An Explainable Hybrid Fraud Detection Engine for Online Payments Using Rule-Based Analysis and Machine Learning

Key Points

• The central aim is to evaluate the effectiveness of a hybrid fraud detection engine that combines rule-based analysis and machine learning.
• Utilized a hybrid approach involving both rule-based and machine learning methods for fraud detection.
• Evaluated the performance of the engine using various online payment scenarios and datasets.
• The hybrid engine demonstrated significant accuracy in detecting fraudulent transactions.
• Improvements were observed in processing speed and reduction of false positives compared to traditional methods.