Rating Models and Validation - Oesterreichische Nationalbank

The best-practice segmentation presented here on the basis of individual
loans and credit facilities for retail customers reflects customary practice in
banks, that is, scoring procedures for calculating the PD of individual customers
usually already exist in the retail customer segment.
The draft EU directive contains provisions which ease the burden of risk
measurement in the retail customer segment. For instance, retail customers
do not have to be assessed individually using rating procedures; they can be
assigned to pools according to specific borrower and product characteristics.
The risk components PD, LGD, and EAD are estimated separately for these
pools and then assigned to the individual borrowers in the pools.
Although the approach provided for in Basel II is not discussed in greater
detail in this document, this is not intended to restrict a bankÕs alternative
courses of action in any way. A pool approach can serve as an alternative or
a supplement to best practices in the retail segment.
2 Best-Practice Data Requirements for
Credit Assessment
The previous chapter pointed out the necessity of defining segments for credit
assessment and presented a segmentation approach which is commonly used in
practice. Two essential reasons for segmentation are the different factors relevant
to creditworthiness and the varying availability of data in individual segments.
The relevant data and information categories are presented below with
attention to their actual availability in the defined segments. In this context,
the data categories indicated for individual segments are to be understood as
part of a best-practice approach, as is the case throughout this document. They
are intended not as compulsory or minimum requirements, but as an orientation
aid to indicate which data categories would ideally be included in rating
development. In our discussion of these information categories, we deliberately
confine ourselves to a highly aggregated level. We do not attempt to present
individual rating criteria. Such a presentation could never be complete due
to the huge variety of possibilities in individual data categories. Furthermore,
these guidelines are meant to provide credit institutions with as much latitude
as possible in developing their own rating models.
The data necessary for all segments can first be subdivided into three data
types:
Quantitative Data/Information
This type of data generally refers to objectively measurable numerical values.
The values themselves are categorized as quantitative data related to the
past/present or future. Past and present quantitative data refer to actual
recorded values; examples include annual financial statements, bank account
activity data, or credit card transactions.
Future quantitative data refer to values projected on the basis of actual
numerical values. Examples of these data include cash flow forecasts or budget
calculations.
Rating Models and Validation
Guidelines on Credit Risk Management 11