Advancing fraud detection through deep learning: A comprehensive review

Abstract

Fraud detection remains a critical challenge across various sectors, necessitating advanced techniques to address the increasing sophistication of fraudulent activities. This review focuses on the role of deep learning techniques in enhancing fraud detection capabilities. Traditional fraud detection methods, including rule-based systems, statistical models, and heuristic approaches, have laid the groundwork but face limitations such as difficulty in adapting to evolving fraud patterns and capturing complex relationships. In contrast, deep learning offers substantial improvements due to its ability to process large datasets and uncover intricate patterns. This paper reviews key deep learning architectures used in fraud detection, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Autoencoders, and Generative Adversarial Networks (GANs). Each model's strengths, weaknesses, and applicability to fraud detection are discussed, highlighting their effectiveness in identifying anomalies and improving detection rates. The review also addresses current challenges such as data quality, interpretability, and emerging trends, offering insights into future research directions. By synthesizing the advancements and applications of deep learning in fraud detection, this paper aims to provide a comprehensive understanding of the field and its potential for addressing evolving fraudulent activities.

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