Claims Program Predictive Fraud Analytics
Description
Deploy advanced AI analytics to detect fraudulent claims and suspicious patterns in compensation claims programs, including the September 11th Victims Compensation Fund, Radiation Exposure Compensation Act, Camp LeJeune Justice Act, and other federal victim assistance programs. This initiative protects program integrity, ensures resources reach legitimate claimants, and maintains public trust in federal compensation systems. Aligns with Administration priorities on combating fraud, protecting taxpayer funds, and ensuring justice for claimants.
Detailed example
Predictions, Classifications, Scores: fraud probability scores, claim authenticity classifications, risk alerts.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
(1) Strengthens the Civil Division's capabilities in administering victim compensation programs by identifying potentially fraudulent medical claims, duplicate submissions, and identity fraud. (2) Builds upon existing Civil Division case management systems and medical claim review processes. Leverages ongoing fraud detection initiatives across DOJ components, integrates with established medical record verification systems, and utilizes existing partnerships with healthcare providers and medical review contractors. (3) Improvement in fraudulent claim detection rates, prevention of fraudulent payouts annually, reduction in false positive flags affecting legitimate claimants, faster claim processing times for verified submissions, and improved coordination metrics with investigative agencies on fraud referrals.
Controls / human review
ATO: Not reported; PIA: Not published