Identifying future defaulters : A hierarchical Bayesian method

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

15 Scopus Citations
View graph of relations

Author(s)

  • Fan Liu
  • Zhongsheng Hua
  • Andrew Lim

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)202-211
Journal / PublicationEuropean Journal of Operational Research
Volume241
Issue number1
Online published13 Aug 2014
Publication statusPublished - 16 Feb 2015

Abstract

Traditional methods of applying classification models into the area of credit scoring may ignore the effect from censoring. Survival analysis has been introduced with its ability to deal with censored data. The mixture cure model, one important branch of survival models, is also applied in the context of credit scoring, assuming that the study population is a mixture of never-default and will-default customers.

We extend the standard mixture cure model through: (1) relaxing the independence assumption of the probability and the time of default; (2) treating the missing defaulting labels as latent variables and applying an augmentation technique; and (3) introducing a discrete truncated exponential distribution to model the time of default. Our full model is written in a hierarchical form so that the Markov chain Monte Carlo method is applied to estimate corresponding parameters.

Through an empirical analysis, we show that both mixture models, the standard mixture cure model and the hierarchical mixture cure model (HMCM), are more advanced in identifying future defaulters while compared with logistic regression. It is also concluded that our hierarchical Bayesian extension increases the model's predictability and provides meaningful output for risk management.

Research Area(s)

  • Bayesian analysis, Credit scoring, Mixture cure model, Risk management

Citation Format(s)

Identifying future defaulters: A hierarchical Bayesian method. / Liu, Fan; Hua, Zhongsheng; Lim, Andrew.
In: European Journal of Operational Research, Vol. 241, No. 1, 16.02.2015, p. 202-211.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review