Operational Risk Quanti}cation in Financial Institutions: A Bayesian Network Approach for Loss Distribution Modeling

Abstract

This research develops a comprehensive framework for quantifying operational risk in }nancial 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 ezectiveness, and loss severity. We analyze a dataset of
2,847 operational loss events from 45 }nancial 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 }nancial institutions to better allocate capital for operational risk while enhancing risk mitigation strategies through improved
understanding of risk drivers and their interdependencies