Sriram, A.Gorti, S.S.Amin, E.G.Anand Kumar, M.A.2026-02-062022ACM International Conference Proceeding Series, 2022, Vol., , p. 289-29321531633https://doi.org/10.1145/3549206.3549259https://idr.nitk.ac.in/handle/123456789/29740Over the last few years, the banking sector has had a pivotal role to play in the global economy, comprising of about 24% of the global GDP and employing millions of people worldwide. Banks have a wide array of products and services to offer, ranging from ATMs, Tele-Banking, Credit Cards, Debit cards, Electronic Fund Transfers (EFT), Internet Banking, Mobile Banking, etc. Machine learning is a method of data analysis that automates analytical model building and can be an essential decision support tool for banks in providing services to certain customers and to help in improving customer satisfaction and experience based on collected data. In this study, we made use of several machine learning models and Artificial Neural Networks (ANN) to help banks make predictions about timely customer loan repayment and customer satisfaction. We explored different machine learning algorithms and have performed SHAP analysis, which has helped make conclusions about the significant features driving these decisions. © 2022 ACM.BankingDeep LearningExplainable AI Machine LearningSHAP Analysis