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Query-Based Versus Tree-Based Classification: Application to Banking Data

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Foundations of Intelligent Systems (ISMIS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10352))

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Abstract

The cornerstone of retail banking risk management is the estimation of the expected losses when granting a loan to the borrower. The key driver for loss estimation is probability of default (PD) of the borrower. Assessing PD lies in the area of classification problem. In this paper we apply FCA query-based classification techniques to Kaggle open credit scoring data. We argue that query based classification allows one to achieve higher classification accuracy as compared to applying classical banking models and still to retain interpretability of model results, whereas black-box methods grant better accuracy but diminish interpretability.

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Notes

  1. 1.

    https://www.kaggle.com/c/GiveMeSomeCredit.

  2. 2.

    https://www.kaggle.com/c/GiveMeSomeCredit.

  3. 3.

    https://github.com/dmlc/xgboost.

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Acknowledgments

The paper was prepared within the framework of the Basic Research Program at the National Research University Higher School of Economics (HSE) and supported within the framework of a subsidy by the Russian Academic Excellence Project ‘5-100’.

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Authors and Affiliations

  1. National Research University Higher School of Economics, Moscow, Russia

    Alexey Masyutin & Yury Kashnitsky

Authors
  1. Alexey Masyutin
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  2. Yury Kashnitsky
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Corresponding author

Correspondence to Alexey Masyutin .

Editor information

Editors and Affiliations

  1. Warsaw University of Technology, Warsaw, Poland

    Marzena Kryszkiewicz

  2. University of Bari Aldo Moro, Bari, Italy

    Annalisa Appice

  3. Institute of Informatics, University of Warsaw, Warsaw, Poland

    Dominik Ślęzak

  4. Faculty of Electronics & Information, Warsaw University of Technology, Warsaw, Poland

    Henryk Rybinski

  5. Institute of Mathematics, Warsaw University, Warsaw, Poland

    Andrzej Skowron

  6. Department of Computer Science, University of North Carolina at Charlotte, North Carolina, USA

    Zbigniew W. Raś

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Masyutin, A., Kashnitsky, Y. (2017). Query-Based Versus Tree-Based Classification: Application to Banking Data. In: Kryszkiewicz, M., Appice, A., Ślęzak, D., Rybinski, H., Skowron, A., Raś, Z. (eds) Foundations of Intelligent Systems. ISMIS 2017. Lecture Notes in Computer Science(), vol 10352. Springer, Cham. https://doi.org/10.1007/978-3-319-60438-1_65

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  • DOI: https://doi.org/10.1007/978-3-319-60438-1_65

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60437-4

  • Online ISBN: 978-3-319-60438-1

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