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Evolutionary Ensemble Approach for Behavioral Credit Scoring

  • Nikolay O. Nikitin
  • Anna V. Kalyuzhnaya
  • Klavdiya Bochenina
  • Alexander A. Kudryashov
  • Amir Uteuov
  • Ivan Derevitskii
  • Alexander V. Boukhanovsky
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10862)

Abstract

This paper is concerned with the question of potential quality of scoring models that can be achieved using not only application form data but also behavioral data extracted from the transactional datasets. The several model types and a different configuration of the ensembles were analyzed in a set of experiments. Another aim of the research is to prove the effectiveness of evolutionary optimization of an ensemble structure and use it to increase the quality of default prediction. The example of obtained results is presented using models for borrowers default prediction trained on the set of features (purchase amount, location, merchant category) extracted from a transactional dataset of bank customers.

Keywords

Credit scoring Credit risk modeling Financial behavior Ensemble modeling Evolutionary algorithms 

Notes

Acknowledgments

This research is financially supported by The Russian Science Foundation, Agreement № 17-71-30029 with co-financing of Bank Saint Petersburg.

References

  1. 1.
    Abdou, H.A., Pointon, J.: Credit scoring, statistical techniques and evaluation criteria: a review of the literature. Intell. Syst. Account. Fin. Manag. 18(2–3), 59–88 (2011)CrossRefGoogle Scholar
  2. 2.
    Ha, S.H.: Behavioral assessment of recoverable credit of retailer’s customers. Inf. Sci. (Ny) 180(19), 3703–3717 (2010)CrossRefGoogle Scholar
  3. 3.
    Serrano-Cinca, C., Gutiérrez-Nieto, B.: The use of profit scoring as an alternative to credit scoring systems in peer-to-peer (P2P) lending. Decis. Support Syst. 89, 113–122 (2016)CrossRefGoogle Scholar
  4. 4.
    Lessmann, S., et al.: Benchmarking state-of-the-art classification algorithms for credit scoring: an update of research. Eur. J. Oper. Res. 247(1), 124–136 (2015)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Wang, G., et al.: A comparative assessment of ensemble learning for credit scoring. Expert Syst. Appl. 38(1), 223–230 (2011)CrossRefGoogle Scholar
  6. 6.
    Kaggle Ensembling Guide [Electronic resource]Google Scholar
  7. 7.
    Westley, K., Theodore, I.: Transaction Scoring: Where Risk Meets Opportunity [Electronic resource]Google Scholar
  8. 8.
    Mullen, K.M., et al.: DEoptim: an R package for global optimization by differential evolution. J. Stat. Softw. 40(6), 1–26 (2009)MathSciNetGoogle Scholar
  9. 9.
    Wolters, M.A.: A genetic algorithm for selection of fixed-size subsets with application to design problems. J. Stat. Softw. 68(1), 1–18 (2015)MathSciNetGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Nikolay O. Nikitin
    • 1
  • Anna V. Kalyuzhnaya
    • 1
  • Klavdiya Bochenina
    • 1
  • Alexander A. Kudryashov
    • 1
  • Amir Uteuov
    • 1
  • Ivan Derevitskii
    • 1
  • Alexander V. Boukhanovsky
    • 1
  1. 1.ITMO UniversitySt. PetersburgRussian Federation

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