TITLE:
Unlocking Causal Relationships in Commercial Banking Risk Management: An Examination of Explainable AI Integration with Multi-Factor Risk Models
AUTHORS:
Bing Hu, Yi Wu
KEYWORDS:
Artificial Intelligence, Commercial Banking, Causal Relationships, Explainable Artificial Intelligence, Banking Risk Management, Transparency and Interpretability
JOURNAL NAME:
Journal of Financial Risk Management,
Vol.12 No.3,
September
28,
2023
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 traditional 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.