2 research outputs found
Multinomna logistiÄka regresija u kreditnom skoringu
Jedna od metoda kvantitativne kreditne analize je logistiÄka regresija kojom se klijent svrÅ”tava u
jednu od dvije kategorije, u dobre ili loŔe. Podaci o srednjim klijentima, iako postoje u bazi klijenata
svake banke, uglavnom su iskljuÄeni u procesu kreditnog skoringa. Mnoge studije do sada
nisu pokazale znaÄajnost upotrebe srednjih klijenata pri kreiranju kredit skoring modela, uglavnom
zbog toga sto je slabija granica meÄu definicijama dobrih, srednjih i loÅ”ih, te su konaÄne procjene
manje toÄne od procjena koje bi dao logistiÄki model u kojem su srednji izostavljeni.
U ovom radu ispitat Äemo znaÄajnost upotrebe srednjih na bazi klijenata jedne banke, na naÄin
da Äemo kreirati multinomni logistiÄki model u kojem ovisna varijabla ima tri kategorije, dobre,
srednje i loÅ”e, te binomni model samo s dobrim i loÅ”im klijentima. Te Äemo usporediti rezultate.One of quantitative credit analysis methods is logistic regression where the client is classified into
the one of two categories, good or bad.
Although there are data of poor clients in the database of any bank. They are generally excluded
in the process of credit scoring. Many studies so far havenāt shown the importance of using poor
clients in credit scoring model, mainly because the lower boundary between definitions of good,
poor and bad, so the final estimates are less accurate than estimates obtained by logistic model in
which poor are omitted.
In this article we will examine the significance of use poor clients of a bank, in a way that will
create a multinomial logistic model in which the dependent variable has three categories, good,
poor and bad, and the binomial model only with good and bad customers. Than the results will be
compared
Multinomna logistiÄka regresija u kreditnom skoringu
Jedna od metoda kvantitativne kreditne analize je logistiÄka regresija kojom se klijent svrÅ”tava u
jednu od dvije kategorije, u dobre ili loŔe. Podaci o srednjim klijentima, iako postoje u bazi klijenata
svake banke, uglavnom su iskljuÄeni u procesu kreditnog skoringa. Mnoge studije do sada
nisu pokazale znaÄajnost upotrebe srednjih klijenata pri kreiranju kredit skoring modela, uglavnom
zbog toga sto je slabija granica meÄu definicijama dobrih, srednjih i loÅ”ih, te su konaÄne procjene
manje toÄne od procjena koje bi dao logistiÄki model u kojem su srednji izostavljeni.
U ovom radu ispitat Äemo znaÄajnost upotrebe srednjih na bazi klijenata jedne banke, na naÄin
da Äemo kreirati multinomni logistiÄki model u kojem ovisna varijabla ima tri kategorije, dobre,
srednje i loÅ”e, te binomni model samo s dobrim i loÅ”im klijentima. Te Äemo usporediti rezultate.One of quantitative credit analysis methods is logistic regression where the client is classified into
the one of two categories, good or bad.
Although there are data of poor clients in the database of any bank. They are generally excluded
in the process of credit scoring. Many studies so far havenāt shown the importance of using poor
clients in credit scoring model, mainly because the lower boundary between definitions of good,
poor and bad, so the final estimates are less accurate than estimates obtained by logistic model in
which poor are omitted.
In this article we will examine the significance of use poor clients of a bank, in a way that will
create a multinomial logistic model in which the dependent variable has three categories, good,
poor and bad, and the binomial model only with good and bad customers. Than the results will be
compared