ABSTRACT
Unfortunately, in the real world, banks often have to face the problem of relatively sparse default data for many retail portfolios. Contingency issues (e.g., banks are recent market entrant for a given retail portfolio or have started to collect data only from a small period) or the nature of the product itself (e.g., mortgages) are the most common reasons. Considering that retail banking is generally based on transactional data (hard information, as Berger [2006] observes) and needs advanced credit risk management tools to be managed in an efficient way, the lack of internal data to develop or validate meaningful credit risk models must be regarded as more dangerous for retail portfolios than for nonretail ones.
