© National Audit Office 2025

Tackling benefit overpayments due to fraud and error

Department for Work & Pensions

Session 2024–2026
22 October 2025
HC 1336

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Report by the Comptroller and Auditor General

Ordered by the House of Commons
to be printed on 20 October 2025

This report has been prepared under Section 6 of the National Audit Act 1983 for presentation to the House of Commons in accordance with Section 9 of the Act

Gareth Davies
Comptroller and Auditor General
National Audit Office

14 October 2025

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The National Audit Office study team consisted of:

Margaret Anderson, Sarah Dixon and Katie Taylor, under the direction of Laura Brackwell.

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Key facts

Summary

1 The Department for Work & Pensions (DWP) makes welfare payments to more than 23 million people across Great Britain. In 2024-25, it paid £290.8 billion in benefits (including State Pension) and spent £7.3 billion on running costs. Some of DWP’s customers are vulnerable or have complex needs, for example due to poverty, age, health problems or disabilities.

2 DWP is required to pay benefits and State Pension to claimants and pensioners on time, in full and in accordance with legislation and the related regulations. Where fraud or error results in the payment of a benefit to an individual who is not entitled to that benefit, or a benefit is paid at a rate that differs from the amount specified in legislation, the overpayment or underpayment does not conform with Parliament’s intention and is irregular.

3 For the past 37 years, successive Comptroller and Auditor Generals have qualified their audit opinions on the regularity of DWP’s accounts due to the material level of fraud and error in benefit expenditure. State Pension is excluded from the qualification because it has a significantly lower level of fraud and error.

4 Fraud and error in benefit expenditure is one of DWP’s most persistent and pressing risks. In its annual report and accounts for 2024-25, DWP rated as ‘red’ the risk that its plans to reduce fraud and error are not successfully executed and/or cannot mitigate the increased propensity for fraud in society.

Focus of our report

5 This report examines whether DWP has an effective approach to tackling overpayments in the welfare system. Our work did not cover benefit underpayments. The evaluative criteria we used to assess value for money included whether DWP:

has made the progress it expected in reducing overpayments due to fraud and error, including whether it has achieved its objectives and implemented effectively key initiatives to tackle overpayments; and

is well placed to reduce overpayments due to fraud and error going forwards, including whether it has set out a clear strategy and specified what success will look like.

6 The report covers: information about benefit overpayments (Part One); DWP’s approach to tackling overpayments (Part Two); and DWP’s key initiatives for tackling overpayments (Part Three). Details of our audit approach are set out in Appendix One.

Key findings

Benefit overpayments

7 The estimated proportion of benefit expenditure overpaid fell from 3.6% (£9.7 billion) in 2023-24 to 3.3% (£9.5 billion) in 2024-25, but has not yet returned to pre-pandemic levels. DWP estimates the monetary value of fraud and error in the benefit system annually. The overpayment rate has been generally declining after rising substantially during the COVID-19 pandemic. In 2019-20, the estimated overpayment rate was 2.4% using the National Statistic for fraud and error in the benefit system, and 3.1% using DWP’s cross-welfare rate, which includes Tax Credit overpayments. DWP regards the latter as a fairer comparator because it considers that many of the overpayments previously observed in Tax Credits can now be expected to occur within Universal Credit (UC). It should be noted that the cross-welfare rate goes wider than irregular expenditure and includes in-year overpayments that were part of the design of the Tax Credit system (paragraphs 1.2, 1.5, 1.7 and 1.8 and Figure 1).

8 The estimated UC overpayment rate dropped significantly from 12.4% in 2023-24 to 9.7% in 2024-25. The rate is now considerably lower than during the COVID-19 pandemic when, to cope with the rapid increase in UC claims, DWP suspended some controls in order to process cases quickly and provide people with prompt support. With the reintroduction of controls and enhanced counter-fraud activity, the overpayment rate for UC has fallen. The main reasons for UC overpayments in 2024-25 were claimants (mainly self-employed claimants) not declaring in full the income they received from work, claimants failing to declare that they lived with a partner, and claimants not declaring all their financial assets. Although UC continued to account for most overpayments by value in 2024-25, for the first time since it was rolled out nationally in 2018, it did not have the highest overpayment rate across all benefit lines. Pension Credit had the highest estimated rate at 10.3% (paragraphs 1.9 to 1.11 and Figure 2).

DWP’s approach to tackling overpayments

9 From 2018 to 2024, DWP produced a series of outputs outlining its high-level approach to tackling fraud and error. In 2018, DWP produced a strategy, setting out its vision to improve the prevention and detection of fraud and error. It later acknowledged this document set out objectives that were principles rather than deliverables. In 2021, DWP supplemented the strategy with a document that set out its approach in more detail and sought to improve accountability for delivery. It had intended to develop a delivery plan for its strategy but did not do so because of the COVID-19 pandemic. In 2022, DWP published a plan for fighting fraud in the welfare system. The plan focused on investing in counter-fraud professionals and data analytics, and creating new legal powers to investigate potential fraud and punish fraudsters. In 2024, DWP published an update on the progress it had made against the commitments in the plan (paragraphs 2.6 to 2.10 and Figures 3 and 4).

10 DWP’s current strategy places a greater focus on prevention, alongside continuing its efforts to detect fraud and error. DWP took a two-stage approach to developing a new strategy. It first assessed its fraud and error commitments, progress made and operating model. It then used the findings to develop a refreshed strategy, which was approved in November 2024. It has set five strategic objectives that focus on preventing inaccurate payments through improvements in areas such as accountability, decision-making and use of customer data. At the time of our work, DWP had started to develop implementation and evaluation plans to support delivery of the strategy and measure its success. Its current prevention activities focus on UC and Pension Credit, such as its UC continuous improvement initiatives which seek to tackle the main causes of overpayments. DWP is also in the early stages of assessing its strategic controls framework, with a view to evaluating cost-effectiveness and taking action to strengthen controls where necessary (paragraphs 2.11 to 2.16).

11 DWP faces digital and data challenges that could hamper successful implementation of its fraud and error strategy. DWP’s IT systems are not fully integrated and do not allow staff to view all the information that DWP holds about a claimant, making it less likely that incorrect payments will be prevented or detected. The lack of common data standards, within DWP and across government, also makes it more difficult to identify fraud and error. The success of DWP’s strategy will partly depend on its ongoing Service Modernisation Programme. Relevant parts of the programme include developing an application to provide DWP staff with a single view of customer data from multiple benefit systems. DWP told us it is looking to the centre of government to provide leadership on setting cross-government data standards that will support data sharing between departments. It is also working to learn lessons from other countries. It points to Denmark where interoperable IT systems and government-wide data standards have been important in allowing counter-fraud initiatives to be rolled out at scale (paragraphs 2.26 to 2.30).

12 The government has awarded DWP £6.7 billion of dedicated funding for fraud and error activity over the nine years from 2020-21 to 2028-29. The earmarked funding was given on the basis that the related activity would reduce fraud and error and lead to savings in benefit expenditure, which was reflected in the Office for Budget Responsibility’s forecasting. Since April 2022, DWP has mainly used the funding to: scale up its programme of Targeted Case Review of UC claims; increase its counter-fraud resource; and expand its use of data analytics to tackle fraud and error. The funding is backloaded, with DWP due to receive 52% of the total (£3.5 billion) in the three years from 2026-27 (paragraphs 2.17 to 2.20 and Figure 5).

