Source · Select Committees · Public Accounts Committee
Recommendation 32
32
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 2022–23, page 308 67 Qq 84–85 68 DWP ARA 2022–23, pages 102, 308 69 Committee of Public Accounts, The Department for Work and Pensions’ Accounts 2021–22 – Fraud and error in the benefit system, Twenty-Sixth Report of Session 2022–23, HC 44, 9 November 2022 70 DWP0007; DWP0008 71 Q 101 72 Qq 101–103 73 DWP ARA 2022–23, page 309 74 Q 103 The Department for Work & Pensions Annual Report and Accounts 2022–23 19 working closely with the relevant authorities and that Ministers would be aware of its plans.75 However, DWP claimed that it is “taking it very slowly” with regards to rolling out machine learning. It explained that its pilot algorithm to detect fraud in Universal Credit advances did not work very well at first and needed to be tested and iterated using a small number of cases before being released for wider use. It added that it intends to follow this approach going forward and will not roll out new algorithms more widely until they have reached a level of accuracy that avoids unnecessarily holding up legitimate payments.76
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.
Government Response
Rejected
HM Government
Rejected
6.1 The government disagrees with the Committee’s recommendation. 6.2 The department is committed to reporting annually to Parliament on its assessment of the impact of data analytics on protected groups and vulnerable claimants with the first assessment in the department’s 2023-24 Annual Report and Accounts. In future years the department will iterate the annual assessments to include impacts on customer service. 6.3 While the department is committed to providing information as set out, it must not compromise its ability to tackle fraud and error by revealing details about its models that could be exploited. On that basis, the department disagrees with the Committee’s recommendation detailing specific metrics for publication.