Graph-Based Machine Learning Approaches for Fraud Detection in Financial Networks

Fraud detection in financial networks presents a significant challenge due to the complexity and volume of transactions. Traditional detection methods often struggle with scalability and accuracy when faced with evolving fraudulent schemes. In this paper, we explore the application of graph-based machine learning techniques to enhance fraud detection in financial networks. By representing financial transactions as graph structures, we capture the relationships between entities, allowing for the identification of suspicious patterns that may indicate fraudulent behavior. We examine several graph-based algorithms, including Graph Neural Networks (GNNs) and Random Walk-based approaches, and compare their performance with conventional machine learning methods. The use of graph representations facilitates the extraction of features such as transaction flows and network centrality, which are critical in identifying anomalies. Furthermore, we propose an enhanced detection model that integrates community detection techniques with supervised learning for improved accuracy. Our experimental results, conducted on a real-world financial dataset, demonstrate that graph-based models outperform traditional models in detecting fraud with higher precision and recall. The study highlights the potential of these techniques to provide scalable and robust solutions for real-time fraud detection, contributing to a more secure financial ecosystem.