AI-driven Fraud Detection
Description
Fraud diverts billions in taxpayer funds but manual review of records is too slow to catch early misconduct. Fraud cases involve sifting through vast amounts of structured and unstructured data, often too large for manual review to detect early fraud signals. This initiative will apply AI-enabled anomaly detection to efficiently synthesize insights from vast data collections and uncover fraudulent patterns. It aligns with M-25-21’s public trust priority by safeguarding taxpayer funds and E.O. 14179’s innovation directive.
Detailed example
Predictions, Classifications, Alerts: fraud risk scores, anomaly alerts, pattern classifications.
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) Bolsters DOJ’s mission to prevent waste, fraud, and abuse in taxpayer-funded health programs, strengthening enforcement under the FCA. (2) Builds on existing Medicare/Medicaid data feeds, OIG case frameworks, previous FCA healthcare enforcement analytics, and ongoing interagency fraud task force initiatives.(3) Increase in early identification of false claims; recovery dollars secured; reduced investigation timelines.
Controls / human review
ATO: Not reported; PIA: Not published