Abstract:

Risk management and fraud prevention are major issues in the financial industry that need for creative solutions to find abnormalities and reduce likely dangers.    GraphFinGuard is risk management and fraud prevention.  In Deep learning, uses Graph Neural Networks (GNNs) to solve challenging interactions in financial transactions, hence improving risk assessment and fraud prevention.    GraphFinGuard's capacity to record complex links in transaction networks helps this paper to explore its use in banking, financial forecasting, and FinTech.    GraphFinGuard reduces anomaly prevention, finds fraudulent trends, and increases credit risk assessment by use of graph-based representations with quantum computing.    Moreover, we use self-supervised learning, attention mechanisms, and contrastive learning approaches to raise the model's flexibility in the financial environment.    GraphFinGuard beats conventional machine learning algorithms in predictive risk modeling and fraud prevention accuracy according empirical data.    At last, we discuss pragmatic issues like scalability, explainability, and adversarial robustness, so providing concepts on future research paths for best GNN performance in financial applications.