Validation Techniques for Credit Risk Models - Applying New Methods on Nordea’s Corporate Portfolio

Abstract:   Nordea, being the largest corporate group of its kind in Northern Europe, has a great need of evaluating its customers ability to repay a debt as well as the probability of bankruptcy. The evaluation is done by different statistically derived internal rating models, based on logistic regression. The models have been developed by the use of historical data and attain good predictiveness when a lot of observational data is provided for each specific customer. In order to ameliorate the rating models, Nordea wants to implement two new validation methods, recommended by the reputable credit rating agency Moody’s: information entropy and accuracy ratio with simulated defaults. A default is a customer either being close to or being bankrupt. Information entropy measures how much information is included within a given variable, while accuracy ratio with simulated defaults validates the ability of the model to discriminate between "good" and "bad" customers when simulating default data. The simulation is used when sufficient default data does not exist, which is the case for large corporates. After the implementation of these validation methods, for the same set of data that Moody’s were given, the results that they presented could be confirmed by the chosen implementation method. This method was then used for analysis of a general set of data and it could be concluded that the use of each validation method, recommended by Moody’s, would improve the validation of the model.