The supply chain finance industry will generate the flow of funds and commodities when providing financing services to small and medium-sized enterprises (SMEs). At this time, banks will face multiple risks such as policy, operation, market and credit. The investigation on supply chain finance under information sharing from the aspect of credit risk assessment will be conducted. The genetic algorithm combined with support vector machine and BP neural network is selected to evaluate the credit risk of supply chain finance. In the support vector machine method, the parameter selection method adopts genetic algorithm. In the included data, the gap in growth capacity of SMEs is relatively large. The standard deviations of main business, net profit and total assets are all above 30%, and the standard deviations of current ratio and quick ratio are small, which means that the two are more stable and healthier. In addition, among all the investigated enterprises, the cost gap is large, and the standard deviation of the inventory decline price reserve is small, which means that most enterprises have good inventory quality. After classification, 32 high-quality enterprises, 46 neutral enterprises and 55 risk enterprises are obtained in the total sample. In the test sample, there are 21 high-quality enterprises, 12 neutral enterprises, and 26 risk enterprises. The overall classification accuracy of the support vector machine method optimized by genetic algorithm is relatively lower than that of the BP neural network method. The classification accuracy of the support vector machine method optimized by genetic algorithm is 76.27%, and the classification accuracy of BP neural network method is 89.83%. The supply chain financial risk assessment of SMEs is mainly explored from the perspective of banks. The results can provide theoretical support for reducing the probability of bank’s profit damage and increasing the bank’s profitability.

当为中小企业提供融资服务时，供应链金融业将产生资金和商品的流动。目前，银行将面临政策，运营，市场和信贷等多重风险。从信用风险评估的角度对信息共享下的供应链财务进行调查。选择了结合支持向量机和BP神经网络的遗传算法来评估供应链财务的信用风险。在支持向量机方法中，参数选择方法采用遗传算法。在所包含的数据中，中小企业的增长能力差距相对较大。主营业务，净利润和总资产的标准差均在30％以上，电流比和速动比的标准偏差小，这意味着两者更稳定，更健康。此外，在所有被调查企业中，成本差距较大，库存降价储备的标准差较小，这意味着大多数企业的库存质量较好。分类后，共获得32家优质企业，46家中立企业和55家风险企业。在测试样本中，有21家优质企业，12家中立企业和26家风险企业。遗传算法优化的支持向量机方法的总体分类精度相对低于BP神经网络方法。通过遗传算法优化的支持向量机方法的分类精度为76.27％，BP神经网络方法的分类精度为89.83％。中小企业的供应链财务风险评估主要从银行的角度进行。研究结果可为减少银行利润受损的可能性和提高银行的盈利能力提供理论支持。