Model Risk Management.pdf

Post 2008 crisis, Paul Willmott and Emmanuel Derman pointed out the major challenges of financial institutions, emphasizing the need for transparency and ethical considerations in model use and understanding. Regulators have since focused on risk management supervision, expecting transparent and auditable risk measurement frameworks. The development of computational methods and AI components has increased, but the use of machine learning techniques in credit risk management faces skepticism due to the lack of transparency and the "black box" effect. Artificial Intelligence can help reduce model risk and improve predictive power, but the explainability barrier remains a concern, with a need for governance and control, including proof of algorithm reliability, model explainability, and human-algorithm interactions. The Zen Risk platform is designed to open the black box, providing tools for model explanation, feature importance, and outcome explanation, and can be adapted to various credit risk management processes. Machine learning modeling involves advanced data pre-processing, heterogeneous and homogeneous classifiers, and hyperparameter optimization algorithms to improve model performance. However, even with good performance, it is necessary to understand why a model made a certain decision, taking into account ethical considerations, and methods for opening the black box have increased in recent years.