With the increasing complexity of risk assessment, multi-dimensional, multi-timing and other irregular sample data increases the difficulty of the assessment. In this paper, the establishment of credit risk evaluation of differential evolution automatic clustering model is applyed to our assessment of the credit risk of listed companies. The prior knowledge of classified data is not required in this model, on the contrary, swarm intelligence is used to find the optimal partition. By data simulation and empirical comparative study of credit risk assessment and genetic algorithms, decision tree, BP neural network model, the results show that the model can be very accurately to find the corresponding data partition, which greatly improving the accuracy of the credit assessment, reducing the cost of risk, making a high value of credit risk management and control.
ZHANG Da-bin, ZHOU Zhi-gang, XU Zhi, LI Yan-hui
. Study on Credit Risk Assessment Model Based on Automatic Clustering Using an Differential Evolution Algorithm[J]. Chinese Journal of Management Science, 2015
, 23(4)
: 39
-45
.
DOI: 10.16381/j.cnki.issn1003-207x.2015.04.005
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