AN ADVANCED DEEP LEARNING FRAMEWORK FOR BANKING FRAUD DETECTION USING GNNS AND AUTOENCODERS
Keywords:
Financial Fraud Detection, Deep Learning, Real-Time Analytics, Scalable Architecture, Streaming Data, LSTM, CNN, Attention Mechanism, Online LearningAbstract
An innovative deep learning framework for the detection of banking fraud employs autoencoders and Graph Neural Networks (GNNs) to identify intricate and dynamic fraudulent activities. The model was able to identify concealed account-transaction linkages by analyzing banking transactions as graph-structured data. This discloses network activity that is dubious. The autoencoder is able to identify anomalies through reconstruction error analysis, while the GNN is able to learn the relationships and dependencies between entities. Anomaly detection and relational learning enhance accuracy, minimize false positives, and facilitate the adaptation of fraud schemes. This renders the framework a real-time fraud detection solution for modern financial systems that is both scalable and robust.
