Ma, L.; Wang, Y.; Ren, C.; Li, H., and Li, Y., 2020. Early warning for internet finance industry risk: An empirical investigation of the P2P companies in the coastal regions of China. In: Gong, D.; Zhang, M., and Liu, R. (eds.), Advances in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 106, pp. 295–299. Coconut Creek (Florida), ISSN 0749-0208.

Recent years have seen the rapid development of Internet finance in China, and various peer-to-peer (P2P) lending platforms have been released. However, the emergence of diverse problems, such as absconding with the money and fraudulent platforms, has exposed the lack of supervision of P2P lending platforms. This paper collected 26 dimensions of data from 1292 P2P companies in the coastal regions of China from third-party websites. Risk warning and machine learning were incorporated to construct a risk warning model for P2P lending platforms. This model consists of a decision tree model, naive Bayes model, and support vector machine model. Correlation analysis was used to analyze the data source, which is the data predicted by the above models, and the target variables. Type I error rate was utilized to determine the optimal model. The conclusions are as follows: The indicators of registered capital, changes of business scope, and platform background have a great impact on the accuracy of P2P risk prediction; the decision tree model is capable of predicting problematic platforms; and the decision tree model, support vector machine model, and naive Bayes model have a descending order in accuracy. Hence, the decision tree model is the best model for risk warning.