Measuring Quality of Scoring Models Using Information Value

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F11%3A00052733" target="_blank" >RIV/00216224:14310/11:00052733 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Measuring Quality of Scoring Models Using Information Value

Popis výsledku v původním jazyce

Many statistical tools are avaiable for measuring quality, within the meaning of the predictive power, of credit scoring models. Because it is impossible to use a scoring model effectively without knowing how good it is, quality indexes like Gini, Kolmogorov-Smirnov statisic and Information value are used to assess quality of given credit scoring model. The paper describes a selection of indexes that enjoy high popularity in the industry. However, it deals primarily with the Information value. Commonlyit is computed by discretisation of data into bins using deciles. One constraint is required to be met in this case. Number of cases have to be nonzero for all bins. If this constraint is not fulfilled there are some practical procedures for preserving finite results. As an alternative method to the empirical estimates one can use the kernel smoothing theory. The main contribution of this paper is a proposition of the empirical estimate with supervised interval selection.

Název v anglickém jazyce

Measuring Quality of Scoring Models Using Information Value

Popis výsledku anglicky

Many statistical tools are avaiable for measuring quality, within the meaning of the predictive power, of credit scoring models. Because it is impossible to use a scoring model effectively without knowing how good it is, quality indexes like Gini, Kolmogorov-Smirnov statisic and Information value are used to assess quality of given credit scoring model. The paper describes a selection of indexes that enjoy high popularity in the industry. However, it deals primarily with the Information value. Commonlyit is computed by discretisation of data into bins using deciles. One constraint is required to be met in this case. Number of cases have to be nonzero for all bins. If this constraint is not fulfilled there are some practical procedures for preserving finite results. As an alternative method to the empirical estimates one can use the kernel smoothing theory. The main contribution of this paper is a proposition of the empirical estimate with supervised interval selection.

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

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2011

Kód důvěrnosti údajů

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

Název periodika

Journal of Communication and Computer

ISSN

1548-7709

e-ISSN

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Svazek periodika

8

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

6

Strana od-do

234-239

Kód UT WoS článku

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EID výsledku v databázi Scopus

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