Credit scoring has been described as the most successful application of statistical and operational research methods to financial problems in recent decades. In this thesis methods analogous to those used in spatial modelling and prediction are applied to the problem of application scoring, a branch of credit scoring that involves deciding whether or not to offer credit and how much credit to offer. In particular, Gaussian spatial process (GSP) models, commonly employed in disease mapping, geostatistics and design, are explored in an approach that is novel in the credit scoring field. Credit scoring methods are well established and usually involve computations of scores. By contrast, the focus of this work is to use best linear unbiased predictors in order to predict the probabilities of repayment for credit applications. A spatial structure for the model is provided by reformulating the data. Both theoretical and industry standard methods are used in order to assess the predictive competence of GSP models. In addition, the GSP model approach is compared with standard methods for application scoring and conclusions are made regarding the relevance of such models in this area
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:484771 |
| Date | January 2006 |
| Creators | Aldgate, Hannah Jane |
| Contributors | Crowder, Martin ; Adams, Niall ; Hand, David |
| Publisher | Imperial College London |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/10044/1/1256 |
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