Rating Models and Validation - Oesterreichische Nationalbank

Rating Models and Validation
able as a basis for defining the starting point of the data history. However, it is
necessary to ensure that these cases are indeed good cases, that is, that they do
not default within the forecasting horizon after the information is entered. For
good cases, therefore, it is only possible to use information which was available
within the bank at least 12 months before data collection began (¼ 1st cutoff
date for good cases). Only in this way is it possible to ensure that no credit
default has occurred (or will occur) over the forecasting period. Analogous
principles apply to forecasting periods of more than 12 months. If the interval
between the time when the information becomes available and the start of data
collection is shorter than the defined forecasting horizon, the most recently
available information cannot be used in the analysis.
If dynamic indicators (i.e. indicators which measure changes over time) are to
be defined in rating model development, quantitative information on at least
two successive and viable cutoff dates have to be available with the corresponding
time interval.
When data are compiled from various information categories in the data
record for a specific cutoff date, the timeliness of the information may vary.
For example, bank account activity data are generally more up to date than qualitative
information or annual financial statement data. In particular, annual
financial statements are often not available within the bank until 6 to 8 months
after the balance sheet date.
In the organization of decentralized data collection, it is important to define
in advance how many good and bad cases each bank is to supply for each cutoff
date. For this purpose, it is necessary to develop a stringent data collection
process with due attention to possible time constraints. It is not possible to
make generally valid statements as to the time required for decentralized data
collection because the collection period depends on the type of data to be collected
and the number of banks participating in the pool. The workload placed
on employees responsible for data collection also plays an essential role in this
context. From practical experience, however, we estimate that the collection of
qualitative and quantitative data from 150 banks on 15 cases each (with a data
history of at least 2 years) takes approximately four months.
In this context, the actual process of collecting data should be divided into
several blocks, which are in turn subdivided into individual stages. An example
of a data collection process is shown in chart 31.
Chart 31: Example of a Data Collection Process
68 Guidelines on Credit Risk Management