Recommendation 5

The Department’s lack of transparency over its use of data analytics risks eroding public trust in the benefit system. The Department’s strategy to bring down fraud and error will depend increasingly on the use of data analytics and machine learning to identify potentially fraudulent claims. It has trialled a model to detect fraud in Universal Credit advances. This uses historical fraudulent claim data to predict which claims are likely to be fraudulent in future and flag these to caseworkers for their review. The Department is aware of the potential for data analytics methods to generate outcomes that could have an adverse impact on certain claimants. For instance, some cases flagged as potentially fraudulent will turn out to be legitimate claims. If the model were to disproportionately identify a group with a protected characteristic as more likely to commit fraud, it could inadvertently obstruct fair access to benefits. The Department has taken steps to evaluate the potential impact of data analytics and machine learning on groups with protected characteristics, but the results are inconclusive and it has not made them public. The Department expects it will need to regularly update its assessment of the potential impact on vulnerable claimants as it develops its data analytics over time. Recommendations: The Department should report annually to Parliament on its assessment of the impact of data analytics on protected groups and vulnerable claimants. The Department should also consider what role the Social Security Advisory Committee can play in supporting public trust over the use of data analytics in the welfare system.