IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1706.07466.html
   My bibliography  Save this paper

Identification of Credit Risk Based on Cluster Analysis of Account Behaviours

Author

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
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1706.07466
    File Function: Latest version
    Download Restriction: no

    References listed on IDEAS

    as
    1. Thomas, Lyn C., 2009. "Consumer Credit Models: Pricing, Profit and Portfolios," OUP Catalogue, Oxford University Press, number 9780199232130.
    2. Robert Till & David Hand, 2003. "Behavioural models of credit card usage," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(10), pages 1201-1220.
    Full references (including those not matched with items on IDEAS)

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:1706.07466. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (arXiv administrators). General contact details of provider: http://arxiv.org/ .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.