Multinomna logistička regresija u kreditnom skoringu

Abstract

Jedna od metoda kvantitativne kreditne analize je logistička regresija kojom se klijent svrštava u jednu od dvije kategorije, u dobre ili loše. Podaci o srednjim klijentima, iako postoje u bazi klijenata svake banke, uglavnom su isključeni u procesu kreditnog skoringa. Mnoge studije do sada nisu pokazale značajnost upotrebe srednjih klijenata pri kreiranju kredit skoring modela, uglavnom zbog toga sto je slabija granica među definicijama dobrih, srednjih i loših, te su konačne procjene manje točne od procjena koje bi dao logistički model u kojem su srednji izostavljeni. U ovom radu ispitat ćemo značajnost upotrebe srednjih na bazi klijenata jedne banke, na način da ćemo kreirati multinomni logistički model u kojem ovisna varijabla ima tri kategorije, dobre, srednje i loše, te binomni model samo s dobrim i lošim klijentima. Te ćemo 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 haven’t 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