Pregled bibliografske jedinice broj: 259266
Modeling customer revolving credit scoring using logistic regression, survival analysis and neural networks
Modeling customer revolving credit scoring using logistic regression, survival analysis and neural networks // Proceedings of the 7th WSEAS International Conference on Neural Networks / Nikos Mastorakis (ur.).
Cavtat: WSEAS Press, 2006. str. 164-169 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 259266 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Modeling customer revolving credit scoring using logistic regression, survival analysis and neural networks
Autori
Šarlija, Nataša ; Benšić, Mirta ; Zekić-Sušac, Marijana
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of the 7th WSEAS International Conference on Neural Networks
/ Nikos Mastorakis - Cavtat : WSEAS Press, 2006, 164-169
Skup
7th WSEAS International Conference on Neural Networks
Mjesto i datum
Cavtat, Hrvatska, 12.07.2006. - 14.07.2006
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
credit scoring modeling; logistic regression; revolving credit; survival analysis; neural networks
Sažetak
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.
Izvorni jezik
Engleski
Znanstvena područja
Matematika, Ekonomija, Informacijske i komunikacijske znanosti
Napomena
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)
POVEZANOST RADA
Projekti:
0235002
010-0101195-0872 - Transformacija poduzetničkog potencijala u poduzetničko ponašanje (Pfeifer, Sanja, MZOS ) ( CroRIS)
010-0101195-1048 - Modeli za ocjenu rizičnosti poslovanja poduzeća (Šarlija, Nataša, MZOS ) ( CroRIS)
Ustanove:
Ekonomski fakultet, Osijek,
Sveučilište u Osijeku, Odjel za matematiku
