Analysis and Optimization of Personal Credit Risk Assessment Model Based on Improved BPNN

摘要

In the current situation of "Internet plus Finance", the assessment of personal credit risk is of vital importance to the sustained and steady growth of the whole social economy. In order to more effectively complete the personal credit risk assessment, the innovation of this paper lies in the introduction of time stamp into the 5C evaluation method, and then using the artificial intelligence attribute (crossover factor and mutation factor) of Back Propagation Neural Network (BPNN) to build a personal credit risk assessment analysis. With the dynamic development of thinking, this model constantly adjusts and optimizes the personal credit risk assessment process. Simulation experiment shows that this integrated innovative approach not only reduces the error rate value of personal credit risk assessment, but also improves the efficiency of personal credit assessment.