Purpose

Nowadays, Supply Chain Finance (SCF) has been developing rapidly since the emergence of credit risk. Therefore, this paper used SVM optimized by the firefly algorithm, which is called firefly algorithm support vector machine (FA-SVM), and applied it to SCF evaluation with a different indicator selection.

Design/methodology/approach

In this paper, we used FA-SVM to assess the credit risk of supply chain finance with extracted index through correlation and appraisal analysis, and finally determined 3 first-level indicators and 15 third-level indicators. Through the application analysis, 39 SMEs (117 sample data) were selected from the Computer and Electronic Communications Manufacturing Industry as the characteristics for the input variables, to verify the improvement effect of the method relative to the LIBSVM and the classification pretest effect in the credit risk assessment of the SCF.

Findings

The results showed that FA-SVM could improve the accuracy of classification prediction compared with LIBSVM, and decrease the error rate of falseness recognize credible enterprise to untrusted enterprise.

Originality/value

This paper appliedthe firefly support vector machine in the supply chain financial evaluation for the first time. The output variable was described in a more detailed manner during the index define, and the random selection set in the process of FA-SVM data training.

中文翻译：

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用于供应链金融信用风险评估的萤火虫算法改进支持向量机

目的

如今，随着信用风险的出现，供应链金融（SCF）发展迅速。因此，本文采用了萤火虫算法优化的SVM，称为萤火虫算法支持向量机（FA-SVM），并将其应用于不同指标选择的SCF评估。

设计/方法/方法

本文采用FA-SVM通过相关性和评价分析提取指标对供应链金融信用风险进行评估，最终确定了3个一级指标和15个三级指标。通过应用分析，从计算机电子通信制造业中选取39家中小企业（117个样本数据）作为输入变量的特征，验证该方法相对于LIBSVM的改进效果和信用分类预测效果SCF 的风险评估。

发现

结果表明，与LIBSVM相比，FA-SVM可以提高分类预测的准确率，降低对不可信企业的错误识别错误率。

原创性/价值

本文首次将萤火虫支持向量机应用于供应链财务评估中。输出变量在指标定义过程中进行了更详细的描述，FA-SVM数据训练过程中的随机选择集。