Rating Models and Validation - Oesterreichische Nationalbank

Rating Models and Validation
The result from the inference engine is therefore transformed into a clear and
distinct credit rating in the process of defuzzification.
Applied example:
The Deutsche Bundesbank uses a fuzzy logic system as a module in its credit
assessment procedure. 20
The BundesbankÕs credit assessment procedure for corporate borrowers first
uses industry-specific discriminant analysis to process figures from annual financial
statements and qualitative characteristics of the borrowerÕs accounting practices.
The resulting overall indicator is adapted using a fuzzy logic system which
processes additional qualitative data (see chart 14).
Chart 14: Architecture of Deutsche BundesbankÕs Credit Assessment Procedure 21
In this context, the classification results for the sample showed that the error
rate dropped from 18.7% after discriminant analysis to 16% after processing
with the fuzzy logic system.
3.2 Statistical Models
While heuristic credit assessment models rely on the subjective experience of
credit experts, statistical models attempt to verify hypotheses using statistical
procedures on an empirical database.
For credit assessment procedures, this involves formulating hypotheses concerning
potential creditworthiness criteria: These hypotheses contain state-
20 Cf. BLOCHWITZ, STEFAN/EIGERMANN, JUDITH, Bonita‹tsbeurteilungsverfahren der Deutschen Bundesbank.
21 Adapted from BLOCHWITZ, STEFAN/EIGERMANN, JUDITH, Bonita‹tsbeurteilungsverfahren der Deutschen Bundesbank.
40 Guidelines on Credit Risk Management