Advertisement

Mixed Credit Scoring Model of Logistic Regression and Evidence Weight in the Background of Big Data

  • Keqin ChenEmail author
  • Kun Zhu
  • Yixin Meng
  • Amit Yadav
  • Asif Khan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 940)

Abstract

Aims of this paper is to amalgamate the logistic regression algorithm under big data with the weight of evidence. To construct a new credit scoring model to analyze the user’s credit score and then divide the user into trustworthy customers and non-trustworthy customers respectively. Calculation of credit score shows the relationship between independent and dependent variable. The weight of the evidence is calculated by the maximum correlation orthogonal transform, which can have a significant effect on models with higher correlation. Due to the error in the data collected, the logistic regression error is large. Therefore, it is suggested that by constructing a hybrid scoring model a more accurate credit score can be obtained. This helps to improve the prediction accuracy of the credit score.

Keywords

Logistic regression Weight of evidence Personal credit score 

References

  1. 1.
    The Hong Kong Institute of Bankers: Credit risk management. Wiley, Hong Kong (2012)Google Scholar
  2. 2.
    Gjini, V., Koprencka, L.: Relationship Between Economic Factors and Non-Performing Loans-the Case of Albania, p. 61. International Advisory Board (2018)Google Scholar
  3. 3.
    Akko, S.: An empirical comparison of conventional techniques, neural networks and the three stage hybrid adaptive neuro fuzzy inference system (ANFIS) model for credit scoring analysis: the case of turkish credit card data. Eur. J. Oper. Res. 222(1), 168–178 (2012)CrossRefGoogle Scholar
  4. 4.
    Danenas, P., Garsva, G., Gudas, S.: Credit risk evaluation model development using support vector based classifiers. Procedia Comput. Sci. 4(4), 1699–1707 (2011)CrossRefGoogle Scholar
  5. 5.
    Finlay, S.: Multiple classifier architectures and their application to credit risk assessment. Eur. J. Oper. Res. 210(2), 368–378 (2011)CrossRefGoogle Scholar
  6. 6.
    Tsai, M.C., Lin, S.P., Cheng, C.C., Lin, Y.P.: The consumer loan default predicting model – an application of DEA-DA and neural network. Expert Syst. Appl. 36(9), 11682–11690 (2009)CrossRefGoogle Scholar
  7. 7.
    Bennouna, G., Tkiouat, M.: Fuzzy logic approach applied to credit scoring for microfinance in Morocco. Procedia Comput. Sci. 127, 274–283 (2018)CrossRefGoogle Scholar
  8. 8.
    Louzada, F., Moreira, F.F., de Oliveira, M.R.: A zero-inflated non default rate regression model for credit scoring data. Commun. Stat. Theory Methods 47(12), 3002–3021 (2018)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Sampath, S., Kalaichelvi, V.: Assessment of mortgage applications using fuzzy logic. Int. J. Soc. Behav. Educ. Econ. Bus. Ind. Eng. 8, 3487 (2014)Google Scholar
  10. 10.
    Djeundje, V.B., Crook, J.: Incorporating heterogeneity and macroeconomic variables into multi-state delinquency models for credit cards. Eur. J. Oper. Res. 27, 697 (2018)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Wang, D., Zhang, Z., Bai, R., Mao, Y.: A hybrid system with filter approach and multiple population genetic algorithm for feature selection in credit scoring. J. Comput. Appl. Math. 329, 307–321 (2018)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Baesens, B., Gestel, T.V., Viaene, S., Stepanova, M., Suykens, J., Vanthienen, J.: Benchmarking state-of-the-art classification algorithms for credit scoring. J. Oper. Res. Soc. 54(6), 627–635 (2003)CrossRefGoogle Scholar
  13. 13.
    Mukid, M.A., Widiharih, T., Rusgiyono, A., Prahutama, A.: Credit scoring analysis using weighted k nearest neighbor, vol. 1025, p. 012114 (2018)Google Scholar
  14. 14.
    Bhatia, S., Sharma, P., Burman, R., Hazari, S., Hande, R.: Credit scoring using machine learning techniques. Int. J. Comput. Appl. 161(11), 1 (2017)Google Scholar
  15. 15.
    Stiglitz, J., Weiss, A.: Credit rationing in markets with imperfect information. Am. Econ. Rev. 71(3), 393–410 (1981)Google Scholar
  16. 16.
    Eisenbeis, R.A., Robert, A.: Pitfalls in the application of discriminant analysis in business, finance, and economics. J. Financ. 32(3), 875–900 (2012)CrossRefGoogle Scholar
  17. 17.
    Eisenbeis, R.A.: Problems in applying discriminant analysis in credit scoring models. J. Bank. Financ. 2(3), 205–219 (1978)CrossRefGoogle Scholar
  18. 18.
    Lee, T.S., Chiu, C.C., Lu, C.J., Chen, I.F.: Credit scoring using the hybrid neural discriminant technique. Expert Syst. Appl. 23(3), 245–254 (2002)CrossRefGoogle Scholar
  19. 19.
    Oreski, S., Oreski, G.: Genetic algorithm-based heuristic for feature selection in credit risk assessment. Expert Syst. Appl. 41(4), 2052–2064 (2014)CrossRefGoogle Scholar
  20. 20.
    Tripathi, D., Edla, D.R., Cheruku, R.: Hybrid credit scoring model using neighborhood rough set and multi-layer ensemble classification. J. Intell. Fuzzy Syst. 34(3), 1543–1549 (2018)CrossRefGoogle Scholar
  21. 21.
    Grosan, C., Abraham, A.: Intelligent Systems: A Modern Approach. Intelligent Systems Reference Library. Springer, Heidelberg (2011)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Keqin Chen
    • 1
    • 2
    Email author
  • Kun Zhu
    • 2
  • Yixin Meng
    • 2
  • Amit Yadav
    • 2
  • Asif Khan
    • 3
  1. 1.School of Business AdministrationSouthwestern University of Finance and EconomicsChengduChina
  2. 2.Department of Information and Software EngineeringChengdu Neusoft UniversityChengduChina
  3. 3.Crescent Institute of Science and TechnologyChennaiIndia

Personalised recommendations