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In the credit risk analysis of corporate bonds, multiple risk factors need to be considered comprehensively. These factors often exist in complex nonlinear systems. It is difficult to accurately model these system equations by conventional methods. In view of the complexity and uncertainty in credit analysis, this paper attempts to transform the simulation of complex nonlinear systems into classification and recognition of credit risk factor images, transform risk factors into unstructured data, to propose a credit risk modeling and analysis method based on convolutional neural network (CNN). Experiments show that the accuracy of credit risk identification in corporate bonds in multiple industries is over 80%.
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