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In the financial service industry, discriminant analysis and its variants based upon binary outcome, such as logistic regression or neural networks, are largely used to develop predictive models. However, the two-state assumption of such models over-simplifies customers' behavioral outcomes and ignores the existence of multi-level risk. In many situations, the financial impact of a certain customer is directly related to the frequency and the severity of his/her adverse behaviors. Therefore, it is of interest to model and predict such multi-level risks. Several modeling techniques, from Poisson to Ordered Logit models, have been widely discussed in numerous research literatures about how to predict the multi-level risks.

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Format:PDFSize:110 KB
Date:Feb 2009
Pages:13

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