Fraud Detection in Online Banking Transactions Using Machine Learning and Anomaly Detection Techniques

The paper seeks to evaluate the role of machine learning in fraud spotting in digital transactions, with a specific reference to the contemporary banking industry. This comes due to the increased use of electronic banking, which in turn has seen an increase in fraudulent transactions. It focuses on measuring how effectively various techniques of machine learning may accelerate and enhance fraud detection and prevention with large data sets of transactions to identify abnormal patterns. The debate covers various sophisticated techniques from anomaly detection and deep reinforcement learning to security enhancement through data privacy, all with reference to financial literacy. Based on real-time observations, financial institutions use ML systems and techniques for increasing the efficiency of financial data detection. Some of the challenges include data quality, privacy, costs, and complexity of systems and techniques. In other words, the paper seeks to outline the benefits, challenges, and prospects of using machine learning in detecting financial fraud in the wake of its increasing role in protecting digital financial systems.