Fuzzy Ranking of Financial Statements for Fraud Detection

Abstract

Automatic detection of anomalies in financial statements can decrease the risk of exposure to fraudulent corporate behavior. This paper proposes a method to convert fraud classification rules learned from a genetic algorithm to a fuzzy score representing the degree to which a company's financial statements match those rules. Applying the method to financial data in real time can lead to the early detection of potentially fraudulent corporate behavior.