Public Payment Integrity Recovery Analytics
Centralized AI-powered fraud detection and recovery support for public-sector payment integrity oversight, including cross-agency antifraud analytics and Treasury check fraud identification.
The Problem
“Centralize public payment integrity fraud detection across agencies and Treasury-issued payments”
Organizations face these key challenges:
Fraud signals are fragmented across agencies, programs, and payment systems
Rule-based controls miss novel and coordinated fraud schemes
Investigators spend too much time on low-value alerts and manual triage
Entity identities are inconsistent across source systems, limiting linkage analysis
Impact When Solved
The Shift
Human Does
- •Review payment, claims, and check activity in separate agency reports and spreadsheets
- •Apply manual rules and investigator judgment to flag suspicious payments or duplicate claims
- •Triage hotline referrals and audit findings into cases for follow-up
- •Investigate payees, endorsements, and deposit patterns using limited cross-agency context
Automation
- •Run basic rule checks for thresholds, duplicates, and watchlist matches
- •Generate static alerts from agency-specific payment control systems
- •Produce periodic exception reports for manual review
Human Does
- •Approve investigation priorities and decide which high-risk cases move forward
- •Review linked-entity findings and resolve identity or case exceptions
- •Authorize holds, referrals, document requests, and recovery actions
AI Handles
- •Continuously monitor payments, claims, payees, and Treasury check events across programs
- •Score anomalies and prioritize suspicious transactions, claims, entities, and checks for review
- •Link related identities, accounts, addresses, and endorsers across agencies to surface fraud networks
- •Generate shared risk queues, case summaries, and recommended next actions for investigators
Operating Intelligence
How it works
AI surfaces what is hidden in the data.
Humans do the substantive investigation.
Closed cases sharpen future detection.
Who is in control at each step
Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.
Step 1
Scan
Step 2
Detect
Step 3
Assemble Evidence
Step 4
Investigate
Step 5
Act
Step 6
Feedback
AI lead
Autonomous execution
Human lead
Approval, override, feedback
AI scans and assembles evidence autonomously. Humans do the substantive investigation. Closed cases improve future scanning.
The Loop
6 steps
Scan
Scan broad data sources continuously.
Detect
Surface anomalies, links, or emerging signals.
Assemble Evidence
Pull related records into a working case file.
Investigate
Humans interpret evidence and make case judgments.
Authority gates · 1
The system must not place payment holds or initiate recovery actions without approval from an authorized investigator or program official [S2].
Why this step is human
Investigative judgment involves ambiguity, legal considerations, and stakeholder impact that require human expertise.
Act
Carry out the human-directed next step.
Feedback
Closed investigations improve future detection.
1 operating angles mapped
Operational Depth
Technologies
Technologies commonly used in Public Payment Integrity Recovery Analytics implementations:
Key Players
Companies actively working on Public Payment Integrity Recovery Analytics solutions:
Real-World Use Cases
Permanent analytics center of excellence for payment integrity oversight
Create a dedicated expert team that uses data tools to spot suspicious payments and help agencies prevent fraud and errors.
AI-enabled Treasury check fraud detection and recovery
Treasury uses AI to quickly spot unusual patterns in government checks and warn banks before bad checks are cashed.