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Identification of Credit Risk Based on Cluster Analysis of Account Behaviours

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Listed:
  • 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 paper 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, 2017. "Identification of Credit Risk Based on Cluster Analysis of Account Behaviours," Papers 1706.07466, arXiv.org.
    • Handle: RePEc:arx:papers:1706.07466
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    References listed on IDEAS

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    1. Robert Till & David Hand, 2003. "Behavioural models of credit card usage," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(10), pages 1201-1220.
    2. N M Adams & D J Hand & R J Till, 2001. "Mining for classes and patterns in behavioural data," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(9), pages 1017-1024, September.
    3. Thomas, Lyn C., 2009. "Consumer Credit Models: Pricing, Profit and Portfolios," OUP Catalogue, Oxford University Press, number 9780199232130.
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    Cited by:

    1. Eva Kalinová, 2021. "Artificial Intelligence for Cluster Analysis: Case Study of Transport Companies in Czech Republic," JRFM, MDPI, vol. 14(9), pages 1-36, September.

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