Most banks believe that because they can differentiate their risk at facility-level, there is not much point to develop more pooling criteria than what it is required in Basel. Therefore pooling tends to be fairly straight forward based on international best practice.

More over the page.

There are three main approaches,

1. Historical Observed PD pooling approach This key to this approach is to perform segmentation to the most granular(detail) level as possible. The selection of the risk drivers will have an direct impact to the final discrimination power of the pools, therefore choosing the pooling criteria is similar to the variables selection under a scoring approach (discrete vs continuos). Most people prefer to use their judgement in choosing the risk drivers such as account level score, customer level scoring, delinquency status, Time on book, products type, etc. The number of pools identified based on this approach is usually higher compare to the other two methods. This is because the more the risk drivers (up to a certain point) or the higher number of branches within each criteria, the better the separation powers between the pools.

2. PD model pooling approach Typically behaviour score or application score have been one of the major input of the PD model. Banks adopting this approach will build their pooling criteria for the purpose of the PD models, that is they will build different PD models based on the segment. For example, for time on book < 6mths, the bank will include application score into their PD models while for time on book >= 6 mths, they will use the behaviour score instead. Since model can be applied at account level, the objective of pooling criteria is to optimise the segment for model development instead of the discriminative of the pools.

3. Scorecard to PD pooling approach Banks can re-calibrate their scorecard model to estimate PD over the standard default definition (90dpd) and time horizon(1 year). Calibration can be done using observed default frequency over time, or by developing a single characteristic logistic regression model using the application score as the explanatory variable, and the Basel default state as the dependent variable.

This method, the score can be used as a pooling criteria such that different scorebands equate to different pools.

Will be great to hear what other are doing in the industry. Please post your comments in relation to your experiences

in this area!