The application of machine learning model for detection of falsification of accounting

Abstract Machine learning is a broad discipline that has designed learning algorithms that can guide stimuli, detect spoken language, and discover hidden adjustments in data volume growth, of which financial data is no exception. Therefore, the purpose of this study is to investigate the application of machine learning in providing a model for detecting accounting distortions in companies listed on the Tehran Stock Exchange. Statistical analysis of the research was performed based on the extracted data of 308 companies listed on the Tehran Stock Exchange in the period 1389 to 1398 (3080 years - company) and the screening method was used for sampling. Accounting distortions of the dependent variable obtained through the virtual variable zero and one and the variables of non-discretionary accruals, change in accounts receivable, change in inventory, soft assets, change in cash sales, change in return on assets and issuance of securities as Independent variables are considered and nonlinear regression is used to test the hypotheses and R statistical calculation software is used to implement the Medians-K clustering algorithm and related calculations.