engleski

Modeling customer revolving credit scoring using logistic regression, survival analysis and neural networks

The aim of the paper is to discuss credit scoring modeling of a customer revolving credit depending on customer application data and transaction behavior data. Logistic regression, survival analysis, and neural network credit scoring models were developed in order to assess relative importance of different variables in predicting the default of a customer. Three neural network algorithms were tested: multilayer perceptron, radial basis and probabilistic. The radial basis function network model produced the highest average hit rate. The overall results show that the best NN model outperforms the LR model and the survival model. All three models extracted similar sets of variables as important. Working status and client's delinquency history are the most important features for customer revolving credit scoring on the observed dataset.

credit scoring modeling; logistic regression; revolving credit; survival analysis; neural networks

Zbornik je citiran u bazama: ISI (ISINET), INSPEC (IEE), CSA (Cambridge Scientific Abstracts), ELSEVIER and Elsevier Bibliographic Database, AMS (American Mathematical Soceity), Mathematical Reviews, ZENTRABLATT, ELP, NLG, Engineering Index, Directory of Published Proceedings, British Library, Swets Information Services. ISSN: 1790-5109 (hard copy), ISSN: 1790-5117 (CD-ROM)