Design and Evaluation of FraudNet: An Intelligent Graph-Based Architecture for Fraud Ring Detection

Boochi Adikeshavulu, Dr. S. Usharani

Graph Neural Networks; Fraud Detection; Fraud Rings; Transaction Graph; Graph Attention Networks; GCN; GAT; GraphSAGE; Financial Security.

Financial fraud continues to evolve with increasing sophistication, particularly through coordinated fraud rings that exploit distributed transaction patterns to evade detection. Traditional rule-based systems and tabular machine learning models are limited in capturing relational dependencies across entities such as accounts, devices, and identities. This paper presents FraudNet AI, a graph-based deep learning framework that models financial transactions as interconnected nodes within a transaction graph to identify complex fraud structures. The proposed system utilizes the IEEE-CIS Fraud Detection dataset and constructs a heterogeneous graph by linking transactions through shared attributes including device identifiers, IP addresses, billing information, and email domains.A hybrid Graph Neural Network (GNN) architecture combining Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), and GraphSAGE is employed to learn rich node embeddings that encode both intrinsic transaction features and neighborhood relationships. The framework includes preprocessing, graph construction, model training with class imbalance handling, and deployment via a Flask-based API and interactive dashboard. Experimental results demonstrate improved performance over baseline models in terms of AUC-ROC, precision, and recall.

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