A new dynamic modeling framework for credit risk assessment

dc.contributor.author Sousa,MR en
dc.contributor.author João Gama en
dc.contributor.author Brandao,E en
dc.date.accessioned 2018-01-03T10:35:36Z
dc.date.available 2018-01-03T10:35:36Z
dc.date.issued 2016 en
dc.description.abstract We propose a new dynamic modeling framework for credit risk assessment that extends the prevailing credit scoring models built upon historical data static settings. The driving idea mimics the principle of films, by composing the model with a sequence of snapshots, rather than a single photograph. In doing so, the dynamic modeling consists of sequential learning from the new incoming data. A key contribution is provided by the insight that different amounts of memory can be explored concurrently. Memory refers to the amount of historic data being used for estimation. This is important in the credit risk area, which often seems to undergo shocks. During a shock, limited memory is important. Other times, a larger memory has merit. An application to a real-world financial dataset of credit cards from a financial institution in Brazil illustrates our methodology, which is able to consistently outperform the static modeling schema. en
dc.identifier.uri http://repositorio.inesctec.pt/handle/123456789/5314
dc.identifier.uri http://dx.doi.org/10.1016/j.eswa.2015.09.055 en
dc.language eng en
dc.relation 5120 en
dc.rights info:eu-repo/semantics/openAccess en
dc.title A new dynamic modeling framework for credit risk assessment en
dc.type article en
dc.type Publication en
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