Quantitative Risk Assessment in Corporate Accounting: A Bayesian Network Approach for Financial Fraud Detection

Posted: Mar 25, 2024

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

Abstract

This research develops a comprehensive Bayesian network framework for quantitative risk assessment in corporate accounting, specifically targeting financial fraud detection. The study analyzes financial statements from 500 publicly traded companies over a five-year period, incorporating 35 financial ratios and corporate governance indicators. Our methodology integrates Bayesian probability theory with traditional accounting metrics to create a dynamic risk assessment model that adapts to evolving fraud patterns. Results demonstrate that the proposed framework achieves 92.3% accuracy in identifying high-risk financial statements, significantly outperforming traditional rule-based systems. The model successfully identifies subtle patterns of financial manipulation that conventional methods often miss, providing accounting professionals with a robust tool for proactive risk management. This approach represents a paradigm shift from reactive to predictive risk assessment in corporate accounting.