Zhang, Q. Loan Risk Prediction Model Based on Random Forest. Advances in Economics, Management and Political Sciences 2023, 5, 216–222, doi:10.54254/2754-1169/5/20220082.
Zhang, Q. Loan Risk Prediction Model Based on Random Forest. Advances in Economics, Management and Political Sciences 2023, 5, 216–222, doi:10.54254/2754-1169/5/20220082.
Zhang, Q. Loan Risk Prediction Model Based on Random Forest. Advances in Economics, Management and Political Sciences 2023, 5, 216–222, doi:10.54254/2754-1169/5/20220082.
Zhang, Q. Loan Risk Prediction Model Based on Random Forest. Advances in Economics, Management and Political Sciences 2023, 5, 216–222, doi:10.54254/2754-1169/5/20220082.
Abstract
As people's consumption habits change, loan plays a crucial role in our modern society. It provides individuals who do not have sufficient money with funds to purchase residential property or start a business. However, for avoiding unpleasant loan defaults, all financial institutions will first assess the borrower's risk index. By predicting the default risk of the borrower to decide whether to lend money. Machine learning algorithms, including random forest, linear regression and so on, have been benefited most of the real-world applications. With the development of machine learning methods, this paper, based on the personal history loan data of an institution studies the loan default risk, and uses the random forest classification model to predict the possibility of loan default. The result showed that the accuracy of this method was 85.62%, which show its application ability of real-world loan prediction and benefits the manager to decide the degree of risk for loan grant.
Keywords
Random Forest; Loan Risk
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
