Official Statistics

Child Maintenance Service statistics: Quality Assurance of Administrative Data report

Published 7 August 2026

1. Introduction

1.1 Background

This report contains information on the Child Maintenance Service (CMS) administrative data sources used by the Department for Work and Pensions (DWP), as well as quality assessments on each of them. 

The UK Statistics Authority have published a regulatory standard including a Quality Assurance of Administrative Data (QAAD) toolkit. The standard was developed in response to concerns about the quality of administrative data and in recognition of the increasing role that such data is playing in the production of official statistics.

1.2 List of administrative data sources

Data sources

The statistics in the publication come from different data sources. Some of these are from administrative data, management information and some from clerical data. We have ensured that all our tables are based on the most accurate and relevant data available.

Administrative data

This is generated and stored on the CMS computer systems (CS). Data on all parents using the service is collected and the quality of this information is good.

Management information

This is aggregate information and statistics collected and used by the CMS to manage the business, monitor performance, and inform policies. It is usually based on administrative data but can also come from survey data. The quality of this information is good.

Clerical data

This is gathered manually and usually stored in spreadsheets. As the data is entered manually it goes through multiple checks to ensure its accuracy. The quality of this information is good. 

The following table sets out the source(s) for each table.

Table	Measure	Source
1	Applications to the CMS	Management information
2	Intake	Management information
3	Service type changes	Administrative data
4	Money due and paid each quarter	Management information
5	How much Child Maintenance the CMS has arranged	Management information
6.1	Enforcement actions	Administrative and clerical data
6.2	Enforcement actions – detail on sanctions	clerical data
7	Financial investigations unit – actions	Management information
8	Change of circumstances	Administrative data
9	Mandatory reconsiderations	Management information
10	Appeals	Management information
12	Telephony	Management information
Stat-Xplore	Children, arrangements and paying parents	Administrative data

2. QAAD assessment

2.1 UK Statistics Authority QAAD toolkit

The assessment of the CMS administrative data sources has been carried out in accordance with the QAAD toolkit.

The QAAD toolkit sets out 4 levels of quality assurance that may be required of a dataset: 

• A0 – no assurance 

• A1 – basic assurance 

• A2 – enhanced assurance 

• A3 – comprehensive assurance

The UK Statistics Authority states that the A0 level is not compliant with the Code of Practice for Statistics. The assessment of the assurance level is in turn based on a combination of assessments of data quality risk and public interest. The toolkit sets out the level of assurances required as follows:

Level A1 – basic assurance

The statistical producer has reviewed and published a summary of the administrative data quality assurance (QA) arrangements.

Level A2 – enhanced assurance

The statistical producer has evaluated the administrative data QA arrangements and published a fuller description of the assurance.

Level A3 – comprehensive assurance

The statistical producer has investigated the administrative data QA arrangements, identified the results of independent audit, and published detailed documentation about the assurance and audit.

To determine which assurance level is appropriate for a statistics publication it is necessary to take a view of the level of risk of quality concerns and the public interest profile of the statistics.

Each administrative data source has been evaluated according to the toolkit’s risk and profile matrix (Table 1), reflecting the level of risk to data quality and the public interest profile of the statistics.

Table 1: UK Statistics Authority QAAD risk and profile matrix

	Lower public interest profile	Medium public interest profile	Higher public interest profile
Low level of risk of quality concerns	Statistics of lower quality concern and lower public interest (A1)	Statistics of low-quality concern and medium public interest (A1 or A2)	Statistics of a low-quality concern and higher public interest (A1 or A2)
Medium level of risk of quality concerns	Statistics of medium quality concern and lower public interest (A1 or A2)	Statistics of medium quality concern and medium public interest (A2)	Statistics of medium quality concern and higher public interest (A2 or A3)
High level of risk of quality concerns	Statistics of higher quality concern and lower public interest (A1 or A2 or A3)	Statistics of higher quality concern and medium public interest (A3)	Statistics of higher quality concern and higher public interest (A3)

Source: Office for Statistics Regulation.

2.2 Assessment and justification against the QAAD risk and profile matrix

The data risk of quality concern and public interest profile in CMS statistics are rated by assessing: (a) the possibility of quality concerns arising in the administrative data that may affect the statistics’ quality; and (b) the nature of the public interest served by the statistics.

