Research on Default Prediction Model of Corporate Credit Risk Based on Big Data Analysis Algorithm

Abstract

In recent years, with the rise of many technologies such as big data and artificial intelligence, the digitalization and information transformation of enterprises have gradually penetrated into the financial industry. Based on different big data analysis algorithms, we aim to establish a default prediction model for corporate credit risk, further optimize different models, compare the model performance, and analyze the robustness of the optimal model. We collect 21 items of financial and non-financial index data from more than 1,000 listed companies, standardize, balance and normalize the data, use correlation coefficient to screen the index, and establish two in-depth learning models, convolutional neural network model and recurrent neural network model, based on Pytorch framework of Spark platform, to complete the model optimization, and compare them with two traditional machine learning models: random forest model and logistic regression model. Finally, the comparison experiment shows that the recurrent neural network is the optimal model with an accuracy rate of 0.93, a recall rate of 0.96 and a F1 value of 0.93. For the optimal recurrent neural network model, the robustness of the model is analyzed by modifying the number of indicators, changing the number of samples and eliminating non-financial factors. The results show that the evaluation indicators of the model do not change much under the three conditions, and the model has a good robustness.