An Intelligent Risk Scoring Framework for Fraud Detection in Online Financial Transactions

With the rapid growth of online payment systems and digital financial services, the risk of fraudulent transactions has increased significantly. This creates a strong need for efficient, reliable, and real-time fraud detection mechanisms. This project presents an intelligent fraud detection system that combines rule-based evaluation with a risk scoring framework to identify suspicious transactions effectively. The system analyzes each transaction using key parameters such as transaction amount, frequency, and predefined thresholds. Based on these factors, a risk score is calculated and used to classify transactions into different categories such as low, medium, and high risk. High-risk transactions are flagged for further verification, while normal transactions are processed without delay. This approach ensures quick decision-making and reduces the chances of financial loss. The implementation is designed to support real-time monitoring, where transactions are continuously evaluated as they occur. The system also provides a user-friendly interface for administrators to review, analyze, and take appropriate actions on suspicious transactions. By using a rule-based approach, the system maintains transparency and simplicity while still achieving effective fraud detection. The results demonstrate that the proposed system is capable of accurately identifying fraudulent activities with minimal processing time. It reduces manual effort, improves efficiency, and ensures reliable performance in handling large volumes of transactions. Overall, the system provides a practical and scalable solution for enhancing security in modern digital financial environments.