(a) The CMS data is regarded as being a medium risk of data quality concern. While every effort is made to collect data to the highest quality, as with all administrative data it is dependent on the accuracy of information entered into the system. Checks are made throughout the process to minimise errors from collection of the data to producing the statistics.

(b) The CMS official statistics are regarded as medium public interest due to regular coverage of CMS policies and statistics in the media and their impact on the lives of vulnerable UK citizens.

Therefore, as defined by the risk and profile matrix (Table 1), the combination of medium level of data risk concerns, and higher public interest profile indicate that enhanced assurance [A2] is the minimum level required for CMS statistics.

The QAAD toolkit outlines 4 specific areas for assurance, and the rest of this report will focus on these areas in turn. These are:

• operational context and administrative data collection 

• communication with data supply partners 

• quality assurance principles, standards and checks applied by data suppliers

• producer’s quality assurance investigations and documentation

Each of the 4 practice areas are evaluated separately, and the respective level of assurance is stated. This approach enabled an in-depth investigation of the areas of particular risk or interest to users.

The overall level of assurance for CMS statistics is outlined in the summary section.

3. Areas of QAAD

3.1 Operational context and administrative data collection (QAAD matrix score)

This section provides an overview of the operational and administrative procedures involved in collecting Child Maintenance Statistics data.

To apply for Child Maintenance, parents must use the Get Help Arranging Child Maintenance (GHACM) service, which offers impartial information and support to help separated parents make informed decisions about their Child Maintenance arrangements. GHACM was introduced in November 2021, replacing the previous Child Maintenance Options.

GHACM operates exclusively as an online platform that can be accessed on this link Get help arranging child maintenance. Upon visiting the GHACM website, parents are directed to a landing page, which is monitored by the operations team. This monitoring generates metrics that indicate interest in the scheme and track fluctuations in the number of applications. 

To proceed beyond the landing page and initiate a Child Maintenance application, parents must obtain a Unique Reference Number (URN). These URNs are generated for both new and returning users of the service. Once a URN is issued, a caseworker is assigned, and the case transitions into an official application. At this stage, SAS begins tracking the data, which will undergo quality assurance at a later stage in the process. 

During the application process, there is also an investigation of the receiving parent to ensure that sufficient information is available to proceed with the case. This includes verifying details, such as the parent’s National Insurance number, necessitating an operational review at this stage.

The GHACM system, including the website, is managed, and maintained by the Digital team, who oversee the functionality and support the overall process.

Strengths:

• centralised process: the use of the GHACM platform for every application centralises the data collection process, ensuring consistency

• data analysis: the use of a centralised GHACM platform and each case being given URNs allows for real-time analysis of application trends. This can help in forecasting demand and trends

• operational checks: the investigation into the receiving parents’ details allows for a reduction in the possibility in potential fraud as personal information is collected and verified for each case

• accessibility and safety: inbuilt features on the webpage allow both privacy and safety for users

Weaknesses:

• online only access: since GHACM is an online-only platform, this may be limiting to those who do not have the means or resources to use online services, as well as possibly for people with disabilities

• potential for errors: the manual aspect of the operational procedures, such as data records being dependent on inputs from both customers and case workers. This leaves the procedure open to human error

• potential for misleading information: if a parent is given a URN and does not complete the application, their intent to apply/ not apply may not be fully captured by the system

3.2 Communication with data supply partners (QAAD matrix score)

This relates to the need to maintain effective relationships with suppliers (through written agreements such as service level agreements or memoranda of understanding), which include change management processes and the consideration of statistical needs when changes are being made to relevant administrative systems.

CMS data is owned by DWP and provided to analysts as a business requirement for management information (MI), policy analysis and statistics.

The quality assurance checks that we take along with our data suppliers are quarterly meetings with operations and policy teams to discuss trends in operational data. These meetings are with our formal QA group where all members are selected due to their ability to give the producers of the statistics the necessary insights about policy, operations, and expected trends for the producers of the statistics to be able to ensure the quality of the figures, and to ensure we have suitable accurate explanations, context, and interpretations of the figures to maintain the trustworthiness, quality and value of the stats.

