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Model Risk Management for Generative AI In Financial Institutions

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arxiv 2503.15668 v1 pith:P2K4RCC5 submitted 2025-03-19 q-fin.RM cs.LG

Anwesha Bhattacharyya , Ye Yu , Hanyu Yang , Rahul Singh , Tarun Joshi , Jie Chen , Kiran Yalavarthy This is my paper · ORCID
classification q-fin.RMcs.LG
keywords modelfinancialapplicationsgenerativeriskadditionalenterprisesmanagement
verification ladder T0 review T1 audit T2 compute T3 formal
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The success of OpenAI's ChatGPT in 2023 has spurred financial enterprises into exploring Generative AI applications to reduce costs or drive revenue within different lines of businesses in the Financial Industry. While these applications offer strong potential for efficiencies, they introduce new model risks, primarily hallucinations and toxicity. As highly regulated entities, financial enterprises (primarily large US banks) are obligated to enhance their model risk framework with additional testing and controls to ensure safe deployment of such applications. This paper outlines the key aspects for model risk management of generative AI model with a special emphasis on additional practices required in model validation.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Regulatory Graphs and GenAI for Real-Time Transaction Monitoring and Compliance Explanation in Banking

    cs.AI 2025-06 reject novelty 3.0 of 10

    A GNN plus retrieval-augmented LLM pipeline for transaction monitoring is described, but the 98.2% F1 claim rests on an incomparable baseline setup and unreleased synthetic data.

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