Applying Fuzzy Theory and Neural Network in Business Rating
|Keywords:||企業信用評等;類神經網路;模糊理論;business rating;neural network;fuzzy theory|
Enterprises trade with each other by carefully considering business ratings to reduce investment risks. However, conventional mathematic models have difficulty in discriminating between multiple ranks. A prediction accuracy of only 60% among departed models exposes traders to unnecessarily high risks. Based on the above, we should develop a flexible and accurate neural network structure that applies artificial intelligence in fuzzy theory to combine business ratings and bankruptcy prediction. The proposed model can discriminate multiple ranks with 80% accuracy in dissimilar industry and scales of enterprise. By incorporating subjective judgment and objective fact, the neural network structure proposed herein allows an organization to assesses the degree of risk and whether an enterprise will become insolvent. Most organizations evaluate business ratings subjectively, normally based on their professional knowledge and experience. Most enterprises also lack objective models capable of assessing trade partners’ credit actively. However, neural networks discriminate between multiple ranks inaccurately, and some samples that locate an ambiguous situation cannot be easily separated. For instance, while applying Back Propagation Network and Fuzzy Theory for multiple ranks of investment and two ranks of bankruptcy, prediction of bankruptcy can usefully reduce the risk in business rating alone. Combining different outputs in network structure can improve accuracy in multiple ranks and control the risk in limited area. Applying fictitious variables can enhance to discriminating multiple ranks effectively.
|Appears in Collections:||Thesis|