Modeling customer revolving credit scoring using logistic regression, survival analysis and neural networks
Šarlija, Nataša; Benšić, Mirta; Zekić-Sušac, Marijana;
Modeling customer revolving credit scoring using logistic regression, survival analysis and neural networks;
Proceedings of the 7th WSEAS International Conference on Neural Networks;
Ed. Nikos Mastorakis, Cavtat, Croatia, June 12-14, 2006, pp. 164-169.
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.