Abstract
SYNOPTIC ABSTRACTThe retail banking and consumer credit industry is experiencing great change. New challenges, some arising from tougher competition, some from the accumulation of huge data sets, some from the requirements to market new kinds of product, and some from the demands of customers, are appearing. Examples of such challenges are outlined. Confronting them requires statistical tools beyond the staples of linear models. We outline such new tools, including neural networks, recursive partitioning methods, indirect supervised classification, generalized linear models, generalized additive models, multivariate adaptive regression splines, methods of longitudinal data analysis, path analysis, LISREL models and Bayesian belief networks, survival analysis, Markov transition models, and the various tools of data mining.
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