Accredited official statistics

Background Information and Methodology: Fraud and error in the benefit system, Financial Year Ending (FYE) 2025

Published 15 May 2025

Applies to England, Scotland and Wales

Purpose of the statistics 

Context and purpose of the statistics    

This document supports our main publication which contains estimates of the level of fraud and error in the benefit system in Financial Year Ending (FYE) 2025.  

We measure fraud and error so we can understand the levels, trends, and reasons behind it. This understanding supports decision making on what actions DWP can take to reduce the level of fraud and error in the benefit system. The National Audit Office takes account of the amount of fraud and error when they audit DWP’s accounts each year. 

Within DWP these statistics are used to evaluate, develop, and support fraud and error policy, strategy and operational decisions, initiatives, options, and business plans through understanding the causes of fraud and error.        

The fraud and error statistics published in May each year feed into the DWP accounts. The FYE 2025 estimates published in May 2025 feed into the FYE 2025 DWP annual report and accounts. 

The statistics are also used within the annual HM Revenue and Customs National Insurance Fund accounts. These are available in the National Insurance Fund Accounts section of the HMRC reports page.      

The fraud and error estimates are also used to answer Parliamentary Questions and Freedom of Information requests, and to inform DWP Press Office statements on fraud and error.  

Limitations of the statistics

The estimates do not include reviews of every benefit each year. This year, Carer’s Allowance was measured for the first time since FYE 2020. Also, this year the passported Working Age Housing Benefit client group was measured rather than the Pension Age Housing Benefit client groups that were measured in FYE 2024. 

This document includes further information on limitations – for example, on benefits reviewed and changes this year (section 1 and section 2, omissions to the estimates (section 3), and our sampling approach (section 4).  

Longer time series comparisons may not be possible for some levels of reporting due to methodology changes. Our main publication and reference tables indicate when comparisons should not be made. 

We do not provide sub-national estimates of fraud and error as we are unable to break the statistics down to this level.  

Comparisons between the statistics

These statistics relate to the levels of fraud and error in the benefit system in Great Britain. All percentages in this document are rounded to the nearest 0.1%.

Social Security Scotland report the levels of fraud and error for benefit expenditure devolved to the Scottish Government within their annual report and accounts. 

Northern Ireland fraud and error statistics are comparable to the Great Britain statistics within this report as their approach to collecting the measurement survey data, and calculating the estimates and confidence intervals, is very similar. Northern Ireland fraud and error in the benefit system high level statistics are published within the Department for Communities annual reports. 

HM Revenue and Customs produce statistics on error and fraud in Tax Credits. 

When comparing different time periods within our publication, we recommend comparing percentage rates of fraud and error rather than monetary amounts. This is because the amount of fraud and error in pounds could go up, even if the percentage rate of fraud and error stays the same or goes down, if the amount of benefit we pay out in total goes up compared to the previous year. 

Source of the statistics

We take a sample of benefit claims from our administrative systems. DWP’s Performance Measurement (PM) team contact the benefit claimants to arrange a review. The outcomes of these reviews are recorded on a bespoke internal database called FREDA. We use data from here to produce our estimates. 

We also use other data to inform our estimates – for example:

• benefit expenditure data (aligning with the Spring Statement published forecasts) 

• benefit recovery data (DWP benefits and Housing Benefit) to allow us to calculate estimates of net loss 

• other DWP data sources and models to improve the robustness of, or categorisations within, our estimates – for example, to allow us to see if claimants who leave benefit as a consequence of the fraud and error review process then return to benefit shortly afterwards, and to understand the knock-on effect of fraud and error on disability benefits on other benefits

Further information on the data we use to produce our estimates is contained within section 4, section 5 and section 6 of this report. 

Definitions and terminology within the statistics

The main publication presents estimates of Fraud, Claimant Error, and Official Error. The definitions for these are as follows:

• Fraud: This includes all cases where the following three conditions apply:

  • the conditions for receipt of benefit, or the rate of benefit in payment, are not being met
  • the claimant can reasonably be expected to be aware of the effect on entitlement 
  • benefit stops or reduces as a result of the review 

• Claimant Error: An overpayment has occurred where the claimant has provided inaccurate or incomplete information, failed to report a change in their circumstances, or failed to provide requested evidence, but there is no fraudulent intent on the claimant’s part

• Official Error: The benefit has been paid incorrectly due to a failure to act, a delay or a mistaken assessment by DWP, a Local Authority (LA) or HMRC, to which no one outside of that department has materially contributed.

We report overpayments (where we have paid people too much money), and underpayments (where we have not paid people enough money).

We present these in percentage terms (of expenditure on a benefit) and in monetary terms, in millions of pounds.

We also report a measure on the percentage of cases with Fraud or an overpayment Error calculated as follows:

Proportion of claims with an overpayment or underpayment (reference tables 12 and 13) 

Proportion of claims with Fraud or an overpayment error = (number of claims in the sample with at least one Fraud or at least one overpayment error) / (number of claims in the sample) 

Proportion of claims with an underpayment error = (number of claims in the sample with at least one underpayment error) / (number of claims in the sample) 

Since the same claim can be included in both the proportion of claims with an overpayment error or Fraud and the proportion of claims with an underpayment error, these figures cannot be summed together to obtain the total proportion of claims paid incorrectly.

Proportion of claims paid the incorrect amount (reference table 11)

Proportion of claims overpaid = (number of claims in the sample ultimately overpaid) / (number of claims in the sample) 

Proportion of claims underpaid = (number of claims in the sample ultimately underpaid) / (number of claims in the sample) 

These figures can be summed together to obtain the total proportion of claims paid incorrectly. 

Further information about the types of errors we report on, abbreviations commonly used and statistical methodology can be found in the appendices at the end of this document. 

Revisions to the statistics

Revisions to our statistics may happen for a number of reasons. When we make methodology changes that impact our estimates, we may revise the estimates for the previous year to allow meaningful comparisons between the two. Where we introduce major changes, we may denote a break in our time series and recommend that comparisons are not made back beyond a certain point.  

In our FYE 2025 publication we have revised:

• the monetary value and rates of overpayments for Universal Credit, Pension Credit, and Housing Benefit for FYE 2024  

• the proportion of cases overpaid, underpaid and incorrect across the benefits for FYE 2024 

• the proportion of Universal Credit, Housing Benefit and Pension Credit cases with at least one Official Error overpayment or underpayment, or Claimant Error overpayment in FYE 2024 

For more information and the reason behind the revisions please see section 2.   

The National Statistics Code of Practice allows for revisions of figures under controlled circumstances: “Statistics are by their nature subject to error and uncertainty. Initial estimates are often systematically amended to reflect more complete information. Improvements in methodologies and systems can help to make revised series more accurate and more useful.” 

Unplanned revisions of figures in reports in this series might be necessary from time to time. Under this Code of Practice, the Department has a responsibility to ensure that any revisions to existing statistics are robust and are freely available, with the same level of supporting information as new statistics. 

Status of the statistics

National statistics 

National Statistics are accredited official statistics. National Statistics status means that these statistics meet the highest standards of trustworthiness, quality and public value, and there is a responsibility to maintain compliance with these standards. 

These official statistics were independently reviewed by the Office for Statistics Regulation in December 2017. They comply with the standards of trustworthiness, quality and value in the Code of Practice for Statistics and should be labelled ‘accredited official statistics’. 

Our statistical practice is regulated by the Office for Statistics Regulation (OSR). 

OSR sets the standards of trustworthiness, quality and value in the Code of Practice for Statistics that all producers of official statistics should adhere to.  

You are welcome to contact us directly at enquiries.fema@dwp.gov.uk with any comments about how we meet these standards.

Alternatively, you can contact OSR by emailing regulation@statistics.gov.uk or via the OSR website. 

Read further information about National Statistics on the UK Statistics Authority website. 

Quality Statement

Quality in statistics is a measure of their ‘fitness for purpose’. The European Statistics System Dimensions of Quality provide a framework in which statisticians can assess the quality of their statistical outputs. These dimensions of quality are relevance, accuracy and reliability, timeliness, accessibility and clarity, and comparability and coherence.

Section 6 gives information on the application of these quality dimensions to our fraud and error statistics.

Feedback

We welcome any feedback on our publication. You can contact us at:

enquiries.fema@dwp.gov.uk  

Lead Statistician: Milosz Waszak

DWP Press Office: 020 3267 5144

Report Benefit Fraud: 0800 854 4400

Useful links

Collection page for the fraud and error statistics   

FYE 2025 estimates, including reference tables   

1. Introduction to our measurement system

The main statistical release and reference tables and charts provide estimates of fraud and error for benefit expenditure administered by the Department for Work and Pensions (DWP). This includes a range of benefits for which we derive estimates using different methods, as detailed below. For further details on which benefits are included in the total fraud and error estimates please see Appendix 2. More information can be found online about the benefit system and how DWP benefits are administered. 

The fraud and error estimates provide estimates for the amount overpaid or underpaid in total and by benefit, broken down into the types of Fraud, Claimant Error and Official Error for benefits reviewed this year.  

Estimates of fraud and error for each benefit have been derived using three different methods, depending on the frequency of their review (see section 5 for details): 

Benefits reviewed this year 

Fraud, Claimant Error and Official Error (see definitions above) have been measured for FYE 2025 for:

• Universal Credit (UC)
• Housing Benefit (HB)
• Carer’s Allowance (CA)
• Pension Credit (PC)
• State Pension (SP)
• Personal Independence Payment (PIP) 

Expenditure on measured benefits accounted for 87% of all benefit expenditure in FYE 2025.  

Estimates are produced by statistical analysis of data collected through annual survey exercises, in which independent specially trained staff from the Department’s Performance Measurement (PM) team review a randomly selected sample of cases for benefits reviewed this year. See section 4 for more information on the sampling process. 

The review process involves the following activity: 

• previewing the case by collating information from a variety of DWP or Local Authority (LA) systems to develop an initial picture and to identify any discrepancies between information from different sources 

• interviewing the claimant (or a nominated individual where the claimant lacks capacity) using a structured and detailed set of questions about the basis of their claim. The interview is completed as a telephone review in the majority of cases. However, where this is not appropriate, there is usually also the option for a completed review form to be returned by post  

• the interview aims to identify any discrepancies between the claimant’s current circumstances and the circumstances upon which their benefit claim was based

If a suspicion of Fraud is identified, an investigation is undertaken by a trained Fraud Investigator with the aim of resolving the suspicion. 

Benefits were measured within the period September 2023 to October 2024. For more information on the sample period for individual benefits please see Appendix 1 of the statistical report. The following number of benefit claims were sampled and reviewed by the PM team:

Benefit	Sample size	Percentage of claimant population reviewed
Universal Credit	3,995	0.07%
State Pension	1,556	0.01%
Housing Benefit	2,989	0.13%
Pension Credit	1,987	0.15%
Carer’s Allowance	850	0.12%
Personal Independence Payment	1,494	0.06%
Total	12,871	0.05%

Overall, approximately 0.05% of all claims in payment for the benefits measured this year were reviewed by the PM team.

Information about the Performance Measurement Team can be found online. 

Benefits reviewed previously

Since 1995, the department has carried out reviews for various benefits to estimate the level of fraud and error in a particular financial year following the same process outlined above. In FYE 2025 around 12% of total expenditure related to benefits reviewed in previous years. Please see Appendix 2 for details of benefits reviewed previously.

Benefits never reviewed

The remaining benefits, which account for around 2% of total benefit expenditure, have never been subject to a specific review. These benefits tend to have relatively low expenditure which means it is not cost effective to undertake a review. For these benefits the estimates are based on assumptions about the likely level of fraud and error (for more information please see section 5).

2. Changes to the statistics this year

This section provides detail of changes for the FYE 2025 publication. Any historical changes can be found in Appendix 5. 

Revisions

Incorrectness 

We have identified 2 issues that mean we need to restate the incorrectness figures for FYE 2024: 

• failing to exclude not reasonably expected to know overpayments on DLA – Many changes of circumstances on  Disability Living Allowance (DLA) are gradual improvements or deteriorations in their medical needs, and it is difficult for some claimants to know at what point these needs have changed sufficiently to affect their benefit entitlement. We exclude those overpayments where the customer is not reasonably expected to know (not RETK) to report the change due to it being so gradual, from both the monetary amount of Fraud and Error but also the Incorrectness. For more information on the not RETK adjustment, please see section 5: Personal Independence Payments: Not Reasonably Expected to Know

Due to an error, this exclusion was not carried out on DLA in the FYE 2024 statistics. This change reduces the proportion of DLA claims overpaid from 3.6% to 1.0% and the proportion of DLA claims incorrect from 6.6% to 3.9%. 

• removal of non-Causal Link errors on cases found Inconclusive or Not Fraud, which we were unable to review – For any case that we were unable to review and was subsequently suspended and then terminated, we check to see if they return to benefit within 4 months. If the claimant does not return within that period, we say the case is Inconclusive. If the claimant does return within that period and their circumstances are the same, we say the case is “Not Fraud”. In both these circumstances only any Causal Link Fraud is removed with any other Fraud or Error retained. For more information on this adjustment please see section 5: Cannot Review

For incorrectness purposes, due to an error, all Fraud and Error was removed on these Inconclusive and Not Fraud cases, not just the Causal Link Fraud. This change mainly impacts Universal Credit and increases the total incorrectness from 23.8% to 24.3%. 

Changes

Changes to benefits reviewed

Each year we use decision making methodology called multiple-criteria decision analysis (MCDA) to help evaluate which benefits will be reviewed. 

For FYE 2025 we measured CA for the first time since FYE 2020. 

Changes to New Case Factors

Previously to FYE 2024, for some benefits, the payment (and the period) that the Official Error and the Claimant Error/Fraud check was carried out on was different. However, in FYE 2024 these were aligned for all benefits. To reflect this change, we have aligned the New Case Factors so there is no difference to how they are carried out for Official Error and for Claimant Error/Fraud. For more information on New Case Factors please see section 5: New Case Factors. 

FYE 2024 figures have been recalculated to reflect this change. New Case Factors only affect Employment and Support Allowance (ESA), Pension Credit (PC), Jobseeker’s Allowance (JSA) and Housing Benefit (HB). Making this change has led to a small increase in HB and PC overpayments only (increasing total HB overpayments by £4m and total PC overpayments by £3m). 

Living Together 

Any overpayment (or underpayment) that is Living Together (where a claimant declares to be single but has failed to declare they actually live with another person and maintain a joint household) is calculated by Performance Measurement as the amount of the benefit the claimant and the partner were getting before the review, to the amount they are eligible for post review. However, the partner’s award (if they are on benefit) is not included in the start award, it is just the claimant’s award. This means that in the small number of cases where both claimant and partner are on the benefit we are reviewing, we have been potentially overstating the Fraud/Error.  

We have made a change to now proportion off the Living Together Fraud/Error, so we only take the proportion of the overpayment (or underpayment) that relates to the claimant’s start award when combining that with the partner’s start award. 

Example 1: claimant and partner are both single UC claimants getting £500 each and after review this becomes one payment of £800, leading to a Living Together overpayment of £200. Since our claimant is getting half the total amount used to calculate the overpayment before review, we would halve the overpayment so it would become £100 (as the other £100 would technically fall on the partner but they are not being reviewed).  

Example 2: claimant and partner are both single UC claimants. The claimant is getting £750, and the partner is getting £250. After review this becomes one payment of £800, leading to a Living Together overpayment of £200. Since our claimant is getting three quarters of the total amount used to calculate the overpayment before review, we would change the value of the overpayment to £150 (as the other £50 would technically fall on the partner but they are not being reviewed).  

