信用突变下商业银行信用风险预警模型及应用

在信用突变环境下，传统的基于模糊评价技术的信用风险预警模型存在较大的功能局限性。对此，文章运用偏好信息熵与物元可拓理论相融合的偏好熵权物元可拓方法，构建了基于偏好熵权物元可拓的商业银行信用风险预警模型，并进行了预警模型的实证分析。文章认为，基于偏好熵权物元可拓的信用风险预警模型的优势在于，通过偏好信息熵与物元可拓理论相融合的偏好熵权物元可拓方法，使得信用突变下信用风险的预警结果具有较好的平滑性与客观性，此外，该模型的综合关联度预警功能，提高了信用风险预警结果的精确度，很好地解决了信用突变下商业银行信用风险的预警问题。

Empirical Research and Commercial Bank Credit Risk Early-Warning Model under Credit Mutation Status

Traditional credit risk early-warning model based on fuzzy assessment technique exists larger functional limitations under credit mutation status. This paper constructs Commercial bank credit risk early-warning model based on Preference entropy-weight and matter-element extension by applying preference entropy-weight and matter-element extension technique, and gives a relative example analysis. It believes that the superiority of model is to fufil double smothing function to credit risk under credit mutation status by merging Preference information entropy into matter-element extension. In addition, complex relativity early-warning function of the model enhances accuracy of commercial bank credit risk early-warning. Which solves the problem to commercial bank credit risk early-warning under credit steadiness status.