The present study was conducted with the aim of identifying the factors affecting the occurrence of financial violations in the network of senior managers of companies listed on the Tehran Stock Exchange using artificial intelligence. Its importance is in promoting transparency and accountability of organizations. Data was collected in 1403 through interviews with 25 senior managers of companies listed on the Tehran Stock Exchange and questionnaires and analyzed using qualitative and quantitative methods. This research is applied in terms of its purpose and in terms of its data collection method, it is of a survey and exploratory type and has a deductive and inductive research philosophy. The statistical population of the research is experts, including senior managers of companies listed on the Tehran Stock Exchange, whose sample members were selected using a purposive sampling method and based on the principle of theoretical saturation. The data collection tool in the qualitative part is an interview and in the quantitative part is a questionnaire, and the validity and reliability of the tools were analyzed using content validity and intra-coder and inter-coder reliability for the interview and content validity and test-retest reliability for the questionnaire, respectively. In this process, 180 open codes were extracted, which, after review and integration, were converted into 60 core codes and finally 20 main codes. These codes indicated the factors affecting the occurrence of financial violations by managers and their consequences. Among the core codes, we can mention transparency and financial reporting, internal control and monitoring systems, c