FYE 2024 figures have been restated to reflect this change. Making this change has led to the overpayment rate for FYE 2024 to go from 3.7% to 3.6% and a reduction in the FYE 2024 Universal Credit overpayment rate of £50m (all from Living Together Fraud).

Causal Link non residency Fraud on Housing Benefit

If during a review the claimant informs the department that they are no longer at the address, then it would be recorded as Causal Link Fraud since it is a change of circumstances reported due to the review (for more information on Causal Link Fraud please see section 5: Causal Links). However, in some circumstances the claimant has no financial gain as they were not receiving the money, which was going to their landlord (this could be a private landlord, Housing Association or Local Authority depending on their accommodation). For those cases, we have made the change to recode the Fraud to a Claimant Error. 

FYE 2024 figures have been restated to reflect this change. Making this change has no impact on the overall Housing Benefit overpayment rate but it does mean that £31m moves from Fraud to Claimant Error (all in the Residency category). 

Changes to the Error Code Framework

Every year we review the Error Code Framework, which maps errors to our error reason publication categories. After this year’s review we have made the decision to: 

• Move “Foreign State Pension” and “Child Benefit” from Income – Other Benefits to Income - Other, meaning the former category now only relates to DWP benefits (and it has also been renamed to reflect this)  

• Move Universal Credit “Not Resident at Address” and “Changed Address” errors from Housing Costs to Residency 

FYE 2024 figures have been restated to reflect this change. Making this change has no impact to the overall rates in FYE 2024, however there are some small fluctuations in the error reason breakdowns, in particular in Pension Credit and Universal Credit. 

Change to Winter Fuel Payment Proxy

Some benefits have a small amount of expenditure and therefore are unlikely to be selected for measurement. For each of the benefits that have never been reviewed, we use a similar benefit’s rate of F&E or the passporting benefit’s loss of entitlement rate as a proxy measure. We then apply that to the expenditure on that unreviewed benefit to get an estimate of the monetary value of F&E. 

Winter Fuel Payment (WFP) is now paid via passporting from Pension Credit. As a result, we need to change the proxy measure used to estimate its levels of Fraud and Error. Previously the proxy measure used was the result of a Social Fund Service Centre Measurement carried out in the previous year. However, the change to becoming a passported benefit means that only overpayments can now occur on WFP, and these are due to the claimant not being entitled to Pension Credit. Therefore, we are changing the proxy measure used for WFP to be the loss of entitlement rate on Pension Credit. This aligns with the methodology used for other passported benefits that have never been reviewed. 

WFP F&E is not split out within our publication or supplementary tables, it is grouped with other benefits that have never been reviewed. F&E lines for benefits never reviewed should not be compared year on year due to the benefits that make up this line changing over time. 

3. Interpretation of the results

Care is required when interpreting the results presented in the main report:

• the estimates are based on a random sample of the total benefit caseload and are therefore subject to statistical uncertainties. This uncertainty is quantified by the estimation of 95% confidence intervals surrounding the estimate. These 95% confidence intervals show the range within which we would expect the true value of fraud and error to lie

• when comparing two estimates, users should consider the confidence intervals surrounding each of the estimates. The calculation to determine whether the results are significantly different from each other is complicated and takes into account the width of the confidence intervals. We perform this robust calculation in our methodology and state in the report whether any differences between reporting years are significant or not 

• unless specifically stated within the commentary in the publication or in the reference tables, none of the changes for benefits reviewed this year are statistically significant at a 95% level of confidence when compared to the previous measurement  

As well as sampling variation, there are many factors that may also impact on the reported levels of fraud and error and the time series presented.  

• these estimates are subject to statistical sampling uncertainties. All estimates are based on reviews of random samples drawn from the benefit caseloads. In any survey sampling exercise, the estimates derived from the sample may differ from what we would see if we examined the whole caseload. Further uncertainties occur due to the assumptions that have had to be made to account for incomplete or imperfect data or using older measurements  

• the sample year and the financial year do not align. This means that a proportion of expenditure for benefits reviewed this year cannot be captured by the sampling process. This is mainly because of the delay between sample selection and the interview of the claimant, and the time taken to process new benefit claims, which excludes the newest cases from the review. The estimates in the reference tables in this release have been extrapolated to account for the newest benefit claims which are missed in the benefit reviews and cover all expenditure 

• the estimates do not encompass all fraud and error. This is because Fraud is, by its nature, a covert activity, and some suspicions of Fraud on the sample cases cannot be proven. For example, cash in hand earnings are harder to detect than those that get paid via PAYE. Complex official error can also be difficult to identify. More information on omissions can be found later in this section 

• some incorrect payments may be very difficult to prevent. The measurement methodology will treat a case as incorrect, even where the claimant has promptly reported a change and there is only a short processing delay

De minimis

A de minimis approach to the proportion of claims paid incorrectly is taken across all benefits, removing all errors that are 10p or less. This means that when discussing the number of cases with an error, only material overpayments and underpayments are considered. 

Where the proportion of claims paid incorrectly is discussed in the publication we use headline figures, i.e. those with de minimis errors removed.

The proportion of cases with a de minimis error are also published in the reference tables and can be added to the headline figures. In Table 11 this will give the proportion of cases that have a net overpayment or net underpayment of any size. In Table 12 this will give the percentage of all cases with at least one overpayment error of any size, and in Table 13 it will give the percentage of cases with at least one underpayment error of any size. 

Omissions from the estimates 

The fraud and error estimates do not capture every possible element of fraud and error. Some cases are not reviewed due to the constraints of our sampling or reviewing regimes (or it is impractical to do so from a cost or resource perspective), some cases are out of scope of our measurement process, and some elements are very difficult for us to detect during our benefit reviews. The period that our reviews relate to means that any operational or policy changes in the last five months of the financial year are not usually covered by our measurements.  

For most omissions from our estimates, we apply adjustments or assumptions to those cases. For some omissions we assume that the levels of fraud and error for those cases are the same as for the cases that we do review, and for other omissions we apply specific assumptions where we expect the levels of fraud and error to be different.  

This section details the omissions from the estimates as far as possible. The examples that follow are not an exhaustive list but are an attempt at providing further details on known omissions in the estimates. 

There are a number of groups of cases that we are unable to review or which we do not review. Some of the main examples of these are as follows: 

New and short-term cases

We are unable to review short duration cases (of just a few weeks in duration) due to the time lags involved in accessing data on the benefit caseloads, drawing the samples and preparing these for review. For these cases, we assume the rates of fraud and error are the same as in the rest of the benefit caseloads. We do, however, make an adjustment using “new cases factors” to try to ensure that the results are representative across the entire distribution of lengths of benefit claims (see section 5 for further details). 

It can take time for new cases to be available for sampling, meaning they are potentially under-represented in the sample. Analysis was undertaken to quantify the impact of these potential exclusions.  

New cases make up a small proportion of cases for most benefits. The table below shows the yearly average percentage that are less than three months old at a given time. 

Benefit	Average number of cases less than three months old	Source
Pension Credit	1.9%	Official quarterly data for August 2023 to May 2024
Carer’s Allowance	3.2%	Official quarterly data for August 2023 to May 2024
Personal Independence Payment	4.1%	Official monthly data for November 2023 to October 2024
Housing Benefit	4.4%	Official monthly data September 2023 to August 2024
Universal Credit	9.2%	Official monthly data for November 2023 to October 2024
State Pension	1.9%	Estimated using quarterly pension data for May 2023 and ONS population data

To investigate the impact of this exclusion, a simulation of the sampling process was performed and repeated multiple times with these cases included.  Sensitivity analysis was then carried out across all benefits to estimate the impact of excluding new cases if the error rate were doubled, halved, or remained the same.  

The age of a case when it becomes available for sampling differs by benefit. Based on these timescales, analyses were undertaken using an exclusion period of either 6 weeks or 3 months. 

Overall, this analysis showed that, because short term cases make up such a small percentage of total cases at any given time and many are available to be sampled later in the period, the impact on final published figures for all benefits is negligible. 

The impact of excluding new cases is no more than 0.2 percentage points difference in the estimated error overpayment rate across all benefits except UC. For UC overpayments, the scenario of doubling the error rate increases the total overpayment rate by 0.5 percentage points and halving the error rate decreases it by 0.2 percentage points. The higher numbers reflect the fact that UC has a higher proportion of new cases. The impact of this omission on the underpayment rate has been estimated as 0.0 percentage points across all benefits. 

It would be expected that the rate of fraud and error in new cases would typically be lower than the full population of claimants since they have recently been assessed. The outcomes of this analysis fall within the estimated confidence intervals and there is little impact on the published statistics, therefore no adjustment is required. 

Unclaimed Benefits 

We are only able to sample claimants who are in receipt of a benefit payment. Eligible claimants that have not made a benefit claim are not included in these figures. Statistics on benefit take-up can be found online.  

Disallowed claims   

Claims which do not receive an award are not included in our sample. These claims may have been disallowed in error, resulting in a possible underpayment.   

Using data on the number of disallowed claims and appeal success rates, sensitivity analysis was carried out to investigate the impact of various assumed error rates for disallowed cases. 

A summary across benefits for various error rate scenarios is shown below. The worst-case scenario is not plausible and is included to illustrate that even assuming an unrealistic level of error, the adjusted estimate would still fall within or close to the published confidence intervals. 

Results show that there is likely to be little to no impact on our estimate of underpayments as a result of not sampling disallowed claims. 

Carer’s Allowance   

Underpayments of Carer’s Allowance can only occur in very rare situations, where there is either a benefit overlap, or the claim is declined in error. To estimate the impact of not sampling the disallowed Carer’s Allowance claims, we carried out sensitivity analysis using the global underpayment rate, since the underpayment rate found in the measured Carer’s Allowance cases was zero. In an extreme worst-case scenario, where the appeal success rate was applied to all disallowed claims, the estimated underpayments would account for 2.0% of the Carer’s Allowance expenditure. If we assumed that the declined claims have the same error rate as the measured cases across all benefits, these underpayments would account for 1.1% of the Carer’s Allowance expenditure.  

Estimated change in underpayment	Impact on CA underpayment estimate	Impact on global underpayment estimate
+£86.0m	+2.0 p.p	0.0 p.p
+£48.3m	+1.1 p.p	0.0 p.p

Personal Independence Payment 

Level of underpayment in excluded group	Estimated change in underpayment	Impact on PIP underpayment estimate	Impact on global underpayment estimate
Extreme worst case: All disallowed cases are appealed with the same success rate	+£315.5m	+1.2 p.p	+0.1 p.p
Disallowed cases have the same error rate as measured cases	+£6.1m	0.0 p.p	0.0 p.p

Note: PIP worst case analysis is slightly different due to its high appeal rate. 

Pension Credit 

The data on disallowed cases shows a steep increase in the proportion of cases declined with no award which started in FYE 2021 and is continuing into the current financial year. Rejection rates in FYE 2021 and FYE 2022 have been affected by the financial strain people experienced due to the pandemic, causing more ineligible people to try and claim the benefit. Furthermore, following the government campaign published in April 2022, urging eligible pensioners to apply for Pension Credit, there has been an even larger increase in the volume of claims received by Department for Work and Pensions.

In the meantime, many financial journalists started encouraging people to apply without checking the eligibility criteria due to the complex nature of the benefit. This meant that many people who were not eligible for Pension Credit tried to claim anyway, naturally increasing the rejection rate throughout FYE 2023 and FYE 2024. This continued in FYE 2025, mainly due to the recent policy changes regarding eligibility for Winter Fuel Payment. Because of these events, the high rejection rate, and the low appeal rate, the sensitivity analysis had to be adjusted, and a different worst-case measure has been chosen. 

Level of underpayment in excluded group	Estimated change in underpayment	Impact on PC underpayment estimate	Impact on global underpayment estimate
Extreme worst case: Reconsiderations success rate used as an appeal rate	+£31.5m	+0.5 p.p	0.0 p.p
Disallowed cases have the same error rate as measured cases	+£23.9m	+0.4 p.p	0.0 p.p

Universal Credit 

Appeals information was not available for UC therefore the sensitivity analysis could not be carried out fully as in the above tables.  

Using numbers of disallowed cases, analysis shows that even if the error rate in disallowed cases was double that found in the sample, the total difference in the UC rate would be around £13.2 million, or 0.0% of expenditure, and therefore well within the confidence interval – no adjustment is appropriate. 

State Pension 

Recent data was not available on the number of disallowed State Pension claims, however, given the data available it is expected to be very low. For the most recent year for which data on appeal rates was available (November 2023 to October 2024), applying sensitivity analysis based on a series of reasonable worst-case assumption showed that even if the error rate in disallowed cases was double that found in the sample, the total difference in the State Pension rate would be £0.2 million, which is negligible. Looking at the worst-case scenario, where all disallowed claims (even the ones that were not challenged) are reconsidered with the same error rate that was found in the sample, the total difference in the State Pension rate would be £4.5 million, or 0.0% of the expenditure. Therefore, no adjustment is required. 

Housing Benefit 

Applications for Housing Benefit are processed by local authorities, not centrally by DWP as with the other benefits reviewed, and it was not possible to obtain numbers of disallowed cases. 

Since analyses of other benefits has shown negligible impact in excluding disallowed claims, the assumption is made that the impact on Housing Benefit is also negligible. 

In conclusion, no adjustments are required to the estimates to account for the exclusion of disallowed cases from the sample. 

Nil payment claims 

A case is considered to be nil-payment if there is a claim in place but the total award being paid is zero. These cases are not included when the sample is selected. Some benefits do not allow a nil-award, meaning all active claimants are receiving some payment. 

Nil-award allowed	No nil-award allowed
Pension Credit	Housing Benefit
Universal Credit	State Pension
Carer’s Allowance	Personal Independence Payment

Note: For a very small proportion of the PIP caseload (0.1%), the combination of award rates (daily living and mobility) is reported as nil-nil. Investigations suggest that award rates may be temporarily shown as nil for a short period whilst a claim review is in process, after which the new award rate is set. These cases will be monitored. 

Note: A nil-award of Carer’s Allowance is referred to as an ‘underlying entitlement’. 

Nil-payment claims are a potential source of underpayment that is not included in the sample.  

Pension Credit 

Only a very small percentage of Pension Credit claimants are in nil-payment at any given time. For the most recent year of data available this number was always below one fifth of one percent. Simulating the sampling process shows that over 99.8% of cases sampled would be the same even if the nil-payment cases were included in the group available for sampling. The potential impact of a different error rate in this group thus rounds to zero, even in a worst-case scenario of doubling the error rate in the excluded cases. 

Universal Credit 

For the year up until August 2024 the most recent year for which data was available for analysis, the number of nil-payment claims ranged from 8.4% to 10.5%, with 9.5% being the average. Simulating the sampling process shows around 9.5% of claims that would otherwise be sampled are missed due to this. The potential impact of doubling the error rate in this group is 0.1% of the UC expenditure. 

Carer’s Allowance 

Claimants cannot receive Carer’s Allowance and some other benefits, such as State Pension, because of overlapping benefit rules. If the other benefit pays more than the current Carer’s Allowance rate (£81.90 for FYE 2025), the claimant is awarded an ‘underlying entitlement’ to Carer’s Allowance only.  

