基于差分进化自动聚类的信用风险评价模型研究

随着风险评价的日益复杂化, 多维度、多时序等不规则的样本数据增加了评估的难度。本文建立信用风险评价的差分进化自动聚类模型, 并将其应用到我国上市公司信用风险评价中。该模型不要求事先知道分类的数据, 相反, 通过群体智能去寻找最优的分区。通过数据仿真, 并与遗传算法、决策树、BP神经网络模型进行信用风险评价的实证对比研究, 结果表明, 该模型能够非常准确的找到数据对应的分区, 大大提高了信用评估的准确性, 降低了风险成本, 对信用风险的管理和控制具有很高的利用价值。

Study on Credit Risk Assessment Model Based on Automatic Clustering Using an Differential Evolution Algorithm

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.