Operational Risk Quantification in Financial Institutions: A Bayesian Network Approach for Loss Distribution Modeling

Posted: Jun 11, 2024

• Chen Wei
• Maria Rodriguez
• Kenji Tanaka
• Fatima Al-Mansoori

Abstract

This research develops a comprehensive framework for operational risk quantification in financial institutions using Bayesian networks. Traditional operational risk models often fail to capture the complex interdependencies between risk factors and loss events. Our methodology integrates historical loss data with expert judgment to construct a Bayesian network that models causal relationships between key risk indicators, control effectiveness, and loss severity. We analyze 5,743 operational loss events from 42 international banks spanning 2000-2003. The results demonstrate that our Bayesian network approach provides superior predictive accuracy compared to conventional loss distribution approaches, with a 23.7% improvement in out-of-sample forecasting performance. The model effectively captures tail dependencies and enables scenario analysis for stress testing. This research contributes to the operational risk management literature by offering a more robust and interpretable framework for regulatory capital calculation under Basel II requirements.