Abstract
In recent years, the government has begun to focus on supporting small and medium-sized enterprises. As an important part of the national economy, small and medium-sized enterprises need to be more cautious about their credit risks. They are generally have the characteristics of small scale, low risk resistance. This often generates more investigation workloads during the review of the lending process. This article proposes to use the random forest model for research, use big data to support, analyze the loan default risk of small and medium-sized enterprises, and predict the repayment probability under each loan line. The purpose is to provide actual reference value for grasping the credit risk of the small and medium-sized enterprises. The output results of the model in this paper are displayed in data visualization.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
