Developing a solid and sound model/scorecard using a reject inference can substantially increase the size, and quality of a customer base or portfolio. Here we look at the use and development of reject inferences.
It can be very dangerous to base lending decisions solely on the behaviors and characteristics of accepted borrowers, or clients. In fact, poor lending rules can be exacerbated and millions of dollars lost if lending institutions do not properly and accurately develop their lending policies, and "acceptance" guidelines. Developing a solid and sound model (or scorecard) using a reject inference can substantially increase the size, and quality of a customer base or portfolio.
In this article, we will look at the use and development of reject inferences for the purpose of raising profits and increasing market share.
1. What is Reject Inference
A Reject Inference is a method for improving the quality of a 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. 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.
To improve our knowledge of potential borrowers, we can use information on those customers who applied for and were refused a loan.
To develop a scorecard, we need to identify each borrower either as a "good" one or a "bad" one. However, there is no 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 applications in order to include them into the development set, based on which we can build a scorecard.
2. Simple Augmentation
The simplest way to include information on rejects is to evaluate them using the existing scorecard; rejected loan applications can be used afterwards to adjust the scorecard.
This method is called Simple Augmentation.
The first step involves developing a scorecard using the information on approved loan applications: Developing a scorecard using the information on approved loan applications
Using the resulting scorecard, we can evaluate the set of rejects; rejects are evaluated as "good" or "bad" based on the acceptable bad rate value.
Read more of
Reject Inference Methods: 2. Simple Augmentation
Reject Inference Methods: 3. Fuzzy Augmentation