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gyambqt
Obsidian | Level 7

hi experts,

I am quite new to scorecard. I am generating a scorecard model to predict fraud application using  scorecard node in EM. The result obtained from one input variable as following:

 

Variable Name:                   Group                scorecard point       weight of evidence             event rate (fraud)  

 

delivery_date  1                         1                          24                         -1.82                                    52.75%  

                        0,  missing          2                          4                             0.06                                   14.56% 

                        2+                       3                          32                           -2.63                                   71.38% 

 

 

coefficent

0.36

0.36

0.36

 

My understanding of the scorecard point indicates how likely the appliciation become fraud. The low scorecard indicate risky application. However this is not the case showed by the above example,delievery_date= 2+ is receiving very high scorecard(mean low risk) at the fraud rate of 71.38% and delievery_date=0 or missing has very low scorecard point of 4 ( high risk application) however only having 14.56% of its application become fraud.

 

If we have an application with delivery_date=0 then it will be given a total score=4 (assume delivery_date WOE is only input variable).

Will that misclassify that application to high risk?

 

 

Thanks

 

 

 

2 REPLIES 2
WendyCzika
SAS Employee

Typically when you have this -- a positive regression coefficient and thus the scorecard points going in the opposite direction than you would expect -- it is due to collinearity among inputs.  So there might be another input highly correlated with delivery_date.  Are you using model selection for the regression model?  That could help eliminate similar inputs.

gyambqt
Obsidian | Level 7

A lot of appreciation for your reply. I have used logistical regression with stepwise selection in the scorecard node.

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