Machine Learning Techniques-Based Banking Loan Eligibility Prediction

Anjali Agarwal, Roshni Rupali Das, Ajanta Das
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Abstract

In our daily life, it is difficult to meet financial demand while in crisis. This financial crisis may be solved with financial assistance from the banks. The financial assistance is nothing but availing loan from the bank with proper agreement to repay the amount including calculated interest within the loan approved tenure. The customer can only avail loans against the submission of some valid and important supportive documents. However, although the customer is aware of the whole process of repayment and installment along with loan approval tenure, most of the time it is hard to get the approved loan within a shorter period. Therefore, the objective of this paper is to automate this manual and long process by predicting the chance of approval of the loan. The novelty of this research article is to apply machine learning techniques and classification algorithms to predict loan eligibility through an automatic online loan application process
基于机器学习技术的银行贷款资格预测
在我们的日常生活中,在危机中很难满足金融需求。这次金融危机可能会在银行的财政援助下得到解决。财政援助只不过是通过适当的协议从银行获得贷款,并在批准的贷款期限内偿还包括计算利息在内的金额。客户只有提交一些有效和重要的支持文件才能获得贷款。然而,尽管客户知道还款和分期的全过程以及贷款审批期限,但大多数情况下,很难在较短的期限内获得批准的贷款。因此,本文的目标是通过预测贷款批准的机会来自动化这个手动的长过程。本研究文章的新颖之处在于应用机器学习技术和分类算法,通过自动在线贷款申请过程来预测贷款资格
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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