Predicting Customer Contentment in E-Banking Services Using Machine Learning

The rapid rise of electronic banking has reshaped customer interactions with private banks in India, emphasizing the need to predict service quality's impact on customer contentment using machine learning. This study explores how factors such as convenience, security, usability, and customer support influence satisfaction among e-banking users. Using a machine learning-based approach, data were collected via surveys from 300 respondents and analyzed through model training and testing. Algorithms such as Logistic Regression, Random Forest, and Support Vector Machines (SVM) were applied to classify and predict customer satisfaction levels. The study underscores that superior service quality in private banks fosters customer loyalty, though challenges like technological barriers persist. These insights help private banks enhance digital platforms, addressing security and usability issues to boost contentment. Future research could explore demographic variations and emerging technologies' impacts on predictive accuracy.