Adaptive Fraud Detection Systems: Real-Time Learning from Credit Card Transaction Data

Abstract

The swift growth of digital payment platforms has heightened the demand for effective fraud detection strategies, particularly within credit card transactions. Conventional static rule-based models are becoming less effective in countering the constantly evolving nature of fraudulent activities. Adaptive fraud detection systems that utilize machine learning (ML) and real-time learning methods are gaining traction as a more potent solution. This paper examines the architecture, design, and effectiveness of adaptive fraud detection systems, with a focus on real-time learning from credit card transaction data. We review existing methodologies, discuss the challenges they encounter, and propose innovative techniques to improve the adaptability of fraud detection systems in changing environments.