A time-dependent proportional hazards survival model for credit risk analysis

J. K. Im, Daniel Apley*, C. Qi, X. Shan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

In the consumer credit industry, assessment of default risk is critically important for the financial health of both the lender and the borrower. Methods for predicting risk for an applicant using credit bureau and application data, typically based on logistic regression or survival analysis, are universally employed by credit card companies. Because of the manner in which the predictive models are fit using large historical sets of existing customer data that extend over many years, default trends, anomalies, and other temporal phenomena that result from dynamic economic conditions are not brought to light. We introduce a modification of the proportional hazards survival model that includes a time-dependency mechanism for capturing temporal phenomena, and we develop a maximum likelihood algorithm for fitting the model. Using a very large, real data set, we demonstrate that incorporating the time dependency can provide more accurate risk scoring, as well as important insight into dynamic market effects that can inform and enhance related decision making.

Original languageEnglish (US)
Pages (from-to)306-321
Number of pages16
JournalJournal of the Operational Research Society
Volume63
Issue number3
DOIs
StatePublished - Mar 1 2012

Keywords

  • data analysis: estimating survival models
  • failure models: survival analysis for credit risk modelling
  • risk: predicting consumer credit risk

ASJC Scopus subject areas

  • Management Information Systems
  • Strategy and Management
  • Management Science and Operations Research
  • Marketing

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