Evolution of Machine Learning: A Foundation for Intelligent Systems

Abstract

Machine learning has transformed credit card fraud prevention by enabling more sophisticated detection capabilities compared to traditional rule-based systems. The evolution began with supervised learning algorithms like logistic regression, decision trees, and random forests, which classified transactions based on historical data patterns. Unsupervised learning techniques, including clustering algorithms and autoencoders, emerged as vital tools for detecting previously unknown fraud patterns without labeled data. Deep learning architectures such as Recurrent Neural Networks and Convolutional Neural Networks have further revolutionized transaction monitoring by processing sequential data and recognizing complex patterns across multiple dimensions. These advanced technologies can detect sophisticated fraud schemes that would remain invisible to conventional methods. Future directions include hybrid architectures combining multiple model types, federated learning for privacy-conscious collaborative training, and adversarial techniques to enhance resilience against emerging threats. Challenges persist in balancing detection accuracy with false positive rates, meeting regulatory requirements for transparency and fairness, and developing adaptive systems capable of responding to continuously evolving fraud tactics without complete redesign.