Assessing the performance of dechow F-score and benford's law in detecting financial statement fraud

Abstract

Purpose: This study aims to assess the performance of   Dechow F-Score and Benford’s Law methods in detecting indications of financial statement fraud in healthcare sector companies listed on the Indonesia Stock Exchange during 2020–2024. Methodology/Approach: This study uses a descriptive comparative approach. Secondary data were obtained from annual reports and audited financial statements of 34 healthcare companies. The sample was selected using purposive sampling, resulting in 20 companies with 100 observations. Data were analyzed using Microsoft Excel and SPSS 26 through descriptive statistics and McNemar Test. Findings: The Dechow F-Score indicates a relatively normal or low risk of material misstatement based on accrual-related financial indicators, and similarly, Benford's Law with Excess MAD generally shows conformity with the expected first-digit distribution. The McNemar Test reveals a statistically significant difference between the two methods in classifying fraud indications. Furthermore, Benford's Law shows a higher detection rate than the Dechow F-Score. Practical and Theoritical Contribution/Originality:  This study contributes to fraud detection literature by demonstrating that both methods capture different fraud characteristics and can complement each other as early detection tools. Research Limitation: This study is limited to healthcare sector companies and only compares two fraud detection methods.