13 DWP has exceeded its targets for savings from its counter-fraud activities in each of the two years since it first set a target. Estimated savings in Annually Managed Expenditure (AME) is the main metric that DWP uses to assess the impact of its activities to reduce fraud and error. Savings comprise the value of past overpayments identified and an estimate of future overpayments prevented. From April 2022 to March 2025, DWP saved an estimated £4.5 billion in total from its counter-fraud activities. It first set a target for the amount of AME it would like to save in 2023-24. It achieved savings of £1.35 billion in 2023-24 against a target of £1.3 billion, and savings of £2.0 billion in 2024-25 against a target of £1.7 billion (paragraphs 2.22 and 2.23 and Figure 6).

14 DWP has now set an ambition to reduce overpayment rates to pre-pandemic levels, but will need to go further if the longstanding qualification on the regularity of benefit expenditure is to be removed. The National Audit Office and the Committee of Public Accounts (PAC) have previously recommended that DWP should set annual targets for the level of fraud and error, by benefit, against which its progress can be assessed. The ultimate aim should be to get fraud and error down to a level that represents a cost-effective control environment. In its 2024-25 annual report and accounts, DWP set out a multi-year ambition to reduce cross-welfare overpayment rates to pre-pandemic levels. It is using its cross-welfare overpayment measure – which incorporates Tax Credit overpayments – as its baseline of pre-pandemic performance. Using this measure, the Spring Statement 2025 forecast that overpayments would fall to the pre-pandemic level of 3.1% by 2028-29 (paragraphs 1.8, 2.24 and 2.25).

Key initiatives for tackling overpayments

15 DWP is working to expand its innovative use of machine learning to tackle fraud and error. Data analytics, such as machine learning, are a valuable tool in addressing fraud and error and making sure that benefit claimants receive the right amount of money. Since May 2022, DWP has used a machine learning model to flag potentially fraudulent claims for UC advances, saving an estimated £4.4 million. The model is designed to assess the risk in requests for advances and refer those assessed as high risk to a caseworker for review. DWP is also developing and testing four other models. Three of these models target key areas of fraud loss in UC and have been in development since 2022-23. The fourth model is intended to support DWP’s activity to detect and correct fraud and error in UC claims. DWP told us it is continuing to develop, test and evaluate these models and expects to make decisions on deploying them into live service in 2025-26 (paragraphs 3.3 to 3.5 and Figure 7).

16 DWP has improved its transparency about its use of machine learning, with the published analysis indicating that claimants in some groups are being over-referred for review. Public bodies must balance transparency about their use of data analytics with the risk of making it easier for fraudsters to take advantage. PAC has repeatedly raised concerns about the potential impact of machine learning, including on vulnerable claimants, and recommended that DWP share the results of its fairness impact assessment to provide reassurance. In July 2025, DWP published, for the first time, detailed information on its fairness analysis. The results for 2024-25 indicate that the model is not working as effectively as would be expected in respect of certain characteristics, with older claimants (in age groups 45 to 54 and above) and non-UK nationals being over-referred for review. DWP could undertake fairness analysis on only one of the nine protected characteristics (age) due to limited data, but also covered certain non-protected characteristics, such as whether a claimant is a UK national or a non-UK national. In terms of performance, DWP found the model to be around three times more effective at identifying fraud risk than a randomised control group sample. In light of its assessment, DWP concluded that it remains reasonable and proportionate to continue operating the UC advances model as a fraud prevention control, and said it would continue to seek to improve the model’s effectiveness (paragraphs 3.6 to 3.15).

17 DWP successfully scaled up its Targeted Case Review (TCR) programme to detect and correct fraud and error in existing UC claims. DWP started TCR on a small scale using its own staff. The programme began in February 2022 with seven agents and by April 2024 involved 3,100 DWP staff. In 2023-24, DWP decided to use a contracted-out route to scale up its TCR workforce further and avoid pressure on service delivery, and appointed TP (formerly Teleperformance) to provide additional capacity. By January 2025, TP had around 2,600 full-time equivalent agents undertaking reviews. As well as detecting incorrect payments, TCR provides insights that are helping to inform a range of improvements across UC. For example, DWP is introducing periodic redeclaration of UC claims after TCR helped to identify people failing to regularly report changes of circumstances as a key source of error (paragraphs 3.19 to 3.24).

18 DWP achieved estimated total savings of £581 million from TCR by March 2025, exceeding its expectation by 11%, but has not met its expectation for the proportion of reviewed cases found to be incorrect. DWP’s main measure of success for TCR is the estimated level of AME savings achieved. The total that DWP expects to save has increased significantly over time – from an initial target of £2 billion by 2026-27 to £13.6 billion by March 2030. From August 2022 to March 2025, a total of 1.15 million UC claims were reviewed, generating total estimated savings of £581 million (4% of the amount DWP expects to save by March 2030). DWP also uses other metrics to measure TCR performance such as the ‘hit rate’, which shows the proportion of reviewed cases found to be incorrect. In 2024-25, this was 20%, which was less than DWP’s expectation of 24%. The TCR hit rate achieved was lower than the 24% hit rate achieved from DWP’s testing of a random sample of cases for its 2024-25 annual fraud and error statistics. DWP told us that these two exercises have different objectives and methods, which limits their comparability. It is seeking to identify higher-value errors through TCR, whereas the threshold for incorrect payments in its random sampling is 10p, meaning more cases are recorded as incorrect (paragraphs 3.26 to 3.29 and Figure 9).

19 The government has introduced legislation to strengthen DWP’s powers to tackle fraud and error. DWP considers new legislation to be crucial in allowing it to meet its counter-fraud objectives. The government introduced the Public Authorities (Fraud, Error and Recovery) Bill to Parliament in January 2025, and the Bill is currently towards the end of its Parliamentary passage. The main measures intended to help DWP prevent and detect fraud and error are eligibility verification (whereby banks and other financial institutions could be compelled to provide information to help DWP verify a claimant’s eligibility and entitlement) and an extension to existing information-gathering powers (whereby a larger range of third parties could be required to provide information to DWP in support of criminal investigations). The Bill is expected to deliver gross savings of £1.5 billion by 2029-30 (paragraphs 3.33 to 3.36 and Figure 10).

Conclusion on value for money

20 The proportion of benefit expenditure overpaid remains too high, but the figures for 2024-25 suggest that overpayment levels are now going in the right direction, with a particularly welcome drop in the UC overpayment rate. This provides assurance that DWP has started to make headway – it has successfully deployed a range of counter-fraud interventions in recent years, including making use of data analytics. These are generating savings and helping it to detect and correct overpayments. We encourage DWP to continue to test innovative new approaches that make the most of emerging technologies.

21 Improving processes and controls to stop overpayments before they occur and before debt builds up is the best way to secure value for money in this area. DWP has set an ambition to reduce overpayment rates to pre-pandemic levels, but will need to go further if the longstanding qualification on the regularity of benefit expenditure is to be removed. The next few years will be key to its success. The extra funding it has available for fraud and error activity, and lessons from its interventions to date, present DWP with opportunities to increase the scale and impact of its approach. Its new strategy wisely incorporates a greater focus on prevention and an intention to address systemic challenges, including through better use of data and organisation-wide accountability for tackling fraud and error. DWP must now put its high-level vision into practice and develop an effective approach to implementation, which it can use to guide its actions, track progress and manage risks, including the potential for adverse impacts on claimants.

