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- W36215507 abstract "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. Our paper is also an attempt contributed to this end. Several modeling strategies together with their SAS implementations and related scoring scheme will be illustrated. Our purpose is to demonstrate an application of these complex statistical models with the business touch and how to implement them in a production environment." @default.
- W36215507 created "2016-06-24" @default.
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- W36215507 date "2009-01-01" @default.
- W36215507 modified "2023-09-26" @default.
- W36215507 title "A CLASS OF PREDICTIVE MODELS FOR MULTI-LEVEL RISKS" @default.
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