ESIS2 - Information Value Estimator for Credit scoring models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F15%3A00081966" target="_blank" >RIV/00216224:14310/15:00081966 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/s10614-014-9424-0" target="_blank" >http://dx.doi.org/10.1007/s10614-014-9424-0</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10614-014-9424-0" target="_blank" >10.1007/s10614-014-9424-0</a>

Jazyk výsledku

angličtina

Název v původním jazyce

ESIS2 - Information Value Estimator for Credit scoring models

Popis výsledku v původním jazyce

Information value is widely used to assess discriminatory power of credit scoring models, i.e. models that try to predict a probability of client?s default. Moreover it is very often used to assess the discriminatory power of variables that enter into these models. This means that the Information value is used as a filter for variable selection. However, empirical estimate using deciles of scores, which is the common way how to compute it, may lead to strongly biased results. The main aim of this paperis to give an alternative estimator of the Information value, named ESIS2, which leads to lowered bias and MSE. The implication of this is better credit scoring model. And what is essential, the direct consequence of having better credit scoring model issignificantly higher profitability of credit business.

Název v anglickém jazyce

ESIS2 - Information Value Estimator for Credit scoring models

Popis výsledku anglicky

Information value is widely used to assess discriminatory power of credit scoring models, i.e. models that try to predict a probability of client?s default. Moreover it is very often used to assess the discriminatory power of variables that enter into these models. This means that the Information value is used as a filter for variable selection. However, empirical estimate using deciles of scores, which is the common way how to compute it, may lead to strongly biased results. The main aim of this paperis to give an alternative estimator of the Information value, named ESIS2, which leads to lowered bias and MSE. The implication of this is better credit scoring model. And what is essential, the direct consequence of having better credit scoring model issignificantly higher profitability of credit business.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

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Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název periodika

Computational Economics

ISSN

0927-7099

e-ISSN

—

Svazek periodika

45

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

20

Strana od-do

303-322

Kód UT WoS článku

000348416500007

EID výsledku v databázi Scopus

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