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dc.creatorMartin, Stephen Fredrick
dc.date.accessioned2012-08-16T14:46:20Z
dc.date.available2012-08-16T14:46:20Z
dc.date.created2012-05
dc.date.issued2012-08-16
dc.date.submittedMay 2012
dc.identifier.urihttp://hdl.handle.net/2152/ETD-UT-2012-05-5428
dc.descriptiontext
dc.description.abstractThis goal of this project is to develop a set of business rules to mitigate risk related to a specific financial decision within the prepaid debit card industry. Under certain circumstances issuers of prepaid debit cards may need to decide if funds on hold can be released early for use by card holders prior to the final transaction settlement. After a brief introduction to the prepaid card industry and the financial risk associated with the early release of funds on hold, the paper presents the motivation to apply the CART (Classification and Regression Trees) method. The paper provides a tutorial of the CART algorithms formally developed by Breiman, Friedman, Olshen and Stone in the monograph Classification and Regression Trees (1984), as well as, a detailed explanation of the R programming code to implement the RPART function. (Therneau 2010) Special attention is given to parameter selection and the process of finding an optimal solution that balances complexity against predictive classification accuracy when measured against an independent data set through a cross validation process. Lastly, the paper presents an analysis of the financial risk mitigation based on the resulting business rules.
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.subjectCART
dc.subjectClassification and Regression Trees
dc.subjectBreiman
dc.subjectRisk
dc.subjectPrepaid
dc.subjectDebit cards
dc.subjectRollback
dc.subjectR
dc.subjectRPART
dc.subjectCross validation
dc.titleApplying Classification and Regression Trees to manage financial risk
dc.date.updated2012-08-16T14:46:30Z
dc.identifier.slug2152/ETD-UT-2012-05-5428
dc.description.departmentMathematics
dc.type.genrethesis*
thesis.degree.departmentMathematics
thesis.degree.disciplineStatistics
thesis.degree.grantorUniversity of Texas at Austin
thesis.degree.levelMasters
thesis.degree.nameMaster of Science in Statistics


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