ارزیابی ریسک اعتباری سرمایه گذاری بر اساس الگوریتم شبکه عصبی Enterprise Credit Risk Evaluation Based on Neural Network Algorithm

1. Introduction Credit risk is an important issue in the decision-making and profit of the banking industry. Credit risk is still a single biggest risk that is difficult to offset for banks and it expresses the concept of future loss. Because the customer does not fulfil the repayment obligation, the credit risk also embodies the loss of the bank’s profit. Usually, the general approach of credit risk assessment is to apply the classification model to past customer data, including default and non-default customers, so as to find the relationship between user characteristics and potential default. The credit risk assessment model based on statistical data has become the main analysis tool for the financial institutions to assess the credit risk. By analysing the multiple risk factors of the evaluation object, the credit risk assessment is an independent process of assessing the borrower’s willingness and ability to repay. The credit risk assessment model has been widely used to assess corporate risk by bond investors, debt issuers, and government officials. They provide a means to determine the risk premium and bond market, so that companies can assess the possible return on investment to issue bonds. The advantages of building a credible credit risk assessment system are: reducing the cost of credit analysis, ensuring fast decision-making, guaranteeing credit collection and reducing possible risks. The credit risk assessment was initially judged by the personal experience manager, and then based on the 5C factor. However, with the rapid increase of applicants, it is almost impossible to do the work manually. Many institutions in the credit industry are proposing new models to support credit decisions. Recent studies have shown that the existing artificial intelligence (AI) technology, such as decision tree (DT), support vector machine (SVM) and so on, in the problem of credit risk assessment, shows a better performance than the statistical model and optimization method. Different from statistical models, AI model does not require the assumption of variable distribution, and can acquire knowledge directly from training data sets. In the field of credit risk assessment, especially when the credit risk assessment problem is nonlinear mode classification, the performance of AI model is better than that of the statistical model.