All members of the formal QA group follow the routine standard processes of signing the agreement that they understand and agree to the restrictions on them, including that they cannot share any knowledge they have of the information with anyone outside of the production team and QA group, and that they will not make any other use whatsoever of that information in advance of publication, and the only purpose they serve is to ensure the quality and accuracy of the statistics.

Strengths:

• data provided in timely manner and located same place each quarter. This reduces the likelihood that incorrect data sets will be used by producers
• there is a paper trail
• readily updated list of stakeholders to receive data in a more timely manner
• regular meetings to be able to discuss nuances more easily

Weaknesses:

• the statistics teams are not involved in digital operational system design and have no direct engagement with the system designers. However, communication through policy can raise queries with regards to the system build / design
• there is a small production window to create the datasets and if any issues are encountered, this is likely to cause delays

3.3 Quality assurance principles, standards and checks by data supplier

Data suppliers are responsible for the lawful collection, storage, and transfer of data. They must also ensure that the data is accurate and updated in a timely manner. Additionally, they are required to comply with internal policies that mandate the masking of personal identifiers, thereby safeguarding individual privacy and ensuring no one can be identified from the data.

Table 1: applications.

Table 2: intake.

Table 4: money due and paid.

Table 9: mandatory reconsiderations.

The primary role of the Payments, Systems and Financial Control Directorate team is to provide standard reporting production for CMG, typically on a monthly basis. These reports serve as the data sources for our Tables 1, 2, 4 and 9.

The data compiled by this team is generated through the execution of SAS software codes. These codes are applied to datasets that are compiled, managed, and maintained by a separate Digital team.

Within the Payments, Systems and Financial Control Directorate team, reports are initially compiled by one team member and subsequently reviewed by another to ensure the data’s robustness and that it falls within expected parameters. If any discrepancies or anomalies are detected, the issue is first escalated to the business to determine if there is an operational reason for the irregularity. If necessary, the final step involves consulting the Digital team to verify the accuracy of the output data and ensure that no anomalies have occurred during the coding process.

Strengths:

• qualified verification: since the peer review is conducted by a qualified colleague who is also experienced in report creation, there is a higher likelihood of catching any errors or inconsistencies

• attention to detail: the personal accountability of manual checks can lead to a higher level of care taken to ensure accuracy

• collaboration: peer reviews allow colleagues to catch each other’s mistakes due to different approaches to work, and personal strengths and weaknesses. In this case, outsourcing investigations into discrepancies or inconsistencies to other teams and areas allows for a greater insight into potential issues or explanations for anomalies

Weaknesses:

• automated data checks: automated data checks may not detect subtle inconsistencies in data such as data trends or observations not making logical sense, which would be easier spotted by human judgement

• hubris: producers may become complacent when working on the same data and data processes repeatedly, allowing for more errors to be made

Table 3: switchers.

Table 6.1: enforcements.

Table 8: change of circumstances.

Stat-Xplore data

For administrative data used in Tables 3, 6.1, 8 and the Stat-Xplore tables, a comprehensive logging of quality assurance data checks is implemented. This involves actively monitoring for discrepancies or errors and conducting targeted investigations to understand and resolve any issues as they arise. As a key component of validation, a record count check on the final dataset is conducted to ensure consistency and accuracy. This involves comparing the current record count against previous monthly datasets and raw datasets to verify alignment and detect any significant variances.

In addition to record counts, thorough data population checks for critical columns in the final dataset confirm that all necessary fields are appropriately filled with expected values. This is complemented by null count checks across key columns to identify any missing data that could impact data integrity.

Once these internal checks are complete, the final dataset is verified for accessibility and to confirm it is free from corruption, ensuring it can be opened and utilized without error. For datasets containing masked data, a masking validation check is also performed to ensure that sensitive information is appropriately anonymised in designated fields and locations, maintaining data privacy and compliance with applicable regulations.

Strengths:

• data privacy: the masking step ensures that sensitive information remains anonymous. This helps meet privacy standards
• have a mechanism for investigations: for example if an error or other issue appears, there is a procedure in place regarding how to deal with it

Weaknesses:

• automated data checks: automated data checks may not detect subtle inconsistencies in data such as data trends or observations not making logical sense, which would be easier spotted by human judgement.