For the most recent year of data available, 28.6% of Carer’s Allowance claimants had ‘underlying entitlement’ only, and were in receipt of Carer Premium on top of their other benefit, rather than a Carer’s Allowance award, resulting in a nil payment. These claims are not included in our sample. The main overlapping benefits are ESA, JSA and SP. As SP entitlement can’t be lost, there can’t be an underpayment in SP-CA overlapping claims. 

Only 14.1% were claims with an overlapping benefit that were not State Pension. The potential impact of the exclusion of entitlement cases with overlapping ESA and JSA, even in the worst-case scenario of doubling the error rate in the excluded cases, and assuming that every incorrect claim would result in an underpayment equal to the full rate of Carer’s Allowance (£81.90 for FYE 2025), would amount to £0.6m, or 0.0% of the expenditure. Therefore, the potential impact of the worst-case scenario is within the confidence interval and no adjustment is required. 

Exclusions specific to PIP 

The monthly samples are taken from live PIP claims in advance of the scheduling of the benefit reviews. Any benefit record relating to a claimant who meets specific exclusion criteria (e.g., terminally ill, reviewed in the last three months) will not be reviewed. We assume the rates of fraud and error for these cases are the same as the rest of the PIP caseload. The potential impacts of each excluded group are summarised below. 

Terminally Ill cases  

Terminally ill claimants make up a very small percentage of PIP claimants, typically around 1%.  Sensitivity analysis was carried out to test the impact of their exclusion if the fraud and error rate on these cases were to differ by as much as double that of the sampled population.  

Estimated impact of excluding terminally ill cases on the PIP overpayment rate

Level of overpayment in excluded group	Estimated change in overpayment	Impact on PIP overpayment estimate	Impact on global overpayment estimate
Double the published rate	+£2.9m	0.0 p.p	0.0 p.p
Half the published rate	-£1.4m	0.0 p.p	0.0 p.p

Estimated impact of excluding terminally ill cases on the PIP underpayment rate

Level of underpayment in excluded group	Estimated change in underpayment	Impact on PIP underpayment estimate	Impact on global underpayment estimate
Double the published rate	£0.0m	0.0 p.p	0.0 p.p
Half the published rate	£0.0m	0.0 p.p	0.0 p.p

Note that the PIP underpayment analysis applies to cases that are not on the highest award (enhanced mobility with enhanced daily living) as the estimates are already adjusted to account for this exclusion. See the Benefit-specific adjustments section for more details. 

The above analysis demonstrates that any additional fraud or error that may exist in the excluded PIP terminally ill cases is extremely small. The total estimated levels of fraud and error would have a negligible difference for the above scenarios if terminally ill cases were included in the sample. Therefore, no adjustment has been made to the statistics because of this exclusion.  

Scheduled reviews 

PIP awards are reviewed regularly. The period between reviews is set on an individual basis and ranges from 9 months to 10 years, with the majority of claimants having a short-term award of 0-2 years. If a claim has had a planned award review in the last 92 days, has a review ongoing or a review due in the next six weeks then it is not eligible to be sampled. 

A simulation of the sampling process was performed and repeated multiple times to investigate the impact of this exclusion.  

Cases with an upcoming review would be expected to have a higher propensity for Fraud or Error due to the length of time since their last review. By the same reasoning cases that have had a review completed recently would be expected to have a lower propensity for Fraud or Error. 

In our statistics an assumption is made that the excluded cases are similar to those sampled and so no adjustments are made. We investigated the impact of alternative assumptions on our estimates and results are shown in the table below.  

In these scenarios it was assumed that an award that was increased after a planned review would have an underpayment had we reviewed it, and cases that were decreased or reviewed before being disallowed would have an overpayment. For overpayments, these proportions were taken from PIP data published via StatXplore and fed into the results below. 

Estimated impact of excluding cases with a review that is due, ongoing, or recently completed using review outcomes to estimate fraud and error rates

For PIP overpayments, the vast majority of error was Claimant Error. Therefore, for this analysis we assumed zero error on the recently reviewed claims that were excluded from the sample, given that a recent review would resolve potential Claimant Error.

A worst-case scenario of half the sampled error existing in the excluded recently reviewed cases was also estimated.  

Therefore, for overpayments two assumptions were tested for recently reviewed cases: that they have no errors, and that they have half the error rate as our sample. The table below shows these assumptions and their estimated difference to the published amounts and rates. 

Overpayments

	Estimated difference from published	Impact on PIP estimate	Impact on global estimate
Assumption of zero error on excluded recently reviewed cases	-£24.8m	-0.1 p.p	0.0 p.p
Assumption of half the sampled error on excluded recently reviewed cases	+£5.8m	0.0 p.p	0.0 p.p

As this publication focuses on Official Error underpayments only, a different method of analysis on excluded cases was applied for underpayments. We were unable to split the available review outcome data down into type of error, and therefore applied a simplified calculation based on the sampled Official Error estimates for underpayments. 

For underpayments, we assumed that Official Error is likely to occur in the excluded recently reviewed cases in the same way as for our sampled cases. Therefore, we have estimated the impact of the excluded cases having half the sampled error, and as a worst-case scenario having double the sampled error. 

For underpayments, two assumptions were tested for recently reviewed cases: that they have half the sampled error, and that they have double the sampled error. The table below shows these assumptions and their estimated difference to the published amounts and rates. 

Underpayments

	Estimated difference from published	Impact on PIP estimate	Impact on global estimate
Assumption of half the sampled error on excluded recently reviewed cases	-£4.3m	0.0 p.p	0.0 p.p
Assumption of double the error on excluded recently reviewed cases	+ £8.3m	0.0 p.p	0.0 p.p

These differences all fall within the published confidence intervals for the rates of overpayment and underpayment for PIP in FYE 2025, and the conclusion therefore is no adjustment is needed. 

Time Lags 

The time lags involved in the fraud and error measurement process mean that further omissions are possible. Any policy or operational changes in the last five months of the financial year will not usually be covered by the reviews feeding into the publication, as the reviews tend to finish in the October of that financial year. In addition, some cases do not have a categorisation by the time the estimates are put together, often due to an ongoing fraud investigation. “Estimated outcomes” are generated for these cases for the purposes of the statistics, made by the review officer estimating the most likely outcome of the case, or based on the results from the reviews of similar cases that have been completed.

For all benefits we completed additional work to better understand any implications of major policy/operational changes within the financial year. The conclusion was that we felt the sample period was representative of the financial year. See section 5 for further details. 

Work Capability Assessment 

When ESA was measured between FYE 2014 and FYE 2023, the measurement of ESA did not include a review of the Work Capability Assessment; this is also the case for the Work Capability Assessment for claimants on UC.  

Universal Credit Transitional Protection 

Universal Credit (UC) was introduced to replace older (legacy) benefits, including tax credits. Benefit claimants have gradually moved onto UC through:

• natural migration – when the claimant experiences a change in circumstances, and they need to make a new claim for a benefit that UC has replaced

• voluntary migration – when the claimant voluntarily moves to UC from their existing benefit

• managed migration – when the claimant does not choose to migrate voluntarily and has not migrated naturally

Transitional protection can be applied to claimants who are moved onto UC through the managed migration process. A transitional protection element is applied to ensure that eligible households, with a lower calculated award in UC than their legacy benefit awards, will see no difference in their entitlement at the time they are moved to UC, providing that there is no change in their circumstances during the migration process. 

The transitional protection element is calculated during the managed migration process. It is based on the circumstances for the eligible household and their legacy benefits in payment that are being replaced by UC.  

There is potential for the transitional protection element to be paid incorrectly if the calculation is made incorrectly and/or the legacy benefit awards in payment are incorrect based on the claimant’s circumstances. We are unable to review the transitional protection element calculation or the legacy benefit awards because these can be derived from benefits not administered by DWP. Consequently, fraud and error on this element of UC is omitted from the estimates. 

Latest figures on UC Transitional Protection show there have been around 400,000 UC cases awarded this element. For these cases the transitional protection only accounts for a small fraction of the total UC award in payment. Expenditure on the transitional protection element was low in FYE 2025 compared to the total UC expenditure. Therefore, the omission was taken to be negligible, and no adjustment was made to the UC fraud and error estimates. 

Knock-on impact on other benefits 

We only review the benefit that has been selected for a review, and do not assess any consequential impacts on other benefits. However, in certain circumstances, for some benefits, there may be a knock-on impact on other benefits. For example, changes in entitlement to PIP can affect disability and carer premiums on income-related benefits (specifically IS, PC and HB), as well as CA. We account for this in our estimates by using DWP’s Policy Simulation Model to assess the impact and publish this as “Interdependencies” in the publication tables. The Policy Simulation Model is the main micro-simulation model used by DWP to analyse policy changes and is based on the annual Family Resources Survey.  

However, there are other knock-on impacts which we do not cover. For example, any benefit which considers another benefit as an income. If that other benefit is incorrect then only its impact on the benefit we measured is captured. This means that potentially the global figure is an overestimation as in these scenarios the overpayment on the benefit measured would be offset by an uncaptured underpayment on the other benefit. 

Third party deductions 

The accuracy of third-party deductions is not measured (i.e. whether the deduction is at the correct amount and is still appropriate). Third party deductions can take place to cover arrears for things like housing charges, fuel and water bills, Council Tax and child maintenance. The rate of benefit is not impacted by any third-party deductions, and the amount of any Fraud or Error is based on the “gross” amount of benefit in pay. 

UC sanctions 

For UC, we do not assess whether the Department follows correct “labour market” procedures and takes any necessary follow up action for non-compliance by claimants (i.e. considers whether a sanction should apply if a claimant fails to apply for a job or leaves a job voluntarily). However, if a sanction decision has already been made when we review a case, then we do assess whether the impact this has on the benefit award is correct. 

UC surplus self-employed profit/loss 

For UC, we only measure income in the assessment period we are checking. Self-employed people must report their income monthly. If they receive income that removes their entitlement to UC in one month, and this is above the surplus earnings threshold, then any extra income is carried forward into the next month (the surplus earnings threshold is defined as £2,500 above the amount that removes their entitlement to UC in that month). If a self-employed claimant incurs a loss of any amount within an assessment period, this loss is also rolled forward to the next assessment period. When reviewing the benefit, any rolled forward income or loss is assumed to be correct. 

Earnings from the hidden economy

These are claimants who are working but are not declaring those earnings to the government. For every means tested benefit, where capital and earnings affect the award, we require bank statements for all the claimant’s accounts that cover the period of the payment we are checking. This means that any earnings that go through the claimant’s bank are likely to be picked up when those bank statements are checked.   

Although we think we capture most of the Fraud related to hidden economy earnings, it is unlikely that all of this would end up under the “Earnings/Employment” error reason. If the claimant fails to send in bank statements after multiple prompts, then their benefit is suspended and ultimately terminated. In these circumstances a whole award Fraud would be recorded (see Causal Link part of section 5 for more information). However, given they were hiding their earnings from the government it is likely we would not know the underlying reason so the Fraud would be categorised as “Failure to provide evidence/engage”. 

This means that the only earnings we would not pick up are those which are “cash in hand” and are not being deposited into a bank account. We expect the impact of this to be minimal, particularly since COVID-19, many cash only businesses have diversified into accepting bank transfers/card which further reduces this omission.  

Cyber-crime  

We do find errors relating to this and they would be included within the “Conditions of Entitlement” error reason. The benefit reviews that underpin the statistics are very robust and encompass not only a lengthy interview with the claimant but also evidence to verify all their circumstances. Therefore, it would be very difficult for a fraudulent claimant to meet all these requirements without alerting the suspicions of the reviewing officer.  

Like hidden economy earnings, we think we capture most of the Fraud related to cyber-crime, but it is likely that not all of this would end up under the “Conditions of Entitlement” error reason. If a claim is fraudulent then they are likely to either not attempt the interview or not provide the requested evidence, in which case a whole award Fraud would be recorded (see Causal Link part of section 5 for more information). However, it is likely that we would have no evidence as to why they did this so the Fraud would be categorised as “Failure to provide evidence/engage”. 

Benefit Advances 

One of the largest current omissions from our estimates is benefit advances, which are out of scope of our measurement.  

UC supports those who are on a low income or out of work. It includes a monthly payment to help with living costs. If a claim is made to UC but the claimant is unable to manage financially until their first payment, they may be able to get a UC Advance, which is then deducted a bit at a time from future payments of the benefit. 

The benefit review process for the fraud and error statistics examines cases where benefit is in payment. A benefit advance is not a benefit payment and is not included in the DWP expenditure figures or in our measurement process. Claimants who progress to receive payment of a benefit will be included within the scope of our measurement, but we will only review the existing benefit payment. This will not examine Fraud or Error that may have existed in any prior benefit advance payment. Claimants who only receive a benefit advance, but do not go on to receive a subsequent benefit payment, will not be included within the measurement. Advances are available for a number of benefits but, for FYE 2025, advances for UC constituted most of the expenditure on benefit advances. 

We estimate that for FYE 2025 the monetary value of fraud and error on UC advances lies between £0m and £60m. 

Rounding policy 

In the publication and reference tables, the following rounding conventions have been applied: 

• percentages are rounded to the nearest 0.1%

• expenditure values are rounded to the nearest £100 million

• headline monetary estimates are rounded to the nearest £10 million

• monetary estimates for error reasons are rounded to the nearest £1 million 

The proportion of claims paid incorrectly is rounded to the nearest 1% in the publication and expressed in the format “n in 100 cases”. The reference tables present the same values as a percentage rounded to the nearest 0.1%. 

Individual figures have been rounded independently, so the sum of component items do not necessarily equal the totals shown. 

4. Sampling and Data Collection

The fraud and error statistics are determined using a sample of benefit records, since it is not possible to review every benefit record. The sample of benefit records provide data from which inferences are made about the fraud and error levels in the whole benefit claimant population. 

The number of benefit records to be reviewed is determined by a sample size calculation. The sample size calculation is used to ensure that a sufficient number of benefit records are sampled, which allows meaningful changes in the levels of fraud and error to be detected for the whole benefit claimant population.  

Benefit records are selected on a monthly basis from data extracts of the administrative systems. The population from which the samples are drawn are the benefit records that are in payment in a particular assessment period, that is where there is evidence of a payment relating to the previous month. This is known as the liveload. 

The monthly samples are taken from the liveload in advance of the scheduling of the benefit reviews, to give time for the sample to be checked and for background information to be gathered on each benefit record sampled. Any benefit record relating to a claimant who has been previously sampled in the last 6 months or meets specific exclusion criteria (e.g. terminally ill), will not be reviewed. 

We use Simple Random Sampling to select the sample of benefit records for each benefit that is reviewed in the current year. Benefit records are sampled randomly to ensure an equal chance of being selected for the sample.

The sampling methodology is used to attempt to minimise selection bias in the sample and aims to select a sample that is representative of the entire benefit claimant case population. 

The benefits sampled for this year and the methodologies applied are as follows: 

Simple random sample: 

• Carer’s Allowance

• Pension Credit

• Universal Credit

• State Pension

• Personal Independence Payment 

Housing benefit methodology uses simple random sampling stratified by Primary Sampling Unit (PSU) and four different client groups:

• Working Age in receipt of IS, JSA, ESA, PC or UC

• Working Age not in receipt of IS, JSA, ESA, PC or UC

• Pensioners in receipt of IS, JSA, ESA, PC or UC

• Pensioners not in receipt of IS, JSA, ESA, PC or UC 

Note: For HB the client group reviewed in FYE 2025 was “Working Age in receipt of IS, JSA, ESA, PC or UC” which was last measured in FYE 2019. This group is significantly impacted by the Move to UC which targeted those HB claimants in receipt of tax credits, JSA, and IS to migrate to UC. The cases sampled in FYE 2025 were those in receipt of ESA with no tax credits and those in receipt of UC in supported or temporary accommodation.

