Model Risk Management for Generative AI In Financial Institutions

2025-03-19

AI Summary

Generative AI deployment in finance introduces novel model risks—including hallucination and toxicity—that challenge regulatory compliance and operational safety. Method: This paper proposes the first regulatory-aligned, financial-grade risk management framework for generative AI, featuring a measurable verification pathway integrating red-teaming, adversarial prompt engineering, output consistency checking, controllability quantification, and regulatory compliance mapping. Contribution/Results: The framework establishes, for the first time, a closed-loop governance system spanning risk identification, classification, verification, and mitigation. It delivers a deployable verification checklist, a three-tiered risk taxonomy, and standardized governance workflows. Adopted as a mandatory evaluation standard by multiple major U.S. banks, the framework significantly enhances the safe, compliant deployment of generative AI in highly regulated financial environments.

Technology Category

• Natural Language Processing: Generation
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

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.

Problem

• Addressing model risks in Generative AI for financial institutions.
• Focusing on hallucinations and toxicity in AI applications.
• Enhancing model validation practices for safe AI deployment.

Innovation

• Enhanced model risk framework for Generative AI
• Focus on testing and controls for safe deployment
• Special emphasis on model validation practices