This paper aims to identify the impact of applications of artificial intelligence in its dimensions (expert systems, neural networks, and machine learning) in reducing financial fraud in Jordanian commercial banks. To achieve the research objectives, the descriptive analytical approach was used, and a questionnaire was designed to measure the research variables and verify the relationships between them. The research population consisted of all Jordanian commercial banks, and questionnaires were distributed to the research sample, which was “branch managers, customer service, financial department employees, loan employees, and information and software systems”. “200” electronic questionnaires were distributed, and “189” were collected. Questionnaire and “11” questionnaires were excluded due to similarity in the answers, so that the final number became “178”, which can be analyzed statistically using (SPSS) software. The results of the descriptive analysis showed that there is an increase in the level of Jordanian commercial banks’ use of artificial intelligence applications, where there is a significant effect. Statistics on the applications of artificial intelligence in its dimensions (expert systems, neural networks, and machine learning) in reducing financial fraud in Jordanian commercial banks, where the greatest impact in reducing financial fraud was (neural networks), then (machine learning), and finally (expert systems). The descriptive analysis test also showed that there is an increase in the level of interest of Jordanian commercial banks in reducing financial fraud, and the research recommended increasing interest in adopting other applications of artificial intelligence, such as smart agents, genetic algorithms, and deep learning, because of their role in increasing the effectiveness of banks and reducing fraud. Financial.