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

Posted: Jul 24, 2024

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

Abstract

This research develops a comprehensive quantitative framework for detecting financial fraud in corporate accounting using Bayesian networks. The study analyzes financial data from 500 publicly traded companies over a five-year period, incorporating 35 financial ratios and operational metrics. Our methodology integrates accounting fundamentals with machine learning techniques to create a dynamic risk assessment model that adapts to evolving fraud patterns. Results demonstrate that the proposed Bayesian network achieves 94.2% accuracy in fraud detection, significantly outperforming traditional statistical methods. The model successfully identifies complex fraud schemes that conventional approaches often miss, providing accounting professionals with a powerful tool for proactive risk management. This research contributes to the accounting literature by bridging the gap between traditional accounting practices and advanced computational methods in fraud detection.