Fraud Detection for Financial Transactions: Leveraging Big Data Analytics and Machine Learning

Recently, transaction fraud costs billions of dollars to card issuers. With the increase in fraud rates, it is important to establish a comprehensive monitoring mechanism for detecting abnormal accounts that conform to the characteristics of online fraudulent activities. This paper primarily utilizes statistical methods and data mining techniques to analyze new signals that can represent the latest fraudulent transaction methods based on account static information, account transaction data and fraud blacklists collected by the financial big data system. To safeguard the financial security of customers, interpretable machine learning algorithms are also proposed to construct fraud detection model. Eventually, extensive experiments demonstrate that the proposed framework achieves state-of-the-art results with the highest F1 score, Recall and Accuracy.