Source · Select Committees · Public Accounts Committee
Recommendation 21
21
The Department’s fraud and error strategy relies on modernising its technology and putting more investment...
Conclusion
The Department’s fraud and error strategy relies on modernising its technology and putting more investment into data and data analytics. It told us that “we really do see that putting more investment into data, into data analytics and into that prevention space, is going to get us where we need to go”, with prevention activity not allowing fraud and error into the system in the first place.42
Government Response
Not Addressed
HM Government
Not Addressed
4: PAC conclusion: The Department cannot demonstrate that it is doing everything that is cost- effective to tackle fraud and error. 4: PAC recommendation: The Department needs to be able to monitor and report on the impact and cost effectiveness of each of its fraud and error initiatives and in particular on the impact of its investment in new technology. The Department should monitor and report any discrimination or bias caused by using artificial intelligence and machine learning on different claimant groups. 4. 1 The government agrees with the Committee’s recommendation. Ta rget 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. 12 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.