Generative AI Model Risk Management for Financial Services

Explaining the science

Effectively managing Model Risk in Generative AI demands a comprehensive, multi-dimensional strategy that integrates rigorous evaluation, benchmarking and mitigation. This approach should address the following key dimensions:

• Efficacy and Robustness
  Assess the model’s performance consistency across a variety of conditions and its resilience to anomalies or adversarial inputs. A robust GenAI system should deliver reliable, high-quality outputs even under stress or when encountering unfamiliar data.
• Explainability and Transparency
  Ensure the model's decision-making processes are understandable and well-documented. Transparency enables stakeholders to trust the system by making its inner workings, logic and rationale accessible and interpretable.
• Privacy and Security
  Safeguard sensitive data through strong privacy controls and security mechanisms. This includes preventing data leakage, ensuring data minimisation, and complying with relevant privacy laws and regulations.*
• Fairness and Bias Mitigation
  Promote equitable outcomes by evaluating the model’s behaviour across diverse populations and use cases. The goal is to identify and mitigate any systemic biases that could lead to unfair or discriminatory outputs.
• Sustainability
  Evaluate the environmental, social and economic footprint of GenAI systems. This involves optimising for energy efficiency and considering broader impacts, such as carbon emissions, societal shifts and resource use.
• Legal Compliance and Intellectual Property
  Ensure the model adheres to applicable laws and respects intellectual property rights. This includes verifying that the content it generates does not violate copyright, trademark or other legal protections.