Recommendations & Conclusions
11 items
6
Conclusion
Fourth Report - The Department for Work…
Rejected
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. This includes machine learning algorithms to flag potentially fraudulent benefit …
Government response. 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
1
Conclusion
Fourth Report - The Department for Work…
Rejected
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 benefits it administers.2 We also took evidence from HM Revenue …
Government response. 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
7
Conclusion
Fourth Report - The Department for Work…
Rejected
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 business case has failed to materialise. DWP told us that …
Government response. 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
8
Conclusion
Fourth Report - The Department for Work…
Rejected
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 changes of circumstances through continuous improvements of the Universal Credit …
Government response. 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
9
Conclusion
Fourth Report - The Department for Work…
Rejected
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 reduction in fraud and error from merging legacy benefits into …
Government response. 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
13
Recommendation
Fourth Report - The Department for Work…
Rejected
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 has set a target to achieve £1.3 billion of fraud …
Government response. 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
29
Conclusion
Fourth Report - The Department for Work…
Rejected
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 Universal Credit advances claims.68 The NAO reports that DWP is …
Government response. 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, …
HM Treasury
30
Conclusion
Fourth Report - The Department for Work…
Rejected
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 it understood the concerns of people who have warned of …
Government response. 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, …
HM Treasury
31
Conclusion
Fourth Report - The Department for Work…
Rejected
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 of algorithmic bias is to be expected because of how …
Government response. 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, …
HM Treasury
32
Conclusion
Fourth Report - The Department for Work…
Rejected
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 2022–23, page 308 67 Qq 84–85 68 DWP ARA 2022–23, …
Government response. 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, …
HM Treasury
33
Conclusion
Fourth Report - The Department for Work…
Rejected
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 way to do this would be to report annually in …
Government response. 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, …
HM Treasury