The posts in this thread have retail banking in mind, which leads to an account-oriented approach for “exposures”. This approach is discussed below, and any reader contributions as to different approaches or nomenclatures will be welcome and helpful.

The unit of analysis is the “account” – a retail bank account (HL,PL,CC,..) keyed by its account_number. For credit risk purposes the only accounts of interest are those that are or can become an exposure, so although “account” would also normally apply to term deposits, we won’t be thinking of them here. The bank’s retail exposure comprises a large number of accounts, grouped up in sub-classes and pools. “Account” in this generic context therefore is understood to include credit cards, although when speaking specifically about a credit card portfolio it will be natural to think “card_number” rather than “account_number”.

The units of analysis are not the individual customers. If a customer happens to have a HL as well as a CC, that will be two separate accounts, that would fall into different sub-classes: respectively, exposures secured by residential property, and qualifying revolving retail exposures.

Why not use the terminology “exposures”? It seems to be not specific enough, as it could be interpreted to refer either to accounts or to customers, and also to groupings or pools of the same.

The analysts who grapple with the data will be familiar with the interplay between those two major dimensions of dataset organisation: accounts, and customers. Hence analysts tend to use those terms because they are so specific and so familiar.

For the most part, default analytics is account-oriented and not customer-oriented. However, overlap between these dimensions can be expected, for example:

  • when a customer defaults on any one account this condition may be cascaded across to other accounts held by the same customer, even if the default conditions had not yet been independently met on those other accounts
  • model builders may choose to segment holders of one type of product according to whether or not the customer has a certain other product (like a HL)
  • as a ‘lite’ version of the above point, a PD or LGD model might include as a predictor an indicator variable flagging whether the owner of that account also holds certain other product(s)

Outside the narrow Basel II credit risk view there would be many business reasons to take a customer-centric view rather than an account- or product-oriented approach.  

IIUC the non-retail Basel II world does not take this simple account-oriented approach, because of the greater need to consider the particular details of the exposure entity (such as SME or corporate) as a whole.  

Readers are encouraged to contribute on the above point, and a couple more below:

  • Do retail savings and/or transaction accounts qualify as exposures, if they have a zero overdraft limit, i.e. currently no approved overdraft facility?
  • Do non-recourse reverse mortgages qualify as exposures?