Detect Credit Card Fraud Using Machine Learning Model

With the surge in online transactions, credit card fraud has become a significant issue, demanding efficient solutions for early detection and prevention. The rise in digital transactions has increased the demand for robust fraud detection systems. This project aims to develop a hybrid fraud detection system that integrates facial recognition technology with behavioral analysis to enhance security in credit card transactions. The system employs facial recognition algorithms, including Haar Cascades, Local Binary Patterns (LBP) to verify user identity. When a user initiates a transaction or logs in, their facial image is captured and compared to previously stored data using feature extraction techniques. Additionally, the system analyzes behavioral data, such as login times, transaction locations, and spending habits, to identify anomalies indicative of fraud. By leveraging machine learning algorithms such as Support Vector Machines (SVM), CNNs the system learns from historical data and adapts to emerging patterns. This two-step verification process requires both a valid facial match and consistent user behavior, significantly reducing false positives and negatives.