Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
An Implementation of Logical Analysis of Data
IEEE Transactions on Knowledge and Data Engineering
Knowledge discovery techniques for predicting country investment risk
Computers and Industrial Engineering
Credit rating analysis with support vector machines and neural networks: a market comparative study
Decision Support Systems - Special issue: Data mining for financial decision making
Forecasting stock market movement direction with support vector machine
Computers and Operations Research
Neural network ensemble strategies for financial decision applications
Computers and Operations Research
Credit scoring with a data mining approach based on support vector machines
Expert Systems with Applications: An International Journal
Advanced Engineering Informatics
Credit risk assessment with a multistage neural network ensemble learning approach
Expert Systems with Applications: An International Journal
Using neural network ensembles for bankruptcy prediction and credit scoring
Expert Systems with Applications: An International Journal
Neural nets versus conventional techniques in credit scoring in Egyptian banking
Expert Systems with Applications: An International Journal
Constructing a reassigning credit scoring model
Expert Systems with Applications: An International Journal
Credit scoring algorithm based on link analysis ranking with support vector machine
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Consumer credit scoring models with limited data
Expert Systems with Applications: An International Journal
A new two-stage hybrid approach of credit risk in banking industry
Expert Systems with Applications: An International Journal
Application of an emotional neural network to facial recognition
Neural Computing and Applications
Expert Systems with Applications: An International Journal
Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters
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
The evaluation of consumer loans using support vector machines
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Credit risk analysis using a reliability-based neural network ensemble model
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Bankruptcy prediction for credit risk using neural networks: A survey and new results
IEEE Transactions on Neural Networks
Credit rating using a hybrid voting ensemble
SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
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Credit scoring and evaluation is one of the key analytical techniques in credit risk evaluation which has been an active research area in financial risk management. Artificial neural networks (NNs) have been considered to be accurate tools for credit analysis among others in the credit industry. Lately, emotional neural networks (EmNNs) have been suggested and applied successfully for pattern recognition. In this paper we investigate the efficiency of EmNNs and compare their performance to conventional NNs when applied to credit risk evaluation. In total 12 neural networks; based equally on emotional and conventional neural models; are arbitrated under three learning schemes to classify whether a credit application is approved or declined. The learning schemes differ in the ratio of training-to-validation data used during training and testing the neural networks. The emotional and conventional neural models are trained using real world credit application cases from the Australian credit approval datasets which has 690 cases; each case with 14 numerical attributes; based on which an application is accepted or rejected. The performance of the 12 neural networks will be evaluated using certain criteria. Experimental results suggest that both emotional and conventional neural models can be used effectively for credit risk evaluations, however the emotional models outperform their conventional counterparts in decision making speed and accuracy, thus, making them ideal for implementation in fast automatic processing of credit applications.