Improving Fraud Detection in Financial Transactions using Machine Learning

Abstract

Fraud detection in financial transactions is a critical challenge facing financial institutions worldwide, with billions of dollars lost annually to fraudulent activity. Machine learning (ML) techniques have emerged as a promising tool for improving fraud detection, leveraging the power of statistical algorithms to analyze vast amounts of transaction data and identify patterns indicative of fraudulent behavior. In this paper, we present an overview of the state-of-the-art in ML-based fraud detection techniques, including supervised, unsupervised, and semi-supervised approaches. We also discuss the challenges associated with implementing these techniques in real-world financial settings, such as data preprocessing, model development and training, and deployment and integration with existing fraud detection systems. Additionally, we present case studies highlighting successful applications of ML-based fraud detection in financial institutions, demonstrating the potential of these techniques to improve the accuracy and efficiency of fraud detection while reducing false positives and minimizing the impact on legitimate transactions. Finally, we discuss future directions for research and development in this area, including the incorporation of new data sources and the integration of explainable AI techniques to improve transparency and interpretability.