摘要
信用证作为目前国际贸易结算方式中使用范围最广、使用次数最多的一种支
付方式具有其独到的优势：在国际贸易中，由于进、出口商双方互不了解，所以
彼此之间不信任，而信用证能够很好的解决这一问题。信用证是银行为进、出口
商双方提供的一种银行信用，它能够解决进、出口商双方之前惯用的商业信用所
不能解决的问题，所以信用证得到了广泛的应用

随着全球经济、金融的一体化的进程逐步加快，信用证业务在我国商业银行
中也得到了迅速的发展。尤其在我国加入世界贸易组织之后，大量外资企业加入
到我国信用证业务的市场中来，对我国商业银行形成巨大竞争压力。面对逐渐缩
小的市场份额以及上级银行的业务指标压力，我国的商业银行往往会采取增大信
用证业务规模的措施来增加银行的收益。但是，信用证业务对于银行来说并不是
有百利而无一害的，信用证业务给银行带来高收益的同时也给银行带来高风险

银行可以通过为国际贸易中的买卖双方提供信用证服务而收取手续费等利润，但
是与此同时，银行也是信用证风险的受害者，信用证带来的风险主要有信用风险、
市场风险以及操作风险等。在现实情况中，银行遭遇信用证风险的例子层出不穷，
信用证风险给银行带来的巨大损失也是无法估量的。所以，如何去识别、度量以
及防范信用证业务风险、把信用证风险控制在较低水平应该成为银行关注的焦点

基于以上研究背景，本文在已有对信用证风险的定性研究和对信用证风险评
估之上，利用 BP 神经网络方法来构建银行信用证的风险评估模型，并利用该模
型来对银行信用证风险进行识别、度量和评估。在构建 BP 神经网络银行信用证
风险评估模型之后，本文又以 Z 银行温州分行的信用证业务数据对该模型进行检
验，结果表明该模型具有很好的效度和信度，能够准确的评估银行信用证风险水
平。本文基于 BP 神经网络方法来构建银行信用证风险评估模型，具有很强的理
论与实际意义，能够帮助银行在做出接受某项信用证业务决策之前首先明确该业
务的风险水平，从而减少银行遭受损失的可能性。最后，文章基于该 BP 神经网
络信用证风险评估模型为银行防范信用证风险提供一些建议和措施

关键词：信用证；信用证风险；BP 神经网络浙江理工大学硕士专业学位论文
III
Abstract
The letter of credit has the widest use in the current international trade due to its
unique advantages. In the international trade, importers and exporters do not believe
in each other, so there is no confidence between them, and the letter of credit can be a
good solution to this problem. The letter of credit is a conditional payment promise
provided by the bank, which can save the exporter from the crisis of trust and replace
the commercial credit with the bank credit, thus ensuring the normal and smooth
conduct of the trade between the importer and the exporter. It is because of this
advantage of the letter of credit that the letter of credit has been widely used.
In the current, China&39;s commercial bank&39;s credit business is also in the
competition to get faster and faster development. In particular, after China&39;s accession
to the WTO, a large number of foreign banks first entered the field of credit business
competition, so China&39;s commercial banks are in the face of market share reduction in
the potential crisis and need to increase the letter of credit business scale to increase
bank revenue. In terms of banks, the letter of credit business have both high returns
and risk characteristics, on the one hand, the bank can access fees, such as issuing fees,
negotiable fees and revision fees; on the other hand, banks as risk holders have to face
the credit risk, national risk, operational risk and other risks. Therefore, how to
measure these different types of L / C risk and control the risk at a lower level has
always been the focus of the bank&39;s focus on the credit business.
Based on the above research background, this paper constructs the risk
assessment model of bank letter of credit by using BP neural network method to
qualitatively study the risk of letter of credit and risk assessment of credit. It is
proposed to use the risk assessment model to identify, measure and evaluate the risk
of bank credit from the perspective of risk management. After the construction of BP
neural network bank credit risk assessment model, this paper used the Z bank
Wenzhou branch of the letter of credit business data to test the model, the results show
that the model has a very good degree of validity and reliability, to accurately assess浙江理工大学硕士专业学位论文
IV
the bank credit Risk level. The risk assessment model of bank credit based on BP
neural network is very theoretical and practical. It can help banks to clear the risk
level of the business before making a decision to accept a letter of credit business.
Finally, the paper based on the BP neural network credit risk assessment model for
banks to prevent credit risk to provide some suggestions and measures.
Key Words: letter of credit; letter of credit risk; BP neural network浙江理工大学硕士专业学位论文
V
目 录
摘要.....III
Abstract..........III
目 录.....V
1 绪论.....1
1.1 选题背景与意义.. 1
1.1.1 选题背景.....1
1.1.2 选题意义.....2
1.2 文献综述.. 3
1.2.1 银行信用证风险及防范国内外研究综述.3
1.2.2 BP 神经网络在风险评估领域研究现状...7
1.3 研究内容与研究方法...... 8
1.3.1 研究内容.....8
1.3.2 研究方法.....9
1.4 论文创新与不足之处.... 10
1.5 本文技术路线图.11
2 信用证相关概念界定及理论基础..13
2.1 信用证概述....... 13
2.1.1 信用证概念及分类...........13
2.1.2 信用证所涉及的主体.......14
2.1.3 信用证的特点.......14
2.1.4 信用证遵循的原则...........15
2.1.5 信用证的运作流程...........16
2.2 信用证风险概述........... 17
2.2.1 信用证风险的定义..........17
2.2.2 信用证风险的特征...........17
2.3 BP 神经网络理论概述...19
2.3.1 BP 神经网络结构.19
2.3.2 BP 神经网络学习过程.....20
2.3.3 BP 神经网络与其他评估方法的比较.....20
3 Z 银行温州分行信用证业务发展现状分析..
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