Abstract
Comparing performances and effectiveness of machine learning classifiers in detecting financial accounting fraud for Turkish SMEs
Highlights
According to the Association of Certified Fraud Examiners (ACFE), which is the world’s largest anti-fraud organization, companies lost 5% of their annual revenues through fraud in 2020
It is clear that Random Forest and Bagging classifiers, which are known as ensemble methods, have better performance than other classifiers in financial accounting fraud detection
Since the role of small- and medium-sized enterprises (SMEs) in the global economy is vital, more researches should focus on financial accounting fraud detection for SMEs
Summary
According to the Association of Certified Fraud Examiners (ACFE), which is the world’s largest anti-fraud organization, companies lost 5% of their annual revenues through fraud in 2020. ACFE stated that the total loss at 2,504 cases was $ 3.6 billion, with an average loss per case of $1.51 million [1] Beyond these losses, financial accounting fraud leads to comprehensive negative consequences on investors, employees, suppliers, and other stakeholders of the enterprise [2]. Previous studies have focused mainly on publicly listed companies or large capitalization companies [5,6,7,8,9,10,11] In comparison to these studies, there have been fewer researchers which have focused on fraud risks for small- and medium-sized enterprises (SMEs) [12,13,14].
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