Deep Learning for Financial Fraud Detection: A Hybrid GNN-Transformer Fraud Detection Model

Abstract

Financial fraud has become a major challenge in modern digital banking and online financial systems. Traditional fraud detection systems are unable to detect complex and rapidly changing fraudulent activities efficiently. Deep learning techniques provide intelligent solutions by automatically learning hidden transaction patterns and detecting anomalies in real time. This study presents a comprehensive review of deep learning methods used in financial fraud detection, including Convolution Neural Networks (CNN)[1], Long Short-Term Memory (LSTM), Autoencoders, Graph Neural Networks (GNN), and Transformer models. A new hybrid fraud detection framework combining Graph Neural Networks and Transformer architecture is proposed to improve fraud detection accuracy and reduce false positives. The proposed model effectively handles imbalanced financial datasets using SMOTE and anomaly-based learning techniques. Experimental analysis shows improved performance in terms of accuracy, precision, recall, F1-score, and AUC. The research concludes that hybrid deep learning models can significantly improve financial fraud detection systems in banking, insurance, and online transaction environments.