Abandoned Cases

Of the benefit records sampled, there are some that are not eligible for a review according to strictly defined criteria for abandonment. Benefit records that fall into this category could include: 

• the claimant has a change of circumstances that ends their award before the interview can take place

• the claimant has had a benefit reviewed in the last six months 

• if the claimant or their partner is terminally ill 

When such cases occur in the sample, they are replaced by another case from a reserve list. However, for a small number of abandoned cases replacement is not possible for practical reasons. This occurs when cases are abandoned towards the end of the review year, which means that there is not enough time for a replacement case to complete the full process. 

Abandoned Cases FYE 2025

It is the decision of the Performance Measurement (PM) team, during the preview stage of a case, if a case should be abandoned.

The following table shows the main reason for cases abandoned for each benefit

Abandonment Reason/Benefit	CA	HB	PC	PIP	SP	UC	Total
Benefit not in payment/ceased or suspended	36	74	75	32	0	286	1,171
Planned/Recent activity within 6 months of the start of the sample	2	301	5	138	0	256	702
Sensitive issues	5	14	68	8	22	29	146
Corporate Appointee with no named contact	0	31	12	36	3	7	89
Incorrectly sampled	0	82	0	10	0	0	92
Miscellaneous	5	66	40	66	60	115	352
Total cases abandoned	48	1236	200	290	85	693	2,552
Total cases reviewed	850	2,989	1,987	1,494	1,556	3,995	12,871
Abandonment rate	6%	41%	10%	19%	5%	17%	20%
Percentage point change from previous year	-	32	0	1	0	4	6

For FYE 2025, the abandonment reason ‘New activity within 6 months of the start of the sample’ has been renamed to ‘Planned/Recent activity within 6 months of the start of the sample’ to better capture other benefits abandonment reasons. 

The five reasons listed (excluding ‘miscellaneous’) accounted for around 86% of total abandonment, an increase in total proportion from FYE 2024. This is mainly due to more cases being abandoned within the ‘Benefit not in payment/ceased’ category which accounted for 46% of the total abandonments in FYE 2025, compared with 33% in FYE 2024. This difference mainly comes from the change in HB client group measured between years. 

CA has been reviewed for the first time this year since FYE 2020, as such there is no previous comparison to be made for abandonments.  

In FYE 2025, for HB, the passported working age client group was reviewed, whereas in FYE 2024 the pension age client groups were reviewed. Different client groups have variation in the rate of abandonment due to the nature of the benefits. A large proportion of the HB sample this year was passported via UC, which only happens when the claimant is in supported or temporary accommodation. Once the claimant moves into more permanent accommodation their HB ceases, as their housing costs are met by UC. This has led to a large increase in cases abandoned due to benefit not in payment/ceased or suspended in this year’s HB sample.  

Below are updated descriptions for the top five abandonment reasons for FYE 2025. 

• Benefit not in Payment/ceased or suspended – this remains the largest cause of abandonment, with almost 60% of these abandonments being on UC claims. These are claims no longer in payment either because the claimant is no longer entitled to the benefit or because they are now claiming another benefit. This is primarily related to the time lag of benefit reviews commencing from sample selection period

• Planned/Recent activity within 6 months of the start of the sample – This reason has been renamed to better capture other benefits abandonment reasons. This is due to actions previously done or planned to be done on a claimant’s case by the department such as for PIP this could be appeals, renewals or interventions made on their claim. For UC, it could be linked to reviews within other areas of DWP being done on the claim while for HB it could be due to engagement with the local authority

• Sensitive Issues – this is a reason that affects all benefits reviewed. The claimant/partner being terminally ill or finding out they recently passed away are the main causes for the usage of this reason 

• Incorrectly Sampled – This category was previously in ‘Miscellaneous’. The main usage is for claims that have been sampled for a PM review that are not part of the HB client group(s) being measured or is a migrating case to UC

• Corporate Appointee with no named contact –These claims relate to when the review has been planned and are unable to find the correct official to deal with the review on the claimant’s behalf 

• Miscellaneous – this category covers all remaining categories of abandonment used

These reasons are within the pre-defined criteria for abandonment. All the reasons here are unavoidable, out of our control or can’t be identified at the sample production stage of the process. 

Official Error Checking

Specially trained DWP benefit review officers carry out the Official Error checks. The claimant’s case papers and DWP computer systems are checked to determine whether the claimant is receiving the correct amount of benefit according to their presented circumstances. This identifies any errors made by DWP officials in processing the claim and helps prepare for the next stage: a telephone review of circumstances with the claimant.  

Claimant Error and Fraud Reviews

For all benefits, benefit review officers normally check for Claimant Error (CE) or Fraud by comparing the evidence obtained from the review to that held by the Department. The claimant may not be interviewed if: 

• the case is already under an ongoing fraud investigation

• a suspicion of Fraud arises while trying to secure an interview 

When such cases occur in the sample, the outcome of the fraud investigation is used to determine the review outcome.  

Where, following receipt of a letter informing them of a review, the claimant reports a change of circumstances that results in entitlement to that benefit ending before the review takes place, an outcome of Causal Link would be considered without the claimant being interviewed. See section 5 for information on Causal Link errors.

Types of errors excluded from our estimates

• some failures by DWP staff to follow procedures are not counted as official errors; where the failure does not have a financial impact on the benefit award or where the office have failed to take action which could have prevented a claimant error from occurring. These are called procedural errors

• accounting errors are errors where, despite an error in the claimant’s benefit, an overpayment (or underpayment) of the benefit undergoing a check could be offset against any corresponding underpayment (or overpayment) on the same benefit or in the case of State Pension and Pension Credit each other. For State Pension and Pension Credit these errors are excluded from the monetary estimates but are included in estimates of the proportion of claims paid incorrectly.  For errors where the offset is on the same benefit they are excluded completely 

• a notional error is one where, following PM intervention, the claimant is advised to apply for State Pension or an occupational pension, but the claimant does not apply for it so the DWP decision maker assumes a ‘notional’ amount for this income based on the amount the claimant could receive if they apply for it. These notional amounts are taken into account from a future date so are excluded from our estimates 

Recording Information

Case details relating to the fraud and error reviews are recorded on internal bespoke ‘fraud and error’ software (the system is known as FREDA), to create a centrally held data source. This can then be matched against our original sample population to produce a complete picture of fraud and error against review cases across our sample.

5. Measurement Calculation Methodology

Fraud and error measurement relies on three data sources: 

• Raw Sample held on ‘FREDA’ (the database on which the review outcomes are recorded), is used to identify the Monetary Value of Fraud and Error (MVFE) for individual cases, categorise its cause and quantify it as a proportion of the sample

• Benefit Population data to estimate the extent of fraud and error across the whole claimant caseload from the sample data 

• Expenditure data to estimate the total MVFE to the department

Estimates are categorised into overpayments (OP) and underpayments (UP) and one of three incorrectness types: Claimant Fraud (CF), Claimant Error (CE) or Official Error (OE). Further sub-categories of error reasons are used to provide more details about the nature of the Fraud or Error. Details on error classifications can be found in the glossary at Appendix 3.  

Detailed below are the main calculation steps that the Fraud and Error Measurement and Accuracy (FEMA) team carry out to produce the final Fraud and Error estimates. 

Methodology for Benefits reviewed this year

Benefits that have been reviewed this year account for 87% of the total benefit expenditure.

For each of the benefits reviewed this year a random sample of cases was taken. See section 4 for further details.

An Official Error check is carried out; see Official Error Checking part of section 4. The claimant is then contacted, and a review carried out with evidence requested to verify their circumstances as outlined in Claimant Error and Fraud Reviews part of section 4.

Finally, a case is categorised as Benefit Correct, Official Error, Claimant Error or Fraud (or a combination of the last three). 

There are specific scenarios and adjustments that we then take into account. These are detailed below: 

Causal Link

Cases where there is a change to the claimant’s award as a result of the review activity or, after initial contact, the claimant subsequently fails to engage in the review process, are categorised as Claimant Fraud with causal link. Action is taken to suspend their payment and subsequently terminate their claim. 

Examples of behaviours that can trigger cases to be categorised as Causal Link include; 

• the claimant receives notification of the review and subsequently contacts the department to report an immediate change, for example living with a partner, starting work, self-employment or capital changes. Then supporting evidence needed to verify the change is not provided, resulting in claim suspension and termination

• the claimant completes the review but subsequently declares that a change has happened shortly following the period of the review

• the claimant receives notification of the review and does not engage in the review process or contacts the department with a request to withdraw their claim

• the claimant completes a review and declares a change, however supporting evidence needed to verify the change is not returned, resulting in claim suspension and termination 

For UC there are cases where the claimant fails to engage in the review process, but there is supporting evidence that a change is not due to the review. These are categorised as ‘mitigating circumstances’. For these cases, information is available on our systems to indicate why the person may not have engaged. In most cases, they have moved into paid work following the Assessment Period under review.   

For all benefits post-review, every Causal Link error is categorised as either high suspicion or low suspicion. This categorisation is used in the netting and capping procedure (see section Netting and Capping) to help attribute losses to the error reasons we are most confident about. Any losses attributed to low suspicion Causal Link after netting and capping will have their error reason changed to “Failure to provide evidence/fully engage in the process”.  

Examples of high suspicion Causal Link errors include:

• shortly after review the claimant terminates their claim (rather than send in evidence) 

• the claimant told us at the review of a change of circumstances, but we cannot confirm that the change occurred prior to (or in) the assessment period checked 

• post-review, the claimant made a change of circumstances that cannot be confirmed as starting after the assessment period checked

• when asked to send in more information or to clarify further queries on evidence sent in, the claimant stops engaging 

The main reason that we would class a case as low suspicion is where the claimant fails to send in evidence, but we have no prior suspicions of fraudulent intent. 

Adjustments   

A series of adjustments are made to the sample data, to allow for various characteristics of the benefits and how their data is collected and recorded. The following table highlights which adjustments apply to each of the benefits reviewed in FYE 2025: 

	SP	CA	PC	HB	UC	PIP
Netting and Capping	Y	Y	Y	Y	Y	Y
Estimated Outcomes	Y	N	Y	Y	Y	Y
New Cases Factor	N	N	Y	Y	N	N
Underlying Entitlement	N	N	N	Y	N	N
Cannot Review Cases	Y	Y	Y	Y	Y	Y
Reasonably Expected to know	N	N	N	N	N	Y

Netting and Capping

Where a case has more than one error, these errors can be “netted off” against one another to produce a total value. For example, if a case is found to have two different OEs, one leading to an UP and one leading to an OP, then these are “netted off” to produce a single OP or UP. This is done to better represent the total monetary loss to the public purse (via OPs) or to the claimant (via UPs). 

The monetary loss on each case is the difference between the case award paid at the review/assessment period, and the correct award calculated following the review – the “award difference”. 

A case may have OPs of more than one ‘type’ which sum to a total greater than the award difference. To ensure that the total OP does not exceed the total award difference, we ‘cap’ the OP amount using a hierarchy order of actual CF, Causal Link (high suspicion) CF, Causal Link (low suspicion) CF, CE then OE. This capping process means that a small proportion of CE and OE found during the survey is not included in the estimates, and therefore the final estimates may actually be under-reporting CE and OE in the benefit system, but the total amount of fraud and error is correctly reported.

Estimated outcomes

For a number of cases reviewed this year, the review process had not been completed at the time of the analysis and production of results, often due to incomplete fraud investigations. As a result, predictions for the final outcomes for these cases have been made in the analysis using either the review officer (RO) estimation of the most likely outcome, or the results from the reviews of similar cases that had been completed.

New Cases Factors

New Cases Factors are an adjustment applied to help ensure that the durations on the sample accurately reflect the duration on benefit within the population.  

As a result of the time required to collect the information needed to review a case, as well as other operational considerations, there is an unavoidable delay between sample selection and case review. This delay means that fewer low duration claims will be represented in the sample of cases, which artificially introduces a bias around claim durations at the point of interview.  

Cannot Review

Cases that cannot be reviewed, primarily due to the claimant not engaging in the review process resulting in their benefit claim being suspended and later terminated, are initially recorded as Fraud. These cases are referred to as ‘Cannot Review’ and for most cases the Department holds very little evidence of their current circumstances and their reasons for failing to engage. 

Not all of these cases will be Fraud so for cases where there is a lack of evidence available, additional checks are conducted at a later date. These checks are to determine if the individuals have reclaimed benefit and if there was a suspicion of Fraud recorded on the case at the initial preview. The outcome of these checks will result in these cases being re-categorised for reporting purposes. 1.8% of sampled cases in FYE 2025 did not have an effective review and we had no evidence as to why. 

There are three different categories that are applied to cannot review cases for reporting purposes: 

• Not Fraud – If the individual reclaims benefit within 4 months, with the same circumstances and at a similar rate they were receiving prior to review, then the Fraud is removed

• Fraud remains – If an individual does not reclaim benefit and there was a suspicion of Fraud raised at the preview stage of the review then the case remains as Fraud

• Inconclusive – If the individual does not reclaim benefit and there was no suspicion of Fraud at the preview stage of the review then the case is categorised as inconclusive as there is no evidence to suggest the case is Fraud or not 

Inconclusive cases are excluded from the estimates and reported separately in footnotes in the publication and reference tables.  

Failure to Provide Evidence

If a claimant fails to provide requested evidence after a Performance Measurement review, their benefit is suspended and then subsequently terminated. A whole award error is then recorded relating to the evidence that we failed to receive (for example, if a bank statement which was requested to verify Capital was not received, then a whole award Capital Fraud is recorded). However, if we had no real suspicions about the evidence apart from the claimant not providing it, we reclassify the whole award error into Failure to provide evidence. For more information on this please see section on Causal Link.

We adopt a data driven approach to attempt to reclassify some of the failure to provide evidence errors into known reasons for Fraud or potentially remove the Fraud entirely. This involves looking at data we have on a case four months after classification, to see if this gives us a clearer picture of why they chose not to provide evidence. If we can determine the reason for the fraud, we reclassify the error again from Failure to Provide into the error reason the data suggests. However, if the information gives us reason to believe that the case is not fraudulent, the fraud is removed (i.e., information from HMRC shows the claimant entered full time work before the claim was terminated and their new earnings would have meant they were no longer eligible for the benefit). 

Benefit-specific adjustments 

This section contains details of any benefit-specific sampling issues, or things that we only do for certain benefits when we calculate our estimates.  

Universal Credit

Zero payment cases: Universal Credit cases still live but with zero entitlement are not included in our sample for benefit reviews. However, when calculating the proportion of claims paid incorrectly on Universal Credit we scale the final figure to account for these cases. 

State Pension 

Working for a certain period in the UK means that individuals are entitled to a UK State Pension from State Pension age even if they subsequently move outside of the UK (before or after reaching State Pension age). 

Reviews for State Pension only cover cases from Great Britain (GB). We apply the GB rate to the whole of the State Pension expenditure, including those cases living overseas. We also estimate an additional amount of Claimant Error for the impact of late or non-notification of death on State Pension cases living overseas. We see very low rates of Fraud and Claimant Error on the GB caseload and therefore it is reasonable to assume that, aside from late or non-notification of death, we would find equally low rates for claimants of State Pension living overseas. 

