Open AccessResearch on the Application of Machine Learning Algorithms in Credit Risk Assessment of Minor Enterprises
Author(s) -
Huichao Mi
Publication year - 2021
Publication title -
converter
Language(s) - English
Resource type - Journals
ISSN - 0010-8189
DOI - 10.17762/converter.220
Subject(s) - machine learning , support vector machine , computer science , decision tree , artificial intelligence , relevance vector machine , minor (academic) , artificial neural network , scale (ratio) , algorithm , credit risk , naive bayes classifier , undersampling , finance , business , physics , quantum mechanics , political science , law
Under the influence of COVID-19, minor enterprises, especially the manufacturing industry, are facing greater financial pressure and the possibility of non-performing loans is increasing. It is very important for financial institutions to reduce financial risks while providing financial support for minor enterprises to promote industrial development and economic recovery. In order to understand the function of machine learning algorithms in predicting enterprise credit risk, the research designs five models, including Logistic Regression, Decision Tree, Naïve Bayesian, Support Vector Machine and Deep Neural Network, and adopts SMOTE and Undersampling to process imbalanced data. Experiments show that machine learning algorithms have high accuracy for both large-scale data and small-scale data.