Financial Risk Management and Explainable, Trustworthy, Responsible AI

Area of Science:

• Financial Risk Management
• Artificial Intelligence (AI)
• Machine Learning (ML)

Background:

• Financial institutions increasingly use sophisticated AI/ML models for risk prediction, decision-making, and complex financial modeling.
• Model risk management is critical for productive models across credit, insurance, anti-money laundering, fraud detection, and derivative pricing.

Purpose of the Study:

• To offer practical guidance on establishing robust governance and testing frameworks for AI/ML models in finance.
• To discuss the integration of recent technologies and platforms for responsible and trustworthy AI/ML implementation.
• To consider the implications of the EU Artificial Intelligence Act (AIA) for high-risk models in financial services.

Main Methods:

• Perspective paper based on expert sessions and external input.
• Review of recent publications from central banks, supervisors, and regulators.
• Discussion of practical advice for governance and testing frameworks.

Main Results:

• Identified a broad range of AI/ML applications in financial services, from risk management to sales optimization.
• Highlighted the need for a risk-based approach to AI/ML model governance and testing.
• Emphasized the importance of responsible, trustworthy, explainable, auditable, and manageable AI/ML in production.

Conclusions:

• Financial institutions require adaptable frameworks to manage the evolving risks of AI/ML models.
• The EU AI Act provides a regulatory context for 'high-risk' AI applications, necessitating careful consideration.
• Proactive establishment of governance and testing is crucial for leveraging AI/ML benefits while mitigating risks.