AI-Enhanced Fraud Detection: Novel Approaches and Performance Analysis

Fraudulent activities have become a pervasive and costly problem in today's interconnected world, threatening the stability and trustworthiness of various industries. The rise of sophisticated fraud tactics and the constantly evolving nature of fraudulent behavior necessitate innovative and adaptive solutions for fraud detection. Artificial Intelligence (AI) has proven to be powerful in the battle against fraud, offering promising capabilities to enhance the efficiency and accuracy of detection systems. This research paper discusses AI-powered fraud detection and presents novel approaches to tackle the issue. It begins with an exploration of the current landscape of fraud detection methodologies, encompassing traditional rule-based systems, statistical methods, and machine learning techniques. It highlights the shortcomings of such approaches and emphasizes the need for AI-based solutions to overcome the limitations posed by the dynamic nature of fraud.This paper introduces three AI-based fraud detection approaches - Graph Neural Networks (GNN) , Generative Adversarial Networks (GANs) and Temporal Convolutional Networks (TCN) , each harnessing the strengths of different AI techniques. The performance of the AI-powered approaches is compared against traditional rule-based systems, logistic regression, and random forest models. A comprehensive evaluation is performed to assess the superiority of the novel AI-based methods in detecting fraudulent activities.