Skip to main content
SpringerLink
Log in

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

  • Published:
Computational Economics Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  • Abra, F. D., Huijser, M. P., Pereira, C. S., & Ferraz, K. M. (2018). How reliable are your data? Verifying species identification of road-killed mammals recorded by road maintenance personnel in São Paulo state, Brazil. Biological Conservation., 225, 42–52.

    Article  Google Scholar 

  • Aldridge, I. (2019). Big data in portfolio allocation: A new approach to successful portfolio optimization. The Journal of Financial Data Science, 1(1), 45–63.

    Article  Google Scholar 

  • Alsayed, A., & Bilgrami, A. (2017). E-banking security: Internet hacking, phishing attacks, analysis and prevention of fraudulent activities. International Journal of Emerging Technology and Advanced Engineering, 7(1), 109–115.

    Google Scholar 

  • Campus, K. (2018). Credit card fraud detection using machine learning models and collating machine learning models. International Journal of Pure and Applied Mathematics, 118(20), 825–838.

    Google Scholar 

  • Chanmee, S., & Kesorn, K. (2020) Data quality enhancement for decision tree algorithm using knowledge-based model. Current Applied Science and Technology, 20: 259–277.

    Google Scholar 

  • Chen, R., Yu, J., Jin, C., & Bao, W. (2019a). Internet finance investor sentiment and return comovement. Pacific-Basin Finance Journal, 56, 151–161.

    Article  Google Scholar 

  • Chen, R., Yu, J., Jin, C., & Bao, W. (2019b). Internet finance investor sentiment and return comovement. Pacific-Basin Finance Journal, 56, 151–161.

    Article  Google Scholar 

  • Chen, T., Xu, J., Ying, H., Chen, X., Feng, R., Fang, X., Gao, H., & Wu, J. (2019c). Prediction of extubation failure for intensive care unit patients using light gradient boosting machine. IEEE Access : Practical Innovations, Open Solutions, 7, 150960–150968.

    Article  Google Scholar 

  • Cong, L. W., & He, Z. (2019). Blockchain disruption and smart contracts. The Review of Financial Studies, 32(5), 1754–1797.

    Article  Google Scholar 

  • Ding, H., Peng, C., Tian, Y., & Xiang, S. (2019). A risk adaptive access control model based on Markov for big data in the cloud. International Journal of High Performance Computing and Networking, 13(4), 464–475.

    Article  Google Scholar 

  • Florio, C., & Leoni, G. (2017). Enterprise risk management and firm performance: The Italian case. The British Accounting Review, 49(1), 56–74.

    Article  Google Scholar 

  • Fuyong, Y. (2016). Internet finance statistics dilemma and implementation path. Financial Development Review, (10), pp. 4–9.

  • Georganos, S., Grippa, T., Niang Gadiaga, A., Linard, C., Lennert, M., Vanhuysse, S., Mboga, N., Wolff, E., & Kalogirou, S. Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling. Geocarto International. 2019, 36: pp. 1–16.

    Google Scholar 

  • Gomber, P., Kauffman, R. J., Parker, C., & Weber, B. W. (2018). On the fintech revolution: Interpreting the forces of innovation, disruption, and transformation in financial services. Journal of Management Information Systems, 35(1), 220–265.

    Article  Google Scholar 

  • Gulsoy, N., & Kulluk, S. (2019). A data mining application in credit scoring processes of small and medium enterprises commercial corporate customers. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(3), e1299–e1311.

    Google Scholar 

  • Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223–2273.

    Article  Google Scholar 

  • Hasan, R., Chatwin, C., & Sayed, M. Examining alternatives to traditional accident causation models in the offshore oil and gas industry. Journal of Risk Research. 2019, pp. 1–16.

  • Ivanov, D., Dolgui, A., & Sokolov, B. (2019). The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production Research, 57(3), 829–846.

    Article  Google Scholar 

  • Kim, H., Hong, T., & Kim, J. (2019b). Automatic ventilation control algorithm considering the indoor environmental quality factors and occupant ventilation behavior using a logistic regression model. Building and Environment, 153, 46–59.

    Article  Google Scholar 

  • Kim, H., Kim, J., Kim, Y., Kim, I., & Kim, K. J. (2019a). Design of network threat detection and classification based on machine learning on cloud computing. Cluster Computing, 22(1), 2341–2350.

