Deep Learning Models for Robust Fraud Detection: A Comparative Study of Architectures and Techniques

Abstract

Fraudulent activities pose a significant threat to businesses and organizations, leading to substantial financial losses and reputational damage. With the advent of deep learning, fraud detection systems have witnessed a remarkable improvement in their ability to identify and prevent fraudulent transactions. This research article presents a comprehensive comparative study of various deep learning architectures and techniques employed for robust fraud detection. By evaluating the performance, scalability, and adaptability of these models, we aim to provide valuable insights into the most effective approaches for combating fraud in diverse domains. Through extensive experiments and analysis, this study contributes to the advancement of fraud detection systems and offers practical recommendations for implementing deep learning-based solutions in real-world scenarios.