Conceptual study on e-banking systems and customer satisfaction using deep learning and blockchain

The rise of digital payments enhances global internet and mobile usage. However, there are still issues with customer satisfaction in mobile e-banking. This study examines how mobile banking service quality impacts customer satisfaction, detects hackers, and offers solutions for improvement through blockchain integration. This study compares artificial neural network performance with ML models like naive Bayes and XGBoost. The validated data is first sent to cloud for verification, and then securely stored on blockchain to protect customer information. The study uses ANN, a DL model to reduce hacking and ensure secure transactions for enhanced security. The proposed approach is implemented using Python platform and Ethereum tool. The study shows that the ANN model outperforms the ML models in terms of security, achieving an accuracy rate of 99.44%, making the proposed model ideal for e-banking applications. This approach not only enhances security against hacking but also builds customer trust and satisfaction.