Description

Credit card transaction fraud costs billions of dollars to card issuers every year. A well-developed fraud detection system with a state-of-the-art fraud detection model is regarded as essential to reducing fraud losses. Fraudulent credit card transactions cost firms and consumers large financial losses every year, and fraudsters continuously attempt to find new technologies and methods for committing fraudulent transactions. The detection of fraudulent transactions has become a significant factor affecting the greater utilization of electronic payment. The main contribution of our work is the development of a fraud detection system that employs a machine learning architecture together with an advanced feature engineering process. To demonstrate the effectiveness of our proposed system for detecting fraud in credit card transactions, experiments were performed using real-world public credit card transaction data sets consisting of fraudulent transactions and legitimate ones. The managerial implication of our work is that credit card issuers can apply the proposed methodology to efficiently identify fraudulent transactions to protect customers’ interests and reduce fraud losses and regulatory costs.