Credit Card Fraud Detection System

1. ABSTRACT The growing dependency on electronic payments has resulted in a sharp escalation of credit card fraud, severely threatening financial security globally. The present study emphasizes the creation of an effective Credit Card Fraud Detection System that employs machine learning and data analysis algorithms to detect suspicious transactions quickly and precisely. The system analyzes transaction information to determine anomalies and separate authentic from false activities. Different classification techniques like logistic regression, decision trees, random forests, and support vector machines are used and compared to identify the best method. The research also solves problems like class imbalance, which is a typical problem in fraud detection, using methods like oversampling and cost-sensitive learning. Performance measures like accuracy, precision, recall, and the F1-score are utilized to measure the performance of the models. The outcomes indicate that the suggested system has a high fraud detection rate with very low false alarms, while providing both improved security and user satisfaction. This research adds to the development of fraud detection systems through the presentation of a scalable, robust, and feasible solution that can be tailored to changing fraud patterns, thus enhancing confidence in electronic financial transactions. 2. KEYWORDS Credit Card Fraud, Machine Learning, Anomaly Detection, Financial Security, Classification, Fraud Detection, Digital Payments.