FFDM − GNN:A Financial Fraud Detection Model using Graph Neural Network

Financial Fraud Detection Model (FFDM) is used to develop an advanced detection framework utilizing Graph Neural Networks (GNNs) to accurately identify fraudulent transactions within the transactions. Traditional fraud detection systems often struggle with the dynamic and intricate nature of fraudulent activities. In this paper, we introduce an innovative fraud de-tection model leveraging the power of Graph Neural Networks (GNN) to address these challenges. Our model synergistically combines the strengths of graph-based learning with deep neural networks to effectively capture the complex relationships and patterns inherent in financial transactions. By utilizing a multi-layered approach, our GNN model not only identifies anomalous patterns indicative of fraud but also adapts to evolving fraudulent tactics.A key feature of our model is the integration of node and edge features, which enhances the representation of transaction networks.