Abstract

The mobile banking system allows consumers to access financial services through mobile phones. It includes services like money transfers, account balance checks and bill payments. Robust data security methods, including encryption and multi-factor authentication, secure user information while preserving the confidentiality and integrity of financial transactions, therefore increasing confidence in the system. In this research, we intend to develop an innovative novel machine learning (ML)-based approach for securing cloud data in mobile banking systems. The cloud computing risk management paradigm enhances financial security. To improve consumer services and banking experiences while maintaining information security, this study explains how mobile cloud architecture might be implemented. This article addressed several difficulties associated with mobile banking, including processing speed and storage capacity, by concentrating on cloud-based risk management for banking systems. We proposed versatile Adaptive Boosting (V-AdaBoost) to improve effectiveness in learning by selecting characteristics and training bank individuals. We implemented our suggested approach in Python software. The performance assessment step employs various metrics to assess the efficacy of the suggested prediction model. We performed a comparative study with other existing methods and the results demonstrate that the suggested model performed better than other conventional methods for securing cloud data in mobile banking systems.