Credit Card Fraud Detection

The widespread adoption of digital payment platforms has enhanced convenience but also increased the risk of fraud, unauthorized access, and financial loss. This study proposes a simplified, efficient, and secure framework to protect users during online financial transactions by continuously monitoring activities in real time using key parameters such as transaction amount, user behavior patterns, device identification, location consistency, and transaction frequency. Based on this analysis, transactions are classified as safe, suspicious, or high-risk, where legitimate transactions proceed normally, suspicious transactions require additional authentication, and high-risk transactions are immediately blocked to prevent potential losses. For highvalue transfers, the system enforces identity verification through facial recognition, adding a strong biometric security layer with minimal user inconvenience. Additionally, the framework provides a userfriendly interface for transaction history tracking and alert notifications, along with administrative tools for auditing logs and identifying emerging fraud patterns. By emphasizing real-time detection and reducing false positives, the proposed system enhances digital payment security, ensures data integrity, and builds user confidence in online financial systems.