Recommendations

22 To improve its approach to tackling overpayments in the welfare system, DWP should:

a finalise its approach to implementing its fraud and error strategy and use cross-departmental governance arrangements to advance delivery and monitor progress. DWP’s approach should consider how its main interventions are expected to contribute to achieving its objectives and a timetable for delivery. DWP should update on progress in its 2025-26 annual report and accounts.

b progress the work it has started on reviewing its controls framework and use its detailed findings to strengthen the framework, removing or improving ineffective controls and prioritising those which prevent overpayments in the most cost-effective way.

c improve data quality by:

getting its data about benefit claimants into a common format; and

continuing to engage with cross-government data standards and ensuring DWP alignment with these.

d build on its existing use of data analytics to explore how emerging technologies may help to detect and prevent fraud and error, taking account of cost-effectiveness.

e progress its ambition to reduce the overpayment rate to the pre-pandemic level. Beyond that, DWP should focus on getting the overpayment rate down to a level that represents a cost-effective control environment. DWP should develop its evidence base on cost-effectiveness and target its activities accordingly.

f extend those detection and prevention activities which currently focus on UC to encompass other benefits where this is appropriate and cost-effective, in particular building on activity underway in Pension Credit, which had the highest rate of overpayments in 2024-25.

Part One

Benefit overpayments

1.1 This part of the report sets out information about benefit overpayments due to fraud and error.

How DWP estimates the level of overpayments due to fraud and error

1.2 The Department for Work & Pensions (DWP) estimates the monetary value of fraud and error in the benefit system annually. It does this through direct measurement of five or six benefits each year using a statistical sampling exercise. For those benefits not covered, it typically rolls forward the rate from when the benefit was last tested or uses a similar benefit as a proxy. The estimates are published annually as National Statistics.1

1.3 For 2024-25, DWP measured fraud and error in State Pension, Universal Credit (UC), Personal Independence Payment (PIP), Housing Benefit, Pension Credit and Carer’s Allowance. Overall, 87% of benefit expenditure was subject to sampling.

1.4 DWP groups overpayments into three categories:

Fraud: occurs when DWP considers a claimant should reasonably have been aware they were receiving money that they were not entitled to.

Claimant error: occurs when a claimant has provided inaccurate or incomplete information, or failed to report changes, and DWP believes they did not have fraudulent intent.

Official error: occurs when a benefit is paid incorrectly due to action, delay or a mistake by DWP, a local authority or HM Revenue & Customs.

Overpayment trends

1.5 DWP estimates that it overpaid 3.3% of benefit expenditure in 2024-25 (Figure 1). This equated to £9.5 billion of the £292.2 billion that it spent on benefits.2 Fraud accounted for an estimated £6.5 billion; claimant error, £1.9 billion; and official error, £1.0 billion.3

1.6 The Comptroller and Auditor General qualified his opinion on the regularity of DWP’s 2024-25 financial statements due to the material level of fraud and error in benefit expenditure (except for expenditure on State Pension, for which the level of fraud and error was significantly lower).4 This was the 37th year in which DWP’s accounts had been qualified due to material fraud and error.

1.7 The overpayment rate has been generally declining after rising substantially during the COVID-19 pandemic. The rate fell by 0.3 percentage points in 2024-25, from 3.6% (£9.7 billion) in 2023-24 (Figure 1). However, overpayment rates are yet to return to pre-pandemic levels.

1.8 In 2019-20, 2.4% of benefit expenditure was overpaid. However, DWP uses a wider ‘cross-welfare overpayment rate’ as its baseline of pre-pandemic performance. As well as benefit overpayments, the cross-welfare measure incorporates Tax Credit overpayments in order to account for the migration of Tax Credit claimants to UC.5 DWP considers that many of the overpayments previously observed in Tax Credits can now be expected to occur within UC and so should be included in the 2019-20 baseline in order to provide a fair comparison. In 2019-20, the cross-welfare overpayment rate was 3.1%. It should be noted that the cross-welfare rate does not represent wholly irregular expenditure – as well as fraud and error, the measure includes in-year Tax Credit overpayments that were part of the design of the Tax Credit system. The cross-welfare rate should not therefore be conflated with the National Statistic on fraud and error in benefit expenditure.

Benefits with the highest overpayment rates

1.9 The estimated overpayment rate for UC fell significantly from 12.4% (£6,410 million) in 2023-24 to 9.7% (£6,350 million) in 2024-25. The rate is now considerably lower than during the COVID-19 pandemic when, to cope with the rapid increase in claims, DWP suspended some controls in order to process cases quickly and provide people with prompt support. It accepted that the increased caseload and changes to the UC application process would lead to an increase in fraud and error. With the reintroduction of controls and enhanced counter-fraud activity, the overpayment rate for UC has fallen. In its 2022 update of the UC business case, DWP restated its aim that the level of fraud and error in UC would reduce to 6.5%.

1.10 DWP estimated that the main reasons for UC overpayments in 2024-25 were:

claimants (mainly self-employed claimants) not declaring in full the income they received from work (accounting for overpayments of £1,621 million, 2.5% of UC expenditure);

claimants failing to declare that they lived with a partner (£1,164 million, 1.8%); and

claimants not declaring all their financial assets (£938 million, 1.4%).

1.11 UC accounts for most overpayments by value – 67% of the total in 2024-25 (Figure 2). However, for the first time since it was rolled out nationally in 2018, UC did not have the highest rate of overpayments but was overtaken by Pension Credit. The estimated overpayment rate for Pension Credit was 10.3% (£610 million) in 2024-25, compared with 9.7% (£530 million) in 2023-24. The main causes of Pension Credit overpayments were:

claimants not fully declaring their financial assets (accounting for overpayments of £235 million, 4.0% of Pension Credit expenditure); and

claimants remaining abroad for longer than is allowed (£122 million, 2.1%).

Number of claimants with identified overpayments

1.12 DWP does not report the number of claimants who receive overpayments. In 2024-25, it recorded new overpayments involving 1.0 million UC claimants on its debt management system. The value of these identified overpayments was £1.35 billion. This is less than the estimated UC overpayments of £6.35 billion as the latter is calculated by extrapolating the results from DWP’s sampling exercise. DWP only identifies which specific claims contain fraud and error if they have been reviewed as part of its sampling exercise or flagged as incorrect through other means. DWP seeks to recover all overpayment debt where it has the legal basis to do so unless recovery would cause financial hardship or would not be cost-effective.

Factors that affect the likelihood of fraud and error

1.13 A range of factors shape the environment that DWP works within and affect the likelihood of fraud and error. These issues include the following.

The complexity of the benefit system: In many instances, Parliament has targeted benefits to claimants’ needs and circumstances with the aim of ensuring that resources are used efficiently. However, this can introduce complexity and increase the risk of fraud and error. The complexity of the benefit system can cause confusion and genuine error, both for claimants providing information and for DWP officials responsible for processing benefits.

The growth of serious and organised crime: Benefit payments are susceptible to organised crime attacks. A National Crime Agency assessment stated that the threat of serious and organised crime continued to increase in 2024 and was highly unlikely to be reversed in the next 18 months, a trend facilitated by advances in technology and online connectivity.6

Changes in society: DWP points to evidence that suggests there is a growing propensity for fraud in society and a softening of attitudes towards fraud. For example, in the British Social Attitudes Survey, the percentage of respondents who thought that failing to report £3,000 in earnings to the benefit office was “not wrong” or “a bit wrong” increased between 2016 and 2022; and analysis from Cifas showed an annual rise in fraud cases against large organisations of 11% in 2021-22 and again in 2022-23.7 DWP has assumed a 5% increase in the underlying propensity for fraudulent behaviour each year in its forecasting. In January 2025, the Committee of Public Accounts reiterated its view that there is no reason why an increasing propensity for fraud must inevitably lead to increasing losses to the taxpayer.8

1 The most recent publication is: Department for Work & Pensions, Fraud and error in the benefit system, financial year ending 2025, May 2025.

