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?
I addition to that paper, there's an article that highlights different approaches available here: Theoretical approaches of reject inference.
It gives overviews of: