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Curated Insight: New AI Lexicon for financial services

Picture of Yusuf Moolla
TL;DR
• This featured resource is from the US Treasury.
• A new AI Lexicon for financial services.
• It provides single, sector-specific definitions for key terms.

 

Earlier this month, the US Department of the Treasury released the Financial Services AI Risk Management Framework (FS AI RMF). It is long (four documents, 230 control objectives, more than 500 pages) and new, so will take some time to work through properly.

Of interest, the US Treasury also released an AI Lexicon, created by the Artificial Intelligence Executive Oversight Group (described in the document).

It is different to NIST’s Trustworthy AI Glossary:

  • shorter, at 9 pages (vs 22)
  • maps a single definition to each term (vs multiple definitions per term)
  • is meant to be FS focused (e.g. it defines “Algorithmic trading system”).

 

Highlights (of the Lexicon)

It adopts definitions from NIST (e.g. from the Risk Management Framework for Information Systems and Organisations), the Comptroller’s Handbook (Model Risk Management), and other sources.

Notably, it:

    • was “developed to promote a shared vocabulary for communication and collaboration on AI related matters in the financial sector.”
    • “compiles commonly used risk management and technical terms that have specific meanings in the context of AI use in financial services.”
    • is “an optional tool and is not intended for use…” in legal interpretations, etc.

 

It could help reduce arguments within (and between) teams and become a handy reference point.

 

The AI lexicon is available here: https://fsscc.org/wp-content/uploads/2026/02/AIEOG-AI-Lexicon-February-2026.pdf

If you have the time and inclination, the FS AI RMF is here: https://cyberriskinstitute.org/artificial-intelligence-risk-management/

 

Disclaimer: The info in this article is not legal advice. It may not be relevant to your circumstances. It was written for specific contexts within banks and insurers, may not apply to other contexts, and may not be relevant to other types of organisations.