2 Audited total expenditure on benefits in 2024-25 was £290.8 billion, as reflected in DWP’s Statement of Comprehensive Net Expenditure. Note 19 to the 2024-25 accounts set out estimated total expenditure on benefits of £292.2 billion, which represented the latest available forecast for 2024-25 at the time DWP produced the fraud and error estimates.

3 Figures do not sum to the total due to rounding.

4 Comptroller and Auditor General, Department for Work & Pensions Report on Accounts 2024-25, National Audit Office, July 2025.

5 Tax Credits were administered by HM Revenue & Customs until April 2025 and have been replaced by UC for people of working age.

6 Serious and organised crime encompasses a wide range of criminality, from money laundering and fraud to other economic crime, bribery and corruption.

7 Cifas is a not-for-profit member organisation that seeks to tackle fraud and financial crime through the sharing of data, intelligence and learning.

8 Committee of Public Accounts, DWP Customer Service and Accounts 2023-24, Sixth Report of Session 2024-25, HC 354, January 2025.

Part Two

The Department for Work & Pensions’ approach to tackling overpayments

2.1 This part of the report covers the Department for Work & Pensions’ (DWP’s) approach to tackling overpayments and some of the challenges it faces in seeking to reduce the level of fraud and error.

Accountabilities

2.2 DWP is required to pay benefits and State Pension to claimants and pensioners on time, in full and in accordance with legislation and the related regulations. Expenditure on overpayments due to fraud and error does not conform with Parliament’s intention and is therefore irregular.

2.3 DWP’s work to tackle fraud and error is led by its fraud, error and debt (FED) policy and operational functions, which had around 13,600 staff at March 2025. DWP established a FED board in 2024 to provide cross-cutting oversight and scrutiny of activity and performance. The board brings together senior officials from across the department, including people with direct responsibility for fraud and error such as those responsible for service delivery, and staff from cross-cutting teams such as digital.

2.4 Responsibility for tackling fraud and error involves many parts of DWP. For example, work coaches in jobcentres may check a person’s identity when they apply for a benefit, staff in service centres process changes of circumstances reported by claimants, and design and digital teams work on developing user-friendly guidance and system interfaces. DWP recognises that it needs a culture of accountability for fraud and error across the department, at all levels, as many staff have a role to play in ensuring payments are correct.

2.5 To avoid making incorrect payments, DWP partly relies on claimants fully declaring their circumstances and reporting any changes. In January 2025, the Committee of Public Accounts (PAC) concluded that DWP needed to do more to encourage people to report changes of circumstances, which hinges on making it easy for people to get in touch and on customers trusting that they will be treated fairly.9

DWP’s strategy

Previous strategy

2.6 In 2018, DWP produced an internal FED strategy for 2018 to 2022, setting out its vision to improve the prevention and detection of fraud and error. It later acknowledged that this document set out objectives that were principles rather than deliverables.

2.7 In 2021, DWP supplemented the strategy with a document that set out its approach in more detail and sought to improve accountability for delivery. This included describing high-level ambitions for what DWP wanted its data and digital systems to look like, the organisational culture and processes it wanted to create, and the change in customer behaviour it wanted to encourage. Figure 3 summarises DWP’s vision and objectives for 2018 to 2022 and how it expected to achieve them. DWP told us it had intended to develop a delivery plan for its strategy but did not do so because of the COVID-19 pandemic.

2.8 DWP refined its approach to tackling fraud and error in response to the changing external environment, including the increase in fraud and new technologies. In 2022, it published its plan for fighting fraud in the welfare system.10 The plan comprised three main areas of activity:

investment in front-line counter-fraud professionals and data analytics;

creating new legal powers to investigate potential fraud and punish fraudsters, when Parliamentary time allowed; and

working closely with the public and private sectors.

2.9 DWP also created a Fraud Prevention Fund, worth £30 million, to research, test and trial creative ways to tackle new and emerging threats. To date, projects supported by the fund include piloting a tailored communications campaign to encourage claimants to notify DWP about changes of circumstances, and reviewing the ‘gold standard’ of fraud detection and prevention in the private sector to understand potential applications in a DWP context.

2.10 In 2024, DWP published an update reporting the progress it had made against the commitments in the plan (Figure 4).11

Current strategy

2.11 DWP took a two-stage approach to developing a new strategy. In 2023, it assessed its fraud and error commitments, progress made and operating model. It then used the findings to develop a refreshed strategy, which was approved in November 2024.

2.12 DWP told us it will be focusing more on prevention than was previously the case, alongside continuing its efforts to detect fraud and error. Better preventative controls are more effective than detection because they stop incorrect payments before they occur, removing the need for organisations to seek to recover money. DWP’s vision to 2030 and beyond is to prevent inaccurate payments by addressing internal challenges, which are reflected in five strategic objectives:

Better use of customer data by combining information from different sources to have a unified view of customers’ circumstances.

Strategic decision-making that consistently considers how changes to policy, service design or delivery might affect payment accuracy.

Staff who understand how their work contributes towards payment accuracy and who have the skills, systems and support to perform their role effectively.

Resources that are optimally allocated to prevent inaccurate payments.

Accountability for payment accuracy that is organisation-wide, with relevant business areas taking responsibility for managing risks to accuracy through effective controls.

2.13 At the time of our work, DWP was in the process of developing a ‘roadmap’ setting out its approach to delivering its new strategy. This will be important in articulating how in practice DWP intends to achieve its high-level objectives. DWP also intends to use the roadmap to track progress on key workstreams. DWP is also in the early stages of determining how it will evaluate the impact of its strategy to reduce fraud and error, including establishing what success will look like and how this will be measured.

Ongoing prevention activities

2.14 DWP has a range of activities intended to improve how it prevents fraud and error from occurring. It told us that these have particularly focused on UC because of the scale of expenditure on this benefit and because UC has modern digital systems that make it easier to implement changes. However, it wants to take a similar approach to other benefits and has activities underway relating to Pension Credit.

2.15 DWP’s UC continuous improvement activity aims to tackle the main causes of overpayments by taking insights from initiatives such as the Targeted Case Review of UC claims and from its annual exercise to estimate the level of fraud and error in benefit expenditure. Examples of continuous improvement initiatives include:

introducing a process to check household composition by asking claimants with children/young people aged 16 to 18 to confirm their education status;

testing an enhanced earnings verification process for self-employed claimants by requesting evidence of business activity, such as receipts for expenses;

introducing periodic redeclaration of UC claims, which will prompt claimants to review their declared circumstances and report any changes; and

developing ways to verify a claimant’s financial assets, such as open banking whereby claimants have the option of allowing DWP to view their bank balance to check their savings.

2.16 DWP is also in the early stages of assessing its strategic controls framework. It is starting with UC and Pension Credit as the benefits with the highest overpayment rates. It is undertaking work to map and evaluate the controls it has in place to prevent fraud and error in these benefits, with a view to assessing cost-effectiveness and acting to strengthen controls where necessary. The Comptroller and Auditor General and PAC have recommended that DWP should establish a framework for reporting on the impact and cost-effectiveness of its controls.12 Demonstrating that it has a cost-effective control environment is a key component of DWP achieving value for money in tackling fraud and error and could help it move towards an unqualified audit opinion on its accounts.

Funding for fraud and error activity

2.17 The government has provided DWP with earmarked funding for fraud and error activity on the basis that these activities will reduce fraud and error and lead to savings in benefit expenditure, which has been reflected in the Office for Budget Responsibility’s forecasting. In 2023, the then Civil Service Chief Operating Officer set a general expectation for departments to achieve returns of 3:1 on counter-fraud investment. DWP was not required to comply with this target return on investment as it had committed to setting a public target for reducing fraud and error across the welfare system.

