MACHINE LEARNING METHODS FOR ANTIFRAUD SYSTEMS

Abstract

Fraud in the financial sector, e-commerce, and online services is becoming increasingly frequent and sophisticated. Traditional rule-based systems, while still helpful in detecting known fraud patterns, struggle to keep up with new, evolving attack vectors, as static rules are quickly circumvented. In contrast, machine learning (ML) provides a dynamic and scalable approach that can process vast amounts of transactional and behavioral data to identify subtle anomalies and suspicious activity.
This paper provides a comprehensive overview of current ML techniques used in fraud detec-tion, categorized into three main groups: classification models, anomaly detection methods, and deep learning architectures. It discusses real-world applications across various fraud scenarios, including credit card abuse, account takeovers, cybercrime, and scams in digital comerce.
Emphasis is placed on the strengths and limitations of each approach, with attention to real-world considerations like scalability, model transparency, and the challenge of class imbal-ance. The paper also reviews recent advances, including graph-based representations of fi-nancial interactions, IP-based behavioral profiling, and the emergence of hybrid systems that integrate multiple ML techniques – such as combining autoencoders with boosting algorithms for improved accuracy, especially when labeled data is scarce.
The findings aim to support the development of flexible, high-performance fraud detection solutions that leverage the most effective ML practices and capitalize on the synergy of hybrid model architectures.