The International Pension Centre collects information on deaths of overseas State Pension claimants, but do not consistently collect any information on other changes of circumstance on these cases. This means that it is only possible to measure Fraud and Error overpayments relating to late or non-notification of death. 

Two methods are used by DWP to confirm that overseas SP claimants are still alive and entitled to the benefit. These are as follows: 

Life Certificates 

A life certificate (LC) is a paper-based form that should be completed by the claimant, signed by a witness and then returned by the claimant. If the completed form is not returned after 16 weeks, then the claimant’s benefit is suspended and another LC is sent out, then following a further 16 weeks with no response, the claimant’s benefit is terminated. If the LC is returned by the claimant, then their benefit entitlement continues (subject to any changes in rate due to changes of circumstance reported by the claimant). 

Death Exchange

DWP exchanges death data with Australia, Germany, Isle of Man, Malta, Netherlands, New Zealand, Poland, and the United States of America. Most of this data is received monthly. The process for death exchanges begins with these countries requesting lists of claimants living in their country and receiving a UK State Pension. Following this, they send the death data for DWP to process. 

Methodology 

We estimate the additional amount of Claimant Error due to late or non-notification of death using an estimate calculated in 2006. This was based on data from the January 2004 life certification exercise and is applied to the whole of the State Pension overseas expenditure. 

Life certificates were suspended during COVID, restarting in November 2021. The life certificate exercise runs over a two-year period. We are looking to update the 2006 estimate in the future, but need to ensure that the data is complete and robust before doing so.  

Personal Independence Payment 

For disability benefits, there are some changes which the claimant should report (for example, hospitalisation). However, many changes are gradual improvements or deteriorations in their medical needs, and it is difficult for some claimants to know at what point these needs have changed sufficiently to affect their benefit entitlement. 

PIP legislation states that when a case is reassessed and their benefit is reduced, the Department will only seek to recover an overpayment when it is reasonable for the claimant to have known they should have reported the change. In other cases, the benefit will be treated as correct up to the point of reassessment. 

Not Reasonably Expected to Know 

PIP claimants are flagged during the review process as being Reasonably Expected to Know (RETK) or Not Reasonably Expected to Know (Not RETK) when they meet certain criteria: 

• Overpayment

• Error type Claimant Error

• Error reason Functional Needs 

When the claimant is assessed as having such a gradual improvement in their functional needs that they could not reasonably be expected to know when to report it, the Not RETK flag is applied.  

Cases identified as Not RETK are excluded from the headline overpayment estimates. However, as the monetary amounts are recoverable by the Department, the associated estimates of Claimant Error overpayments are reported in note 5 within the publication supporting tables.

Note that this rule only applies to overpayments. 

Special Rules Terminally Ill (SRTI) 

PIP claimants are considered to be particularly vulnerable, therefore it is not always deemed appropriate to put a claimant through the review process. This is the case for terminally ill PIP claimants. 

Instead, an adjustment is made to the PIP estimates to account for their exclusion from the sample. 96% of terminally ill PIP claimants receive the highest award. For this reason, the fraud and error rate in this group is not considered to be the same as for the rest of the PIP population, and an adjustment is made based on this assumption. 

The impact of the SRTI adjustment can be found in section 3.

Grossing 

Grossing is the term used to describe the creation of population estimates from the sample data; sample results are scaled up to be representative of the whole population.  

Example of a simple grossing factor ‘G’, if we were to sample 100 cases from a population of 1,000: 

G = N ÷ n

= 1000 ÷ 100

= 10

Where ‘N’ is the population or sampling frame from which the cases are selected and ‘n’ is the sample size taken.  

The above grossing factor shows that, in this example (sampling 100 cases from a population of 1,000), then each case would have a grossing factor of 10 (i.e. each sample case represents 10 cases from the population). Hence if a case was shown to be in error, this would represent 10 errors once grossed. 

UC is replacing a selection of legacy benefits. As this process continues the UC caseload will increase whilst those other benefit caseloads will decrease. As a result, grossing for all benefits are calculated monthly. This ensures that an error identified at the start of the year is grossed up by less than an error identified at the end of the year if the caseload is increasing (or vice versa if it is decreasing). 

Grossing factors are different for each benefit due to the sample, population and adjustments made. 

Percentage overpaid and underpaid 

The grossing factors are then applied to the sample data to calculate values for the grossed awards, the grossed overpayments and grossed underpayments i.e. these are scaled up proportionally to what we would expect to find in the population. In turn, the resulting grossed values are used to calculate the total (global) annual percentage overpaid and underpaid. 

Extrapolation  

The grossed results provide a core estimate of levels of fraud and error. Extrapolation aligns the monetary amount with the benefit expenditure, which is particularly important given the sample period and the financial year do not fully align. 

Monetary Value of Fraud and Error (MVFE) 

To then calculate the MVFE across the benefits, we apply the OP or UP percentage rates to the total annual expenditure for each benefit. This means that the MVFE is affected by the increases and decreases in expenditure, even if the OP and UP percentages are stable. We see the impacts of this in our estimates for benefits not reviewed in the current year, where we use the same rate of fraud and error from previous years but apply it to the expenditure on the benefit in the current year (which will have changed from the year before). 

Although expressing fraud and error in monetary terms (i.e., MVFE terms) might be helpful for a reader to contextualize the figures, we recommend making comparisons on a year-on-year basis based on the percentage rates of fraud and error. This is particularly important for benefits where the expenditure changes a lot each year, as comparisons of monetary amounts can be misleading. For example, on a benefit with growing expenditure, it can be possible for the monetary amount of fraud and error to increase, even if the percentage rate of fraud and error has actually gone down. 

Central Estimates and Confidence Intervals 

The central estimates produced following extrapolation are based on reviews of random samples and hence are subject to variability. Therefore, confidence intervals are provided with the central estimates to quantify the uncertainty associated with these estimates.  

The central estimates and confidence intervals are incorporated into the Global (overall) Estimates of fraud and error. These combine all separate DWP benefits to calculate an overarching set of Fraud, Claimant Error and Official Error rates for overpayments and underpayments. See section 6 on Measurement of Total Overpayment and Underpayments for more detail.  

Total Overpayment and Underpayments 

The fraud and error estimates need to include all expenditure on benefits by DWP. Some benefits have been reviewed for fraud and error in the current year, and some have been reviewed in previous years. We also need to include estimates for benefits which have never been reviewed. A full list of the benefits that are in scope for each release of the Fraud and Error in the Benefit system estimates is included within Appendix 2 of this document. 

We have an estimate of Interdependencies, the knock-on effect of DLA and PIP fraud and error on other benefits, where receipt of DLA or PIP is a qualifying condition. This is only included within the overpayments calculation and not the underpayments. 

Total Housing Benefit Calculations

Housing Benefit (HB) is composed of working age and pension age client groups i.e. those that receive housing benefit who are of working age or who have reached pension age respectively. These groups are subdivided further into standard and passported groups: 

• Working Age in receipt of IS, JSA, ESA, PC or UC (Passported)

• Working Age not in receipt of IS, JSA, ESA, PC or UC (Standard)

• Pensioners in receipt of IS, JSA, ESA, PC or UC (Passported)

• Pensioners not in receipt of IS, JSA, ESA, PC or UC (Standard) 

It is not possible to conduct a review of the total HB population in one year, therefore one or more client groups are usually selected. The rates of the selected group(s) are measured to find those client group’s specific rates. The rates of client groups not reviewed are carried over from when they were previously measured. The total HB rate is then calculated by extrapolating the rate of the groups measured, and those carried over from previous measurements, by their expenditure proportion. Any changes observed at a total level can only be attributed to the group(s) reviewed. To provide a simple example: 

Assume that all 4 groups are of equal proportion (25% of expenditure) and the last time each group was measured found an overpayment (op) rate of 1%, resulting in a total HB op rate of 1%. If HSWA was found to have an op rate of 2%, the total HB op rate would change based on this new proportion – in this instance being 25% (or 1 over 4) of the total. The equation below shows this relationship: 

HB Total =

(HSWA rate ÷ HSWA proportion) + (HSPA rate ÷ HSPA proportion) +

(HPWA rate ÷ HSWA proportion) + (HPPA rate ÷ HPPA proportion)

= (0.02 ÷ 4) + (0.01 ÷ 4) + (0.01 ÷ 4) + (0.01 ÷ 4)

= 0.0125 = 1.25%

An additional factor in total HB rates is the shifting proportions of the client groups between each year. If one group is more prone to higher rates of op and that group saw a reduced proportion of the total HB expenditure relative to the other groups, the total HB op rate may appear to fall when that group simply makes up a smaller proportion of the total. Using the same rates as the previous example, if the proportion of HSWA dropped from 25% to 12.5% (or 1 over 8) and HSPA increased from 25% to 37.5% (or 3 over 8) compared to the other client groups, it would result in a lower total HB op rate as shown below: 

HB Total =

= (0.02 ÷ 8) + 3 x (0.01 ÷ 8) + (0.01 ÷ 4) + (0.01 ÷ 4)

= 0.0113 = 1.13%

Comparing 1.13% to the previous example’s 1.25% shows that simply changing the proportions of the client groups can result in total HB op changing.  

Our Fraud and Error statistics take into account these expenditure proportions based on the most recent Spring Statement figures provided.  

Methodology for Benefits previously reviewed 

Some benefits which were not measured this year were measured in previous years. For these benefits we apply the rate from the last time the benefit was measured to the current year’s expenditure, to get an estimate of the monetary value of fraud and error. 

Benefits that have been previously reviewed account for 12% of the total benefit expenditure.  

Methodology for Benefits never reviewed 

As mentioned in section 2 “Changes”, we use multi criteria decision analysis to choose which benefits we should measure each year. Some benefits have a small amount of expenditure and therefore are unlikely to ever be selected for measurement. For each of the benefits that have never been reviewed, we use a similar benefit or passporting benefit’s rate of fraud and error as a proxy. We then apply that to the expenditure on that unreviewed benefit to get an estimate of the monetary value of fraud and error. 

For more information on the proxies used, please see the “Benefits never reviewed” section of Appendix 2. 

Benefits that have never been reviewed account for 2% of the total benefit expenditure. 

Central Estimate and Confidence Intervals 

The percentage estimate (i.e., the overall rate of fraud and error) is the sum of the monetary value of fraud and error for all benefits reviewed this year, those reviewed in previous years, those never reviewed and interdependencies, divided by the overall expenditure. This is done independently for Fraud, Claimant Error, Official Error and the overall fraud and error.  

The central estimate is the estimate obtained from the sample data. It provides our best guess of the unknown value that we are trying to measure. 

Confidence intervals are calculated for the percentage estimates, to quantify the statistical uncertainty associated with the central estimate. Some adjustments are made to the individual benefits before this is calculated for overall fraud and error: 

• Confidence intervals for benefits reviewed previously are deliberately widened

• Confidence intervals may be widened further if it is believed that non-sample error could impact the accuracy of the estimates. For more information on non-sample error please see Section 6 “Accuracy and Reliability”

• Confidence intervals are widened for the benefits never reviewed, whereby the standard error is assumed to be 40% of the central estimate, to reflect greater uncertainty given the less robust method of estimation for these benefits

The uncertainty surrounding a central estimate is associated with both the variance of the outcome within the sample and the size of the sample from which it is calculated. A 95% confidence interval is used to indicate the level of uncertainty. It shows the range of values within which we would expect the true value of the estimate to lie. A wider range for the confidence interval implies greater uncertainty in the estimate. 

Confidence intervals are calculated using a statistical technique called bootstrapping. It is used to approximate the sampling distribution for the central estimate. The sampling distribution describes the range of possible values, for the central estimate, that could occur if different random samples had been used. 

Bootstrapping is a computationally intensive technique that simulates resampling. A computer program is used to take 4,000 resamples with replacement, of equal size, from the initial sample data. The percentage rate of fraud and error is calculated for each of the resamples. These estimates are ordered from smallest to largest and this gives the approximated sampling distribution. 

The 95% confidence intervals are obtained from the bootstrapping results, by taking the 100th estimate (2.5th percentile) and the 3,900th estimate (97.5th percentile). We also check the median estimate (50th percentile) against our actual central estimate to ensure that no bias exists. 

Calculation of Net Loss estimate 

Recoveries refer to money recovered in the same financial year as the overpayment estimates, regardless of the period the debt is from. They include debt recovered by both the Department and Local Authorities (who administer Housing Benefit payments). The recovery data for Housing Benefit covers the period October 2023 to September 2024, due to a time lag on the data being available.  

Net loss is calculated as a monetary amount by subtracting the value of the recoveries from the value of the overpayments. The percentage estimate is then calculated by dividing the monetary net loss by the expenditure. Net loss can only be calculated at the overall fraud and error level because error classification differs between overpayments and recoveries. 

As the recoveries are actual values rather than estimates, the calculation of net loss does not affect the uncertainty around the overpayment estimates. The confidence intervals for net loss are calculated by subtracting the value of the recoveries from the upper and lower confidence limits. Percentage confidence intervals are obtained by dividing by the expenditure. 

Net loss is calculated overall and individually for benefits reviewed this year and previously reviewed benefits. Net loss is also calculated for the group of benefits never reviewed combined, by subtracting all recoveries relating to these benefits from the total overpayments. 

Some recoveries have no associated overpayments for the same period, as these benefits are no longer administered by the Department. This is because the debt relates to expenditure from previous years. In addition, some recoveries the Department makes are not included in our net loss estimate as they do not relate to our fraud and error reporting.  For example, recoveries of tax credits and of benefit advances (which are outside the scope of our measurement; see section 3 for more details). 

The overall net loss estimate includes the benefits reviewed this year, benefits reviewed in previous years, benefits never reviewed and recoveries for which there is no overpayment. 

6. Quality Report

Introduction

This section of the document assesses the quality of the fraud and error in the benefit system national statistics using the European Statistics System Quality Assurance Framework. This is the method recommended by the Government Statistical Service Quality Strategy. Statistics are of good quality when they are fit for their intended use.

The European Statistics System Quality Assurance Framework measures the quality of statistical outputs against the dimensions of:

• relevance

• accuracy and reliability

• timeliness and punctuality

• comparability and coherence

• accessibility and clarity

The Government Statistical Service also recommends assessment against 3 other principles in the European Statistics System Quality Assurance Framework. These are: 

• trade-offs between output quality and components

• balance between performance, cost and respondent burden

• confidentiality, transparency and security

These dimensions and principles cross the three pillars of trustworthiness, quality and value in the Code of Practice for Statistics. 

Relevance

Relevance is the degree to which statistics meet the current and potential needs of users.

The Department for Work and Pensions (DWP) fraud and error in the benefit system statistics provide estimates of fraud and error for benefits administered by the DWP and local authorities. 

The series has been developed to provide information to various users for policy development, monitoring and accountability, as well as providing academics, journalists and the general public, data to aid informed public debate. 

The statistics: 

• include DWP benefits and those administered by local authorities

• are the primary DWP indicator for levels of fraud and error in the benefit system

• are in the DWP business plan

• are important for DWP assurance on the impact of anti-fraud and error activity across the business

The publication is essential for providing our stakeholders with: 

• a consistent time series for assessing fraud and error trends over time

• data to assess current DWP fraud and error policy and evaluate recent changes to these or business processes

• the evidence base for assessing the potential effect of future fraud and error policy options and programmes

• robust data to inform future measurement options

• estimates of fraud and error for the DWP annual report and accounts

• data to measure government performance relating to objective 5 of the DWP single departmental plan: transform our services and work with the devolved administrations to deliver an effective welfare system for citizens when they need it while reducing costs, and achieving value for money for taxpayers. Read the latest plan (correct at the time of publication of this document, May 2025)

• estimates that feed into the annual HM Revenue and Customs National Insurance Fund Accounts 

We recognise that our users will have different needs and we use a range of different methods to contact them. We frequently meet internal DWP users to discuss their requirements. As for external stakeholders, we often contact the National Audit Office as they audit DWP’s annual report and accounts which include estimates of the amount of money overpaid and underpaid to claimants. We also occasionally contact HM Revenue and Customs and the Cabinet Office. 

