Advanced use of Machines in Detecting and Preventing Financial Fraud

الملخص

In recent years، the rapid expansion of digital technologies and the fundamental transformation of financial activities have led to a significant increase in online financial transactions. While these advancements have provided advantages such as speed and convenience، they have also created opportunities for more sophisticated forms of financial fraud.Financial fraud، manifested in forms such as credit card fraud، account manipulation، and the misuse of sensitive user data، has become a serious threat to public trust and the stability of financial institutions.Traditional methods for detecting such fraudulent activities، which are primarily based on rule-based systems and manual analysis، are no longer sufficient to address the increasing complexity of fraud patterns. Therefore، this study aims to investigate the application of advanced machine learning techniques in the detection and prevention of financial fraud.This research adopts a descriptive–analytical approach and draws upon credible academic sources to evaluate algorithms such as decision trees، artificial neural networks، and clustering models.The findings indicate that machine learning algorithms not only demonstrate a strong capability in identifying hidden patterns but also outperform traditional methods in reducing false positives، improving detection speed، and adapting to evolving conditions. Furthermore، the study examines the fundamental concepts of financial fraud.

Keywords: Information Security، Financial Fraud، Machine Learning، Algorithmic Analysis، Artificial Intelligence.