Richard Vale takes us through a data challenge:
In credit risk modelling, you want to calculate the probability that a loan will default. Since different financial institutions gather different data and offer different products, there is no one-size-fits-all approach to doing this. Therefore, credit risk models are usually built using the institution’s own data. For example, if I’m building a credit risk model for XYZ Bank, I look at loans which XYZ bank has previously granted, and try to estimate the probability that a future loan will default based on principal, tenor, the borrower’s credit rating, and so on.
For those who haven’t heard of the through-the-door problem before, this is a good moment to pause and think about what is wrong with this. Why does this process contain a huge pitfall?
Click through for the answer, as well as an example of the problem and one way to get around this. H/T R-Bloggers.