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A loan provides an extra amount of money for a certain period to the borrower. The borrower needs to repay the loan amount with certain interest as he or she agreed for it at the time of loan sanction. In most cases, the borrower avails loan from the bank to fulfill some pre-defined purposes, like personal or educational, or medical as and when urgency arrives. The distribution of loans is nearly every bank's primary activity. Most of the bank's resources are directly derived from the profits made on loans provided by the bank. The primary goal in the banking system is to put their funds in safe hands wherever they are. Today, many banks/financial firms issue loans after a lengthy process of verification and validation, but there is no guarantee that the chosen applicant is the worthiest of all applicants (Zhu, L. et al. 2019). Banking systems utilize human techniques to determine whether or not a consumer is qualified for a loan based on data. Manual techniques were usually efficient, but they were insufficient when there were many loan applications. At such a moment, it might take an indefinite time to make the decision (Kumar, R., Jain et al. 2019). As technology advances, banking systems are computerized and most of the tasks are performed with the help of the software. Despite several computer failures, content mistakes, and weight correction in an automated prediction model, the need for banking software is immensely in demand. Soon, banking software might be more dependable, accurate, and dynamic, and it could be integrated with an automated forecasting unit.
At the outset, each person is not eligible for availing loan facility from the bank. As bank provides various kinds of loans, such as house building loan, educational loan, business loan, purchase loan or start-up capital loan, etc. So, based on the type of loan, the categories of the borrower also vary. There are many factors to select the eligible borrower for the specific type of loan. The important factors for sanctioning the loan are credit history, age, repayment, service history, residential locality, criteria for a loan request, etc. Therefore, the prediction of eligibility criteria for a specific loan is always beneficial. Even the clients or person may need not visit the bank several times to check whether he or she is eligible for the specific loan request or not. Once they know that they are eligible for an availing loan, then they may proceed with all the rules in the corresponding bank. Hence, the prediction of loan eligibility is extremely advantageous to both bank employees and applicants.
The objective of this paper is to give a rapid, immediate, and simple method for the company or bank that wants to automate the loan eligibility procedure based on the information given by the consumer while filling out the application form. Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History, and other information are included. This study used data from prior clients of several banks who had loans authorized based on a set of characteristics. As a consequence, the machine learning model is trained on that record to produce correct results. As a result, the suggested loan prediction machine learning model may be utilized to determine client loan status and develop solutions. This model extracts and applies the essential aspects of a client that impact the customer's loan status. Finally, it provides the expected output. This prediction model will make a bank/financial manager's job easier and faster.
In response to the aforementioned circumstance, this paper discusses the application of various machine learning models in the loan lending procedure to determine the best strategy for a financial firm to check eligibility for loans. They will be utilized independently to analyze the dataset, find patterns in the dataset, and learn from them. Predict if an application is eligible for a loan based on that study. The primary goal of this research is to forecast loan approval.