Abstract
With the rapid development of China’s Internet finance industry and the continuous growth of transaction amount in recent years, a variety of financial risks have increased, especially credit risk in the financial industry. Also, the credit risk evaluation is usually made by using the application card scoring model, which has the shortcomings of strict data assumption and inability to process complex data. In order to overcome the limitations of the credit card scoring model and evaluate credit risk better, this paper proposes a credit evaluation model based on extreme gradient boosting tree (XGBoost) machine learning (ML) algorithm to construct a credit risk assessment model for Internet financial institutions. At the same time, an Internet lending company in China is taken as a case study to compare the performance of the traditional credit card scoring model and the proposed machine learning (ML) algorithm model. The results show that ML algorithm has a very significant advantage in the field of Internet financial risk control, it has more accurate prediction results and has no particularly strict assumptions and restrictions on data, and the process of processing data is more convenient and reliable. We should increase the application of ML in the field of financial risk control. The value of this paper lies in enriching the related research of financial technology and providing a new reference for the practice of financial risk control.
Highlights
Since the 1970s, human society has entered the industrial 3.0 era marked by the application of electronic information technology, and computer technology and Internet have been widely used in various fields and integrated with traditional industries, giving birth to new business models and formats [1]
True positive (TP) refers the number of defaults that are correctly predicted as defaults; false positive (FP) refers the number of nondefaults that are mistakenly predicted as defaults; true negative (TN) refers the number of nondefaults that are correctly predicted as nondefault; false negative (FN) refers the number of defaults that are mistakenly predicted as nondefaults
We propose an improved machine learning (ML)-based technique for credit card scoring in Internet financial risk control, which has better performance than the traditional credit scoring modern in Internet financial risk control
Summary
Since the 1970s, human society has entered the industrial 3.0 era marked by the application of electronic information technology, and computer technology and Internet have been widely used in various fields and integrated with traditional industries, giving birth to new business models and formats [1]. Note that many methods and technologies for risk control in the financial field have been proposed in the existing literature, including traditional methods for credit risk management in Internet finance. Rough literature review, it can be seen that focusing on the research of ML algorithm applied in traditional credit scoring model in the field of Internet financial credit risk management research is insufficient. Erefore, on the basis of reading the relevant literature at home and abroad, this paper uses ML algorithm to construct the credit risk model, verifies the performance of ML model better than the traditional credit score card model through empirical verification, makes a deep discussion on how to convert the ML model into the score card model, and puts forward suggestions on the construction of risk control system of Internet financial industry by ML There are few literatures comparing the traditional credit evaluation model and ML model, and the research on the integration of the two methods to evaluate the credit risk of Internet finance is relatively rare. erefore, on the basis of reading the relevant literature at home and abroad, this paper uses ML algorithm to construct the credit risk model, verifies the performance of ML model better than the traditional credit score card model through empirical verification, makes a deep discussion on how to convert the ML model into the score card model, and puts forward suggestions on the construction of risk control system of Internet financial industry by ML
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