Measuring the Quality of Credit Scoring Models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F09%3A00036504" target="_blank" >RIV/00216224:14310/09:00036504 - 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 the Quality of Credit Scoring Models

Popis výsledku v původním jazyce

In the current strong competitive environment it is quite fundamental good care of the quality of client portfolio. Credit scoring models are widely used to achieve this business aim. For a measurement of quality of the scoring models it is possible to use quantitative indexes such as Gini index, K-S statistics, Lift, Mahalanobis distance and Information statistics. They can be used for comparison of several developed models at the moment of development. It is possible to use them for monitoring of quality of models after the deployment into real business as well. Figures like ROC curve (Lorenz curve), Lift chart (Gains chart) can be used as well. This paper deals with definition of good/bad client, which is crucial for further computations. Parametersaffecting this definition are discussed. The main part is devoted to quality indexes based on distribution functions (Gini, K-S and Lift) and on density functions (Mahalanobis distance, Information statistics).

Název v anglickém jazyce

Measuring the Quality of Credit Scoring Models

Popis výsledku anglicky

In the current strong competitive environment it is quite fundamental good care of the quality of client portfolio. Credit scoring models are widely used to achieve this business aim. For a measurement of quality of the scoring models it is possible to use quantitative indexes such as Gini index, K-S statistics, Lift, Mahalanobis distance and Information statistics. They can be used for comparison of several developed models at the moment of development. It is possible to use them for monitoring of quality of models after the deployment into real business as well. Figures like ROC curve (Lorenz curve), Lift chart (Gains chart) can be used as well. This paper deals with definition of good/bad client, which is crucial for further computations. Parametersaffecting this definition are discussed. The main part is devoted to quality indexes based on distribution functions (Gini, K-S and Lift) and on density functions (Mahalanobis distance, Information statistics).

Druh

O - Ostatní výsledky

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

2009

Kód důvěrnosti údajů

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