Abstract
The banking sector is an integral part of an economy as it helps in capital formation. One of the most critical issues of banks is the risk involved in loan applications. Employing machine learning to automate the loan approval process is a significant advancement. For this topic, all classification algorithms have been tested and assessed in previous researches; however, it is still unclear which methodology is best for a particular type of dataset. It is still difficult to identify which model is the most effective. Since each model is dependent on a certain dataset or classification approach, it is critical to create a versatile model appropriate for any dataset or attribute collection. The aim of the study is to provide detailed analysis of previous studies and to propose a predictive model for automatic loan prediction using four classification algorithms. Exploratory data analysis is performed to obtain correlation between various features and to get insights of banking datasets.
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