Real-Time Banking Fraud Detection with Hybrid Deep Learning

Abstract

The banking industry faces more sophisticated fraud schemes, requiring real-time fraud detection. Hybrid deep learning systems with many models aim to detect suspicious transactions faster, more correctly, and more flexiblely. Combining autoencoders, recurrent neural networks (RNNs), and convolutional neural networks increases feature extraction and temporal pattern analysis, two crucial steps in identifying fraud in diverse and high-volume transaction streams. Real-time anomaly detection and warning provision reduce financial loss and increase banking operational security in the suggested paradigm. Data augmentation, parallel processing, and attention techniques address computing efficiency, model interpretability, and data balance. Experimental data shows that this hybrid deep learning model outperforms conventional methods in detection accuracy and reaction time. This study shows how hybrid deep learning can detect real-world fraud, making banking systems more trustworthy and safe.