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Using Machine Learning Approach to Evaluate the Excessive Financialization Risks of Trading Enterprises
Computational Economics ( IF 2 ) Pub Date : 2021-01-29 , DOI: 10.1007/s10614-020-10090-6
Zhennan Wu

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.



中文翻译:

使用机器学习方法评估贸易企业过度金融化的风险

随着Internet技术的发展和广泛传播,使用Internet进行财务管理已成为一种新型的金融技术。尽管Internet金融带来了便利和利润,但也出现了严重的金融风险,例如混乱的信誉管理,不良贷款和恶意欺骗。因此,为了增强贸易金融企业应对过度金融化风险的能力,利用机器学习算法建立了决策树模型,随机森林模型和梯度提升模型。利用平均融合方法建立融合控制模型。分析了在上述模型下该算法的性能和风险预测指标。最后,通过分析六个月内贸易企业的贷款数据,评估了优化的风险控制模型的实际影响。结果表明,如果数据集在1G-5G的较小范围内(平均20分钟),则支持向量机(SVM)的训练速度将比其他模型更快。如果数据集在5G-30G的较宽范围内(平均35分钟),则融合模型(FM)将消耗较短的时间。不同的型号在不同的性能上具有独特的优势;融合算法的精度,查全率和准确性较高,分别为79.35%,39.28%和78.28%。随机森林算法(RFA)的精度为72.48%,也更高。通过模型融合来改善风险控制模型的性能,以提高贸易融资企业抵御金融贷款风险的能力,

更新日期:2021-01-31
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