Bankruptcy can happen to any company, but it is very difficult to identify intentional bankruptcies that are carried out for personal gain. Currently, there is no precise methodology for identifying intentional bankruptcies, so the process depends on the skills and qualifications of the investigator. The purpose of this research is to provide a method for identifying intentional bankruptcies after examining fraud in the financial statements and their impact on the probability of bankruptcy. The paper identifies the main methods of fraud bankruptcy detection, distinguishing forensic science as the main method for doing so. The paper conducts research, which was modeled on research conducted by other authors to test the effectiveness of bankruptcy prediction methods and the effectiveness of financial indicators in detecting fraud. The research evaluated the trends of the Altman Z'-Score model and the application of binary logistic regression analysis to a sample of intentional and unintentional bankruptcies. The regression analysis provided a model for determining intentional bankruptcies and identified the following indicators: net profit/assets, liabilities/assets, liabilities/equity, and Altman Z'-Score. An independent t-test was also performed to show the differences in the means of financial ratios between intentional and unintentional bankruptcies. The results of the T-test indicated that it is important to calculate and evaluate the following additional indicators: current assets/assets, receivables/income. The results of the research may help to identify the likelihood of intentional corporate bankruptcies and thus facilitate the sophisticated methods used to date.