Financial Fraud Detection with Altman Z-Score and Beneish M-Score via Random Forest: Verified by Borsa Istanbul Fines (2018–2022) - Özari, Çiğdem; Can, Esin Nesrin; Demirkale, Özge (SAGE open, 2025)
The main aim here is the prediction of financial errors or fraud considering how effective Altman Z-Score and Beneish M-Score models are in determining financial statement errors or frauds without traditional coefficients. Therefore, these models have been utilized to assess whether a firm has indulged in financial manipulations using a random forest technique that employs the features for both models yet is devoid of the coefficients of either. This will offer greater accuracy in predicting the issue of financial manipulation. To test the efficiency of these models, we analyze those companies that were subject to an administrative fine by the CMB, assuming that in the year in which this fine was levied, and aldo in the previous year, these companies engaged in financial manipulation. The research focuses on firms operating in Borsa Istanbul between 2018 and 2022, those subject to administrative fines, and, for comparison, firms from the same sector that did not receive any penalties. This comparison aims to evaluate the consistency of the outcomes obtained from the models and assess whether such outcomes would correspond to the real findings. The novelty of this research is an integration of random forest analysis with the Altman Z-Score and Beneish M-Score variables to make a coefficient-free prediction about financial fraud, hence shedding new light on the use of these models in fraud detection. JEL Codes: C38, M49, H83. Financial Fraud Detection with Altman Z-Score and Beneish M-Score via Random Forest The main aim here is the prediction of financial errors or fraud considering how effective Altman Z-Score and Beneish M-Score models are in determining financial statement errors or frauds without traditional coefficients. Therefore, these models have been utilized to assess whether a firm has indulged in financial manipulations using a random forest technique that employs the features for both models yet is devoid of the coefficients of either. This will offer greater accuracy in predicting the issue of financial manipulation.