Smart Predictive Modeling in Mobile Banking: Improving Customer Satisfaction through Cybersecurity, Data Privacy, and Best Practices

This study explores the role of artificial intelligence (AI) and computing techniques in enhancing customer satisfaction in mobile banking through best practices, cybersecurity, and data privacy. Critical elements influencing customer satisfaction—system reliability, technological innovation, privacy policies, security measures, and customer engagement—were examined using data collected from bank customers and employees via a validated questionnaire. Ordinal logistic regression a machinelearning approach was used to develop the model regarding the relationships between the independent variables and their influence on customer satisfaction. The results highlighted AI-driven technological innovations that strongly focus on strategies centered on customer needs that enhance customer satisfaction. Moreover, strong cybersecurity measures and data privacy policies are necessary to foster customer trust and loyalty. The study indicated no significant differences in the perceptions of cybersecurity and data privacy as perceive by bank employees and bank clients. This indicated the need to have a comprehensive mobile banking strategy The practical application of machine learning in mobile banking was illustrated in this research, showing how ordinal logistic regression which is a technique of machine-learning should be integrated into AI systems specifically in the analysis of complicated data about customer satisfaction. By using AI powered analytics, banks can enhance their capability to address dynamic customer needs. This study contributes to the body of knowledge in areas such as customer experience, enhances customer delivery, strengthens security and emphasizes the potential to drive data-informed decision-making.