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Statements

Subject Item
n2:RIV%2F00216224%3A14310%2F09%3A00036504%21RIV10-MSM-14310___
rdf:type
n15:Vysledek skos:Concept
dcterms:description
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. Parameters affecting 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). 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. Parameters affecting 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).
dcterms:title
Measuring the Quality of Credit Scoring Models Measuring the Quality of Credit Scoring Models
skos:prefLabel
Measuring the Quality of Credit Scoring Models Measuring the Quality of Credit Scoring Models
skos:notation
RIV/00216224:14310/09:00036504!RIV10-MSM-14310___
n4:aktivita
n5:S
n4:aktivity
S
n4:dodaniDat
n7:2010
n4:domaciTvurceVysledku
n10:4304373 n10:8918457
n4:druhVysledku
n16:O
n4:duvernostUdaju
n11:S
n4:entitaPredkladatele
n13:predkladatel
n4:idSjednocenehoVysledku
325127
n4:idVysledku
RIV/00216224:14310/09:00036504
n4:jazykVysledku
n12:eng
n4:klicovaSlova
Predictive modeling; Quality indexes; Credit scoring; Normally distributed scores
n4:klicoveSlovo
n6:Predictive%20modeling n6:Credit%20scoring n6:Quality%20indexes n6:Normally%20distributed%20scores
n4:kontrolniKodProRIV
[719B096C00AD]
n4:obor
n14:BB
n4:pocetDomacichTvurcuVysledku
2
n4:pocetTvurcuVysledku
2
n4:rokUplatneniVysledku
n7:2009
n4:tvurceVysledku
Řezáč, František Řezáč, Martin
n8:organizacniJednotka
14310