Presenting a Model for Financial Reporting Fraud Detection using Genetic Algorithm

both academic and auditing firms have been searching for ways to detect corporate fraud. The main objective of this study was to present a model to detect financial reporting fraud by companies listed on Tehran Stock Exchange (TSE) using genetic algorithm. For this purpose, consistent with theoretical foundations, ۲۱ variables were selected to predict fraud in financial reporting that finally, using statistical tests, ۹ variables including SALE/EMP, RECT/SALE, LT/CEQ, INVT/SALE, SALE/TA, NI/CEQ, NI/SALE, LT/XINT, and AT/LT were selected as the potential financial reporting fraud indexes. Then, using genetic algorithm, the final model of fraud detection in financial reporting was presented. The statistical population of this study included ۶۶ companies including ۳۳ fraudulent and ۳۳ non-fraudulent companies from ۲۰۱۱ to ۲۰۱۶. The results showed that the presented model with the accuracy of ۹۱.۵% can detect fraudulent companies. These findings extend financial statement fraud research and can be used by practitioners and regulators to improve fraud risk models.

Keywords:

financial reporting fraud , fraud detection , Genetic Algorithm , Data mining