GCNXG: Detecting Fraudulent Activities in Financial Networks: A Graph Analytics and Machine Learning Fusion

The detection of fraudulent actions has become a major challenge for upholding the integrity of financial systems in today’s complex and ever-changing financial world. This study recommends a novel method for detecting and preventing financial network fraud by combining the strengths of graph analytics and machine learning. To begin, the paper defines financial networks and describes the intricate relationships and transactions that characterize them. The subtle patterns and abnormalities that indicate fraudulent behaviours in these networks are difficult for conventional fraud detection technologies to capture. Using the abundant structural information available in financial graphs, our proposed integration of graph analytics and machine learning fills this void. When it comes to modelling the complex relationships between entities in financial networks, graph analytics provides a natural framework. An exhaustive graph structure is generated, capturing the complex web of relationships, by modelling entities as nodes and monetary transactions as edges. The use of sophisticated graph algorithms allows us to unearth previously unseen patterns and identify outliers that may point to fraudulent behaviour. Machine learning approaches complement graph analytics by providing the capacity to learn complicated patterns from massive datasets. The graph structure, transaction history, and context data are mined using these methods in our method. When graph-derived characteristics are combined with machine learning algorithms, subtle, high-dimensional patterns that could otherwise go undetected might be found. We run trials on real-world financial datasets, contrasting the results with those of more conventional methods, to verify the efficacy of our proposed approach. The outcomes show a considerable improvement in fraud detection accuracy, with fewer false positives. We also demonstrate the model’s flexibility by using an incremental learning framework to account for new forms of fraud. This paper presents a novel approach to tackling the difficulties of financial network fraud detection by combining graph analytics and machine learning. Our method demonstrates a resilient and flexible way to tackle the ever-changing landscape of financial fraud by combining the structural insights of graph analytics with the pattern recognition skills of machine learning.