Dynamic Resilience in Credit Card Fraud Detection: The Adaptive Accuracy Weighted Ensemble Approach

Abstract

This paper introduces the Adaptive Accuracy Weighted Ensemble (AAWE) algorithm, strategically devised to fortify credit card fraud detection against the constant threat of concept drift. Centered on the foundational Multilayer Perceptron (MLP) classifier, AAWE seamlessly incorporates dynamic weighting mechanisms to skillfully acclimate to the ever-shifting landscape of fraudulent activities. This innovative progression substantiates AAWE's capacity to substantially augment the precision of credit card fraud detection, demonstrating adeptness in negotiating the intricate challenges presented by perpetually evolving fraudulent strategies. In the realm of credit card fraud detection, wherein subtle deviations in fraud methodologies can yield profound consequences, AAWE's intrinsic adaptability emerges as a pivotal asset. The algorithm's ability to promptly respond to concept drift nuances becomes a critical edge. With empirical evidence showcasing its commendable accuracy rates, AAWE emerges as a compelling remedy to the exigent concept drift quandary within credit card fraud detection. This research sheds a compelling light on AAWE's potential to deliver robust and pinpoint-accurate fraud detection outcomes, solidifying its stature as an invaluable instrument for real-world scenarios where timely accommodation to emerging fraud patterns is of paramount significance.