A Data Science Central Community
When a Credit Risk Models like PAY& NO-PAY (Binary) Models are developed, we get the probability
whether an account/customer can pay. Only on Paid customers we develop
"Payment projection Scorecard"(Linear Model).
At the time of Implementation, how do we combine the information?
First Model gives P(Pay) and the second Model provides Expected Payment value?
If the customer who can pay falls in bottom decile, and if his payment
value is high, how this can be justified by using two Models?
Tags:
© 2019 AnalyticBridge.com is a subsidiary and dedicated channel of Data Science Central LLC Powered by
Badges | Report an Issue | Privacy Policy | Terms of Service