The model did not replace fraud investigators; it changed which transactions reached them first and compressed the time available for recovery.
AI value · Fraud and improper-payment recovery attributed to expedited ML check-fraud identification
Company-reported
$1 billion recovered in FY2024; Treasury separately reported $375 million recovered in FY2023
Treasury-reported attribution. The agency does not publish model precision, false-positive rate, or a causal decomposition from other process changes.
Before
Review payment and bank information through existing fraud processes. → Pursue suspicious payments after identification.
After
Scores and prioritizes potentially fraudulent Treasury checks in near real time. → Investigate prioritized cases and expedite recovery actions.
Human boundary
The model prioritizes risk; authorized officials and financial institutions decide whether and how to investigate or recover funds.
Why it matters
That potentially fraudulent checks must be found through slower post-payment reviews rather than prioritized.
This case is company-reported. Use it for the operating-model shift; do not treat the numbers as independently measured.
How the work changed
Before
How the work ran before the change.
Step 1 of 2
Payment-integrity analysts
Review payment and bank information through existing fraud processes.
ControlRules, referrals, and manual prioritization
Step 2 of 2
Recovery teams
Pursue suspicious payments after identification.
ControlRecovery procedures
What changed
That potentially fraudulent checks must be found through slower post-payment reviews rather than prioritized.
Decision rightHuman sets the threshold; AI decides each instance
After
How the same work runs now.
Step 1 of 2
Machine-learning fraud process
Scores and prioritizes potentially fraudulent Treasury checks in near real time.
ControlRisk-based screening
Step 2 of 2
Analysts and partner institutions
Investigate prioritized cases and expedite recovery actions.
ControlHumans determine investigative and recovery action
Process model built from the published workflow evidence for U.S. Department of the Treasury, Bureau of the Fiscal Service. Every step, actor, and control appears in full below.Every step, actor, and control
Exception path
Flagged checks proceed through human review and institutional recovery processes; the public source does not disclose thresholds or appeal procedures.
Decision authority
The model prioritizes risk; authorized officials and financial institutions decide whether and how to investigate or recover funds.
Before
#
Actor
Action
Control
01
Payment-integrity analysts
Review payment and bank information through existing fraud processes.
Rules, referrals, and manual prioritization
02
Recovery teams
Pursue suspicious payments after identification.
Recovery procedures
After
#
Actor
Action
Control
01
Machine-learning fraud process
Scores and prioritizes potentially fraudulent Treasury checks in near real time.
Risk-based screening
02
Analysts and partner institutions
Investigate prioritized cases and expedite recovery actions.
Humans determine investigative and recovery action
Work that left the path
Undifferentiated review of lower-risk checks
Delay between suspicious-pattern emergence and recovery prioritization
Human role before
Analysts detected and prioritized suspicious checks through existing processes.
Human role after
Analysts work a machine-prioritized queue and retain investigative, recovery, and law-enforcement decision authority.
AI role
Machine-learning risk detection that strengthens and expedites identification of potentially fraudulent Treasury checks.
Outcomes
Fraud and improper-payment recovery attributed to expedited ML check-fraud identification
Company-reported
Before the enhanced AI process at the start of FY2023→$1 billion recovered in FY2024; Treasury separately reported $375 million recovered in FY2023
Fiscal years 2023 and 2024 · U.S. Treasury checks processed through the Office of Payment Integrity
Treasury-reported attribution. The agency does not publish model precision, false-positive rate, or a causal decomposition from other process changes.
What leaders can reuse
Anti-pattern
Equating dollars recovered with model precision or allowing risk scores to trigger adverse action without human review.
Questions
01Does the model only prioritize, or can it block payment?
02What precision and appeal data should be public?
Portability conditions
High-volume transaction data
A lawful human investigation and recovery process
Monitoring of false positives and disparate effects
Reputation risk
medium
Evidence and authority
What the public record supports.
Current · updated
2 primary; publication outcomes are reported.
Bundle 1.0.0 · reviewed 2026-09-06 · stable ID 37f86dbf3a4048b3
