  We present the problem of "Reject Inference" for credit acceptance. Because
of the current legal framework (Basel II), credit institutions need to
industrialize their processes for credit acceptance, including Reject
Inference. We present here a methodology to compare various techniques of
Reject Inference and show that it is necessary, in the absence of real
theoretical results, to be able to produce and compare models adapted to
available data (selection of "best" model conditionnaly on data). We describe
some simulations run on a small data set to illustrate the approach and some
strategies for choosing the control group, which is the only valid approach to
Reject Inference.
