AI-Powered Neural Networks Detecting Anomalous Patterns in Real-Time Financial Transactions

Abstract

This article looks at how artificial intelligence and machine learning have been transforming banking fraud detection systems and provides a detailed discussion of their use and the effects of these technologies. The article examines how the conventional rule-based system has developed into more advanced AI-based systems, covering details of particular methods such as real-time anomaly detection, supervised and unsupervised learning, deep learning structures, and natural language processing applications. The article uses major financial institution case studies to show quantifiable increases in fraud detection rates, cost-benefit factors, and comparative effectiveness in the detection of different types of fraud. The regulatory and compliance aspects are also extensively studied, covering the existing frameworks, privacy, explainability issues, and cross-border issues. The article ends with the identification of future directions that encompass the new hybrid technologies, organizational adaptation mechanisms, collaborative ecosystems building, and research opportunities, which give a holistic perspective of how AI is transforming risk management in the banking industry.