Internet financial supervision based on machine learning and improved neural network

The research on the risk pricing of Internet finance online loans not only enriches the theory and methods of online loan pricing, but also helps to improve the level of online loan risk pricing. In order to improve the efficiency of Internet financial supervision, this article builds an Internet financial supervision system based on machine learning algorithms and improved neural network algorithms. Moreover, on the basis of factor analysis and discretization of loan data, this paper selects the relatively mature Logistic regression model to evaluate the credit risk of the borrower and considers the comprehensive management of credit risk and the matching with income. In addition, according to the relevant provisions of the New Basel Agreement on expected losses and economic capital, starting from the relevant factors, this article combines the credit risk assessment results to obtain relevant factors through regional research and conduct empirical analysis. The research results show that the model constructed in this paper has certain reliability.

Feature Selection to Optimize Credit Banking Risk Evaluation Decisions for the Example of Home Equity Loans

Measuring credit risk is essential for financial institutions because there is a high risk level associated with incorrect credit decisions. The Basel II agreement recommended the use of advanced credit scoring methods in order to improve the efficiency of capital allocation. The latest Basel agreement (Basel III) states that the requirements for reserves based on risk have increased. Financial institutions currently have exhaustive datasets regarding their operations; this is a problem that can be addressed by applying a good feature selection method combined with big data techniques for data management. A comparative study of selection techniques is conducted in this work to find the selector that reduces the mean square error and requires the least execution time.

Bankruptcy Prediction: A Survey on Evolution, Critiques, and Solutions

After the economic crisis and the BASEL agreement, the bankruptcy prediction research has evolved substantially due to its importance in corporate finance. This paper summarizes the short history of bankruptcy prediction from the beginning until quite recently. First, it presents a short summary of bankruptcy prediction evolution pointing to the most used models. Then, it provides a summary of the most cited papers that discuss the evolution of bankruptcy prediction and of those papers that have contributed to bankruptcy prediction. Finally, it summarizes some critiques about bankruptcy prediction that the literature has formulated over time and provides some suggestions for future research on bankruptcy prediction.