Source · Select Committees · Public Accounts Committee
Recommendation 23
23
The Department told us that it is starting to build a system that is based...
Conclusion
The Department told us that it is starting to build a system that is based on ‘transaction risking’; its vision is to be in a place where it can, in real time, or near real time, assess every claim as it is coming through and take a view of how much it trusts the information that is in the claim. For example, having one ‘customer journey’ (quicker and easier) for claimants where the Department trusts the information, and a different ‘customer 38 Qq 27, 40 39 DWP ARAC 2019–20, page 185 40 Qq 17, 19 41 Q 19 42 Q 24; DWP ARAC 2019–20, page 17 43 Department for Work & Pensions, Annual Report and Accounts 2018–19, HC 2281, 27 June 2019, page 122 44 DWP ARAC 2019–20, pages 73 and 76, 188 Figure 2 14 Department for Work and Pensions Accounts 2019–20 journey’ with more intervention where the Department does not trust the information.45
Government Response
Not Addressed
HM Government
Not Addressed
The government agrees with the Committee’s recommendation. Target implementation date: July 2021 4.2 The department is able to track the effectiveness of new technologies. The department is also conscious of the need to address any potential for bias in its approach to fraud and error and is taking steps to do so. 4.3 There are benefit realisation plans in place to monitor the impact of new digital technologies such as those being delivered through initiatives such as the Counter Fraud and Error Management System, Verify Earnings and Pensions, Transaction Risking and the Data Services Platform. These projects now form part of the new Fraud, Error and Debt Portfolio, which will track initiatives and potential savings between now and 2023-24. 4.4 The department’s Monetary Value of Fraud and Error estimates are published annually. Alongside that, the department continually monitors a huge range of data on fraud and error detected through both interventions and customer reporting. The department also tracks its results from internal accuracy checks. The Integrated Risk and Intelligence Team now acts as a central unit for all this data and provides a single view of risk for the whole department. Collectively, this approach helps gauge the strength of particular initiatives and identifies remaining gaps. 4.5 The department has a draft Data Science Ethics Framework for machine learning that ensures it considers bias and discrimination in the design of predicative models. The Integrated Risk and Intelligence Service is working with legal experts to ensure that the ethical and legal position of all of its products have been properly considered ahead of any wider automation. 4..6 The department will provide an update on how it is using data to tackle loss as part of the annual report and accounts fraud and error narrative.