Fourth Report - The Department for Work & Pensions Annual Report and Accounts 2022–23

Select Committee
Public Accounts Committee HC 290 6 December 2023
Report Status Government responded
Conclusions & Recommendations 33 items (11 recs)
Source View on Parliament website
Government Response (AI assessment · 32 of 33 classified)
Accepted 20
Acknowledged 1
Rejected 11
Government response
Treasury minutes: Government response to the Committee of Public Accounts on the Fourth report from Session 2023-24 · published 8 Mar 2024
Read the government response ↗ Response on the tracker
Clear

Recommendations & Conclusions

11 results
6 Conclusion Rejected
Assess impact of data analytics and machine learning on legitimate claims and specific groups.
Conclusion
DWP has not yet done enough to understand the impact of machine learning on customers and provide them with confidence that it will not result in unfair treatment. DWP is expanding its use of advanced data analytics to tackle fraud. … Read more
Government Response Summary
The government disagrees with detailing specific metrics for publication, citing a need to avoid compromising fraud detection. However, it will report annually on the impact of data analytics on protected groups and vulnerable claimants, starting with the 2023-24 Annual Report.
HM Treasury
View Details
1 Conclusion Rejected
Committee took evidence from DWP and HMRC on fraud, error, and National Insurance records.
Conclusion
On the basis of a Report by the Comptroller & Auditor General (C&AG), we took evidence from the Department for Work & Pensions (DWP) on its 2022–23 Annual Report & Accounts and the level of fraud and error in the … Read more
Government Response Summary
The government disagrees with an implied recommendation regarding a 5% assumption, stating it cannot be compared to official fraud and error statistics in isolation due to various influencing factors.
HM Treasury
View Details
7 Conclusion Rejected
Universal Credit overpayments remain high, with significant build-up from pandemic-era claims.
Conclusion
DWP estimates that it overpaid 12.8% (£5.5 billion) of all Universal Credit payments in 2022–23, which is much higher than any other benefit.10 We challenged DWP to explain why the fall in fraud and error promised in the Universal Credit … Read more
Government Response Summary
The government rejects the committee's implied direction to explain the failure of fraud and error reduction, stating its commitment to a cost-effective control environment but highlighting external fraud trends beyond its direct control.
HM Treasury
View Details
8 Conclusion Rejected
Universal Credit system complexity contributes to incorrect claims despite ongoing simplification efforts.
Conclusion
We asked DWP to what extent the fact that 1 in 3 Universal Credit claims is incorrect is a result of the complexity of the system. DWP told us it is trying to make it easier for claimants to declare … Read more
Government Response Summary
The government rejects the committee's implied criticism regarding system complexity, stating it is committed to reducing fraud and error but acknowledges external trends impacting fraud levels are not directly in its control.
HM Treasury
View Details
9 Conclusion Rejected
Universal Credit overpayment target of 6.5% now unachievable due to increased baseline fraud and error.
Conclusion
We challenged DWP to explain whether it still expects Universal Credit overpayments to fall to 6.5% as it had previously committed to. DWP explained that 6.5% was the level implied in the business case as a result of the expected … Read more
Government Response Summary
The government rejects the committee's implied direction to explain how it will achieve the 6.5% overpayment target, stating it's committed to reducing fraud and error but external trends impact the level of fraud, which is outside its direct control.
HM Treasury
View Details
13 Recommendation Rejected
DWP's new fraud and error measures are expected to improve accountability and transparency.
Recommendation
We have previously found that the DWP lacks the ability to demonstrate that its counter-fraud activities are having the intended impact and are cost-effective.22 Alongside its forecast that benefit overpayments will not return to pre-pandemic levels until 2027– 28, DWP … Read more
Government Response Summary
The government rejects the recommendation, stating that while DWP is committed to reducing fraud and error, external trends impacting the level of fraud in the benefit system are not directly within its control.
HM Treasury
View Details
29 Conclusion Rejected
DWP invests £70 million to expand machine learning for detecting fraudulent benefit claims.
Conclusion
DWP is investing some £70 million to March 2025 in expanding its use of advanced analytics to tackle fraud. This includes using machine learning algorithms to flag potentially fraudulent benefit claims. DWP has already piloted an algorithm to detect fraudulent … Read more
Government Response Summary
The government rejects detailing specific metrics for publication on data analytics' impact, citing the need to avoid compromising fraud detection. However, it reaffirms its commitment to reporting annually on the impact of data analytics on protected groups and vulnerable claimants, with the first assessment in its 2023-24 Annual Report and Accounts.
HM Treasury
View Details
30 Conclusion Rejected
Concerns persist regarding machine learning's potential unfairness and bias for vulnerable claimants.
Conclusion
We received written evidence from the Child Poverty Action Group and from the Public Law Project expressing concern about the potential unfairness of machine learning, particularly with regard to vulnerable claimants and people with protected characteristics.70 We asked DWP whether … Read more
Government Response Summary
The government rejects detailing specific metrics for publication on data analytics' impact, citing the need to avoid compromising fraud detection. However, it reaffirms its commitment to reporting annually on the impact of data analytics on protected groups and vulnerable claimants, with the first assessment in its 2023-24 Annual Report and Accounts.
HM Treasury
View Details
31 Conclusion Rejected
Algorithmic bias in DWP systems acknowledged, but evidence of unfair impacts remains inconclusive.
Conclusion
We challenged DWP to explain how it would address the risk that legitimate benefit claims are unfairly delayed or reduced as a result of an algorithms targeting innocent behaviour, such as frequent changes of circumstances. DWP acknowledged that some level … Read more
Government Response Summary
The government rejects detailing specific metrics for publication on data analytics' impact, citing the need to avoid compromising fraud detection. However, it reaffirms its commitment to reporting annually on the impact of data analytics on protected groups and vulnerable claimants, with the first assessment in its 2023-24 Annual Report and Accounts.
HM Treasury
View Details
32 Conclusion Rejected
DWP cautiously implementing machine learning for fraud detection due to initial accuracy issues.
Conclusion
DWP also told us it did not want to reveal when it planned to go live with machine learning on a large scale to avoid informing potential fraudsters, but added it was 65 Qq 84–90 66 Q 90; DWP ARA … Read more
Government Response Summary
The government rejects detailing specific metrics for publication on data analytics' impact, citing the need to avoid compromising fraud detection. However, it reaffirms its commitment to reporting annually on the impact of data analytics on protected groups and vulnerable claimants, with the first assessment in its 2023-24 Annual Report and Accounts.
HM Treasury
View Details
33 Conclusion Rejected
DWP committed to annual reporting on data analytics impact on protected groups.
Conclusion
In our November 2022 report on DWP’s 2021–22 accounts we recommended that DWP should report annually to Parliament on its assessment of the impact of data analytics on protected groups and vulnerable claimants.77 DWP told us it thought the right … Read more
Government Response Summary
The government rejects detailing specific metrics for publication on data analytics' impact, citing the need to avoid compromising fraud detection. However, it reaffirms its commitment to reporting annually on the impact of data analytics on protected groups and vulnerable claimants, with the first assessment in its 2023-24 Annual Report and Accounts.
HM Treasury
View Details