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Financial credit risk prediction in internet finance driven by machine learning

  • Xiaomeng MaEmail author
  • Shuliang Lv
Machine Learning - Applications & Techniques in Cyber Intelligence
  • 35 Downloads

Abstract

The development of science and technology promotes the constant changes of consumer finance, but also brings some financial credit risks. In particular, with the continuous development of Internet finance, financial credit risk is increasingly difficult to control. Based on machine learning algorithm, this study improved the machine learning algorithm and named it MLIA algorithm. Meanwhile, this study decomposes the objective function into weighted sums of several basis functions. This study uses three typical test functions to compare the performance of MLIA prediction algorithm and logistic prediction algorithm. Simultaneously, this study analyzes the performance of MLIA financial credit risk prediction model by taking the data of an Internet financial company as an example. In addition, this study used the AUC (area under curve) value as a specific indicator of model performance verification. Research shows that machine learning has a good predictive effect on MLIA financial credit risk prediction and can provide theoretical reference for subsequent related research.

Keywords

Machine learning Finance Risk prediction Logistic model 

Notes

Acknowledgements

This work is supported by Project of the China Postdoctoral Science Foundation (No. 2018M643213).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Cao M, Xiao Y, Liu M (2015) Fuzzy support vector machines for credit risk prediction of SMEs under internet finance. Icic Express Lett 9(1):45–52Google Scholar
  2. 2.
    Kamalloo E, Abadeh MS (2014) Credit risk prediction using fuzzy immune learning. Adv Fuzzy Syst 16:1–11MathSciNetzbMATHGoogle Scholar
  3. 3.
    Imazato A, Hotta K, Higo Y et al (2014) Risk prediction for code clones based on machine learning. Tech Rep Ieice Ss 114:1–6Google Scholar
  4. 4.
    Puyalnithi T, Viswanatham VM (2016) Preliminary cardiac disease risk prediction based on medical and behavioural data set using supervised machine learning techniques. Indian J Sci Technol 9(31)Google Scholar
  5. 5.
    Zhu Y, Xie C, Wang GJ et al (2016) Predicting China’s SME credit risk in supply chain finance based on machine learning methods. Entropy 18(5):195CrossRefGoogle Scholar
  6. 6.
    Lakshmi D C (2014) Competency comparison between logistic classifier and partial decision tree classifier for credit risk prediction 1(1): 31–40Google Scholar
  7. 7.
    Lee MC (2014) Business bankruptcy prediction based on survival analysis approach. Int J Comput Sci Inf Technol 6(2):103–119Google Scholar
  8. 8.
    Dlugosch TJ, Klinger B, Frese M et al. (2017) Personality based selection of entrepreneurial borrowers to reduce credit risk: two studies on prediction models in low and high stakes settings in developing countries. J Organ Behav 39(6)Google Scholar
  9. 9.
    Attigeri GV, Pai MMM, Pai RM (2016) Credit risk assessment using machine learning algorithms. Adv Sci Lett 23(4):3649–3653CrossRefGoogle Scholar
  10. 10.
    Punniyamoorthy M, Sridevi P (2016) Identification of a standard AI based technique for credit risk analysis. Benchmarking Int J 23(5):1381–1390CrossRefGoogle Scholar
  11. 11.
    Na W, Zhu Q, Su Z et al (2015) Research on well production prediction based on improved extreme learning machine. Int J Model Ident Control 23(3):238CrossRefGoogle Scholar
  12. 12.
    Zhang L, Rao K, Wang R et al (2015) Risk prediction model based on improved adaboost method for cloud usersse. Open Cybern Syst J 9(1):44–49CrossRefGoogle Scholar
  13. 13.
    Yoo S, Bang J, Lee C et al (2014) A quantum speedup in machine learning: Finding a N-bit Boolean function for a classification. N J Phys 16(10):013014CrossRefGoogle Scholar
  14. 14.
    Su ZE, Wang XL, Lu CY et al (2015) Entanglement-based machine learning on aquantum computer. Phys Rev Lett 114(11):110504CrossRefGoogle Scholar
  15. 15.
    Jordan MI, Mitchell TM (2015) Machine learning: trends, perspectives, and prospects. Science 349(6245):255–260MathSciNetCrossRefGoogle Scholar
  16. 16.
    Takeda A, Kanamori T (2014) Using financial risk measures for analyzing generalization performance of machine learning models. Neural Netw 57:29–38CrossRefGoogle Scholar
  17. 17.
    Takeda A, Fujiwara S, Kanamori T (2014) Extended robust support vector machine based on financial risk minimization. Neural Comput 26(11):2541–2569MathSciNetCrossRefGoogle Scholar
  18. 18.
    Sun J, Li H, Huang QH et al (2014) Predicting financial distress and corporate failure: a review from the state-of-the-art definitions, modeling, sampling, and featuring approaches. Knowl-Based Syst 57(2):41–56CrossRefGoogle Scholar
  19. 19.
    Sugiyama M, Hirowatari E, Tsuiki H et al (2013) Learning figures with the Hausdorff metric by fractals—towards computable binary classification. Mach Learn 90(1):91–126MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Management School, Shenzhen PolytechnicShenzhenChina
  2. 2.Post-Doctoral Scientific Research WorkstationChina Merchants BankShenzhenChina
  3. 3.Zhengzhou Branch China CITIC BankZhengzhouChina

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