Credit Card Fraud Detection System

Author(s)	Ms. Yoshithaa B S, Ms. Subitha K, Mr. Sakthiswar M, Mrs Krishnaveni K
Country	India
Abstract	Credit card fraud has become a major concern in the digital payment ecosystem due to the rapid growth of online transactions. Traditional fraud detection systems often fail to identify dynamic and user-specific fraud patterns. This paper proposes a behavior-based credit card fraud detection system that analyzes the transactional patterns of users to detect anomalies. The system monitors user behavior such as transaction time, location, spending category, and frequency. If any deviation from the normal behavior is detected, the system sends a real-time notification to the user for confirmation. Based on the user's response, the transaction is either approved or declined. This approach enhances security by combining machine learning-based anomaly detection with user verification, thereby reducing fraudulent transactions effectively.
Keywords	Credit Card Fraud Detection, Behavioral Analysis, Machine Learning, Anomaly Detection, Transaction Monitoring, User Authentication, Financial Security
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-04-17

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International Journal For Multidisciplinary Research