Program Integrity (RRAD-PI) AI Counter-Fraud Enhancement Measures

Overview

Development stage

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

High-impact designation

Not high-impactSource: c) Not high-impact

Impact justification

Not reported

Start date

Not reported

Withheld from public reporting

Not reported

Mission

Topic area

Service Delivery

Operational functions

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

Problem the AI is intended to solve

The AI use case aims to further expand and address RRAD-PI’s fraud detection and prevention challenges within disaster recovery programs. Specifically, it seeks to mitigate fraudulent activities, identity theft, and deceptive practices that compromise program integrity, ensuring that resources are allocated efficiently and equitably to eligible individuals and entities.

Expected benefits

The expected benefits include: • Enhanced Fraud Detection: Improved identification of fraudulent activities, reducing financial losses and ensuring program integrity. • Operational Efficiency: Automation of manual processes, such as document review and identity verification, leading to faster application processing and reduced administrative burden. • Proactive Fraud Prevention: Early detection of fraud risks enables timely intervention, minimizing harm and protecting public funds. • Improved Resource Allocation: Ensures disaster recovery resources are distributed to legitimate recipients, fostering public trust in government programs. • Cross-Agency Collaboration: Facilitates secure data sharing across agencies, enabling a unified approach to combating fraud schemes that span jurisdictions. • Public Confidence: Strengthened program integrity enhances public trust in the agency’s ability to manage disaster recovery efforts effectively.

System outputs

The AI model/system may generate outputs such as: • Fraud Risk Scores: Quantitative assessments of fraud likelihood for transactions, applications, or entities. • Anomaly Alerts: Notifications of unusual patterns or behaviors indicative of potential fraud. • Network Maps: Visual representations of relationships between entities, highlighting connections to fraudulent actors. • Document Analysis Reports: Summaries of inconsistencies, deceptive language, or forgery detected in submitted documents. • Real-Time Monitoring Flags: Alerts for suspicious activities requiring immediate intervention. • Behavioral Biometrics Insights: Reports on user behavior anomalies, such as unusual typing patterns or device usage. • Image/Video Verification Results: Validation of authenticity for submitted visual evidence. • Threat Intelligence Updates: Integration of external threat data into fraud detection models. • Geospatial Analysis Findings: Location-based discrepancies in claims, such as mismatched disaster relief applications. • Cross-Agency Fraud Insights: Aggregated data analysis highlighting fraud schemes across jurisdictions.

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