Rating Models and Validation - Oesterreichische Nationalbank

Rating Models and Validation
The system transforms all of the individual creditworthiness characteristics
into grades and then combines them to yield an overall grade. This involves two
steps: First the system compresses grades from an individual information category
into a partial grade by calculating a weighted average. The weights used
here were determined on the basis of expert surveys. Then the system aggregates
individual assessments to generate an overall assessment. The aggregation
process uses the expert systemÕs hierarchical aggregation rules, which the credit
analyst cannot influence.
3.1.4 Fuzzy Logic Systems
Fuzzy logic systems can be seen as a special case among the classic expert systems
described above, as they have the additional ability to evaluate data using fuzzy
logic. In a fuzzy logic system, specific values entered for creditworthiness criteria
are no longer allocated to a single linguistic term (e.g. Òhigh,Ó ÒlowÓ); rather they
can be assigned to multiple terms using various degrees of membership.
For example, in a classic expert system the credit analyst could be required to
rate a return on equity of 20% or more as good and a return on equity of less than
20% as Òpoor.Ó However, such dual assignments are not in line with human assessment
behavior. A human decision maker would never rate a return on equity of
19.9% as ÒlowÓ and a return on equity of 20.0% as ÒhighÓ at the same time.
Fuzzy logic systems thus enable a finer gradation which bears more similarity
to human decision-making behavior by introducing linguistic variables. The basic
manner in which these linguistic variables are used is shown in chart 12.
18 Adapted from HEITMANN, C., Neuro-Fuzzy, p. 47.
Chart 12: Example of a Linguistic Variable 18
38 Guidelines on Credit Risk Management