致谢
矿大的学习生活已接近尾声，入学以来的点点滴滴都还深深的印在脑海中，
一幕幕学习的场景恍若昨天。感谢在中国矿业大学学习的每一天，让我不仅学到
了管理学相关的知识，同时学到了很多做人、做事的道理，在论文即将完成之际，
心中充满了感慨，选择在忙碌的工作之余开始我的研究生的生活，对我来说，是
机遇，更是挑战。支撑我一路走来的，是我敬爱的老师们，亲爱的家人和最可爱
的同学们，在此，我由衷的表示感谢！
首先，感谢我敬爱的导师李秀枝副教授，从论文选题、到撰写开题报告、最
后到正文撰写，李老师都提出了很多宝贵意见，她提出的每一个问题，指导的每
一个思路，都让我有醍醐灌顶之感。让我感受最深刻的是李老师严谨治学的态度
和敬业的精神，无论从格式规范、论文要点、还是文章结构，李老师都不厌其烦，
给予我及时的帮助，让我倍感温暖，为我最后顺利完成论文的撰写奠定了基础。
同时，感谢中国矿业大学管理学院的全体老师，正是你们无私的指导、支持
与帮助、爱岗敬业的治学精神，让我对管理理论有了更深一步的理解，从而将理
论结合实际，与工作相结合，将所学理论充分应用于现实工作中，进一步提升自
己的管理水平。
我要感谢我的家人，是他们对我生活中的无微照顾，让我在工作之余能够顺
利完成学业；感谢我的同事们，为我的论文提供了数据支持，为我全篇论文的完
成奠定了基础；感谢我最可爱的同学们，是你们让我在离开校园多年后再次感受
到了同学情的珍贵，是你们在我论文的写作过程中，提供了很多技术指导和帮助，
在此表示最真诚的谢意。
最后，再次感谢中国矿业大学提供给我的宝贵学习机会，让我踏上一段新的
征程，开始一段新的人生!

摘要
我国现阶段由于资本市场仍然不够成熟，商业银行贷款依然是中、小型企业
最为常使用的融资渠道。近五年以来，在政府推动的普惠金融政策的引导下，中、
小型企业的普惠金融贷款业务越来越受到各商业银行的重视，成为各商业银行机
构考核的重要指标之一，普惠金融贷款在整个银行的信贷业务占比日益提升，但
是受到中、小型企业抗风险能力弱、客户信息不对称、财务管理制度不完善以及
违约率高等因素影响，造成各商业银行中、小型企业授信业务风险事件频发，不
良处置难度大。同时国家目前经济的显著特征就是步入新常态，在这种宏观经济
下行压力大的情况下，银行向企业发放贷款的风险必然会变大，因为企业业务经
营的风险增加了。因此，如果想要减轻企业融资的困难，就需要协助银行增强自
身评价信贷风险的能力，从而达到将风险限制在可控范围以内的目的。
本文以B银行作为研究对象，分析了该银行现行信贷评级体系存在的问题，
着重构建针对中型民营企业客户的信用评级指标体系。通过对该行165户中型民
营企业不良授信客户违约前一年的客户信用评级结果分析，发现其未能准确反映
该类客户的风险状况，导致该行信用评级体系在中型民营企业客户授信准入、退
出、审批决策等环节未能发挥其应有的作用。进一步分析造成该现象的原因，一
是未设计开发适用于中型民营企业客户的信贷风险评价模型；二是评级模型定量
指标对财务数据依赖过高；三是信用评级结果存在人为调整的动机等。为此，本
文通过调查问卷形式筛选出31个对中型民营企业客户影响较大的信用评级指
标，在此基础上采用层次分析法，对B银行中型民营企业客户信用评级体系进行
了研究，并以浙江A纺织有限公司为例，进行了案例分析，结果表明，新指标体
系能够为该行开展中型民营企业客户授信提供更为科学的定量评价依据。
关键词：信用评级；信贷风险；授信客户；层次分析法；中型民营企业
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Abstract
At the present stage in China, because the capital market is still not mature
enough, commercial bank loans are the most commonly used legal financing channels
for small and medium-sized enterprises. In recent years, under the guidance of the
inclusive financial policy, although the inclusive financial loan business of small and
medium-sized enterprises has been paid more and more attention by commercial
banks and accounts for an increasing proportion in the credit business volume of the
whole bank, it is affected by the factors such as weak anti risk ability, imperfect
financial situation, asymmetric information and high default rate of small and
medium-sized enterprises, As a result, the risk events of SME credit business of
commercial banks occur frequently and the recovery rate of non-performing disposal
is low. At the same time, the remarkable feature of the country's current economy is to
enter the new normal. In this case of great downward pressure on the macro economy,
the risk of banks issuing loans to enterprises is bound to increase, because the risk of
enterprise business operation increases. Therefore, if you want to reduce the financing
difficulties of enterprises, you need to help banks enhance their ability to evaluate
credit risk, so as to limit the risk to a controllable range.
This thesis takes the medium-sized private enterprise customers of bank B as the
research object, and focuses on constructing its credit rating index system. Through
the analysis of the customer credit rating results of 165 non-performing credit
customers of medium-sized private enterprises in the bank one year before default, it
is found that it fails to accurately reflect the risk status of such customers, resulting in
the failure of the bank's credit rating system to play its due role in the credit access,
exit and approval decisions of medium-sized private enterprise customers. In depth
analysis of the causes of this phenomenon, first, the credit risk evaluation model
suitable for medium-sized private enterprise customers is not designed and developed;
Second, the quantitative indicators of rating model rely too much on financial data;
Third, the credit rating results have the motivation of artificial adjustment. Therefore,
this thesis selects 31 credit rating indicators that have a great impact on the customers
of medium-sized private enterprises in the form of questionnaire, uses analytic
hierarchy process, so as to optimize the customer credit rating system of
medium-sized private enterprises of bank B. And a case study is carried out with
Zhejiang a textile Co. , Ltd. as an example. The results show that the new index
II
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