ANALYZING THE IMPACT OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN DETECTING AND PREVENTING FRAUDULENT TRANSACTIONS IN REALTIME

Abstract

The rise in electronic payment systems has increased cases of fraud and this makes real time fraud detection highly critical to the success of financial organizations. AI and ML technologies have become potent tools for realtime fraud detection and prevention by analyzing large datasets, detecting patterns, and predicting suspicious behavior. This research investigates the role of AI and ML in improving fraud detection and prevention systems, specifically their ability to be effective, and to scale, and to adapt to a dynamic environment. It explores the application of supervised and unsupervised learning models such as decision trees, neural networks, and clustering algorithms, to identify anomalies and block fraudulent transactions. The study further discusses challenges like false positives, data privacy, and the adaptability of models to changing patterns in fraud. The importance of AI/ML for preventing fraud in the future is supported by the findings in its ability to dramatically reduce the number of fraudulent transactions (more than 25%) and increase detection accuracy (90%). The paper closes by making recommendations to better fit AI/ML frameworks for fraud detection with ethical standards and user trust.