Estimating Information Value for Credit Scoring Models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F11%3A00052735" target="_blank" >RIV/00216224:14310/11:00052735 - 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

Estimating Information Value for Credit Scoring Models

Popis výsledku v původním jazyce

The paper deals with the Information value, which enjoy high popularity in the industry. Commonly it is computed by discretisation of data into intervals using deciles. One constraint is required to be met in this case. Number of cases have to be nonzerofor all intervals. If this constraint is not fulfilled there are some issues to solve for preserving reasonable results. To avoid these computational issues, I proposed an alternative algorithm for estimating the Information value, named the empirical estimate with supervised interval selection. This advanced estimate is based on requirement to have at least k, where k is a positive integer, observations of scores of both good and bad clients in each considered interval. Simulation study with normallydistributed scores shows high dependency on choise of the parameter k. If we choose too small value, we get overestimated value of the Information value, and vice versa. The quality of the estimate was assessed using MSE.

Název v anglickém jazyce

Estimating Information Value for Credit Scoring Models

Popis výsledku anglicky

The paper deals with the Information value, which enjoy high popularity in the industry. Commonly it is computed by discretisation of data into intervals using deciles. One constraint is required to be met in this case. Number of cases have to be nonzerofor all intervals. If this constraint is not fulfilled there are some issues to solve for preserving reasonable results. To avoid these computational issues, I proposed an alternative algorithm for estimating the Information value, named the empirical estimate with supervised interval selection. This advanced estimate is based on requirement to have at least k, where k is a positive integer, observations of scores of both good and bad clients in each considered interval. Simulation study with normallydistributed scores shows high dependency on choise of the parameter k. If we choose too small value, we get overestimated value of the Information value, and vice versa. The quality of the estimate was assessed using MSE.

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

Aplimat - Journal of applied mathematics

ISSN

1337-6365

e-ISSN

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Svazek periodika

4

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

SK - Slovenská republika

Počet stran výsledku

10

Strana od-do

1619-1628

Kód UT WoS článku

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EID výsledku v databázi Scopus

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