Engagement with other external users is usually through the DWP statistical pages of this website where we: 

• invite users to share their comments or views about our National Statistics, or to simply advise us how they use our statistics

• advise users of updates and changes to our statistics through the future statistics release calendars and our fraud and error in the benefit system collection page

• consult with customers on developments and changes to our statistical methodologies, publications or publication processes. We last carried out a consultation in the Summer of 2018

Accuracy and Reliability 

Accuracy is the closeness between an estimated result and the unknown true value. Reliability is the closeness of early estimates to subsequent estimated values. 

The statistics are calculated from the results of a survey sample, which are recorded on an internal DWP database. The survey combines data collated from DWP administrative systems and local authority owned Housing Benefit systems, with data collected from the claimant during an interview. 

The estimates obtained are subject to various sources of error, which can affect their accuracy. Both sampling and non-sampling error are considered in producing the statistics. 

Sampling error arises because the statistics are based on a survey sample. The survey data is used to make conclusions about the whole benefit caseload. Sampling error relates to the fact that if a different sample was chosen, it would give different sample estimates. The range of these different sample estimates expresses the sample variability. Confidence intervals are calculated to indicate the variability for each of the estimates. More detail on central estimates and confidence intervals is provided in section 3. 

Sources of non-sampling error are difficult to measure. However, where possible, these uncertainties have been quantified and combined with the sampling uncertainties, to produce the final estimates. Quality assurance processes are undertaken to mitigate against particular types of error (for example, data entry error). 

Possible sources of non-sampling error that may occur in the production of the fraud and error statistics include: 

• Data entry error – the survey data is recorded on a database by DWP staff. Data may be transcribed incorrectly, be incomplete, or entered in a format that cannot be processed. This is minimised by internal validation checks incorporated into the database, which can prevent entry of incorrect data and warn staff when an unusual value has been input. Analysts undertake further data consistency checks that are not covered by the internal database validations 

• Measurement error – the survey data collected from the benefit reviews are used to categorise an outcome for each case. The correct categorisation is not always obvious, and this can be recorded incorrectly, particularly for complex cases. To reduce any inaccuracies, a team of expert checkers reassess a selection of completed cases before any statistical analysis is carried out. This evidence is used as a feedback mechanism for the survey sample staff and for the statistical analysis 

• Processing error – errors can occur during processing that are caused by a data or programming error. This can be detected by a set of detailed quality assurance steps that are completed at the end of each processing stage. Outputs are compared at each stage to identify any unexpected results, which can then be rectified 

• Non-response error – missing or incomplete data can arise during the survey. Supporting evidence to complete the benefit review may not be provided, or the claimant may not engage in the review process altogether. In other cases, the benefit review may not have been completed in time for the analysis and production of results. An outcome is imputed or estimated in these cases, by different methods that are detailed in this document 

• Coverage error – not all of the benefit caseload can be captured by the sampling process. There is a delay between the sample selection and the claimant interview, and also a delay due to the processing of new benefit claims, which excludes the newest cases from being reviewed. An adjustment is applied to ensure that the duration of benefit claims within the sample accurately reflects the durations within the whole caseload 

The list above is not exhaustive and there are further uncertainties that occur due to assumptions made when using older measurements for benefits that have not been reviewed this year. There are also some benefit-specific adjustments that are part of the data processing. 

More detailed information about the quality of the statistics can be found earlier in this document. This includes discussion of the limitations of the statistics, possible sources of bias and error, and elements of fraud and error that are omitted from the estimates.

Timeliness and Punctuality

Timeliness refers to the time gap between the publication date and the reference period for the statistics. Punctuality is the time lag between the actual and planned dates of publication for the statistics. 

The fraud and error in the benefit system report is usually published around 7 months after the main reference period. 

Due to the time taken to undertake the interviews and gather follow up information, final data from the reviews is not made available to analysts until 4-5 months after the start date of the last interviews. The production of the statistics and reference tables, and the associated clearance processes, then takes the analytical team about 2 months to prepare.  

DWP pre-announce the date of release of the fraud and error in the benefit system report at least 4 weeks in advance on this website and the UK Statistics Authority publication hub, in accordance with the Code of Practice for Statistics. 

The statistics are published at 9.30am on the day that is pre-announced. The release calendar online is updated at the earliest opportunity to inform users of any change to the date of the statistical release and will include a reason for the change. All statistics will be published in compliance with the release policies in the Code of Practice for Statistics. 

Comparability and Coherence 

Comparability is the degree to which data can be compared over time, region or another domain. Coherence is the degree to which the statistical processes that generate two or more outputs use the same concepts and harmonised methods. 

Our publication provides information on the estimates over time. Where breaks in the statistical time series are unavoidable, users are informed within the report by a text explanation, with clear sectioning within the time series reference tables and detailed footnotes. 

Any changes made to the DWP or local authority administrative system data are assessed in terms of their impact on fraud, error and debt strategy and policy. These are then impacted against the fraud and error measurement review process and communicated to our internal users and the National Audit Office through our change of methodology log. The same is true for any changes made to business guidance, processes and review methodology, as well as our own calculation methodology. 

We agree some methodology changes in advance with internal stakeholders using change request and change notification procedures. 

External users are notified of any changes to methodology in the ‘Methodology Changes’ section of the fraud and error in the benefit system report. Substantial changes to the report structure or content will be announced in advance on the fraud and error in the benefit system collection. 

The fraud and error in the benefit system statistics form the definitive set of estimates for Great Britain. They are underpinned by reviews of benefit claimants in England, Wales and Scotland. 

The benefit expenditure figures used in the publication also include people resident overseas who are receiving United Kingdom benefits, except for Financial Assistance Scheme payments, which also cover Northern Ireland. All other benefit expenditure on residents of Northern Ireland is the responsibility of the Northern Ireland Executive. The benefit expenditure figures do not include amounts devolved to Scottish Government (which is forecasted to be £5.2 billion in FYE 2025). Reporting the levels of fraud and error of this benefit expenditure is the responsibility of Social Security Scotland. Their estimates for FYE 2024 were published as part of their annual report. 

Northern Ireland fraud and error statistics are comparable to the Great Britain statistics within this report, as their approach to collecting the measurement survey data, and calculating the estimates and confidence intervals is very similar. Northern Ireland fraud and error in the benefit system high level statistics are published within the Department for Communities annual reports. 

HM Revenue and Customs produce statistics on error and fraud in Tax Credits. Again, these estimates can be compared to form a whole benefit view. 

Accessibility and Clarity

Accessibility is the ease with which users can access the statistics and data. It is also about the format in which data are available and the availability of supporting information. Clarity refers to the quality and sufficiency of the commentary, illustrations, accompanying advice and technical details. 

The reports and reference tables can be accessed on the statistics pages on this website and the UK Statistics Authority publication hub. 

Fraud and error in the benefit system statistics follow best practice and guidance from the Government Digital Service and Government Statistical Service, in publishing statistics that give equality of access to all users. 

For data protection reasons, the underlying datasets are not available outside DWP. However, the reference tables published alongside the report provide detailed estimates, giving a breakdown of overpayments and underpayments into the different types of fraud and error, for the benefits measured in that year. The reference tables are available in both standard and accessible formats. 

Technical language is avoided where possible within the report. To help users, the report contains definitions of key terms such as: Fraud, Official Error and Claimant Error. A more extensive glossary of terms and error types is included in Appendix 1. 

Contact details are provided for further information on the statistics, guidance on using the statistics, data sources, coverage, data limitations and other necessary relevant information to enable users of the data to interpret and apply the statistics correctly. 

Trade-offs Between Output Quality and Components

Trade-offs are the extent to which different dimensions of quality are balanced against each other. 

The main trade-off for these statistics is timeliness against accuracy. We always assess the right balance taking into account fitness for purpose, and fully explaining any compromises in accuracy for improved timeliness. 

We wait a considerable amount of time for the data to be as complete as possible before our publication process begins, to ensure that the estimates are based on data which is as final and robust as possible. This means that we usually publish data around 7 months after the main reference period. 

Balance Between Performance, Cost and Respondent Burden 

The DWP fraud and error in the benefit system statistics are produced from survey data which have a high respondent burden. A compulsory interview, lasting between approximately 30 minutes and 2 hours, is required for all cases sampled for claimant error and fraud checking. 

The total DWP cost for production of these statistics is approximately 154 staff (full-time equivalent).  

DWP are continuously looking at more cost effective and efficient options for sourcing and collecting data, reducing the burden on the respondent and the production of the estimates. 

Confidentiality, Transparency and Security 

All of our data is handled, stored and accessed in a manner which complies with government and departmental standards regarding security and confidentiality, and fully meets the requirements of the Data Protection Act (2018). 

Access to this data is controlled by a system of passwords and strict business need access control. 

Any revisions to our publications are handled in accordance with the department’s revisions policy. 

7. Future Reporting

The future coverage and scope of the national statistics “Fraud and Error in the Benefit System” is kept under review and users are kept informed of our plans via our Publication Strategy document.

Appendix 1: Glossary of abbreviations

AA	Attendance Allowance
CA	Carer’s Allowance
CE	Claimant Error
CF	Claimant Fraud
DLA	Disability Living Allowance
DWP	Department for Work and Pensions
ESA	Employment and Support Allowance
FEMA	Fraud and Error Measurement and Accuracy
FYE	Financial Year Ending
HB	Housing Benefit
HMRC	His Majesty’s Revenue and Customs
IS	Income Support
JSA	Jobseeker’s Allowance
LA	Local Authority
MVFE	Monetary Value of Fraud and Error
OE	Official Error
OP	Overpayment
PC	Pension Credit
PIP	Personal Independence Payment
PM	Performance Measurement team
PSU	Primary Sampling Unit
SP	State Pension
UC	Universal Credit
UP	Underpayment

Appendix 2: List of benefits included in fraud and error estimates

Benefits reviewed this year

Universal Credit  

State Pension

Housing Benefit - Working Age in receipt of IS, JSA, ESA, PC or UC (or passported working age client group). The cases sampled in FYE 2025 were those in receipt of ESA with no tax credits and those in receipt of UC in supported or temporary accommodation.

Carer’s Allowance

Pension Credit

Personal Independence Payment

Benefits reviewed previously

Employment Support Allowance (FYE 2023) 

Attendance Allowance (last reviewed FYE 2022) 

Disability Living Allowance (last reviewed FYE 2024) 

Housing Benefit: 

• pension age (last reviewed FYE 2024) 

• non-passported working age (last reviewed FYE 2023) 

Jobseeker’s Allowance (last reviewed FYE 2019) 

Income Support (last reviewed FYE 2015) 

Incapacity Benefit (last reviewed FYE 2011) 

Benefits never reviewed

Maternity Allowance (proxy measure: Employment and Support Allowance rates relating to Abroad, Conditions of Entitlement, Earnings and Contributions only) 

Severe Disablement Allowance (proxy measure: Employment and Support Allowance rates relating to Abroad, Conditions of Entitlement, Earnings and Contributions only) 

Financial Assistance Scheme (proxy measure: State Pension Official Error only) 

Industrial Death Benefit (proxy measure: Pension Credit Living Together rate only) 

Winter Fuel Payments (proxy measure: Rate of whole award errors found on the last measurement of Pension Credit) 

State Pension Transfers (proxy measure: State Pension) 

Cold Weather Payments (proxy measure: Rate of whole award errors found on the last measurement of Employment and Support Allowance, Income Support, Jobseeker’s Allowance, Pension Credit and Universal Credit) 

Widow’s Benefit / Bereavement Benefit (proxy measure: Employment and Support Allowance Contributory only element and rates relating to Conditions of Entitlement, lack of National Insurance Contributions or incorrect recording of National Insurance Contributions only) 

Industrial Disablement Benefit (proxy measure: Personal Independence Payments) 

Armed Forces Independence Payment (proxy measure: Personal Independence Payments) 

Christmas Bonus (proxy measure: Rate of whole award errors found on the last measurement of Attendance Allowance, Carer’s Allowance, Employment and Support Allowance, Pension Credit, Personal Independence Payments and State Pension) 

Cost of Living Payments (proxy measure: Rate of whole award errors found on the last measurement of Universal Credit, Jobseeker’s Allowance, Income-related Employment and Support Allowance, Pension Credit, Personal Independence Payments, Attendance Allowance, Attendance Allowance, Armed Forces Independence Payments, Disability Living Allowance, Winter Fuel Payments) 

Statutory Sick Pay (proxy measure: No fraud and error) 

Statutory Maternity Pay (proxy measure: No fraud and error) 

Appendix 3: Further information on types of errors reported

The definitions of the key terms of Fraud, Claimant Error and Official Error are included at the start of this document. This section includes additional information on how we classify errors, including a detailed list of the types of errors we report for benefits reviewed in the current year. 

Note that our methodology states that all errors (Fraud, Claimant Error and Official Error) found on a case are recorded separately and the full values of each error are recorded in isolation of one another. This can lead to the sum of the error values being higher than the benefit award. In such cases a capping calculation is performed (using a Fraud, Claimant Error, Official Error hierarchy) to ensure that the sum of the errors does not exceed the award, so that the monetary value of fraud and error is not over-reported. This can lead to some of the originally captured Fraud, Claimant Error and Official Error raw sample values being reduced during the calculation of the estimates. 

In addition, it should be noted that an error which is initially categorised as claimant error, will instead be categorised as official error where the business unit within the Department/LA is in possession, from whatever source, of the true facts, regardless of whether the information has been processed by that business unit. 

A glossary of the current error types for overpayments and underpayments is given below:  

• Abroad – claimant and/or partner left Great Britain after claim began, did not notify DWP before leaving and it is established that they were abroad for a period that exceeds any allowable absence limit. State Pension abroad errors are different in that being abroad does not remove their entitlement to the benefit, however, the uprating of State Pension differs depending on the country of residence  

• Award Determination – where a Case Manager from the DWP makes an incorrect award of PIP, DLA or AA based on the declared functional needs of the claimant. This includes failing to consider the qualifying period  

• Capital – concealed or incorrect declarations, or failure to declare changes in the amount of savings in bank or building society accounts, cash, ISA/PEPs, premium bonds, other property interests or shares that exceed the minimum value for capital limits  

Capital official errors include incorrect calculation by DWP staff of the value of declared savings, money and other financial assets available to the claimant, or failure to correctly adjust tariff levels and amend the benefit entitlement due.  

• Childcare Costs – Childcare costs incorrectly included or excluded or an incorrect declaration of the amount of childcare costs for the childcare element of UC 

• Conditions of entitlement – Incorrect declarations or undeclared changes in the personal circumstances of a claimant or their partner, that would end entitlement to a benefit. Examples are being in full-time education, long-term hospitalisation, imprisonment, death and assuming a false identity

Includes staff failing to act on information received raising doubt on basic entitlement to benefit. For Universal Credit this includes the agent accepting the claimant commitment on behalf of a claimant who has the capacity required to accept their claimant commitment. 

