Dr. Prashant Kumar Srivastava 03 06 In todays digital financial landscape the rise of internet banking electronic payment systems blockchain and cryptocurrencies has created a significant challenge for financial fraud Due to their inability to identify complicated relations between connected entities conventional fraud detection techniques such rulebased and statistical models are inadequate for detecting more sophisticated fraud schemes Thanks to AIs use of cuttingedge machine learning ML and deep learning DL technologies fraud detection has never been more effective Graph Neural Networks GNNs are one approach that has attracted a lot of attention they employ GNNs to examine the relationships between commodities customers merchants and devices as well as between accounts and devices GNNs can identify hidden fraud trends coordinated fraud rings money laundering identity theft and cryptocurrency crimes among other things using relational and structural information This article covers all aspects related to AI fraud detection solutions ranging from simple statistical techniques to more advanced methods like ML DL and graphbased solutions Furthermore the paper explores the primary GNN architectures including Graph Attention Networks Graph Convolutional Networks and Temporal Graph Neural Networks delving into their applications advantages and disadvantages in financial fraud detection The review finds that the GNNbased methods demonstrated high detection accuracy good realtime surveillance ability and could be applied to develop scalable interpretable and privacypreserving financial security frameworks Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026