Heuristic models attempt to take experiences and use them as a basis to methodically gain new insights. These experiences can stem from:
Conjectured business interrelationships
Subjective practical experiences and observations
Business theories related to specific aspects
In credit assessment, therefore, these models constitute an attempt to use experience in the lending business to make statements as to the future creditworthiness of a borrower.
The quality of heuristic models thus depends on how accurately they depict the subjective experience of credit experts.
Therefore, not only the factors relevant to creditworthiness are determined heuristically, but their influence and weight in overall assessments are also based on subjective experience.
In the development of these rating models, the factors used do not undergo statistical validation and optimization.
In practice, heuristic models are often grouped under the heading of expert systems
Empirical statistical models, by contrast:
Try to assess a borrower’s credit standing on the basis of objectifying processes.
For this purpose, certain credit review criteria of the exposure under review are compared to the existing database which was established empirically.
This comparison makes it possible to classify the credit exposure.
The goodness of fit of an empirical statistical model depends to a great extent on the quality of the database used in developing the system. First, the database must be sufficiently large to allow significant findings.
In addition, it must be ensured that the data used also represent the credit institution’s future business adequately.