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Identification of credit risk based on cluster analysis of account behaviours

Author

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  • Maha Bakoben
  • Tony Bellotti
  • Niall Adams

Abstract

Assessment of risk levels for existing credit accounts is important to the implementation of bank policies and offering financial products. This article uses cluster analysis of behaviour of credit card accounts to help assess credit risk level. Account behaviour is modelled parametrically and we then implement the behavioural cluster analysis using a recently proposed dissimilarity measure of statistical model parameters. The advantage of this new measure is the explicit exploitation of uncertainty associated with parameters estimated from statistical models. Interesting clusters of real credit card behaviours data are obtained, in addition to superior prediction and forecasting of account default based on the clustering outcomes.

Suggested Citation

  • Maha Bakoben & Tony Bellotti & Niall Adams, 2020. "Identification of credit risk based on cluster analysis of account behaviours," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 71(5), pages 775-783, May.
    • Handle: RePEc:taf:tjorxx:v:71:y:2020:i:5:p:775-783
    DOI: 10.1080/01605682.2019.1582586
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    Cited by:

    1. Li, Aimin & Li, Zhiyong & Bellotti, Anthony, 2023. "Predicting loss given default of unsecured consumer loans with time-varying survival scores," Pacific-Basin Finance Journal, Elsevier, vol. 78(C).
    2. Chrysovalantis Gaganis & Panagiota Papadimitri & Fotios Pasiouras & Menelaos Tasiou, 2023. "Social traits and credit card default: a two-stage prediction framework," Annals of Operations Research, Springer, vol. 325(2), pages 1231-1253, June.
    3. Kellner, Ralf & Nagl, Maximilian & Rösch, Daniel, 2022. "Opening the black box – Quantile neural networks for loss given default prediction," Journal of Banking & Finance, Elsevier, vol. 134(C).

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