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

Posted: Feb 07, 2024

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

Abstract

This research develops a comprehensive framework for quantifying operational risk in financial institutions using Bayesian networks. Traditional operational risk models often fail to capture the complex interdependencies between risk factors and loss events. We propose a novel methodology that integrates historical loss data with expert judgment to construct dynamic Bayesian networks capable of modeling operational risk dependencies. Our approach addresses key limitations in existing loss distribution approaches (LDA) by incorporating causal relationships and conditional probabilities. The model was validated using a dataset of 2,347 operational loss events from multinational banks spanning 2000-2003. Results demonstrate significant improvements in risk estimation accuracy and capital allocation efficiency compared to conventional methods. The framework provides financial institutions with enhanced tools for operational risk management and regulatory compliance under Basel II requirements.