credit scorecard problems

[ManOfHonor]

Hi, guys.

My goal is to get a new scorecard better than the old one. But on test month it has higher bad rate and lower approval...

What am I doing wrong?

I try to build a scorecard model in Enterprise Miner: diagram

 I keep the only non-correlating predictors:variable correlation

 It seems my variables are pretty valuable:from scorecard results before reject inference

from interactive grouping before reject inference

And I meet several problems:

1) very few target events:def_6_30 - overdue 30+ on 6 months

 to overcome this limitaion I involved frequency variable, but I suspect model to bias:

  2) cannot get stable model

before reject inference

3) train / validation gini varies drastically: before reject inference, data partition 50/50 stratified, train gini=0.52, validation gini=0.49

before reject inference

And some questions:

1) How to estimate bad rate and approval of scorecard model?

All I need  - is to improove old scorecard model, to archive this I tried to exclude predictors (start from the lowest information value) and add new ones (with high IV) . Honestly speaking have no other ides of doing that.

P.s. I used Naim Siddiqui's  book and "developing credit scorecards using credit scoring for sas"

[Ksharp]

Your bad percent is too small. I think your Logistic model would suffer Overdisperse Problem.

Why not using Oversample like good:bad= 1:1    or 2:1  .

[ManOfHonor]

What node do I use to implement this?

[Ksharp]

Sorry . I never use EM. but the following could oversample.

proc surveyselect data=have sampsize=(1000 1000) out=data_oversample;

strata good_bad;

run;

Note: assuming you have 1000 bad obs.