Identification of credit risk based on cluster analysis of account behaviours

Maha Bakoben, Tony Bellotti, Niall Adams

Research output: Journal PublicationArticlepeer-review

12 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)775-783
Number of pages9
JournalJournal of the Operational Research Society
Volume71
Issue number5
DOIs
Publication statusPublished - 3 May 2020
Externally publishedYes

Keywords

  • Behavioural credit scoring
  • clustering parameter uncertainty
  • credit behaviour clusters
  • default prediction

ASJC Scopus subject areas

  • Modelling and Simulation
  • Strategy and Management
  • Statistics, Probability and Uncertainty
  • Management Science and Operations Research

Fingerprint

Dive into the research topics of 'Identification of credit risk based on cluster analysis of account behaviours'. Together they form a unique fingerprint.

Cite this