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AI Governance for Institutional Readiness in Finance

Author

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  • Irene Aldridge
  • Steve Krawciw

Abstract

Agentic AI is gaining acceptance in asset management, but governance has not kept pace: 88\% of surveyed finance professionals report no operational governance framework for agentic AI, and only 24 of 75 large U.S. money managers disclosing AI use in Form ADV filings report a formal governance policy. We argue this gap is architectural: governance built for static validation does not survive continuously retrained agentic policies. We propose a four-layer framework (Policy, Engineering, Composition, Systemic) grounded in two distinct kinds of evidence, kept explicitly separate: two calibrated synthetic illustrations (a regret-covariance drift monitor; a crowding simulation showing joint drawdown risk rising from 39.2\% to 79.3\%), and three real, documented cases (a deployed LLM-embedding trading strategy, a \$45 billion discretionary fund's forced-deleveraging blowup, and a tribunal ruling holding an airline liable for its chatbot). The synthetic examples demonstrate computability from observable data; the cases demonstrate that the failure modes are not hypothetical. We provide a 90-day implementation sequence spanning trading and payments/customer-facing systems.

Suggested Citation

  • Irene Aldridge & Steve Krawciw, 2026. "AI Governance for Institutional Readiness in Finance," Papers 2608.02311, arXiv.org, revised Aug 2026.
    • Handle: RePEc:arx:papers:2608.02311
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    References listed on IDEAS

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    1. Andreas Fuster & Paul Goldsmith‐Pinkham & Tarun Ramadorai & Ansgar Walther, 2022. "Predictably Unequal? The Effects of Machine Learning on Credit Markets," Journal of Finance, American Finance Association, vol. 77(1), pages 5-47, February.
    2. Hui Chen & Antoine Didisheim & Luciano A. Somoza, 2026. "Out of the Black Box: Uncertainty Quantification for LLMs via Conditional Probabilities," NBER Working Papers 34965, National Bureau of Economic Research, Inc.
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