LEVERAGING MACHINE LEARNING TECHNIQUES FOR INFORMED DECISION MAKING IN BANKING SECTOR
DOI:
https://doi.org/10.29121/shodhkosh.v4.i1.2023.2120Keywords:
Machine Learning, Classification, Prediction, Support Vector Machine, Decision-TreeAbstract [English]
An increasing number of customers are applying for personal loans for their purchases. Personal loans help the consumer to meet any shortfall they experience in buying a needful item. fulfill their needs consumer apply for loans. As many loan application increases, day by day, bank needs to detect and analyze which customer is eligible for a personal loan. The term banking can be defined as receiving and protection of money that is deposited by the individual or the entities. The primary objective of the bank is to provide their wealth in safer hands. In recent times, banks approve the loan after verifying and validating the documents provided by the customer. Yet there is no guarantee whether the applicant is deserving or not. This paper classifies customers based on certain criteria. In this paper, the main focus is to identify and analyze the risk of giving a loan in the banking sector. The Machine Learning techniques are used to analyze risk giving loan, it helps in summarize into a piece of valuable information. This will improve the quality of banking system thus improving customer retention. Applying it on a dataset of the customer and predicting the risk percentage for an individual to give loan. These techniques facilitate useful data analysis and can help to get better outcomes into the processes behind the data. The banks and many investment companies are pioneers in taking advantage of Machine Learning. The main objective is to predict whether assigning the loan to a particular person will be safe or not.
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