Improved Credit Scoring with Multilevel Statistical Modelling
| dc.contributor.advisor | Krämer, Walter | |
| dc.contributor.author | Khudnitskaya, Alesia S. | |
| dc.contributor.referee | Recht, Peter | |
| dc.date.accepted | 2010-12-17 | |
| dc.date.accessioned | 2011-01-18T13:45:47Z | |
| dc.date.available | 2011-01-18T13:45:47Z | |
| dc.date.issued | 2011-01-18 | |
| dc.description.abstract | This dissertation introduces a new type of credit scoring model which assesses credit worthiness of applicants for a loan by forecasting their probability of default. The multilevel scorecard is an improved alternative to the conventional scoring techniques which are regularly applied in retail banking such as discriminant analysis, decision trees and logistic regression scorecards. In addition, this thesis proposes a new way of data clustering for a multilevel structure which is more intuitive and relevant for efficient credit worthiness assessment. The motivation for the topic and the core idea of this research project are closely related to the main advantages of improved credit scoring and its application to the decisionmaking process in retail banking. The main advantages are accuracy gain and cost-saving. Improving credit scoring techniques helps to increase operating efficiency by increasing predictive quality and reducing misclassification errors. From the cost-saving prospective, it also leads to profit growth and gives a higher return on capital. In credit scoring the main goal is to define factors which influence riskiness of individuals who apply for a bank loan. Accordingly, the thesis develops two types of multilevel structures which allow including random-effects at the higher-level of the hierarchy. The first structure nests applicants for a loan within second-level groups, microenvironments. Each microenvironment determines the living area of a borrower with a particular combination of socio-economic and demographic conditions. Microenvironment-specific effects impact the riskiness of borrowers additionally to the observed personal characteristics. The second type of multilevel structure extends the first. It cross-classifies individuals with different classifications according to similarities in particular characteristics of their occupational activities, living area condition and infrastructure of shopping facilities in their residence areas. | en |
| dc.identifier.uri | http://hdl.handle.net/2003/27572 | |
| dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-257 | |
| dc.language.iso | en | de |
| dc.subject | Credit scoring | en |
| dc.subject | Logistic regression scorecards | en |
| dc.subject | Retail banking | en |
| dc.subject.ddc | 310 | |
| dc.title | Improved Credit Scoring with Multilevel Statistical Modelling | en |
| dc.type | Text | de |
| dc.type.publicationtype | doctoralThesis | de |
| dcterms.accessRights | open access |