Rating Models and Validation - Oesterreichische Nationalbank

adapts the network according to any deviations it finds. Probably the most commonly
used method of making such changes in networks is the adjustment of
weights between neurons. These weights indicate how important a piece of
information is considered to be for the networkÕs output. In extreme cases,
the link between two neurons will be deleted by setting the corresponding
weight to zero.
A classic learning algorithm which defines the procedure for adjusting
weights is the back-propagation algorithm. This term refers to Òa gradient
descent method which calculates changes in weights according to the errors
made by the neural network.Ó 30
In the first step, output results are generated for a number of data records.
The deviation of the calculated output od from the actual output td is measured
using an error function. The sum-of-squares error function is frequently
used in this context:
e ¼ 1 X
ðtd odÞ
2
2
d
The calculated error can be back-propagated and used to adjust the relevant
weights. This process begins at the output layer and ends at the input layer. 31
When training an artificial neural network, it is important to avoid what is
referred to as overfitting. Overfitting refers to a situation in which an artificial
neural network processes the same learning data records again and again until it
begins to recognize and ÒmemorizeÓ specific data structures within the sample.
This results in high discriminatory power in the learning sample used, but low
discriminatory power in unknown samples. Therefore, the overall sample used
in developing such networks should definitely be divided into a learning, testing
and a validation sample in order to review the networkÕs learning success using
ÒunknownÓ samples and to stop the training procedure in time. This need to
divide up the sample also increases the quantity of data required.
Application of Artificial Neural Networks
Neural networks are able to process both quantitative and qualitative data
directly, which makes them especially suitable for the depiction of complex rating
models which have to take various information categories into account.
Although artificial neural networks regularly demonstrate high discriminatory
power and do not involve special requirements regarding input data, these rating
models are still not very prevalent in practice. The reasons for this lie in the
complex network modeling procedures involved and the Òblack boxÓ nature of
these networks. As the inner workings of artificial neural networks are not
transparent to the user, they are especially susceptible to acceptance problems.
One example of an artificial neural network used in practice is the BBR
(Baetge-Bilanz-Rating â BP-14 used for companies which prepare balance
sheets. This artificial neural network uses 14 different figures from annual financial
statements as input parameters and compresses them into an ÒN-score,Ó on
the basis of which companies are assigned to rating classes.
30 See HEITMANN, C., Neuro-Fuzzy, p. 85.
31 Cf. HEITMANN, C., Neuro-Fuzzy, p. 86ff.
Rating Models and Validation
Guidelines on Credit Risk Management 47