Validating quantitative data model

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), you could estimate a regression model for predicting this measure and rely on it for identifying higher risk customers as well.In statistics, regression validation is the process of deciding whether the numerical results quantifying hypothesized relationships between variables, obtained from regression analysis, are acceptable as descriptions of the data.If you don't have a significant amount of losses in your portfolio to validate the model, you should be able to obtain external loss data and adjust it where necessary to better fit your organization.

Is there an obvious component of validation that I am omitting?

Hi dmanuge, I'm not suggesting firms openly publish their loss distributions (I don't think they do, not certain, but doubt it).

I do believe there are sources you can find/buy this data from.

Also, note that if you could come up with some (real-valued) "measure of riskiness" for your current data (perhaps some of the customers in your dataset are more late in their payments than others?

perhaps you have external bureau credit scores for them?

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