The most demanding service provided by any banking system is facilitating the lending of different type of loans. Bank generally provides loans to any individual for wide range of purposes. Various factors of loan applicants are analyzed before the approval of their loan. The process of eligibility check of any loan applicant is becoming very challenging for the banks, day by day. Every year, Indian banks are suffering with high financial losses due to improper identification of potential defaulters in the system. Hence, it becomes a pivotal need of banks to identify the type of customer by analyzing the customer’s personal and financial data. In this work, an intelligent loan defaulter identification system is developed using Machine Learning (ML). The different ML algorithms that are used for model training are Random Forest (RF), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM). The dataset for the model training was obtained from Kaggle. The highest accuracy of 95% was obtained by RF Model after hyperparameter tuning.