Identification of credit risk based on cluster analysis of account behaviours

Maha Bakoben, Tony Bellotti and Niall Adams

Journal of the Operational Research Society, 2020, vol. 71, issue 5, 775-783

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.

Date: 2020
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Citations: View citations in EconPapers (3)

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DOI: 10.1080/01605682.2019.1582586

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