Using Machine Learning Approach to Evaluate the Excessive Financialization Risks of Trading Enterprises

Zhennan Wu ()
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Zhennan Wu: Renmin University of China

Computational Economics, 2022, vol. 59, issue 4, No 16, 1607-1625

Abstract: Abstract As Internet technology develops and spreads widely, using the Internet for financial management has become a new type of financial technology. Despite Internet finance’s convenience and profits, severe financial risks, such as chaotic reputation management, bad loans, and malicious deception, also appear. Hence, in order to enhance the ability of trading financial enterprises to respond to over-financialization risks, the machine learning algorithms are utilized to build a decision tree model, a random forest model, and a gradient boosting model; the average fusion method is utilized to build a fusion control model. The performances and risk prediction indicators of the proposed algorithm under the models mentioned above are analyzed. Finally, by analyzing a trading enterprise’s loan data within six months, the optimized risk control model’s actual impacts are evaluated. The results show that the support vector machine (SVM) will be quicker trained than other models if the data set is in the smaller range of 1G-5G, with an average of 20 min. The fusion model (FM) will consume a shorter time if the data set is in the broader range of 5G-30G, with an average of 35 minutes. Different models have unique advantages in different performances; the precision, recall rate, and accuracy of the fusion algorithm are higher, 79.35%, 39.28%, and 78.28%. The precision of the random forest algorithm (RFA) is 72.48%, which is also higher. The performance of the risk control model is improved through model fusion, in an effort to improve the ability of trade finance enterprises to withstand financial loan risks, which provides a reference for the risk control of financial enterprises.

Keywords: Big data; Data mining; Machine learning; Over‐financialization; Financial risk (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10614-020-10090-6

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