Application of binary logistic regression in credit scoring
| dc.contributor.advisor | Pärna, Kalev, juhendaja | |
| dc.contributor.author | Torosyan, Nare | |
| dc.contributor.other | Tartu Ülikool. Loodus- ja täppisteaduste valdkond | et |
| dc.contributor.other | Tartu Ülikool. Matemaatika ja statistika instituut | et |
| dc.date.accessioned | 2017-07-05T08:14:01Z | |
| dc.date.available | 2017-07-05T08:14:01Z | |
| dc.date.issued | 2017 | |
| dc.description.abstract | Nowadays, demand for loan products is growing day by day. Also, loan applicants have become more demanding, than they were before. They want to receive the response from bank as soon as possible. In order to resist the growing competition banks develop new quantification techniques which accelerate and automate the decision making process. One of these techniques is credit scoring. Credit Scoring is one of the most widely used instruments which is applied by lenders decide whether to approve or reject the loan application. In this Master’s thesis an overview of credit scoring is given. The most essential objective of this thesis is to show the application of logistic regression in Credit score models. Other methods of credit scoring will also be noted, but not in extensive detail. The study ends with a practical application of logistic regression for a credit scoring model on real data of loan applicants. | en |
| dc.identifier.uri | http://hdl.handle.net/10062/57102 | |
| dc.language.iso | eng | et |
| dc.publisher | Tartu Ülikool | et |
| dc.subject | credit scoring | en |
| dc.subject | logistic regression | en |
| dc.subject | krediidiskooring | et |
| dc.subject | logistiline regressioon | et |
| dc.title | Application of binary logistic regression in credit scoring | en |
| dc.type | Thesis | en |