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"