Credit risk analysis is complex because data is always scarce and not fully representative of the future. Therefore no single data set will suffice and multiple sources of information (varying from internal data to expert input) are needed. A dedicated statistical framework is required to combine these multiple sources coherently and an accomplished analyst will need to account for uncertainty in the model inputs.
At OSIS, we have created a framework based on Bayesian Statistics, which in essence is more conservative than any traditional approach. However, if many data sources of good quality are used, it does not necessarily lead to more conservative outcomes (see our use case where we updated the Basel 2 model with the default frequencies of 14 banks).
In our modeling approach we distinguish several ways credit risk is measured in the financial industry, which seem very different, but in essence are very similar:
Therefore the statistical approach of these measurements should use the same building blocks in order to get to consistent and comparable outputs.
In credit risk, there is no single historical data set which will completely calibrate a credit model. Historical data are scarce and seldom fully representative of what the future holds. Therefore on one hand it is important that different data sets can be combined in a coherent framework, on the other hand the ultimate model results in an intuitive tool that can be understood (even leaving the maths aside) and where the intuition of the user can be organised into an auditable process.