Intensity models and transition probabilities for credit card loan delinquencies

Mindy Leow, Jonathan Crook

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

We estimate the probability of delinquency and default for a sample of credit card loans using intensity models, via semi-parametric multiplicative hazard models with time-varying covariates. It is the first time these models, previously applied for the estimation of rating transitions, are used on retail loans. Four states are defined in this non-homogenous Markov chain: up-to-date, one month in arrears, two months in arrears, and default; where transitions between states are affected by individual characteristics of the debtor at application and their repayment behaviour since. These intensity estimations allow for insights into the factors that affect movements towards (and recovery from) delinquency, and into default (or not). Results indicate that different types of debtors behave differently while in different states. The probabilities estimated for each type of transition are then used to make out-of-sample predictions over a specified period of time.
Original languageEnglish
Pages (from-to)685-694
JournalEuropean Journal of Operational Research
Issue number2
Early online date7 Jan 2014
Publication statusPublished - 16 Jul 2014

Keywords / Materials (for Non-textual outputs)

  • risk analysis
  • probability of default
  • intensity modelling
  • time-varying covariates
  • state space modelling
  • retail loans


Dive into the research topics of 'Intensity models and transition probabilities for credit card loan delinquencies'. Together they form a unique fingerprint.

Cite this