What are the most effective techniques for reject inference in the context of retail credit scoring. Parcelling is something I use frequently... Any other approaches out there?
Have you looked at the bivariate probit model at all?
Bivariate probit model with sample selection assumes that the distribution of the accepted applicant population is different from that of the rejected applicant population. That is, it is assumed that
$P(default|X, rejected) \neq P(default|X, accepted)$
for some vector of explanatory variables X of the model predicting the default of companies.
I addition to that paper, there's an article that highlights different approaches available here: Theoretical approaches of reject inference.
It gives overviews of:
- Several different parceling methods
- Fuzzy reclassification
- Iterative reclassification
- Three-groups approach