A Few Tricks in the Bag of a Credit Risk Modeler

I revealed solutions you can’t find in any popular textbook that I have applied for problems faced while developing credit risk models. Credit risk models predict whether a borrower will pay their loan back, and people like me in effect decide if you will be approved for a credit card or a mortgage loan. Statistical classification algorithms work best for such problems with a binary outcome. In particular, the logistic regression is preferred in practice due to its quantification of a probability of default between 0 and 1.


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