Table 5: How much maintenance the CMS has arranged, GB

Table 5 data is provided to producers by the Client Fund Accounting Team (CFAT). The CFAT data itself is sourced from both QLICK and SAGE, with SAGE serving as a general ledging system.

The Operations team inputs data into SIEBEL which is their primary computer system. QLICK then receives a nightly data feed from SIEBEL, which the CFAT team uses for their processes.

The CFAT team perform their own quality assurance checks with their data prior to providing said data to producers. This is done by performing monthly data reconciliation SIEBEL and QLICK to ensure consistency, including verification at the bank account level for both Receiving Parents and Paying Parents.

The final figures that CFAT produces that are in turn sent to CMS producers do not include off system, or Exceptional Case Handling (ECH) cases, as these are clerical cases that cannot be loaded onto SIEBEL.

Strengths:

• qualified verification: since the peer review is conducted by a qualified colleague who is also experienced in report creation, there is a higher likelihood of catching any errors or inconsistencies
• attention to detail: the personal accountability of manual checks can lead to a higher level of care taken to ensure accuracy
• collaboration: peer reviews allow colleagues to catch each other’s mistakes due to different approaches to work, and personal strengths and weaknesses
• frequency: data is produced on a nightly and monthly frequency ensuring that there are fewer delays in receiving this data

Weaknesses:

• human error: manual checks are more susceptible to human error than automated checks. Mistakes are more likely to go unnoticed
• ECH not included

Table 6.1: enforcements actions.

Table 6.2: enforcements actions – sanctions.

The data in these tables is classified as Child Maintenance Clerical Data, which is derived from SAS datasets.

For service requests in England and Wales, certain outcomes (for example, rows 13 to 20) are not initially available during the service request process. However, a system workaround allows these outcomes to be captured and included in the table.

This workaround involves leaving a service request open over the weekend. When a request remains open on a Friday, the outcome can be captured during this period and is finally able to be eventually included in the public statistics.

The enforcement team ensures that sub-statuses are only used when they intend to complete the service request with a specific desired outcome. This practice prevents erroneous counting, as a service request cleared for one outcome is not repurposed for another.

When a sanctions outcome is determined, the service request should not be closed immediately. Instead, it is set to the appropriate sub-status before the close of practice on Friday, allowing it to be cleared from Monday morning onwards.

Regarding quality assurance, the team conducts manual data checks to identify any duplicate entries. The performance team exports each individual entry and cross-references them to ensure there are no duplicates. This weekly review process is essential to ensure they avoid missing any discrepancies that occur over the weekend, ensuring consistency across weeks.

If any duplicates or discrepancies are identified, or if an entry reflects 2 different outcomes (for example, both passport confiscation and prison sentence), the team consults with the enforcement team to confirm whether the entry should be counted twice or if it is an error.

Strengths:

• qualified verification: since the peer review is conducted by a qualified colleague who is also experienced in report creation, there is a higher likelihood of catching any errors or inconsistencies
• attention to detail: the personal accountability of manual checks can lead to a higher level of care taken to ensure accuracy
• collaboration: peer reviews allow colleagues to catch each other’s mistakes due to different approaches to work, and personal strengths and weaknesses

Weaknesses:

• human error: manual checks are more susceptible to human error than automated checks. Mistakes are more likely to go unnoticed
• workaround: longevity of a workaround is a weakness
• manual checks at regular intervals (weekly): frequency of checks means production is more vulnerable to absences

Table 7: FIU

The returns come from one source only which is our own app. Every case is input onto it and there are certain points during the life of the case where a manager is involved in checking the case progress as well as having to sign off all cases that are cleared. There are some daily checks done as part of the app maintenance that identify some anomalies. There are also limitations for the input of dates to avoid case outcomes being missed within a monthly return.

The daily maintenance runs an automated process that runs a compact and repair of the Access database and saves a backup, which it then compares to the previous day’s backup. This comparison only looks for any new cases added or changes to the names of the subject of an investigation. This is all done via excel and produces a tab for the current sheet of data, the previous sheet and a separate sheet showing the changes. The list of new/changed cases allows the team to check that there isn’t any duplication of cases registered.

The new backup also allows checks that all rejected cases have a rejected reason, with all “Compliance Achieved” cases including the amount of money collected. These checks are all done manually. Due to the low number of new, rejected or compliance achieved cases daily, this manual process is not onerous.

