Influence of variable interactions versus segmentation in credit scoring: a case study

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F12%3A00060319" target="_blank" >RIV/00216224:14310/12:00060319 - isvavai.cz</a>

Result on the web

<a href="http://www.sas.com/events/analytics/europe/pres/rezac.pdf" target="_blank" >http://www.sas.com/events/analytics/europe/pres/rezac.pdf</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Influence of variable interactions versus segmentation in credit scoring: a case study

Original language description

Credit scoring is a set of predictive models and their underlying techniques that aid financial institutions in the granting of credit. These techniques decide who will get credit, how much credit they should get, and what further strategies will enhancethe profitability of borrowers to lenders. The industry standard for modeling the probability of client default is the logistic regression. However, details such as inclusion of interactions of predictors or segmentation of given data sample may determine the success. The paper will present the influence of inclusion of variable interactions versus segmentation on quality of a credit scoring model.

Czech name

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Czech description

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Type

O - Miscellaneous

CEP classification

BB - Applied statistics, operational research

OECD FORD branch

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Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2012

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů