Paper: arXiv 2503.15668

Authors: Anwesha Bhattacharyya, Ye Yu, Hanyu Yang, Rahul Singh, Tarun Joshi, Jie Chen, Kiran Yalavarthy

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.

Complexity vs Empirical Score

  • Math Complexity: 1.5/10
  • Empirical Rigor: 3.0/10
  • Quadrant: Philosophers — conceptual discussion, limited math and data

Why this score: This paper presents a qualitative risk management framework for adapting SR 11-7 regulatory standards to Generative AI in banking. While it offers clear conceptual value and operational insights for industry practitioners, it lacks formal mathematical modeling and empirical quantitative validation.

Research Flowchart

  flowchart TD
    A["Research Goal: Enhance Model Risk Management (MRM)<br/>for Generative AI in Financial Institutions"] --> B["Identify GenAI Specific Risks<br/>(Hallucinations, Toxicity, Bias)"]
    
    B --> C["Data Inputs: Financial Multi-Asset Data,<br/>Regulatory Standards (e.g., SR 11-7), Prompts"]
    
    C --> D["Validation & Computational Processes:<br/>Automated Hallucination Testing,<br/>Toxicity Evaluation & Output Filtering"]
    
    D --> E["Enhanced MRM Framework:<br/>Controls for Generative AI Deployment"]
    
    E --> F["Key Outcome: Safe & Compliant GenAI Adoption<br/>in Highly Regulated US Banks"]