Source · Select Committees · Public Accounts Committee
Recommendation 25
25
The Department told us that the big fraud and error saving that it knew would...
Conclusion
The Department told us that the big fraud and error saving that it knew would come from Universal Credit is using real-time information (RTI) on earnings from HMRC in an automated way to calculate the award, and that it ‘knows’ it is “doing well on the RTI part”. However, the Department accepted that there is “more fraud and error in self-reported earnings than had been anticipated” and it has “more to do on the self-employment part”.50 The Department told us that is has other datasets from HMRC, “because everybody has to make returns”, for claimants with self-employment or self -reported income. However, the Department said that there are time lag issues with this data so it needs to supplement it with data from other sources e.g. different agencies, financial institutions or financial companies that would have information on people.51 The third risk area identified by the Department where further progress is required is ‘living together’ (e.g. where an undeclared partner might be living in a household). It informed us that it is looking at using other types of data matching in this area and reported that IRIS has developed data matching rules to help identify cases where an undeclared partner might be living in a household. Alongside looking for data matching opportunities, it also told us that a lot of work is going into making reporting a change of circumstance easier for capital, living together and self-reported and self-employed earnings.52
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.