2.18 DWP told us it does expect its fraud and error activities to exceed the target set by the Chief Operating Officer. However, it does not use the rate of return achieved from the funding received for fraud and error activity as a performance measure. It noted that spending in one year can generate savings over many years, making it difficult to identify the relevant costs and benefits to include in any analysis.

2.19 In total, the government has awarded £6.7 billion of dedicated funding for DWP’s fraud and error activity across the nine years from 2020-21 to 2028-29 (Figure 5). The funding is backloaded, with DWP due to receive 52% of the total (£3.5 billion) in the three years from 2026-27.

2.20 Over the three years to March 2025, the following three areas received the largest amounts of earmarked funding:

Targeted Case Review of UC claims, which received £447 million (see paragraphs 3.17 to 3.32);

counter-fraud resource, which received £364 million; and

data and analytics, which received £73 million (see paragraphs 3.3 to 3.16).

2.21 In general, DWP’s activities to stop fraud and error occurring by improving processes and controls are funded from its baseline Departmental Expenditure Limit budget. This funding covers the costs of running the benefit system and other day-to-day spending.

Measuring success

Savings in benefit expenditure

2.22 The main metric that DWP uses to assess the impact of its activities to reduce fraud and error is estimated savings in Annually Managed Expenditure (AME).13 This represents the cost reduction to the Exchequer resulting from DWP’s interventions. DWP calculates the savings by adding together the value of past overpayments identified (discounted to take account of debt recovery rates) and an estimate of future overpayments prevented (based on assumptions about how long the overpayment would have continued to exist if it had not been found).

2.23 In 2022-23, DWP revised how it calculates estimated AME savings, moving to recognising savings in the year they were realised, rather than the year in which an intervention took place. Because of this change some savings may have been recognised in more than one year, meaning that savings achieved before the change in methodology cannot be added to savings achieved afterwards to calculate an overall total. The estimated amount of AME saved since 2022-23 is £4.5 billion.14 Since 2023-24, DWP has set an annual savings target which it has exceeded. It achieved estimated savings of:

£1.35 billion in 2023-24 against a target of £1.3 billion; and

£2.0 billion in 2024-25 against a target of £1.7 billion (Figure 6).

Overall rate of fraud and error

2.24 The NAO and PAC have previously recommended that DWP should set annual targets for the gross level of fraud and error, by benefit, against which its progress can be assessed. Targets would aid accountability over how DWP is tackling fraud and error and support it to focus its efforts, with the ultimate aim being to get fraud and error down to a level that represents a cost-effective control environment. DWP accepted PAC’s recommendation in 2021 but deferred implementation as it considered that the COVID-19 pandemic had led to changes in its caseload and operations and created significant uncertainty, meaning that recent fraud and error rates would not represent a stable baseline against which to set a target.

2.25 DWP has now set out, in its 2024-25 annual report and accounts, a ‘multi-year ambition’ to reduce overpayment rates to pre-pandemic levels. It is using its cross-welfare measure as its baseline of the level of overpayments before the pandemic (see paragraph 1.8). Using this measure, the Spring Statement 2025 central estimate forecast that overpayments would fall to the pre-pandemic level of 3.1% by 2028-29, although DWP has stated that it may be possible to reach this ambition sooner. It has said it will publish its forecast in its annual report and accounts each year to indicate progress. DWP will need to go further than returning overpayment rates to pre-pandemic levels if the longstanding qualification on the regularity of benefit expenditure is to be removed.

Wider challenges and enablers of success

2.26 DWP told us that addressing data and digital issues is central to preventing and detecting fraud and error. For example, some benefit processing is still paper-based or involves outdated legacy IT systems, increasing the risk of error; and DWP’s IT systems are not fully integrated and do not allow staff to view at the same time all the information that DWP holds about a claimant, making it more difficult to prevent or detect fraud and error. Across the benefits measured in 2024-25, DWP estimated it overpaid £73 million because it failed to consider income from other benefits in calculating a claimant’s entitlement.

2.27 The lack of common data standards, within DWP and across government, also makes it more difficult to identify fraud and error. By way of example, on a visit to a DWP service centre, we saw there were multiple formats in which staff could enter a customer’s address, making it harder for them to match the different records they held for the same claimant. DWP told us it is advocating for common data standards and modern interoperable IT systems across government, and is looking to the centre of government to provide leadership on these areas.15 Improvements in data standards and interoperability would help DWP to link up the customer data it holds and access relevant data from other departments such as HM Revenue & Customs. As a result, benefit processing could become more accurate and efficient.

2.28 DWP also has an ongoing Service Modernisation Programme, which it intends will help reduce fraud and error.16 Relevant parts of the programme include the development of a ‘customer view’ application that will enable customers to view and update their own information, and a ‘colleague view’ application to bring together information from multiple benefit systems so DWP staff can access and update a customer’s entire record at once. In its 2024-25 annual report and accounts, DWP noted that the programme delivery risk was assessed as ‘amber’, reflecting the scale and complexity of the programme.

2.29 DWP’s digital and transformation group is responsible for building, procuring and designing systems to support the department’s ambitions to reduce fraud and error. It has a programme of data transformation projects that are intended to support detection and prevention. These include, for example, an initiative looking at the accuracy of DWP’s data and the barriers to accessing real-time data, such as issues with staff capability or technology.

2.30 DWP is also working to learn relevant lessons from other countries and points to Denmark to illustrate what is possible. It told us that Denmark has around 100 machine learning models to help tackle fraud, and that modelling at this scale is only possible because of new interoperable IT systems, with government-wide data standards. To develop this infrastructure, the Danish government spent three to four years investing in accurate data across its public services. It established data owners in each service area, who were responsible for ensuring their data were accurate and compatible.

9 Committee of Public Accounts, DWP Customer Service and Accounts 2023-24, Sixth Report of Session 2024-25, HC 354, January 2025.

10 Department for Work & Pensions, Fighting Fraud in the Welfare System, CP 679, May 2022.

11 Department for Work & Pensions, Fighting Fraud in the Welfare System: Going Further, CP 1072, May 2024.

12 Comptroller and Auditor General, Department for Work & Pensions Report on Accounts 2022-23, National Audit Office, July 2023; and Committee of Public Accounts, The Department for Work & Pensions Annual Report and Accounts 2022–23, Fourth Report of Session 2023-24, HC 290, December 2023.

13 Annually Managed Expenditure (AME) is spending that is difficult to predict or control as it is spent on areas that are demand-led. Most welfare spending is classified as AME.

14 In 2023-24, DWP altered its assumptions on the proportion of each overpayment it expects to recover, how long the fraud or error would have remained in payment had it not intervened, and the indirect impact of its interventions (for example, where it is suspected that a person has closed their claim as a result of DWP initiating counter-fraud activity). This resulted in overpayments detected from 2024-25 onwards being worth more in terms of AME saved than those detected in earlier years.

15 The Government Digital Service leads the government digital and data function and, among other things, sets the digital strategy for government and maintains guidance and tools to support best practice. It is part of the Department for Science, Innovation & Technology.

16 The Service Modernisation Programme is an 11-year organisation-wide programme running from 2022-23 to 2032-33, which is seeking to modernise services and deliver benefits for customers, staff and taxpayers.

Part Three

Key initiatives for tackling overpayments

3.1 This part of the report covers three of the Department for Work & Pensions’ (DWP’s) key initiatives for tackling overpayments due to fraud and error – machine learning, Targeted Case Review of Universal Credit (UC) claims and new legal powers.

