The Role of Artificial Intelligence and Machine Learning in Financial Fraud Detection: A Data-Driven Approach

This paper dives into fresh strategies for combating financial fraud, highlighting how Artificial Intelligence (AI) and Machine Learning (ML) are outshining traditional rule-based detection systems. By recognizing the shortcomings of older methods, the study seeks to map out the current landscape and thoughtfully evaluate the pros and cons of using AI/ML, while also pinpointing areas ripe for future exploration. It traces the evolution of fraud detection from manual techniques to smart algorithms that can reveal hidden anomalies. The research discusses important models like Random Forests, Support Vector Machines (SVM), and neural networks, which significantly boost the accuracy of fraud detection. As digital financial services expand rapidly, the complexity of fraud cases has increased, prompting institutions and regulators to embrace more intelligent, data-driven approaches. This study assesses how various models—including both supervised and unsupervised learning techniques—enhance the effectiveness of fraud detection. By utilizing real-time analytics and anomaly detection, it shows how AI can help minimize financial losses and bolster compliance. The findings underscore the practical benefits of AI/ML in fraud detection and offer recommendations for effective implementation.