The new backup allows to check that all rejected cases have a rejected reason showing and that all cases cleared with a specific reason of “Compliance Achieved” also have an amount for money collected. These checks are all done manually. Don’t have many new, rejected or compliance achieved cases every day, so although it’s a manual process, it is not a difficult or time-consuming one.

Strengths:

• collaboration: peer reviews allow colleagues to catch each other’s mistakes due to different approaches to work, and personal strengths and weaknesses
• qualified verification: since the peer review is conducted by a qualified colleague who is also experienced in report creation, there is a higher likelihood of catching any errors or inconsistencies

Weaknesses

• automated data checks: automated data checks may not detect subtle inconsistencies in data such as data trends or observations not making logical sense, which would be easier spotted by human judgement

Table 10: Appeals

The appeals team receives cases from HM Court and Tribunal Service (HMCTS) via the manage cases/Core Case Data (CCD) portal (it’s software they share with them to pass documents and update cases). Once received, these cases are then entered into the Decision Maker and Case Recorder (DMACR), a reporting software used to track and manage case progress.

The appeals team receives cases from HMCTS via the manage cases and CCD portal.

As work is completed, the DMACR software is updated with daily records of the actions taken. This ensures that the status of each case is accurately reflected on the system.

Daily quality assurance tasks involve running reports in DMACR for the previous day, capturing key metrics such as work-on-hand figures, clearances and the type of clearances processed. These reports are compiled to provide both an overall summary and a breakdown at the team and individual levels. Each HEO (Higher Executive Officer) team leaders’ responsibility is to verify the accuracy of their team’s data. If any discrepancies are identified, a team member is assigned to investigate and determine the cause. Any anomalies are documented in a daily log to ensure that they can be referenced later when reporting information to other stakeholders.

On a monthly basis, a deputy colleague consolidates the figures for submission to Mark Weber. During this process, reports for the entire month are generated simultaneously to ensure alignment with the daily reports. If discrepancies arise between the monthly and daily reports, an investigation is conducted to identify the cause, as certain items may not appear in the DMACR reports.

Before finalising and distributing the tables, they undergo a peer review by another colleague. If any discrepancies are found, a discussion is held to identify the source of the data issues and to verify the accuracy of the calculations used by each team member.

The monthly data is then incorporated into a quarterly spreadsheet, which is subsequently distributed to other areas within DWP.

Additionally, the team maintains a separate spreadsheet for manual cases that are considered sensitive and cannot be recorded on the DMACR. These special access cases are checked daily and are manually counted and included in figures for daily, monthly, and quarterly reporting.

Strengths:

• smaller data sets: done daily working with smaller data sets- may be beneficial in reducing error
• daily monitoring: daily quality assurance checks can catch issues promptly. This can minimise errors before they escalate
• collaboration: peer reviews allow colleagues to catch each other’s mistakes due to different approaches to work, and personal strengths and weaknesses
• qualified verification: since the peer review is conducted by a qualified colleague who is also experienced in report creation, there is a higher likelihood of catching any errors or inconsistencies
• strong Documentation of errors/anomalies
• have a mechanism for investigations: for example if an error or other issue appears there is a procedure in place regarding how to deal with it

Weaknesses:

• human error: manual checks are more susceptible to human error than automated checks. Mistakes are more likely to go unnoticed
• inconsistency: the fact that some checks are handled manually, and others are automated could lead to inconsistencies and difficult to maintain a uniform standard across the quality checks

Table 12: Telephony

The majority of reports produced by the telephony team are generated from the GI2 system, which is an historical database. The Telephony team thoroughly examines this system to produce tailored reports based on the various business needs in the department.

For quality assurance, the telephony team conducts peer reviews, where a qualified colleague verifies the accuracy of the reports. This process involves a thorough review to ensure the information produced is correct.

All quality assurance checks conducted manually by a qualified colleague, who also is experienced in report creation, carefully reviews the work.

