Credit scoring by segmented modelling
| dc.contributor.advisor | Pärna, Kalev, juhendaja | |
| dc.contributor.author | Özay, Tevfik Can | |
| dc.contributor.other | Tartu Ülikool. Loodus- ja täppisteaduste valdkond | et |
| dc.contributor.other | Tartu Ülikool. Matemaatika ja statistika instituut | et |
| dc.date.accessioned | 2016-07-08T06:50:50Z | |
| dc.date.available | 2016-07-08T06:50:50Z | |
| dc.date.issued | 2016 | |
| dc.description.abstract | This study is devoted to small loan evaluation modelling which is known as credit scoring. Credit scoring models help the decision takers (such as credit offices, banks …) decide customers’ creditworthiness in short time without prejudice. Main goal of this master thesis was to understand feasibility and effectiveness of credit scoring model by using logistic regression technique and obtaining important variables for credits scoring models. Furthermore we targeted to reveal how segmentation (creating different score cards for different age groups) can help to predict more accurately. In this study, we worked with real data which was provided by local company in Estonia. In conclusion, our results showed that credit scoring by logistic regression helped to discriminate good customers effectively and the use of segmentation improves the model’s accuracy. | en |
| dc.identifier.uri | http://hdl.handle.net/10062/52455 | |
| dc.language.iso | en | en |
| dc.subject | regression analysis | en |
| dc.subject | logistic regression | en |
| dc.subject | credit scoring | en |
| dc.subject | krediidiskooring | et |
| dc.subject | regressioonanalüüs | et |
| dc.subject | logistiline regressioon | et |
| dc.subject.other | magistritööd | et |
| dc.title | Credit scoring by segmented modelling | en |
| dc.type | Thesis | en |