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dc.contributor.advisorTyssedal, John Sølve
dc.contributor.authorHarto, Anine
dc.date.accessioned2018-09-13T14:00:52Z
dc.date.available2018-09-13T14:00:52Z
dc.date.created2018-06-13
dc.date.issued2018
dc.identifierntnudaim:20057
dc.identifier.urihttp://hdl.handle.net/11250/2562556
dc.description.abstractMonthly customers are sent to debt collection, where most of them are able to pay the demanded amount and are therefore reinstated as normal customers. However, some do not. These are the ones that constitute the main loss. The problem at hand is therefore to build a model which segments customers into three (or more) groups: the ones who most probably will pay (green), the ones who most probably will not pay (red), and the ones in between (yellow). Obtaining such a model could play an important role in turning yellow customers into green, which yields great profit. Furthermore, a sensitivity analysis of the obtained models will be performed.
dc.languageeng
dc.publisherNTNU
dc.subjectFysikk og matematikk, Industriell matematikk
dc.titlePredicting Recovery Rates of Defaulted Credit Card Accounts
dc.typeMaster thesis


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