Strengths:

• qualified verification: since the peer review is conducted by a qualified colleague who is also experienced in report creation, there is a higher likelihood of catching any errors or inconsistencies
• attention to detail: the personal accountability of manual checks can lead to a higher level of care taken to ensure accuracy
• collaboration: peer reviews allow colleagues to catch each other’s mistakes due to different approaches to work, and personal strengths and weaknesses

Weaknesses:

• human error: manual checks are more susceptible to human error than automated checks. Mistakes are more likely to go unnoticed

3.4 Producers quality assurance investigations and documentation (QAAD matrix score A2)

Pre-processing checks

Before moving to production the Child Maintenance Statistics team, undergoes a review of the necessary code on the SAS platform to confirm that all relevant data sets are available and the code is correct/up to date, so that the appropriate data will be accurately output.

Additionally, at the start of every production cycle, the CMS team review the previous publications and update and action the Development Log. From this the timeline of the next production is reviewed and refined to ensure maximum efficiency. Appropriate folders are updated and organised to maintain a clear and orderly structure minimising the risk of confusion between quarters when dealing with similar datasets. This includes creating QA logs for the next cycle and allocating team members tasks.

The team ensures that all data suppliers have been contacted and that the necessary data has been received well in advance of production. Production of the data is guided by standardised instructions to reduce opportunities for errors. Care is taken so that tasks are QA’d by a colleague not involved in the production to limit bias and highlight any production errors that the producer may have overlooked. Furthermore, meetings are scheduled with appropriate stakeholders so that specialists have oversight into the data produced and are able to highlight any errors and provide context for trends. Checkpoints are arranged with seniors prior to production to ensure accountability and approval.

This step is essential to ensure that the final data sets are comprehensive and free of any missing or invalid data, thereby maintaining the integrity of the data and the reliability of the results.

Data processing checks

Quality assurance checks are conducted at multiple stages throughout the production process. These checks include manual procedures, such as reviewing and comparing figures with those from previous quarters, as well as automated checks within SAS. Additionally, producers monitor data trends during production and document any anomalies or inconsistencies that may arise, ensuring the integrity and accuracy of the final output.

Output validation

Once statistics have been produced, they undergo quality assurance checks conducted by policy and analytical experts in the department. This external review ensures that the statistics are thoroughly evaluated, with ample time allocated for expert feedback. This external assessment provides an additional layer of scrutiny being the Child Maintenance Statistics team. Additionally, the team conducts peer reviews, where members perform quality assurance on each other’s work after the tables or release have been produced, further enhancing the reliability and accuracy of the final output.

Strengths:

• qualified verification: since the peer review is conducted by a qualified colleague who is also experienced in report creation, there is a higher likelihood of catching any errors or inconsistencies
• attention to detail: the personal accountability of manual checks can lead to a higher level of care taken to ensure accuracy
• collaboration: peer reviews allow colleagues to catch each other’s mistakes due to different approaches to work, and personal strengths and weaknesses

Weaknesses:

• human error: manual checks are more susceptible to human error than automated checks. Mistakes are more likely to go unnoticed
• inconsistency: the fact that some checks are handled manually, and others are automated could lead to inconsistencies and difficult to maintain a uniform standard across the quality checks
• automated data checks: automated data checks may not detect subtle inconsistencies in data such as data trends or observations not making logical sense, which would be easier spotted by human judgement
• external reliance: relies on the chain of data suppliers, producers, and reviewers (who perform the quality assurance checks) to provide services efficiently in order to not delay publication of release

4. Summary

DWP considers the main strengths of the CMS CS data to be that:

• information in the CMS CS is updated in a timely manner
• some information is verified by automated checks in addition to a sample of management checks
• the feedback that data supply partners receive during the data transformation process from the data handling team increases the likelihood of issues being identified and resolved
• the statistics team carry out extensive quality assurance throughout the statistical production process

The current limitations are that:

• there are potential risks to the consistency of data collection when any developments occur
• there is the potential for fraud and administrative error as the system relies on the information submitted by claimants and verification by operational teams
• the statistics team are not involved in the CMS CS design and have no direct engagement with the system designers

CMS official statistics are assessed as being assured to level A2 (enhanced assurance) as outlined by the UK Statistics Authority QAAD toolkit.

This document is accurate as of June 2026 but will be reviewed and updated periodically.

If you are of the view that this report does not adequately provide this level of assurance, or you have any other feedback, please contact us via email at cm.analysis.research@dwp.gov.uk with your concerns.