Self organizing neural networks for financial diagnosis
Decision Support Systems
Hybrid neural network models for bankruptcy predictions
Decision Support Systems
A global learing algorithm for a RBF network
Neural Networks
Algorithms to determine the feasibilities and weights of multi-layer perceptions with application to speech classification
Prediction of mortality and in-hospital complications for acute myocardial infarction patients using artificial neural networks
A comparison of supervised and unsupervised neural networks in predicting bankruptcy of Korean firms
Expert Systems with Applications: An International Journal
Auto claim fraud detection using Bayesian learning neural networks
Expert Systems with Applications: An International Journal
Empirical determination of sample sizes for multi-layer perceptrons by simple RBF networks
WSEAS Transactions on Computers
The effect of training set size for the performance of neural networks of classification
WSEAS Transactions on Computers
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In this paper, we use feed forward neural networks with the back-propagation algorithm to build decision models for five insurances including life, annuity, health, accident, and investment-oriented insurances. Six features (variables) were selected for the inputs of the neural networks including age, sex, annual income, educational level, occupation, and risk preference. Three hundred insurants from an insurance company in Taiwan were used as examples for establishing the decision models. Six experiments were conducted in this study. These experiments were mainly categorized into two phases: Phase 1 (Experiments 1 to 3) and Phase 2 (Experiments 4 to 6). In Phase 1, we used the six features as the inputs of the neural networks. In Phase 2, we employed the factor analysis method to select three more important features from the six features. In Phase 1, Experiment 1 used a single neural network to classify the five insurances simultaneously while Experimental 2 utilized five neural networks to classify them independently. Experiments 1 and 2 adopted the purchase records of primary and additional insurances as experimental data. Experiment 3, however, utilized the primary insurance purchase dada only. In Phase 2, we repeated the similar experimental procedure as Phase 1. We also applied statistical methods to test the differences of the classification results between Phases 1 and 2. Discussion and concluding remarks are finally provided at the end of this paper.