FSOC's 2024 annual report, approved December 6, 2024, sharpened its AI analysis, warning that lack of explainability and high complexity could heighten instability beyond individual firms and that concentration in a few models or providers could create interconnection, herding, and contagion. It listed five persistent risk factors — explainability, data quality, performance assessment, third-party risk, and model bias — and recommended that member agencies keep monitoring AI, build interagency expertise on systemic AI risk, and continue Treasury-FBIIC-FSSCC cooperation. The report also references a staff-level FSOC Artificial Intelligence Working Group.
| Document | FSOC 2024 Annual Report — Financial Stability Oversight Council 2024 Annual Report — Section 3.3.3, The Use of Artificial Intelligence in Financial Services |
| Issued by | U.S. Department of the Treasury (including the Financial Stability Oversight Council) |
| Type | Report |
| Status | Superseded |
| Published | Dec 6, 2024 |
| Applies to | FSOC member agencies (recommendations); informs supervisory priorities for all U.S. financial institutions |
| Supersedes | FSOC 2023 Annual Report |
| Superseded by | FSOC 2025 Annual Report |
| Official source | home.treasury.gov ↗ |
| Use cases | AI governance (general) · Model risk management · Third-party & vendor AI · Fair lending & discrimination · Generative & agentic AI |
What are the key points of FSOC 2024 Annual Report?
- Warns that concentration in AI models or providers may lead to interconnections, herding behavior, and contagion
- Five risk factors: explainability, data (higher volume and less structured), assessing performance and robustness, third-party AI vendors, and model bias/fair lending
- Notes generative AI 'hallucinations' and degraded analysis from low-quality or fake training data
- Recommends member agencies monitor AI so oversight structures address emerging risks while facilitating efficiency
- Supports interagency development of expertise to analyze and monitor systemic risks from AI
- Supports continued Treasury, FBIIC, and FSSCC public-private cooperation on AI
- References a staff-level FSOC Artificial Intelligence Working Group and Treasury's 2024 RFI
What did FSOC 2024 Annual Report change for banks?
The 2024 report moved FSOC from noting AI risks to naming concrete systemic channels — model and vendor concentration, herding, contagion — and gave supervisors a shared five-factor risk vocabulary that appears in subsequent agency guidance on third-party AI and model risk.
What AI concentration risk did FSOC identify in 2024?
That many financial entities relying on a small number of base models or data providers could create interconnections, herding behavior, and contagion, amplifying instability beyond any single firm.
Which AI risk factors does the FSOC 2024 report list?
Explainability challenges, data governance and quality, assessing performance and robustness, third-party vendor risk, and model bias including fair-lending compliance.
| Date | Document | Status |
|---|---|---|
| Dec 14, 2023 | FSOC 2023 Annual Report — Financial Stability Oversight Council 2023 Annual Report — Section 3.3.3, The Use of Artificial Intelligence in Financial Services | Superseded |
| Dec 11, 2025 | FSOC 2025 Annual Report — Financial Stability Oversight Council 2025 Annual Report — Section 3.4, Harnessing Artificial Intelligence to Promote Financial Stability | Final |
| Jun 24, 2026 | FSOC AI Innovation Series (Mar–May 2026) — Artificial Intelligence Innovation Series — FSOC and Treasury AI Transformation Office roundtables | Final |
| Feb 19, 2026 | Treasury FS AI RMF and AI Lexicon (Feb 2026) — Financial Services AI Risk Management Framework (FS AI RMF) and Artificial Intelligence Lexicon | Final |
| Dec 19, 2024 | Treasury AI in Financial Services report (Dec 2024) — Artificial Intelligence in Financial Services — Report on the Uses, Opportunities, and Risks of AI in the Financial Services Sector | Final |
| Oct 17, 2024 | Treasury $4B AI fraud-prevention announcement (Oct 2024) — Treasury Announces Enhanced Fraud Detection Processes, Including Machine Learning AI, Prevented and Recovered Over $4 Billion in Fiscal Year 2024 | Final |
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