How Department for Work and Pensions uses AI

DWP scores requests for Universal Credit advances in real time with a supervised machine learning classifier and refers the highest risk requests to a caseworker before payment. The caseworker is not told the referral came from the model, a random control group is referred alongside, and every decision to decline is made by a person and can be appealed.

DWP's published effectiveness assessment for April 2025 to March 2026 finds the model 2.5 times more effective than random selection with a median payment delay of one day for approved referrals. It also finds that non UK nationals and several age bands were referred more often without a matching increase in confirmed fraud, and that referrals of couples were less often confirmed than those of single claimants; a retrained model was being tested.

The Child Maintenance Service (CMS) in the Department for Work and Pensions (DWP) uses a machine learning model to predict which paying parents on compliant Collect and Pay cases are likely to miss a payment. A model trained each month on case, payment, liability and contact data (an XGBoost classifier with SHAP explanations) scores around 100,000 cases and shows caseworkers a high, medium or low risk with the reasons and suggested actions, so they can step in before a payment breaks down. Caseworkers decide what to do; the score is one input. The tool went to a wider group of caseworkers in March 2025 and DWP reports validation accuracy of 0.86 and precision of 0.79.

Callers to the benefit telephone lines of the Department for Work and Pensions (DWP) are asked what they are calling about. Speech recognition and a natural language model then signpost them to GOV.UK, help them log in to their online account, answer some questions in the IVR (such as the next payment amount and date) or route them to the right adviser. The platform also runs identity and verification questions against the DWP trust hub by API. It makes no decisions; callers can always ask for a human.