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Modeling Credit Risk: A Category Theory Perspective

Author

Listed:
  • Cao Son Tran

    (Science and Engineering Faculty, Queensland University of Technology, Brisbane, QLD 4000, Australia)

  • Dan Nicolau

    (Science and Engineering Faculty, Queensland University of Technology, Brisbane, QLD 4000, Australia
    Green Templeton College, University of Oxford, Oxford OX2 6HG, UK)

  • Richi Nayak

    (Centre for Data Science, Queensland University of Technology, Brisbane, QLD 4000, Australia)

  • Peter Verhoeven

    (Faculty of Business and Law, Queensland University of Technology, Brisbane, QLD 4000, Australia)

Abstract

This paper proposes a conceptual modeling framework based on category theory that serves as a tool to study common structures underlying diverse approaches to modeling credit default that at first sight may appear to have nothing in common. The framework forms the basis for an entropy-based stacking model to address issues of inconsistency and bias in classification performance. Based on the Lending Club’s peer-to-peer loans dataset and Taiwanese credit card clients dataset, relative to individual base models, the proposed entropy-based stacking model provides more consistent performance across multiple data environments and less biased performance in terms of default classification. The process itself is agnostic to the base models selected and its performance superior, regardless of the models selected.

Suggested Citation

  • Cao Son Tran & Dan Nicolau & Richi Nayak & Peter Verhoeven, 2021. "Modeling Credit Risk: A Category Theory Perspective," JRFM, MDPI, vol. 14(7), pages 1-21, July.
    • Handle: RePEc:gam:jjrfmx:v:14:y:2021:i:7:p:298-:d:586887
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    References listed on IDEAS

    as
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