湖北省区域内客户企业信用风险评价数据
收藏浙江省数据知识产权登记平台2024-07-19 更新2024-07-22 收录
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材料制造行业普遍存在交易前先付20至30%的定金,等客户收到全部货物再付其余尾款,会存在有客户迟迟拖欠尾款的情况发生。企业信用风险在经济管理领域中起着非常重要的作用,可以帮助本行业所有企业识别和评估客户企业的信用风险,从而避免因客户违约而造成的损失。通过评价客户的信用风险等级,对于评优秀的企业,本行业所有企业可以放心合作或者降低定金比例促进双方交易量,而对于评良好的企业,本行业所有企业需要与客户企业维持在30%左右的定金比例上合作,而对于评不及格的企业,本行业所有企业一定需要与客户企业在50%以上的定金比例上才能合作,从而避免因客户违约而造成的损失。1.对近一年合作的企业采集相关数据。2.算法规则:(1)a1(注册资本≥1亿元加20分,在0.1至1亿元加10分,其余都只加5分)(2)a2(一线城市加20分,二线加10分,其余加5分)(3)a3(员工≥100人加15分,30至100人加8分,其余加3分)(4)a4(知识产权总数≥25个加15分,15-25个加8分,其余加5分)(5)a5(国家级荣誉加20分,省级荣誉加15分,市级荣誉加8分)(6)a6(企业有≥20司法诉讼减10分,有5至20条减5分,5条以下加5分)(7)a7(凭主观打0至10分)(8)a8(企业等级在A和B级的加15分,企M级的加8分,C和D级的减2分)(9)a9(与本行业所有企业有≥3违约的减10分,有1或者2次违约的减5分,0次违约的加15分)。3.计算信用风险值A=(a1+a2+......a9)*k1,k1不同省份值不同,湖北省k1值为1.08。4.评价企业信用风险等级,A大于等于85分为优秀,65-85分为良好,小于等于65分为不及格,从而帮助本行业所有企业识别和评估客户企业的信用风险,从而避免因客户违约而造成本行业所有企业的损失。
In the material manufacturing industry, a standard practice is to require a 20% to 30% advance deposit prior to transaction, with the remaining balance settled after the customer receives the full shipment. However, issues frequently arise where customers delay payment of the final balance for extended periods. Enterprise credit risk plays a critical role in the field of economic management, as it enables all enterprises within the industry to identify and evaluate the credit risk of their client enterprises, thus mitigating losses caused by customer defaults. By evaluating the credit risk level of client enterprises, all enterprises in the industry can adjust their cooperation terms accordingly: for enterprises rated as Excellent, they can cooperate with confidence or reduce the deposit proportion to boost bilateral transaction volume; for enterprises rated as Good, they should maintain a cooperation deposit proportion of approximately 30%; for enterprises rated as Non-qualified, they must require a deposit proportion of over 50% before cooperation, so as to avoid losses caused by customer defaults.
1. Collect relevant data from enterprises that have cooperated with the industry within the past year.
2. Algorithm scoring rules:
(1) a1: 20 points if the enterprise's registered capital is ≥ 100 million RMB, 10 points if between 0.1 and 100 million RMB, and 5 points for all other cases.
(2) a2: 20 points if the enterprise is located in first-tier cities, 10 points if in second-tier cities, and 5 points for all other locations.
(3) a3: 15 points if the number of employees ≥ 100, 8 points if between 30 and 100, and 3 points for all other cases.
(4) a4: 15 points if the total number of intellectual property rights ≥ 25, 8 points if between 15 and 25, and 5 points for all other cases.
(5) a5: 20 points for national-level honors, 15 points for provincial-level honors, and 8 points for municipal-level honors.
(6) a6: -10 points if the enterprise has ≥ 20 judicial lawsuits, -5 points if the number of judicial lawsuits is between 5 and 20, and 5 points if fewer than 5.
(7) a7: Scored 0 to 10 subjectively.
(8) a8: 15 points if the enterprise's credit rating is Grade A or B, 8 points if Grade M, and -2 points if Grade C or D.
(9) a9: -10 points if the enterprise has ≥ 3 defaults with all enterprises in the industry, -5 points if 1 or 2 defaults, and 15 points if there are 0 defaults.
3. Calculate the credit risk score A = (a1 + a2 + ... + a9) * k1, where k1 varies by province. The k1 value for Hubei Province is 1.08.
4. Evaluate the enterprise credit risk level: Excellent when A ≥ 85, Good when 65 ≤ A < 85, and Non-qualified when A ≤ 65. This framework enables all enterprises in the material manufacturing industry to identify and evaluate the credit risk of their client enterprises, thereby avoiding losses caused by customer defaults.
提供机构:
温州市享通塑磁科技有限公司
创建时间:
2024-06-27
原始信息汇总
数据集概述
数据集名称
浙江省数据知识产权登记平台
数据集描述
浙江省数据知识产权登记平台是由浙江知识产权研究与服务中心推出的区块链数据知识产权登记系统。该平台支持数据知识产权登记、知识产权证书申请、原创作品登记确权、维权服务申请、维权证据出具、知识产权转让等场景。通过登记、确权、维权、交易等多维度为创作者的知识产权提供保护。
关键词
区块链、知识产权、数据存证、知识产权存证、知识产权研究与服务中心、数据知识产权登记、浙江省数据知识产权登记平台
搜集汇总
数据集介绍

特点
该数据集包含14094条记录,每年更新一次,适用于材料制造行业,帮助企业评估客户信用风险,优化交易策略。数据集通过算法规则计算信用风险值,并根据风险等级提供合作建议,避免因客户违约造成的损失。
以上内容由遇见数据集搜集并总结生成



