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

Posted: Oct 28, 2025

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

Abstract

This research develops a comprehensive framework for quantifying operational risk in financial institutions using Bayesian networks. Traditional approaches to operational risk measurement, particularly the Loss Distribution Approach (LDA) under Basel II, often fail to capture the complex interdependencies between risk factors and loss events. Our methodology integrates expert judgment with historical loss data to construct dynamic Bayesian networks that model causal relationships between key risk indicators, control effectiveness, and loss severity. We analyze a dataset of 2,847 operational loss events from 45 financial institutions spanning 2000-2003. The results demonstrate that our Bayesian network approach provides superior predictive accuracy compared to conventional LDA models, with a 23.7% improvement in out-of-sample forecasting performance. The framework enables financial institutions to better allocate capital for operational risk while enhancing risk mitigation strategies through improved understanding of risk drivers and their interdependencies.