Unlocking Causal Relationships in Commercial Banking Risk Management: An Examination of Explainable AI Integration with Multi-Factor Risk Models

Abstract

The 21st century has ushered in transformative digital technologies, notably Artificial Intelligence (AI), which has the potential to redefine commercial banking risk management, especially in the current complicated geopolitical context. This paper examines the integration of Explainable AI into tradition al multi-factor models used in commercial banking. Traditional models, while foundational, often struggle to decipher intricate causal relationships between various risk factors, especially with limited data. With the advent of AI, especially machine learning techniques like Bayesian networks and random forests, there is an opportunity to enhance these models by capturing intricate risk interdependencies and predicting future risks more precisely. We delve deep into the nuances of XAI, emphasizing its potential in making AI’s decision-making transparent and interpretable, addressing the “black box” challenge. Furthermore, we explore the application of Explainable AI in detecting causal relationships within restricted datasets, underscoring the importance of techniques like cross-validation, regularization, and bootstrapping. The paper concludes by highlighting the need for a synergistic approach, combining Explainable AI’s capabilities with the robustness of traditional models, setting the stage for future research in this promising nexus of technology and finance.