Enhancing Fintech Fraud Detection through Graph Convolutional Neural Networks

The swift advancement of fintech has transformed financial services, offering users seamless digital experiences. Nonetheless, this expansion has also presented intricate cybersecurity difficulties, especially in fraud detection. This research investigates novel methods for addressing fintech fraud through the utilization of GCNN and the ResNet101-C model. GCNN adeptly addresses dynamic fraud trends via graph-based feature aggregation, whereas ResNet101-C, augmented with dropout and fully connected layers, attains enhanced classification performance. Preprocessing procedures, such as imputation, noise reduction, and feature standardization, enhance data quality, whereas feature extraction emphasizes essential transaction variables like frequency and value. Assessment of synthetic datasets reveals that ResNet101-C achieves an accuracy of 93.46%, surpassing leading models. These results underscore the transformational capability of GCNN and ResNet101-C in tackling the dynamic nature of fraud, providing scalable, precise, and privacy-preserving solutions for the financial sector.