How do we construct the A/R table?

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This is best demonstrated by an example.

For the historical pattern, let’s take a search for new credit variable, for example, # of Inquiries in last 24 months with 5 bins 0 Inq, 1 Inq, 2-3, 4-5, and 6 or more. The final table is shown below:

# IQ 24 M		History			Proposed
Bin	Counts	Accepts	Reject	Accept %	Accepts	Reject	Accept %
0	2,000	1,700	300	85%	1,800	200	90%
1	4,000	3,500	500	88%	3,500	500	88%
2-3	4,000	3,000	1,000	75%	3,200	800	80%
4-5	3,000	2,000	1,000	67%	1,900	1,100	63%
6+	2,000	1,000	1,000	50%	800	1,200	40%
Total	15,000	11,200	3,800	75%	11,200	3,800	75%

Here’s the explanation for this table. During the development time frame, we had 15,000 applicants, 11,200 were accepted and 3,800 were rejected for a 75% accept rate (it doesn’t matter how many were booked). 2,000 applicants had no inquiries and of those 300 were rejected. 4,000 had 1 inquiry and 500 of those were rejected, … . Basically, you need to a cross tab for each of the variables against the Accept/Reject flag. For the Proposed A/R, rank order the population by the new score and then assume that any account that is in the bottom 25% (historical reject rate) is a reject according to the proposed model and anything in the top 75% is an accept.

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