Risk prediction of enterprise credit financing using machine learning - Gu, Jingjing (Informatica, 2022)
For the credit financing risk of small, medium-sized and micro enterprises, a risk prediction model of enterprise credit financing management based on antagonistic neural network and least squares support vector machine is proposed. The data samples are processed combined with antagonistic neural network, and the risk prediction of enterprise credit financing is realized by using least squares support vector machine model. Compared with many classification models, the effectiveness of the least squares support vector machine model is verified. The results show that the accuracy rate of the least squares support vector machine model is 90.15%, the recall rate is 85.63%, and the prediction error rates of the model in default and non default are 6.48% and 3.09% respectively, which is better than BP neural network, random forest algorithm and Gaussian naive Bayesian algorithm. The least squares support vector machine model can effectively and accurately predict the enterprise credit financing risk, provide scientific and efficient risk early warning for the enterprise credit financing control, and provide technical support for preventing and reducing the occurrence of loan default.