Evaluating and Enhancing AI and ML Model Robustness
Objective: To assess and improve the robustness of AI and ML models in the financial sector under varying financial conditions.
Methodology: Conducted a comprehensive analysis of existing AI and ML models in finance.
Implemented advanced stress testing designs, focusing on scenario development, input variation, and performance metrics.
Results: Insights into AI and ML Model Performance
1. Enhanced Predictive Accuracy: Found that AI and ML models, when subjected to rigorous stress tests, displayed an enhanced ability to predict financial risks under diverse conditions. Identified key factors contributing to model resilience.
2. Regulatory Compliance and Challenges: Analyzed the compliance of these models with Basel Committee guidelines and other regulatory frameworks. Highlighted the challenges in balancing high predictive performance with regulatory standards.
3. Stress Testing Methodologies: Evaluated various stress testing methodologies for their effectiveness in assessing and improving model robustness. Proposed new strategies for stress testing that can be adopted by financial institutions.
4. Policy and Governance Implications: Detailed the implications of these findings for policymakers and financial institutions. Emphasized the need for adaptive strategies in risk governance to accommodate AI and ML models.
Future Directions and Recommendations: Recommended further research into AI and ML methodologies to continuously adapt and improve models in line with evolving financial landscapes. Advocated for a collaborative approach involving academics, industry practitioners, and policymakers to ensure the development of robust, efficient, and transparent financial systems.