Credit scoring by logistic regression
dc.contributor.advisor | Pärna, Kalev, juhendaja | |
dc.contributor.author | Tabagari, Salome | |
dc.contributor.other | Tartu Ülikool. Matemaatika-informaatikateaduskond | et |
dc.contributor.other | Tartu Ülikool. Matemaatilise statistika instituut | et |
dc.date.accessioned | 2015-07-08T12:06:03Z | |
dc.date.available | 2015-07-08T12:06:03Z | |
dc.date.issued | 2015 | |
dc.description.abstract | Today banking business’ most successful products are loans and credits given to the clients. In order to make a decision whether to accept or reject a loan application banks gather information from applicants. In the past, decision was made by individual bank’s expert. It was not efficient way for banks, because competition was growing, thus they introduced better method – credit scoring. Credit scoring is one of the most effective and successful methods in finance and banking. With help of credit scoring methodology it is easier to make correct and fast decisions. An overview of the credit scoring is given in the following thesis. A real data set is used to demonstrate how to calculate applicants’ scores. For this purpose one of the most frequently used statistical method- logistic regression – is used. | et |
dc.identifier.uri | http://hdl.handle.net/10062/47572 | |
dc.language.iso | en | et |
dc.publisher | Tartu Ülikool | et |
dc.subject | laenutaotluste hindamine | et |
dc.subject | logistiline regressioon | et |
dc.subject.other | magistritööd | et |
dc.subject.other | credit scoring | en |
dc.subject.other | logistic regression | en |
dc.title | Credit scoring by logistic regression | et |
dc.type | Thesis | et |