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In retail banking the credit risk of a creditor after the credit had been granted is often modeled using behavioral credit scoring. In this setting the customer already has an account (or a few) and the bank can observe inflow, outflows, arrears and advances and use such information to assess the credibility of the customer. We can think of loans but the setting also applies to current accounts with the possibility to use overdraft.

For the capital requirement we have to consider default within the forthcoming 12 months. To this aim we can think of risk factors observed at some point before the reference date and the outcome default/non-default.

But there are many possible choices for when to observe the risk factors. Thus, what I was wondering is whether there are references or best practice examples on how to choose the observations.

Say we develop a model on the years 2014-2018.

  • Then there are customers that never defaulted and I can use several one year periods for observations of non-defaults.
  • On the other hand I have defaulted customers. In such cases I can use observations before this very default. But which ones? 12 months before, 11 or 10? What if the customer defaults just (e.g.) 6 months after opening the account.

The target to model "default during the forthcoming year" leaves open many degrees of freedom.

Could anyone please point me to references where the structure of observations on the data set where the model is developed is clearly justified?

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