Claims Program Predictive Fraud Analytics

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

Administrative Functions

Operational functions

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

Problem the AI is intended to solve

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.

Expected benefits

(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.

System outputs

Predictions, Classifications, Scores: fraud probability scores, claim authenticity classifications, risk alerts.

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