|
| SD路桥集团供应商信用风险预警模型构建研究 |
| Research on the Construction of Supplier Credit Risk Early Warning Model for SD Road and Bridge Group |
| 投稿时间：2025-12-03 修订日期：2026-01-26 |
| DOI： |
| 中文关键词: 路桥施工 供应商 信用风险预警 机器学习 |
| 英文关键词: Road and bridge construction Supplier Credit risk early warning Machine learning |
| 基金项目: |
|
| 摘要点击次数: 39 |
| 全文下载次数: 0 |
| 中文摘要: |
| 全球经济下行背景下，加强路桥施工企业供应商信用风险预警已成为必然趋势。本文以SD路桥集团为研究对象，通过分析应付账款、预付账款等财务数据，发现其应付账款财务管理存在单一财务数据难反映风险、难以形成统一预警标准等问题。据此，本文从供应商信用风险预警指标、预警模型及模型结果实践应用三个层面，构建供应商信用风险预警模型。研究表明，多模态数据融合可提升风险评级准确度；随机森林模型因准确率高、稳定性好、抗过拟合能力强，更适配该集团；不同类型供应商信用风险预警指标重要性存在差异，需实施分类预警。该研究为SD路桥集团及其他同类企业，提供了供应商信用风险管理的思路与经验。 |
| 英文摘要: |
| Against the backdrop of a global economic downturn, strengthening credit risk early warning for road and bridge construction suppliers has become an inevitable trend. This paper takes SD Road & Bridge Group as a case study, analyzing financial data such as accounts payable and prepayments. It reveals problems in its accounts payable financial management, including the difficulty in reflecting risks with single financial data and the lack of a unified early warning standard.Therefore, this paper constructs a supplier credit risk early warning mechanism from three levels: supplier credit risk early warning indicators, early warning models, and the practical application of model results. Research shows that multimodal data fusion can improve the accuracy of risk rating; the random forest model, due to its high accuracy, good stability, and strong anti-overfitting ability, is more suitable for this group; the importance of risk early warning indicators varies among different types of suppliers, requiring classified early warning. This research provides SD Road & Bridge Group and other similar enterprises with ideas and experience in supplier credit risk management. |
|
View Fulltext
查看/发表评论 下载PDF阅读器 |
| 关闭 |