• Contributions – errors where the National Insurance record is incorrect, including where HMRC has failed to record, or incorrectly recorded Child Benefit within the record. Additionally, errors caused by failure by DWP to action a change to the award of State Pension following receipt of information from HMRC  

• Control Activities are not carried out appropriately– failure of staff to conduct actions at the due time which otherwise may have changed the level of benefit payable. For example, failure to review a Pension Credit claim at the end of an Assessed Income Period 

For State Pension these errors generally relate to failure to take action at age related trigger points or failure to action a change in the claimant’s marital status. 

• Earnings/Employment – concealment or under-declaration of full or part-time work undertaken during the claim by the claimant or their partner. This work can be for an employer or self-employment  

Includes staff failing to correctly calculate the amount of monthly benefit due for claimants who have declared any paid work they or their partner have undertaken during the claim.  

• Element/Premium/Components – Elements (UC only) – The award of UC is made up of a number of different Elements. Some of these are treated in this report in a similar way to Premiums in Legacy benefits, for example Carer Element, Disabled Child Element and Work Capability Elements. Child Care Costs and Housing Costs (also Elements in their own right) are recorded separately 

Premium: DWP can pay additional amounts in means-tested benefits when other benefits are also being paid, for example Disability or Carers Allowance. This often introduces additional criteria for staff to consider before deciding the qualification for the extra amounts.  

Components (SP only): The award of SP is made up of a number of different Components (for example Graduated Pension, State Second Pension, Additional Pension etc). This often introduces additional criteria for staff to consider before deciding the qualification for the additional amounts. 

Includes staff failing to or incorrectly award an element, premium or component. On Universal Credit this could also include errors arising from claimants failing to declare they are no longer caring. 

• Failure to provide evidence/Fully engage in the process – Errors caused by the claimant failing to take part in the review or provide evidence requested during the review and the reason for doing this is unclear. We are confident that the case is fraudulent, as the claimant has forgone their right to benefit, but with the evidence we have available, we cannot be certain as to why 

• Functional Needs – where the claimant has failed to declare a change in their ability to carry out any of the activities on which PIP or DLA or AA is considered or misrepresented their abilities when making their claim (whether intentionally or not) 

• Hospital/Registered Care Home – AA, PIP and DLA (excluding DLA children) is not normally payable for any period or periods of more than 28 days, during which a customer is being maintained free of charge whilst undergoing treatment as an in-patient in a hospital or similar institution or is resident in a care home where the Local Authority (LA) meets the costs of any of the qualifying services 

For UC couple cases, a person who is living away from their partner ceases to be treated as a member of a couple and part of the same benefit unit, where they are absent from the household, or expected to be absent from the household, for 6 months or more. 

Errors are caused by the claimant failing to declare a period in hospital/care. Includes staff failing to action a change where this has been reported. 

• Household composition – failing to disclose changes in household composition, for example a non-dependant leaving. Claiming incorrectly for children which increases the value of DWP benefits payable, or claiming for adult partners who leave the household  

Includes incorrect action by staff, taken in respect of other people the claimant declared living with them. This includes a partner or dependent children incorrectly omitted or included in the assessment (with due regard to entitlement to any disability premium or benefit reductions due to long-term hospitalisation).  

• Housing costs – DWP provides financial assistance with the costs of ground rent and service charges. Claimants fail to declare changes to housing costs. For example, service charges or a change of address which would end the extra amounts payable. For Universal Credit and Housing Benefit this can include calculation of the rent amount, correctly incorporating rent free weeks and size criteria  

Staff can also inaccurately calculate the ground rent or service charges.  

• Income - Occupational and Personal Pensions – concealed or incorrect declarations, or failure to declare changes of income received from a non-state pension, obtained through contributions paid in past employment schemes, annuities or personal investments  

DWP staff failing to take into account the correct amount of non-state pensions declared by the claimant.  

• Income - Other – concealed or incorrect declarations, or failure to declare changes of income coming into the household, from sources such as sick pay from work, spousal maintenance, student income, unemployment or similar insurance policy payments  

Failure by staff to correctly identify or record other money coming into the household, such as Child Benefit, sick pay from work, spousal maintenance, student income, tax credits, unemployment or similar insurance policy payments. 

• Income - DWP benefits – concealed or incorrect declarations, or failure to declare changes of income received from another DWP benefit 

DWP IT systems or staff have failed to take into account the correct value of other social security benefits currently paid to the claimant or partner, including benefits paid by a foreign state. 

• Living Together – where a claimant declares to be single but fails to declare they actually live with another person and maintain a joint household 

• Passporting – relevant to Housing Benefit only. Some claimants gain entitlement to HB through receipt of certain means tested DWP benefits. Errors include communication failures between different IT systems that notify the termination of a claimant’s means-tested DWP benefit to the Local Authority, impacting the HB award, or LA staff failing to act on the information received 

• Residency – errors relating to Housing Benefit claimants only, where DWP confirms that the claimant no longer lives at the address being paid for. For Universal Credit, where the claimant no longer lives at the address for which they are being paid Housing Costs Element, the error would appear in Housing Costs

• Uprating – errors where the IT system that pays State Pension has applied an annual increase to an element of State Pension incorrectly or where the DWP system has incorrectly uprated the benefit being measured

• Other – this covers a range of different cases not covered in the categories above

Appendix 4: Glossary of Statistical terms

Key statistical terms used in this report are explained below.

95% Confidence Interval  

The range of values within which we can be 95% sure that the true value we are trying to estimate lies. It is used as a measure of the statistical uncertainty in an estimate. 

Estimate  

An estimate is an indication of the value of an unknown quantity based on observed data. It provides information about unknown values in the population that we are trying to measure. 

Population 

A population is any entire collection of items from which we may collect data. It is the entire group that we are interested in, which we wish to describe or to draw conclusions about (generally benefit claimants or expenditure in the context of this report). There are two different types of population referred to in sampling: 

1. The Target Population - consists of the group of population units from whom we would like to collect data (e.g. all people claiming a benefit). 

2. The Survey Population - consists of the group of population units from whom we can collect data (e.g. all claimants with sufficient case details on our datasets). The Survey Population is sometimes referred to as the ‘Sampling Frame.’  

Sample 

A group selected (randomly in the context of this report) from a larger group (known as the ‘population’). Through analysing the sample, we aim to draw valid conclusions about the larger group. 

