Mastering Risk Management

DATA REQUIREMENTS: USING ALL FOUR DATA SETS FOR YOUR MODELLING

Having looked at the business benefits and uses of modelling output, it is important to look at the other end of the process, i.e. the input data. Modelling risk management is an inherently difficult process as the quality and quantity of data varies enormously. For example, a firm will have large amounts of data on the creditworthiness of its customers. It will have very little information on internal fraud and probably some, but not a large amount, of data on transactional errors. In short, while the data relating to the financial side of the business are likely to be of sufficient quantity and quality for modelling, the data relating to the non-financial side are clearly suspect ...