Adaptive Hybrid Fraud Detection System with Personalized Transaction Profiling

This research study presents an Adaptive Fraud Detection System that uses personalized user behavior profiles and multiple layers of detection methods to spot fraud in real time. By clustering similar transaction patterns and applying machine learning techniques like ensemble models and autoencoders, the system can continuously improve its ability to detect unusual activity. Frequency of getting false alarm is reduced. Improved functional clarity in the system.