Title: Efficient Sorting of Financial Transactions Using Artificial Intelligence for Fraud Detection and Risk Assessment
Authors: Sojoud Mahmoud Miqdad and Samy S. Abu-Naser
Volume: 9
Issue: 8
Pages: 147-154
Publication Date: 2025/08/28
Abstract:
The global financial sector is witnessing a continuous increase in the volume and complexity of financial transactions, making fraud detection a growing challenge that requires sophisticated solutions. This study aims to explore the role of artificial intelligence algorithms in enhancing the efficiency of sorting and risk assessment within financial fraud detection systems. Through a comprehensive review of scientific literature, the study analyzes how machine learning and deep learning techniques can go beyond merely identifying suspicious transactions to providing intelligent sorting mechanisms that prioritize response based on risk level. The results show that integrating sorting algorithms with AI models significantly improves the efficiency of fraud detection operations, reduces operational pressure on human analysts, and enhances the accuracy of identifying real fraudulent cases. The study also highlights the importance of continuous model adaptation to the evolving fraud methods and proposes a comprehensive conceptual framework for intelligent sorting that can guide the future development of fraud detection systems.