To identify profit management, this research seeks to ascertain the function of artificial intelligence methods, particularly logistic regression and neural networks. The research challenge arises from the fact that fraudulent accounting methods contribute to the high rates of financial and administrative corruption in Iraq, increasing the pervasive manipulation of profits in economic organizations, particularly at the local level. Preventative measures must be implemented to identify and discourage purposeful fraudulent conduct, which is used to control earnings. According to the research’s core hypothesis, earnings management in Iraqi economic organizations cannot be efficiently detected using logistic regression and neural network approaches. Among the many findings, the most important is that auditors and accountants will soon be using neural network techniques and logistic regression more frequently, leading to innovations in the fight against profit manipulation. We will first utilize financial measures and the Beneish model to find profit management. Then, we will use logistic regression and neural network approaches to get there. Researchers believe that accounting information systems may benefit from using and developing artificial intelligence. This will simplify detecting profit manipulation and improve cybersecurity measures, which will eventually secure the computer system’s integrity and dependability. A sample of three monetary units was subjected to the Beneish model, while a second sample of three private Iraqi banks and other economic units registered on the Iraqi Stock Exchange were subjected to logistic regression and neural networks. A single earnings management firm emerged from combining these two strategies. However, in private Iraqi banks that handle profits, the logistic regression approach considers the ratio of payables to total liabilities and the cash to total deposits.