3.2 In addition to these initiatives, DWP carries out a range of other counter-fraud interventions including:

enhanced reviews of UC claims identified as having an increased risk of incorrectness or fraud, followed by corrective action where appropriate;

scripted or in-depth interviews with claimants to check cases identified through data matching and referrals from DWP staff or the public;17 and

investigations into cases of suspected fraud or organised crime, which can lead to criminal sanctions in the most serious cases.

Use of machine learning

Purpose and approach

3.3 Data analytics, like machine learning, have a valuable role to play in addressing fraud and error. As we reported in July 2025, data analytics are a vital tool in making sure that the right amount of money goes to the right recipient, and in finding potentially incorrect transactions.18 DWP has been using data analytics to tackle fraud and error for a long time. Since 2020, its Integrated Risk and Intelligence Service has worked to increase its detection and prevention capabilities by developing new data matching rules to help identify fraud and error.

3.4 Since May 2022, DWP has used a machine learning model to flag potentially fraudulent claims for UC advances. The model is designed to assess the risk in requests for advances and refer those assessed as high risk to a caseworker for review. Decisions about whether an advance should be paid are made by DWP staff, not the model. Figure 7 sets out how DWP uses the model to reduce benefit fraud. DWP estimates that, from 2022-23 to 2024-25, the model generated savings of £4.4 million that would otherwise have been overpaid. It does not have an estimate of the costs of developing and operating the model as it cannot disaggregate these from its wider spending on data analytics.

3.5 DWP is also developing and testing four other machine learning models.

Three models have been in development since 2022-23. These target key areas of fraud loss in UC, which arise from claimants not fully declaring their earnings from self-employment, their financial assets, or that they live with a partner.

The fourth model in development is intended to support DWP’s activity to detect and correct fraud and error in UC claims.

DWP told us it is continuing to develop, test and evaluate these models and expects to make decisions on deploying them into live service in 2025-26.

Impact on claimants

3.6 Public bodies must balance transparency about their use of data analytics with the risk of making it easier for fraudsters to take advantage. Government officials we spoke to as part of our 2025 report on data analytics told us that meeting transparency requirements was sometimes difficult without revealing things that would make it easier for fraudsters to circumvent their controls.19

3.7 In early 2024, DWP carried out fairness analysis on the UC advances model. It did not publish the results of this assessment as it considered this would undermine the effectiveness of the model as a fraud prevention control. However, it stated in its 2023-24 annual report and accounts that the results did not present “any immediate concerns of discrimination, unfair treatment or detrimental impact on customers”.

3.8 The Committee of Public Accounts (PAC) has repeatedly raised concerns about the potential impact of data analytics and machine learning, for example on legitimate benefit claims being delayed or reduced. In January 2025, PAC concluded that it remained concerned about the potential negative impact on protected groups and vulnerable customers of DWP’s use of machine learning. It recommended that DWP should share the results of its 2024 fairness impact assessment to provide reassurance that its use of machine learning was not resulting in claimants being treated unfairly.20 DWP shared this information in a private briefing with PAC Members in June 2025.

3.9 The stakeholders we spoke to also raised concerns about the lack of transparency over how and when DWP uses machine learning and how vulnerable claimants might be affected by potential bias in its models. Stakeholders told us that without greater transparency it was not possible to scrutinise effectively DWP’s use of artificial intelligence.

DWP’s latest fairness assessment

3.10 DWP published its latest fairness assessment in July 2025. The purpose of the assessment was to consider the results of statistical fairness analysis alongside other factors, and review the extent to which any measured statistical disparity may represent risk of discrimination, unfair treatment or detrimental impact on claimants.

3.11 In order to determine whether there are disparities between groups, DWP compared, for 2024-25, the proportion of advances that the model predicted to be high risk with the proportion of advances predicted as high risk that were subsequently confirmed to be fraudulent by a caseworker. It considers a ‘good’ outcome to be where these metrics are consistent, meaning that the claims of individuals in a particular group are selected for review at a rate that is consistent with the level of fraud in that group.

3.12 Due to limited data, DWP could undertake fairness analysis on only one of the nine protected characteristics.21 DWP performs analysis only where it has data for at least 70% of the population to avoid the risk of bias. However, the proportion of claimants referred by the UC advances model as high risk who responded to DWP’s optional equality questionnaire with an answer other than “prefer not to say” was below this threshold. As claimants provide data on their date of birth as part of applying for UC, DWP could undertake analysis based on age, which is one of the protected characteristics. In addition, to help understand how the model was working, DWP analysed certain non-protected characteristics, such as whether the claimant was a UK national or a non-UK national.

3.13 DWP’s published paper provides detailed information on how it carried out the fairness analysis and the results.22 It highlighted two areas where there was a disparity between the likelihood of being referred by the model and the likelihood of those referrals being correct – for advance requests from older claimants (in age groups 45 to 54 and above) and from non-UK nationals. DWP noted that this evidence suggests that the model is not working as effectively as would be expected in respect of certain characteristics. Although legitimate requests will be approved once the request has been reviewed by a DWP caseworker, a referral requires claimants to find and provide additional evidence. Those groups who are over-referred for review are disproportionately impacted by these demands, which create an administrative burden for those selected for review.

3.14 DWP also assessed the impact of the model on the timeliness of payments. It found that the median payment delay was one day when compared with advance requests that were approved automatically. This delay was in line with the delay experienced by requests that were subject to other fraud checks.

3.15 In terms of performance, DWP found the model to be around three times more effective at identifying fraud risk than a randomised control group sample. In light of its assessment, DWP concluded that it remains reasonable and proportionate to continue operating the UC advances model as a fraud prevention control. It said it would continue to seek to improve the model’s effectiveness and conduct further fairness analysis to assess whether the disparities relating to age and nationality had reduced.

3.16 DWP is working to comply with the government’s Algorithmic Transparency Recording Standard and told us it is on track to do so by the end of 2025. The standard establishes a standardised way for public sector organisations to record and publish information on how and why they are using algorithmic tools. In February 2024, the standard was made a mandatory requirement for government departments and arm’s-length bodies that deliver public or front-line services, or interact directly with members of the public.

Targeted Case Review of UC claims

Purpose and approach

3.17 DWP introduced Targeted Case Review (TCR) in 2022 in order to tackle the growth in fraud and error in UC that had occurred during the COVID-19 pandemic. It scaled up TCR using a ‘test and learn’ approach, examining suspected weaknesses in the UC customer journey to identify where reviews would produce the best return on investment.

3.18 Agents are allocated cases for review based on rules that are generated by using data and analytics to look for activity, behaviour or circumstances that suggest a higher risk of error. DWP also selects some cases for review at random to provide assurance that no new types of error are arising in the UC caseload.

3.19 A TCR involves reviewing an existing UC claim to identify and fix incorrect payments – for example, due to unreported changes in a claimant’s circumstances – to recover taxpayers’ money where necessary and to ensure claimants are receiving the right amount. Agents follow a three-stage process, and tailor their approach within this based on the needs of the claimant. For example, they may put in place additional support or pause a review if a claimant is in hospital (Figure 8).

3.20 Since TCR was introduced, DWP has adapted the process with the aim of improving the experience of customers and agents and securing better outcomes. For example, it introduced warm-up calls, whereby agents call claimants when previewing the claim, and before requesting evidence, if they have concerns about the claimant’s welfare or ability to understand the TCR process.