Appendix 5: List of historical methodology changes

Methodology Change	Included in which published report
Changes to sampling and calculation methods were aimed primarily at making the estimates better represent overpayments in the whole of IS, JSA and PC expenditure.	Fraud and Error in the Benefit System April 2005 to March 2006 Spending Review 2004 target baseline
Definitional changes to what is being measured were introduced primarily to make the estimates better relate to the actual impact of fraud and error on expenditure.	Fraud and Error in the Benefit System: April 2008 – March 2009 – Revised Edition
Incapacity Benefit started to be continuously reviewed for fraud and claimant error as well as official error.	Fraud and Error in the Benefit System: April 2008 – March 2009 – Revised Edition
There was a change in this report to the calculation of the sample weightings for Income Support, Jobseeker’s Allowance and Pension Credit.	Fraud and Error in the Benefit System: April 2009 – March 2010
During 2011 the Fraud and Error Measurement (FEM) team have carried out a series of changes to the calculation processes and methodology in order to simplify and align these across the individual benefits. This work has made our processing quicker, more efficient, robust and transparent and easier to quality assure. This in turn will reduce risk in our calculation processes and will enable our team, in the future, to make changes to the computer programs more easily, especially with the advent of Universal Credit, and be more flexible with resources. The new aligned processing system will also make it easier for our customers to interpret and compare findings across the individual benefits.	Fraud and Error in the Benefit System: FYE 2011 Estimates
We have introduced a change to the order in which Income Support, Jobseeker’s Allowance and Pension Credit errors are capped within our calculation methodology. They are now capped for fraud first, then claimant error, then official error. In previous reports they were capped for official error first, then fraud, then claimant error. This change aligns the above benefits with the Housing Benefit capping hierarchy.	Fraud and Error in the Benefit System: FYE 2011 Estimates
Since the introduction of Employment and Support Allowance in October 2008 no new claimants have been awarded Incapacity Benefit as they claim Employment and Support Allowance instead. In addition, all current claimants of Incapacity Benefit are being reassessed and will be moved to either Employment and Support Allowance or Jobseeker’s Allowance in the near future. For this reason, we have stopped measuring Incapacity Benefit for fraud and error on a continuous basis and have re-deployed resources to measure Employment and Support Allowance for Official Error instead, which was reported for the first time in May 2013. For this report and after, the preliminary 2010/11 Incapacity Benefit estimates will be used in our publications and applied to the latest expenditure figures to provide the most up-to-date monetary values of fraud and error.	Fraud and Error in the Benefit System: FYE 2011 Estimates
A new error code framework was introduced in April 2010 following internal stakeholder consultation and agreement to have more meaningful information on the types of fraud and error. The 2010/11 publication was the first report to include these error code breakdowns. The breakdowns are not comparable to previously published error code breakdowns, i.e. reports before 2010/11.	For this report and after, the preliminary 2010/11 Incapacity Benefit estimates will be used in our publications and applied to the latest expenditure figures to provide the most up-to-date monetary values of fraud and error.
A change was made to the way in which the extrapolation adjustment was being calculated for Income Support, Jobseeker’s Allowance and Pension Credit to ensure it was based on up-to-date assumptions for these benefits. This change was introduced from the Preliminary 2011/12 report onwards.	Fraud and Error in the Benefit System: FYE 2012 Estimates
Removal of stratifications for Pension Credit by age (over 80 and under 80): This is a sampling change that was implemented from October 2012; therefore, this is the first set of statistics that is partly affected by this change.	Fraud and Error in the Benefit System: FYE 2013 Estimates
Change to significance testing for benefits reviewed this year: Improved methodology where we use the bootstrapped values of the estimates of both years. We calculate the difference between each of the bootstrapped values and calculate the 95% confidence interval around the mean. If this confidence interval does not straddle zero, the change is marked as “statistically significant”.	Fraud and Error in the Benefit System: FYE 2013 Estimates
Employment and Support Allowance estimates of fraud and claimant error are included in the “Continuously Reviewed” estimates for the first time in the Preliminary 2013/14 results. Full reviews began in October 2012. There is an impact on the Global Estimates as previously fraud and claimant error were estimated using a proxy measure, combining both Incapacity Benefit and Income Support Disabled and Others results.	Fraud and Error in the Benefit System: FYE 2014 Estimates
An additional level of stratification was introduced into the Pension Credit single review sampling from April 2013. The new classification is based on characteristics of the claim, as held in our administrative records. This modification will not lead to any systematic change in our central estimates, but was introduced to reduce the width of our confidence intervals, thereby improving the precision of our central estimates.	Fraud and Error in the Benefit System: FYE 2014 Estimates
A level of stratification was removed from the Income Support sampling from October 2013. Results can no longer be presented as split by “Lone Parents” and “Disabled/Other”. This modification will not lead to any systematic change in our central estimates, but will increase the width of our confidence intervals, thereby reducing the precision of our central estimates. The reason for the change was the reduction in caseload.	Fraud and Error in the Benefit System: FYE 2014 Estimates
Half of the Income Support single review cases were randomly allocated to receive notified visits from October 2013 onwards. This has had no significant effect on the rates of fraud and error reported.	Fraud and Error in the Benefit System: FYE 2014 Estimates
Housing Benefit measurement methodology changed for the 2014/15 preliminary results. We have aligned the treatments of cases with both overpayments and underpayments across Housing Benefit and the other continuously reviewed benefits in the May 2015 release. This means that both the overpayments amounts and the underpayments amounts have been reduced for Housing Benefit and hence for the total of all benefits. We have also reduced the amount of Claimant Untraceable fraud and error that we count.	Fraud and Error in the Benefit System: FYE 2015 Estimates
Introduced a new ‘Global Net Loss’ measure that takes away the overpayments that the department and Local Authorities recover from the overpayments. Instead of counting all overpayments as a loss to the system, we subtract the amount that the department gets back, giving a more accurate representation of the cash loss to the public purse.	Fraud and Error in the Benefit System: FYE 2015 Estimates
From 2014/15, within the measurement system, there was a change in the way some errors were classified as either claimant error or fraud, following a review of the evidence gathering process by the Performance Measurement teams. The outcome from the review emphasised the need for further questioning to establish the facts around any changes in circumstances. The new data appeared from the 2015/16 Preliminary Estimates. The change gives a more accurate classification of the level of fraud and claimant error across benefits, but it does mean that caution should be used in any comparisons between post 2014/15 results and earlier results. The change is thought to be the main reason for the increase in fraud and a corresponding fall in the level of claimant error, and it may have affected the overall level of total overpayments since its introduction after 2014/15. The new process has been applied to all of the continuously reviewed benefits but has had a particularly large effect on the Housing Benefit estimates.	Fraud and Error in the Benefit System: FYE 2016 Estimates
Universal Credit estimates of fraud and error are included in the “Continuously Reviewed” estimates for the first time in the Preliminary 2015/16 results. Full reviews began in October 2014. There is an impact on the total overpayments and underpayments as previously fraud and error were estimated using a proxy measure.	Fraud and Error in the Benefit System: FYE 2016 Estimates
Universal Credit introduced a methodology change where the statistics have been split into Reviewed and Cannot Review cases. The latter cases are included in the final statistics but calculated using assumptions as opposed to measured data.	Fraud and Error in the Benefit System: FYE 2016 Estimates
The composition of the JSA sample has changed within this publication so that we no longer have a separate sample for newer cases,	Fraud and Error in the Benefit System: FYE 2016 Estimates
JSA grossing no longer includes PSU or the yearly split in the calculation of the grossing factors; instead these have been calculated based solely on the client group. The grossing factors now mainly depend upon the national population for JSA, with the same grossing factor applied to all of the sample cases. This reduces the possibility of a relatively small number of cases with very high grossing factors within the sample having a large influence on the reported results, and has led to a decrease in the range of the confidence intervals around the central estimates for JSA.	Fraud and Error in the Benefit System: FYE 2017 Estimates
PIP estimates for fraud and error were published for the first time within the 2016/17 final results. There is an impact on the total overpayments and underpayments as previously fraud and error on PIP was estimated using DLA as a proxy measure.	Fraud and Error in the Benefit System: FYE 2017 Estimates
Rotational sampling has been introduced, for data collection between April 2017 and September 2019, so that a selection of hard to reach areas will only be selected within the sample at least once in a three-year period. This is the first set of published results that incorporate rotational sampling. Analysis completed on published statistics from previous years shows that the impact would be negligible – with changes to fraud and error levels of less than 0.05% for each benefit affected.	Fraud and Error in the Benefit System: FYE 2018 Estimates
In the 2017/18 preliminary estimates we changed the way we gross ESA and PC estimates to using National grossing factors, bringing these benefits in line with JSA, as well as UC and PIP. National grossing tends to reduce the scope for individual cases to have a higher influence on the reported estimates and, consequently, leads to a decrease in the range of the confidence intervals.	Fraud and Error in the Benefit System: FYE 2018 Estimates
In 2018, we started to simplify and align the methodology for each benefits reviewed that year. The 2017/18 final publication was the first one to use this new, standardised publication process for JSA, ESA, PC, SP (Official Error checks only), HB and UC. For HB, there was a significant change to how grossing and extrapolation were carried out, when bringing it in line with the other benefits. At an overall benefit view, the effect of the change is negligible (less than 0.1%) – however, when viewing at lower levels, such as the working age/pension age split, a bigger difference can be seen. The previous methodology increased the values associated with pension age claimants and decreased the values of the working age claimants more than the new process does. This also has a knock-on effect on published error reason categories at a total HB level, as reasons associated to a greater degree with either working age or pension age claimants will be affected.	Fraud and Error in the Benefit System: FYE 2018 Estimates
The measurement processes are subject to a series of validation checks, which aim to check that the measurement methodology is being correctly implemented. A randomly selected sub-sample of cases are used to create an adjustment across the sample population, by assuming that the same rate of incorrectness/change applies to all cases. The movement to a new publication pipeline methodology and introduction of more refined and targeted data cleansing mean that this Data Quality Adjustment (DQA) is no longer required. The changes resulting from data cleansing and DQA checks are used as the final and correct outcome in the data processing and hence are incorporated directly into the calculations.	Fraud and Error in the Benefit System: FYE 2018 Estimates
The 2017/18 final publication included UC estimates that were based on a composite measure of Live and Full Service cases. Calculation of these statistics required UC expenditure to be split by Full and Live Service so that the estimates for each service could be calculated separately before combining for the overall composite measure. The split applied was based on awards information from the UC caseloads data that underpins the national statistics. This was a temporary approach introduced for the 2017/18 final estimates, re-applied for 2018/19 estimates, and will not be required post 2018/19 as the estimates will be based on entirely Full Service samples.	Fraud and Error in the Benefit System: FYE 2018 Estimates
A new assumption was introduced in the 2017/18 final publication for UC cases that did not have an effective review, primarily due to the claimant not engaging in the review process, resulting in their benefit claim being terminated. These cases are referred to as cannot review cases and are recorded as Fraud. The new assumption involves re-categorising these cases for reporting purposes following the outcome of checks to determine if the individuals had reclaimed benefit. The cases are re-categorised as Fraud, Not Fraud or inconclusive based on whether the individual reclaimed benefit or there was a suspicion of a specific type of fraud recorded on a case (for instance Capital Fraud). Inconclusive cases are not included in the headline statistics and are instead reported separately in a footnote in the publication.	Fraud and Error in the Benefit System: FYE 2018 Estimates
For the 2017/18 final publication, changes were made to the Error Code Framework where we aligned the definition of ‘income – other’ and ‘income – other benefits’ across the six benefits reviewed this year, which has resulted in some small changes for Pension Credit and Housing Benefit pension age customers.	Fraud and Error in the Benefit System: FYE 2018 Estimates
Following the evaluation of the pilot of desk based reviews, for the working age passported Housing Benefit client group, between April 2017 and September 2018, it has been decided to continue with face to face reviews for this client group; therefore, the 2017/18 final and the 2018/19 publications are based solely on face to face review cases. The consequence is that for these two publications the sample sizes were smaller for this client group, and the confidence intervals may be wider than in previous years.	Fraud and Error in the Benefit System: FYE 2018 Estimates
The UC fraud and error estimates in the published national statistics were previously based on Live Service cases only which is the intermediary system in place to administer UC until the full online service is fully rolled out. MVFE estimates were based on the assumption that Full Service fraud and error rates would be similar to those being found in Live Service. The 2018/19 Universal Credit fraud and error estimates are based on Live and Full service cases. In total 1,998 Universal Credit cases were sampled, around 70% of these cases were Full service. The inclusion of Full and Live Service cases required expenditure assumptions to be updated to reflect the latest proportional service splits. The assumption to re-categorise cases that did not have an effective review, introduced in 2017/18, has also been applied to 4% of sample cases in 2018/19.	Fraud and Error in the Benefit System: FYE 2019 Estimates
Between the 2017/18 publications in May and December 2018, we started to simplify and align the methodology for each benefits reviewed this year. This new publication pipeline contained Jobseeker’s Allowance, Employment and Support Allowance, Pension Credit, State Pension (Official Error checks only), Housing Benefit, Universal Credit, Personal Independence Payment.	Fraud and Error in the Benefit System: FYE 2019 Estimates
Rotational sampling was introduced so that a selection of hard to reach areas are only selected within the sample, at least once in a three-year period. This rotational sampling was introduced for data collection between April 2017 and September 2019.  The areas excluded this year are: October 2018 – September 2019: Cornwall, West Devon, South Hams, Teignbridge, Torridge, Highlands, Moray, and Argyll and Bute.	Fraud and Error in the Benefit System: FYE 2020 Estimates
We have strengthened the process for how we deal with combination errors on Housing Benefit as new data has become available to us.	Fraud and Error in the Benefit System: FYE 2020 Estimates
We have strengthened the process for how we deal with Housing Benefit cases that have a whole award error and an underpayment. Previously the underpayment would have been taken off the whole award within the netting and capping part of the process, as we would not know how much of the underpayment was valid. In the new process we look to see what the end award of Housing Benefit is after the review, to ensure we more accurately reflect what the loss to the department would have been. For example, if the overpayment would have removed entitlement to Housing Benefit altogether, then we would remove the underpayment.	Fraud and Error in the Benefit System: FYE 2020 Estimates
Removal of Arrears Advance: This adjustment only affected Housing Benefit. This adjustment was applied to account for cases that were in arrears or advance as these cases could have more/less error at the time the payment relates to.	Fraud and Error in the Benefit System: FYE 2020 Estimates
In 2019/20 we have rolled out the Cannot Review assumption from Universal Credit to the other benefits measured in 2019/20 for cases that did not have an effective review, primarily due to the claimant not engaging in the review process, resulting in their benefit claim being terminated. These cases are referred to as cannot review cases and are recorded as Fraud. The new assumption involves re-categorising these cases for reporting purposes following the outcome of checks to determine if the individuals had reclaimed benefit. The cases are re-categorised as Fraud, Not Fraud or inconclusive based on whether the individual reclaimed benefit or there was a suspicion of a specific type of fraud recorded on a case (for instance Capital Fraud). Inconclusive cases are not included in the headline statistics and are instead reported separately in a footnote in the publication. Previously, Official Error was not netted off from Fraud and Claimant Error on Employment and Support Allowance, Jobseeker’s Allowance (when it was last measured in 2018/19), Pension Credit and Housing Benefit, as they are reviewed in two different periods. When a review is carried out for these cases it is noted whether the Official Error continues into the review week. In over 99% of these cases the error was still there in period when the Fraud and Claimant Error was checked, and therefore netting it off would give a more accurate reflection of the true loss to the department. The note on whether the Official Error continued to the point when the Fraud and Claimant Error check is carried out is not on Housing Benefit. However, since the other benefits mentioned above and Housing Benefit have the same time lag between the Official Error checks and the Fraud/Claimant Error checks, we are confident we can apply this to Housing Benefit as well.	Fraud and Error in the Benefit System: FYE 2020 Estimates
We have rolled out the Universal Credit approach for dealing with multiple whole award errors to the other measured benefits.	Fraud and Error in the Benefit System: FYE 2020 Estimates
Removal of the Net Programme Value adjustment: This adjustment is only made on cases where a Living Together error was found. Previously we would have looked to see if they were still eligible for benefits after the review, and then changed the whole award Living Together error, to the difference between the amount of DWP benefits the claimant and partner were getting before the review and the amount of DWP benefits the claimant and partner were getting after the review. This was done to more accurately reflect the loss to the department. We have decided to remove this adjustment as it only affects a small number of cases (there were less than 10 of these cases in 2018/19)	Fraud and Error in the Benefit System: FYE 2020 Estimates
Move to monthly grossing: We have rolled out the Universal Credit approach for grossing to the other benefits.	Fraud and Error in the Benefit System: FYE 2020 Estimates
This year we have made a change to how we attribute the amount overpaid or underpaid to error reasons. For cases that have multiple errors, when capping the error values, we will attribute amounts to reasons in order of which we are most certain of. Any Fraud that is Causal Link (Low Suspicion) has been recategorised to a new category of “Failure to provide evidence/fully engage in the process”. This change has no effect on the amount overpaid or underpaid at a total level or an error type level (i.e. Fraud, Official Error, Claimant Error).	Fraud and Error in the Benefit System: FYE 2021 Estimates
State Pension changes: Prior to this year we removed some accounting errors from SP, where an overpayment (or underpayment) error on SP was offset by an equivalent underpayment (or overpayment) of the same amount on Pension Credit. However, this was not the correct approach. We have now stopped removing these accounting errors from within SP this year. The impact of making this change is small (adding £1m to overpayments this year and £8m to underpayments).	Fraud and Error in the Benefit System: FYE 2021 Estimates
State Pension changes: We have made a change to remove cases that have a deemed error on them from the calculation of the Official Error rate.	Fraud and Error in the Benefit System: FYE 2021 Estimates
State Pension changes: We have made a small change to the calculation of our overall estimates for SP to better reflect changes in the split of expenditure each year between claimants resident in GB and those resident overseas.	Fraud and Error in the Benefit System: FYE 2021 Estimates
State Pension changes: We have also made changes to remove the proportion of fraud and error relating to Dependency Increases from our SP estimates. This has only affected the estimates for FYE 2021 and not previous years.	Fraud and Error in the Benefit System: FYE 2021 Estimates
The pandemic has driven a move to complete almost all reviews by telephone rather than face to face home visits.	Fraud and Error in the Benefit System: FYE 2022 Estimates
Housing Benefit coverage: In FYE 2022, only non-passported working age claims were reviewed. This means that in FYE 2022, the non-passported working age estimates relate to reviews undertaken in FYE 2022, while the passported pension age estimates relate to reviews undertaken in FYE 2020, and the estimates for the remaining groups (passported working age and non-passported pension age) relate to reviews undertaken in FYE 2019. The rates of fraud and error found when each group was last reviewed were applied to the FYE 2022 expenditure, to calculate the total HB estimate for FYE 2022.	Fraud and Error in the Benefit System: FYE 2022 Estimates
In order to calculate the monetary value of fraud and error and the proportion of expenditure overpaid we use DWP expenditure figures. Within these figures any case that was getting Housing Benefit and Universal Credit was classed as a non-passported Housing Benefit case. Although not strictly a passporting benefit, those getting Universal Credit and Housing Benefit are treated in a similar way to passported cases (if they are entitled to any Universal Credit their Housing Benefit is paid in full). Due to this we have made the change to the expenditure on Housing Benefit to classify these cases as passported and revised the last year’s figures.	Fraud and Error in the Benefit System: FYE 2022 Estimates
Proportion of claims with fraud or an error: We introduced a “net” measure of the proportion of claims with fraud or an error to supplement existing tables. This measure is consistent with the calculation of the headline Monetary Value of Fraud and Error (MVFE) and is now reported alongside MVFE in the main statistical release.	Fraud and Error in the Benefit System: FYE 2023 Estimates
De minimis: Following a review, the removal of all uprating  errors of 10p or less on State Pension has been extended to all benefits and reasons for error. Updated figures using this methodology have also been produced for FYE 2022.	Fraud and Error in the Benefit System: FYE 2023 Estimates
Housing Costs Capping on Universal Credit: The value of Housing Costs overpayments will now be limited (capped) to the amount of housing element in payment on a UC claim. This change has been made to more accurately reflect the amount of housing element that is overpaid on UC.	Fraud and Error in the Benefit System: FYE 2023 Estimates
Reclassification of Failure to Provide Evidence Fraud: If a claimant fails to provide requested evidence after a Performance Measurement review, their benefit is suspended and then subsequently terminated. A whole award error is then recorded relating to the evidence that we failed to receive (e.g., if a bank statement which was requested to verify Capital was not received, then a whole award Capital Fraud is recorded). However, if we had no real suspicions about the evidence apart from the claimant not providing it, we reclassify the whole award error into Failure to provide evidence.	Fraud and Error in the Benefit System: FYE 2023 Estimates
We have adopted a data driven approach to attempt to reclassify some of the failure to provide evidence errors into known reasons for Fraud. This involves looking at data we have on a case four months after classification, to see if this gives us a clearer picture of why they chose not to provide evidence. If this can be determined, we reclassify the error again from Failure to Provide into the error reason the data suggests.	Fraud and Error in the Benefit System: FYE 2023 Estimates
Inclusion of an estimate of the fraud and error on Cost of Living Payments: Cost of Living Payments were made by the department for the first time in FYE 2023. These payments were to give claimants extra support during the current cost of living crisis and were paid in addition to any benefit that qualified a claimant for the payment. Since the statistics give an estimate of fraud and error on all benefit expenditure, we have included an estimate of the amount of fraud and error associated with these payments in our publication. Benefit reviews on Cost of Living Payments have not been carried out so an estimate has been derived. If a claimant is not eligible to receive benefit, then they would also not be eligible to receive a Cost of Living Payment. Therefore, to derive an estimate for the rate of fraud and error on these payments we have used the rate of cases that lose entitlement on the qualifying benefits (the majority of which have been measured in the current or recent years).	Fraud and Error in the Benefit System: FYE 2023 Estimates
Changes to proxy benefits used for unreviewed benefits: For benefits which we have never measured we use a rate from a similar benefit we are currently measuring or have previously measured as a proxy. Alongside Cost of Living Payments being included for the first time we have carried out a full review of all the proxy benefits used in an effort to more accurately estimate the fraud and error on the unreviewed benefits.	Fraud and Error in the Benefit System: FYE 2024 Estimates
Inclusion of an estimate of Unfulfilled Eligibility: From FYE 2024 onwards, estimates previously published as Claimant Error underpayments within the fraud and error statistics have been reclassified and published separately as Estimates of Unfulfilled Eligibility. This follows a planned review of the fraud and error statistics to align more closely with benefit legislation. The review determined the estimates previously published as Claimant Error underpayments should not be defined as underpayments. In benefit legislation, claimants are not eligible for increases in their benefit until they accurately report their circumstances to the department. Unfulfilled eligibility refers to claimants already in receipt of a certain benefit but may not be getting the full award they could be eligible for on this benefit. This is not the same as take-up of benefits, where people could have claimed certain benefits based on their circumstances but have not. The department also publishes statistics about take-up of benefits.	Fraud and Error in the Benefit System: FYE 2024 Estimates
Changes to benefits reviewed: Each year we use decision making methodology called multiple-criteria decision analysis (MCDA) to help evaluate which benefits will be reviewed. For FYE 2024 we have measured DLA for the first time since FYE 2005 and stopped the measurement of ESA.	Fraud and Error in the Benefit System: FYE 2024 Estimates
Extra check on Failure to provide evidence cases: Cases where the claimant has participated in the benefit review but failed to send in requested evidence and had their claim terminated for failing to comply. There is already an adjustment applied where we look four months after their claim was terminated for evidence that would allow reclassification of fraud to a known error reason. This year we expanded this to look for evidence that would allow removal of fraud entirely on some of these cases (e.g. evidence of earnings the month after termination would mean their Universal Credit entitlement went to zero). Applying these extra checks to the FYE 2023 statistics removed just over £100m of the “Failure to Provide Evidence/Engage” Fraud on Universal Credit. All other benefits were unaffected	Fraud and Error in the Benefit System: FYE 2024 Estimates
Change to the recording of Deemed Errors: Deemed errors are official errors where evidence that was available when the benefit award was made has been misplaced or is not available. Previously, when this happened it means that the official error check could not be completed. Any case with a deemed error raised against it was excluded from the calculation of the official error rate. This year, an estimated outcome is recorded instead of a deemed error, and the case is retained in the calculation of Official Error. We estimate that had this change been carried out last year it would not have changed the reported figures.	Fraud and Error in the Benefit System: FYE 2024 Estimates

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