Reject Inference

Reject Inference is a method of improving the quality of the scorecard based on the use of data contained in rejected loan applications.

When developing a scorecard, we normally use information on those borrowers who have previously been granted a loan (approved applicants). However, the number of potential customers is significantly higher and a correctly developed scorecard must be able to perform as expected in the context of the entire population of potential customers.

The behavior of new types of borrowers can significantly differ from the behavior of the borrowers included in our credit portfolio (approved applicants).

To improve our knowledge of potential borrowers, we can use information on those customers who applied for and were refused a loan (rejected applicants).

To develop a scorecard, we need to identify each borrower either as "good“ or "bad”. However, there is no such information available for rejected loan applications. We cannot tell for sure, to which group a borrower would have belonged, had he/she been granted a loan. The Reject Inference methods are intended to provide the most correct way to perform the Good-Bad identification of rejected application in order to include them into the development set, based on which we can build a scorecard.