    Article  Google Scholar 

  • Lv, Z., Kong, W., Zhang, X., et al. (2019). Intelligent security planning for regional distributed energy internet. IEEE Transactions on Industrial Informatics, 16(5), 3540–3547.

    Article  Google Scholar 

  • Michael, H., Tian, L., & Ghebremichael, M. (2019). The ROC curve for regularly measured longitudinal biomarkers. Biostatistics, 20(3), 433–451.

    Article  Google Scholar 

  • Norris, G., Brookes, A., & Dowell, D. (2019). The psychology of internet fraud victimisation: A systematic review. Journal of Police and Criminal Psychology, 34(3), 231–245.

    Article  Google Scholar 

  • Rathee, G., Sharma, A., Saini, H., Kumar, R., & Iqbal, R. (2019). A hybrid framework for multimedia data processing in IoT-healthcare using blockchain technology. Multimedia Tools and Applications, 36: 1–23.

    Google Scholar 

  • Saura, J. R., Herráez, B. R., & Reyes-Menendez, A. (2019). Comparing a traditional approach for financial brand communication analysis with a big data analytics technique. IEEE access : practical innovations, open solutions, 7, 37100–37108.

    Article  Google Scholar 

  • Schonlau, M., & Zou, R. Y. (2020). The random forest algorithm for statistical learning. The Stata Journal, 20(1), 3–29.

    Article  Google Scholar 

  • Smith, A. M., Lampinen, J. M., Wells, G. L., Smalarz, L., & Mackovichova, S. (2019). Deviation from perfect performance measures the diagnostic utility of eyewitness lineups but partial area under the ROC curve does not. Journal of Applied Research in Memory and Cognition, 8(1), 50–59.

    Article  Google Scholar 

  • Tupa, J., Simota, J., & Steiner, F. (2017). Aspects of risk management implementation for Industry 4.0. Procedia Manufacturing, 11, 1223–1230.

    Article  Google Scholar 

  • Urbinati, A., Bogers, M., Chiesa, V., & Frattini, F. (2019). Creating and capturing value from big data: A multiple-case study analysis of provider companies. Technovation, 84, 21–36.

    Article  Google Scholar 

  • Ustuner, M., & Balik Sanli, F. (2019). Polarimetric target decompositions and light gradient boosting machine for crop classification: A comparative evaluation. ISPRS International Journal of Geo-Information, 8(2), 97–117.

    Article  Google Scholar 

  • Yang, B. (2020). Construction of logistics financial security risk ontology model based on risk association and machine learning. Safety Science, 123, 104–123.

    Article  Google Scholar 

  • Yang, D., Chen, P., Shi, F., & Wen, C. (2018). Internet finance: Its uncertain legal foundations and the role of big data in its development. Emerging Markets Finance and Trade, 54(4), 721–732.

    Article  Google Scholar 

  • Yue, X., Wang, H., Jin, D., Li, M., & Jiang, W. (2016). Healthcare data gateways: Found healthcare intelligence on blockchain with novel privacy risk control. Journal of Medical Systems., 40(10), 218–223.

    Article  Google Scholar 

  • Zhang, H., Liao, H., Wu, X., Zavadskas, E. K., & Al-Barakati, A. (2020a). Internet financial investment product selection with pythagorean fuzzy DNMA method. Engineering Economics, 31(1), 61–71.

    Article  Google Scholar 

  • Zhang, H., Liao, H., Wu, X., Zavadskas, E. K., & Al-Barakati, A. (2020b). Internet financial investment product selection with pythagorean fuzzy DNMA method. Engineering Economics, 31(1), 61–71.

    Article  Google Scholar 

  • Zhu, Y., Zhou, L., Xie, C., Wang, G. J., & Nguyen, T. V. (2019). Forecasting SMEs’ credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach. International Journal of Production Economics, 211, 22–33.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of International Relations, Renmin University of China, Beijing, China

    Zhennan Wu

Authors
  1. Zhennan Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhennan Wu.

Ethics declarations

Conflict of interest

All Authors declare that they have no conflict of interest.

Human and Animal Rights

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, Z. Using Machine Learning Approach to Evaluate the Excessive Financialization Risks of Trading Enterprises. Comput Econ 59, 1607–1625 (2022). https://doi.org/10.1007/s10614-020-10090-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10614-020-10090-6

Keywords

Search

Navigation