---
title: >-
  U.S. Department of the Treasury, Bureau of the Fiscal Service: Near-real-time
  machine-learning prioritization of potentially fraudulent Treasury checks
slug: us-treasury-check-fraud-ml
stable_id: 37f86dbf3a4048b3
company: U.S. Department of the Treasury, Bureau of the Fiscal Service
function_code: financial_crime
pattern_codes:
  - threshold_as_control
  - exception_based_operations
  - queue_elimination
evidence_strength: reported
publication_tier: showcase
freshness: current
reviewed_at: '2026-08-23'
updated_at: '2026-08-22'
source_quality_summary: 2 primary; publication outcomes are reported.
caveat_summary: >-
  Treasury-reported attribution. The agency does not publish model precision,
  false-positive rate, or a causal decomposition from other process changes.
collections:
  - queue-eliminated
  - regulated-autonomy
bundle_version: 1.0.0
bundle_fingerprint: sha256:c23c6cc2b88153d008ea8fda928f632ce0011fc2d4c5036672a16e5d895bab93
canonical_url: https://brianletort.ai/transformations/us-treasury-check-fraud-ml
---

# U.S. Department of the Treasury, Bureau of the Fiscal Service: Near-real-time machine-learning prioritization of potentially fraudulent Treasury checks

That potentially fraudulent checks must be found through slower post-payment reviews rather than prioritized in near real time.

Function: Financial crime. Patterns: Threshold as the human control; Exception-based operations; Queue elimination. Evidence: reported.

Freshness: current. Reviewed: 2026-08-23. Updated: 2026-08-22.


Source quality: 2 primary; publication outcomes are reported.

## Before

1. **Payment-integrity analysts** — Review payment and bank information through existing fraud processes. (control: Rules, referrals, and manual prioritization)
2. **Recovery teams** — Pursue suspicious payments after identification. (control: Recovery procedures)

## After

1. **Machine-learning fraud process** — Scores and prioritizes potentially fraudulent Treasury checks in near real time. (control: Risk-based screening)
2. **Analysts and partner institutions** — Investigate prioritized cases and expedite recovery actions. (control: Humans determine investigative and recovery action)

## Decision rights

The model prioritizes risk; authorized officials and financial institutions decide whether and how to investigate or recover funds.

## Exception path

Flagged checks proceed through human review and institutional recovery processes; the public source does not disclose thresholds or appeal procedures.

## Outcomes

- **Fraud and improper-payment recovery attributed to expedited ML check-fraud identification** (reported): Before the enhanced AI process at the start of FY2023 → $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.

## Executive lesson

The model did not replace fraud investigators; it changed which transactions reached them first and compressed the time available for recovery.

## Anti-pattern

Equating dollars recovered with model precision or allowing risk scores to trigger adverse action without human review.

## Questions for leaders

- Does the model only prioritize, or can it block payment?
- What precision and appeal data should be public?

## Sources

- [Treasury Announces Enhanced Fraud Detection Processes, Including Machine Learning AI, Prevented and Recovered Over $4 Billion in Fiscal Year 2024](https://home.treasury.gov/news/press-releases/jy2650) — U.S. Department of the Treasury
- [Treasury Announces Enhanced Fraud Detection Process Using AI Recovers $375M in Fiscal Year 2023](https://home.treasury.gov/news/press-releases/jy2134) — U.S. Department of the Treasury