3.21 As well as detecting incorrect payments, TCR provides insights which are helping to inform a range of improvements across UC. For example, DWP is introducing periodic redeclaration of UC claims after TCR helped to identify people not regularly reporting changes of circumstances as a key source of error (see paragraph 2.15). DWP says it expects to save around £1 billion from this measure over the next five years, while also helping claimants to avoid building up avoidable debt.

Resourcing

3.22 DWP started the TCR programme on a small scale and initially expanded it using its own staff. TCR began in February 2022, at one site with seven agents. By the end of 2022-23, the programme involved nearly 900 agents across 17 sites, and by April 2024 it had expanded to 25 sites and 3,100 agents.

3.23 In 2023-24, however, DWP concluded it could not recruit the staff needed for TCR alongside the extra work coaches and other staff required to administer the growth in benefits. It also foresaw problems with flexing its estate to accommodate more TCR agents. DWP therefore decided to use a contracted-out route to scale up its TCR workforce further.

3.24 In June 2024, DWP awarded a contract to TP (formerly Teleperformance) to provide additional TCR capacity until June 2028 as necessary. By January 2025, TP had around 2,600 full-time equivalent agents undertaking TCR. From September 2024 to March 2025, it reviewed nearly 155,000 cases at a cost of around £42 million.23 In total, from 2022-23 to 2024-25, DWP spent around £358 million on TCR, which included its in-house operational costs and overheads as well as the cost of outsourcing.

3.25 DWP told us it has worked closely with TP with the aim of ensuring consistency between reviews carried out by in-house and outsourced agents. For example, DWP provided training and its staff regularly attend TP sites to share expertise and learning. DWP has also learnt from TP’s approach to tracking and measuring quality.

Performance

3.26 DWP’s main measure of success for TCR is the estimated level of Annually Managed Expenditure (AME) savings achieved, in line with its overall fraud and error metric (see paragraph 2.22). Its savings expectation has increased significantly over time as TCR has been scaled up. In December 2021, DWP estimated that it could save £2 billion by 2026-27. By the time of our work, the target had risen to £13.6 billion by March 2030.

3.27 From August 2022 to March 2025, DWP achieved an estimated £581 million in AME savings from TCR, 11% more than its expectation of £525 million (Figure 9). This represented 4% of the total amount it expects to save by March 2030.

3.28 Performance against other metrics that DWP monitors has been mixed (Figure 9):

Average productivity: the number of cases reviewed per agent per day was 1.07 in 2024-25, above DWP’s expectation of 0.89. This was an improvement on the previous year when average productivity fell short of expectation.

Cases completed: 1,151,000 cases were reviewed from 2022-23 to 2024-25, 104% of DWP’s expectation of 1,104,000.

Average ‘hit rate’: the proportion of reviewed cases found to be incorrect has consistently fallen short of DWP’s expectation. In 2024-25, the hit rate was 20%, which was less than the expectation of 24%.

3.29 DWP’s TCR hit rate in 2024-25 was lower than the 24% hit rate it achieved from testing a random sample of cases for its annual fraud and error statistics. DWP told us that these two exercises have different objectives and methods, which limits their comparability. It is seeking to identify higher-value errors through TCR, whereas the threshold for recording an incorrectness from its random sampling is 10p, meaning more cases are recorded as incorrect.

Impact on claimants

3.30 In October 2024, DWP produced an updated equality analysis for TCR, in line with the Public Sector Equality Duty. It concluded there is no direct discrimination in the TCR process as all customers who claim UC could be subject to review. However, there is the potential for indirect discrimination and adverse impacts because people with certain protected characteristics are overrepresented in the TCR population, when compared with the overall UC population. This is not because the rules target those with particular characteristics, but because, in some circumstances, the rates of error for groups with certain protected characteristics are likely to be higher. This makes them more likely to be picked for review. Overall, DWP concluded that, having weighed up the potential for adverse impacts, on balance, its approach is justified because it finds more error on claims, by around 12 percentage points, through targeting.

3.31 DWP recognises that TCR has the potential for both adverse and positive impacts on claimants, depending on whether an overpayment or underpayment is identified and corrected, as well as a claimant’s experience of the process. It told us about a range of ways that it seeks to support people whose claim is subject to review and to ensure that all information has been considered before suspending the benefit of a more vulnerable customer.

3.32 Stakeholders we spoke to raised concerns about the potential demands of the TCR process on claimants. They commented on the volume of information that claimants have to provide, and questioned whether all the information sought was necessary. They also queried whether DWP makes the process easy enough for more vulnerable claimants to engage with, for example through access to appropriate support, a range of communication channels and options for contacting DWP. DWP pointed us to online resources it has made available to support claimants with a review, which are signposted through the UC journal.

New legal powers

3.33 In its 2022 plan for fighting fraud in the welfare system, DWP noted that some of the key legislation that it relied on was over 20 years old. It set out plans to legislate, subject to Parliamentary time, for new powers to help it investigate potential fraud and apply new penalties to punish fraudsters. DWP considers new legislation to be crucial in allowing it to meet its counter-fraud objectives.

3.34 The government introduced the Public Authorities (Fraud, Error and Recovery) Bill (the Bill) to Parliament in January 2025. The Bill had its third House of Commons reading in April 2025 and is currently towards the end of its Parliamentary passage.

3.35 The Bill includes new powers for DWP and the Public Sector Fraud Authority to identify, prevent and deter public sector fraud and error, and recover any associated losses. The Bill is expected to deliver gross savings of £1.5 billion by 2029-30.

3.36 Figure 10 sets out the main changes to DWP’s powers included in the Bill. A key measure is that banks and other financial institutions could be compelled to provide information to help DWP verify a claimant’s eligibility and entitlement for certain benefits. Through this measure, DWP aims to identify incorrect payments arising from fraud or error as early as possible, thereby minimising the amount of debt that claimants build up.

3.37 DWP’s impact assessment on the Bill estimated the costs and benefits of the measures over a 10-year period. The net present social values were estimated to be:

£2,000 million for the eligibility verification measure (using an appraisal period of 2024-25 to 2033-34);

£13.8 million for the information-gathering powers (2025-26 to 2034-35);

-£21.8 million for the entry, search and seizure powers (2027-28 to 2036-37);24 and

-£124.5 million for the overpayment recovery and enforcement measure (2025-26 to 2034-35).25

Impact on claimants

3.38 The stakeholders we spoke to were concerned about the potential impact of the powers in the Bill on vulnerable claimants. The issues they raised included the fact that the new powers might disproportionately affect claimants who have more complex lives and therefore may struggle to engage properly with the process, and that the legislation would target individuals who had made a mistake, rather than genuine fraudsters.

3.39 DWP told us it is putting in place a number of safeguards with the aim of ensuring that the new powers are used proportionately and effectively. In its updated response on the proposed eligibility verification measure, the Information Commissioner’s Office (ICO) welcomed the changes made by the government to mitigate some concerns that the ICO had raised about the measure in the Data Protection and Digital Information Bill, which fell in 2024. The ICO noted, for example, that the measure now more tightly scopes the type of information that can and cannot be shared, and requires that a code of practice must be issued and that the Secretary of State must appoint an independent person to carry out reviews of the functions under the measure. The ICO also noted that it would welcome more guidance in the code of practice to reinforce the fact that only the minimum necessary information should be shared between financial institutions and DWP in order to identify the account and account holders, and how the eligibility indicators have been met to suggest incorrect payments.

17 Data matching involves comparing data from two or more datasets to determine whether they refer to the same individual.

18 Comptroller and Auditor General, Using data analytics to tackle fraud and error, Session 2024-25, HC 988, National Audit Office, July 2025.

