A Novel Framework for Financial Cybersecurity and Fraud Detection Using XAI-RNN-SGRU

Cyber threats involve unauthorized access, alteration, or deletion of private information, extortion, and disruption of business operations. Traditional network security methods need more scalability, data protection, and difficulty detecting advanced threats. The Explainable Artificial Intelligence with Ridgelet Neural Network and Soft Gated Recurrent Units (XAI-RNN-SGRU) is introduced to address these challenges. This model enhances financial data prediction and cybersecurity through advanced preprocessing for handling missing values and data imbalance, and also normalizes numerical features and encodes categorical variables. Enhanced Principal Component Analysis (EPCA) and Improved Fast Random Opposition-based learning Aphid Ant Optimization (AAO) are utilized for feature extraction and desired feature selection. Then, it combines the Ridgelet Neural Network with Gated Recurrent Units for accurate predictions and uses the Improved Homomorphic Encryption (IHE) process to reinforce data protection during computations. Comprehensive testing on three data sets reveals outstanding predictive performance with accuracies of 99.55%, 99.99%, and 99.95%, respectively. The introduced method improves data security, scalability, and efficiency in real financial applications. It outperforms current techniques with a bit error rate of 0.00004 and a data protection score of 98.03%, providing secure and reliable financial operations. In addition, it enhances efficiency in encryption and decryption, with rates of 9.25 and 8.30 seconds, respectively, and is exceptionally scalable for mass-market cybersecurity use. Such improvements offer a real-world solution for financial institutions, cybersecurity companies, and digital payment systems, guaranteeing strong fraud detection, improved data security, and enhanced operational efficiency compared to the current methodologies.