A classification approach using multi-layered neural networks
Decision Support Systems - Special issue on neural networks for decision support
Computers and Operations Research
Neural network credit scoring models
Computers and Operations Research - Neural networks in business
Intelligent Systems for Finance and Business
Intelligent Systems for Finance and Business
Decision Support Systems and Intelligent Systems
Decision Support Systems and Intelligent Systems
Neural Networks in Finance and Investing: Using Artificial Intelligence to Improve Real World Performance
Expert Systems with Applications: An International Journal
Hybrid mining approach in the design of credit scoring models
Expert Systems with Applications: An International Journal
Managing loan customers using misclassification patterns of credit scoring model
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Credit risk evaluation using neural networks: Emotional versus conventional models
Applied Soft Computing
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Assessing scorecard performance: A literature review and classification
Expert Systems with Applications: An International Journal
Genetic algorithm-based heuristic for feature selection in credit risk assessment
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
In this paper we design the neural network consumer credit scoring models for financial institutions where data usually used in previous research are not available. We use extensive primarily accounting data set on transactions and account balances of clients available in each financial institution. As many of these numerous variables are correlated and have very questionable information content, we considered the issue of variable selection and the selection of training and testing sub-sets crucial in developing efficient scoring models. We used a genetic algorithm for variable selection. In dividing performing and nonperforming loans into training and testing sub-sets we replicated the distribution on Kohonen artificial neural network, however, when evaluating the efficiency of models, we used k-fold cross-validation. We developed consumer credit scoring models with error back-propagation artificial neural networks and checked their efficiency against models developed with logistic regression. Considering the dataset of questionable information content, the results were surprisingly good and one of the error back-propagation artificial neural network models has shown the best results. We showed that our variable selection method is well suited for the addressed problem.