19 See footnote 18.

20 Committee of Public Accounts, DWP Customer Service and Accounts 2023-24, Sixth Report of Session 2024-25, HC 354, January 2025.

21 Under the Equality Act 2010, the nine protected characteristics are: age; disability; gender reassignment; marriage and civil partnership; pregnancy and maternity; race; religion or belief; sex; and sexual orientation.

22 Department for Work & Pensions, Fairness assessment including statistical analysis of the Universal Credit advances machine learning model: 1 April 2024 to 31 March 2025, July 2025.

23 Any cases that involve a decision about benefit entitlement are handed over to DWP staff as outsourced agents are not authorised to make decisions on behalf of the Secretary of State.

24 The net present value of the entry, search and seizure powers is negative because DWP cannot monetise the potential benefits of the policy as it is not a cost-saving measure for DWP. It is instead designed to improve the efficiency of investigations into benefit fraud and reduce the burden on police resources.

25 The net present value of the overpayment recovery and enforcement measure represents the cost of administering the measure. The value of the money received by the public purse will be lost by individuals who, before the measure, were not repaying their debt. In social value terms, these benefits and costs cancel each other out.

Appendix One

Our audit approach

Scope

1 We reached our independent conclusions on whether the Department for Work & Pensions (DWP) has an effective approach to tackling overpayments in the welfare system after analysing evidence collected between March and September 2025.

2 The evaluative criteria we used to assess value for money included whether DWP has made the progress it expected in reducing overpayments due to fraud and error, including whether it has achieved its objectives and implemented effectively key initiatives to tackle overpayments; and whether DWP is well placed to reduce overpayments due to fraud and error going forwards, including whether it has set out a clear strategy and specified what success will look like.

3 Our examination covered benefit expenditure in Great Britain, excluding expenditure that has been devolved to the Scottish Government. Benefit expenditure in Northern Ireland is the responsibility of the Northern Ireland Executive and was outside the scope of our work.

4 Our work did not examine DWP’s approach to, and performance in, tackling benefit underpayments, or its management of the debt that results from overpayments.

Evidence base

5 In forming our conclusions, we drew on a variety of evidence sources, as described in the paragraphs below. We collated and analysed the evidence we obtained, using our evaluative criteria as a framework. We looked across different sources of evidence to support our findings.

Interviews with DWP

6 We interviewed departmental officials involved in various aspects of DWP’s fraud and error work. The people we spoke to were selected based on their roles in:

DWP’s counter fraud and compliance function; and

DWP’s key strategies, programmes and activities for tackling overpayments due to fraud and error.

7 We carried out the interviews online, except for a few that were held in person. We took detailed notes, and the interviews typically lasted one hour. The subject of each interview was informed by our audit questions and tailored to the responsibilities of the officials involved. We used information from the interviews to develop our understanding of all the topics that related to our audit questions and to inform our evidence requests. Where possible, we triangulated interview evidence with documentary or other evidence.

Interviews with other bodies

8 We carried out online interviews with other bodies to inform our findings. Our questions were tailored to reflect the role of each organisation.

9 We interviewed officials from the following bodies to obtain their views on DWP’s approach to tackling overpayments:

HM Treasury; and

the Public Sector Fraud Authority.

10 We also interviewed four stakeholders who undertake relevant research on fraud and error, or work with or represent the interests of benefit claimants. We selected these stakeholders to obtain views on DWP’s approach to tackling overpayments, including on its key initiatives and how they could affect claimants. The stakeholders were:

the Child Poverty Action Group;

Citizens Advice;

the Public Law Project; and

rightsnet.

11 We organised our interview notes into a matrix, structured by our audit questions, to support comprehensive and consistent analysis. Our analysis was used to report on stakeholder views on the impact on claimants of DWP’s key initiatives for tackling overpayments – machine learning, Targeted Case Review (TCR) of Universal Credit (UC) claims, and new legal powers.

Document review

12 We reviewed published and unpublished documents to develop our understanding of DWP’s approach and key initiatives for tackling overpayments, including its previous and current strategies and performance. The documents included:

published documents such as DWP’s annual reports and accounts, funding announcements and fiscal statements, policy papers, and Parliamentary select committee reports and evidence; and

unpublished documents relating to fraud and error such as strategy papers, performance packs, ministerial submissions, research papers, governance papers, equality impact assessments and business cases.

13 We reviewed each document against our evaluative criteria. The review was used to inform further discussion with DWP, to determine audit findings and to triangulate findings from other sources.

Site visits

14 We visited two DWP sites – its service centres in Tyneview Park (handling Pension Credit claims) and Benton Park View (undertaking TCR of UC claims), both located in Newcastle upon Tyne.

15 The purpose of the visit to Tyneview Park was to observe how Pension Credit claims are processed and the controls that are in place to prevent and detect overpayments. We spoke with front-line staff and interviewed senior officials. We took a high-level note of the visit to record our meetings and observations.

16 The purpose of the visit to Benton Park View was to develop our understanding of how TCR is carried out, including by observing the review work in operation, speaking with front-line staff and interviewing senior officials. We took a high-level note of the visit to record our meetings and observations.

Quantitative analysis

Data on overpayments due to fraud and error

17 We analysed trends in the estimated levels and monetary value of overpayments – overall and by benefit – using DWP’s published statistics on fraud and error in the benefit system from 2019-20 to 2024-25. We also used the statistics to examine the main reasons for overpayments of specific benefits.

18 DWP estimates the monetary value of fraud and error in the benefit system annually. It does this through direct measurement of five or six benefits each year using a statistical sampling exercise. For those benefits not covered, DWP rolls forward the rate from when the benefit was last tested or, for benefits it has never measured, makes an assumption of the rate, typically using a similar benefit as a proxy.

19 The estimates are based on a random sample of the total benefit caseload and are therefore subject to statistical uncertainties. The figures we present are the central estimates calculated from the sample. DWP also calculates a range around the central estimates called a ‘confidence interval’. This expresses the uncertainty associated with the central estimate and is quantified by the estimation of 95% confidence intervals surrounding the estimate. These 95% confidence intervals show the range within which DWP expects the true value of fraud and error to lie.

Financial data

20 The main sources of financial data that we drew on were DWP’s:

annual reports and accounts;

published and unpublished data on counter-fraud funding awarded in fiscal events from 2018 to 2025;

unpublished data on its spending on fraud and error across the department; and

unpublished data on the cost to DWP of contracting out TCR from September 2024 to March 2025.

21 We used these data to set out:

the scale of DWP’s fraud and error activity, in terms of overall funding and spending;

DWP’s spending on contracting out TCR to TP (formerly Teleperformance); and

DWP’s overall performance in tackling fraud and error in terms of Annually Managed Expenditure (AME) savings.

Performance data

22 We analysed performance data relating to two of DWP’s key initiatives for tackling overpayments:

Machine learning: We used the published results from DWP’s fairness assessment of the UC advances model to examine key findings on data limitations and disparities in how the model treats different groups of claimants. We also reviewed a number of unpublished documents on the fairness assessments that DWP has carried out on the UC advances model and on the other models it has in development and testing. We used our review of these documents, as well as interviews with DWP officials, to develop our understanding of how the models operate.

TCR: Using unpublished performance data for TCR, we examined DWP’s annual performance against its key performance indicators, from 2022-23 to 2024-25. These included its main measure of success, AME savings achieved, and four other metrics:

staffing levels, including in-house and contracted out agents;

average productivity (cases reviewed per agent per day);

total number of cases completed; and

the proportion of reviewed claims found to be incorrect (referred to as the ‘hit rate’).

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