AI-driven Fraud Detection

Overview

Development stage

In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.

High-impact designation

High-impactSource: a) High-impact

Impact justification

Not reported

Start date

Not reported

Withheld from public reporting

NoSource: a) No

Mission

Topic area

Law Enforcement

Operational functions

Fraud, waste and improper payments, Risk scoring and triage Derived by keyword rules; see methodology

Problem the AI is intended to solve

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.

Expected benefits

(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.

System outputs

Predictions, Classifications, Alerts: fraud risk scores, anomaly alerts, pattern classifications.

Technology

AI classification

Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…

System name(s)

Not reported

Custom-developed code

Not reported

Public source code

Not reported

Sourcing

How it was built

Not reported

Vendor (as reported)

Not reported

Vendors (standardized)

None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII

Not reported

Privacy Impact Assessment

Not reported

Authorization to Operate

Not reported

Demographic features

Not reported

Training and evaluation data

Not reported

Federal Data Catalog

Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Potential impacts and how they were identified

Not reported