Online Payment Fraud Detection Model Using Machine Learning Techniques

In a world where wireless communications are critical for transferring massive quantities of data while protecting against interference, the growing possibility of financial fraud has become a significant concern. The ResNeXt-embedded Gated Recurrent Unit (GRU) model (RXT) is a unique approach precisely created for real-time financial transaction data processing. Motivated by the need to address the rising threat of financial fraud, which poses major risks to financial institutions and customers, our technique takes a systematic approach. We initiate the process with data input and preprocessing, addressing data imbalance through the Synthetic Minority Over-sampling Technique (SMOTE). Feature extraction uses an ensemble approach that combines autoencoders and ResNet (EARN) to reveal critical data patterns, while feature engineering further enhances the model's discriminative capabilities. The core of our classification task lies in the RXT model, fine-tuned with hyperparameters using the Jaya optimization algorithm (RXT-J). Our model undergoes comprehensive evaluation on three authentic financial transaction datasets, consistently outperforming existing algorithms by a substantial margin of 10% to 18% across various evaluation metrics while maintaining impressive computational efficiency. This pioneering research represents a significant advancement in the ongoing battle against financial fraud, promising heightened security and optimized efficiency in financial transactions. In defense against wireless communication interference, our work aims to strengthen security, data availability, reliability, and stability against cyber warfare attacks within the financial industry.