I 摘要 随着互联网金融的兴起，大数据技术日益受到关注，借助大数据科技的手段来 监控银行风险，将会有效提高银行业务的盈利能力，实现对银行数据信息的全方位 分析和挖掘，不断推动银行业的发展与进步。《关于规范金融机构资产管理业务的 指导意见》（以下简称为《资管新规》）在2018年4月27日，由央行、证监会、 银保监会、外管局联合印发，《资管新规》的内容涉及到资管业务属性、产品分类、 投资者资质划分、标准化和非标准化的界分、禁止刚性兑付、资金池管理、净值化 管理、统一杠杆水平、严控嵌套和通道、独立托管、准备金管理以及信息披露等诸 多方面。当前国内经济增速放缓，企业经营违约风险凸显，银行业信用风险持续暴 露，疫情全国蔓延导致债券主体和交易对手履约能力降低，针对这种情况，大数据 智能风控对银行债券投资业务来讲显得更加迫切。 基于上述背景，本篇文章通过查阅相关的资料和阅读相关的文献，在归纳评述 国内外债券信用风险管理相关研究动态的基础上，首先基于信用市场理论、信用评 级理论、全面风险管理理论和大数据应用理论四个方面进行研究，然后分析我国商 业银行债券信用风险的现状。以A银行为例，剖析其存在的问题和短板，主要包括 人员流动性大、管理工具和方法落后、风险管理制度和流程不完善；借助大数据技 术多维分析债券投资信用风险，有效挖掘出债券投资风险管理的信息，从优化债券 信用风险管理架构、整合内外部数据信息、投资债券履约风险防投资控全流程信息 三方面提出债券投资过程中的信用风险管理改进的建议。希望通过本文，能够帮助 A银行在未来不断的提高在债券投资等全行各个领域利用大数据的水平，提升债券 投资风险管控能力和技术水平，同时也为加强A银行对风险的管理和控制，将违约 风险降到最低，并且最终提高盈利水平提出有效的建议。最后，提出A银行实施债 券投资风险管理的保障，包括：运营的保障（人力资源的保障、资金和技术投入的 保障和流程优化的保障）、风险意识和文化的保障和企业信用体系的保障（信用体 系完善的保障），最终协助A银行提高风险管控水平的同时实现投资利益最大化。 本文的创新点在于引用大数据跟传统信用风险风控的有机融合机制，融合互联 网金融机构的数据来提供更好的风险判断的标准和依据，实现数据多维化，采用数 据横向和纵向的不同维度的多维组合，用多维度数据帮助企业去判断相应的风险， 广东工业大学硕士学位论文 II 做到精细化管理；实现数据分层，数据共享，降低数据的耦合度，提高数据的适配 性；实现通过金融科技，实现风险信息全面可视化理念。 关键词：大数据；资管科技；债券投资；信用风险； Abstract III Abstract With the development of Internet finance, the big data technology is getting more and more attention. Monitoring bank risk by means of the big data technology will effectively improve the profitability of banking business, realize the all-round analysis and mining of bank data information, and constantly promote the development and progress of banking industry. On April 27, 2018, the guiding opinions on regulating the asset management business of financial institutions (hereinafter referred to as the new regulations on Asset Management) was jointly issued by the central bank, CSRC, CBRC and safe. The contents of the new regulations on asset management involve the attributes of asset management business, product classification, investor qualification classification, standardized and non standardized boundaries, prohibition of rigid cashing, and fund pool Management, net worth management, unified leverage level, strict control of nesting and channels, independent custody, reserve management and information disclosure. At present, the domestic economic growth slows down, the risk of business default highlights the continuous exposure of the credit risk of the banking industry, and the spread of the epidemic nationwide leads to the reduction of the performance ability of bond subjects and counterparties. In view of this situation, the big data intelligent risk control is more urgent for the bond investment business of banks. Based on the above background, this article reviews the relevant research trends of bond credit risk management at home and abroad by consulting relevant materials and reading relevant literature. First, it studies the theory of credit market, credit rating theory, comprehensive risk management theory and big data application theory, and then analyzes current situation of the bond credit risk of commercial banks in China . Taking A bank as an example, analyzes the domestic problems and shortcomings, mainly including large staff mobility, backward management tools and methods, imperfect risk management system and process, multidimensional analysis of bond investment credit risk with the help of big data technology, effectively mining out the information of bond investment risk management, optimization of bond credit risk management structure, 广东工业大学硕士学位论文 IV integration of internal and external data information, investment bond performance .The article puts forward suggestions on improving credit risk management in the process of bond investment from three aspects of information in the whole process of risk prevention and control. It is hoped that this paper can help A bank to continuously improve the level of using big data in various fields of the bank, such as bond investment, improving the risk management and control ability and technical level of bond investment, and also put forward effective suggestions for strengthening the risk management and control of bank a, minimizing the default risk, and ultimately improving the profitability. Finally, the paper puts forward the guarantee of a bank's bond investment risk management, including: the guarantee of operation (the guarantee of human resources, the guarantee of capital and technology investment and the guarantee of process optimization), the guarantee of risk awareness and culture and the guarantee of enterprise credit system (the guarantee of credit system perfection). Last of all, it helps a bank to improve the level of risk control and realize the maximization of investment benefits. The innovation of this article is to integrate the data of Internet financial institutions to provide a better standard and basis for risk judgment by introducing the organic integration mechanism of big data and traditional credit risk risk control, to realize multidimensional data, to use multi-dimensional combination of different dimensions of horizontal and vertical data, to help enterprises to judge the corresponding risks with multi-dimensional data, and to achieve refined management; Realize data stratification, data sharing, reduce the coupling degree of data, improve the adaptability of data; realize the concept of comprehensive visualization of risk information through financial technology. Keywords: Big data; asset technology; bond investment; credit risk; 目 录 V 目 录 摘要..I ABSTRACT ............. III 目 录 ............................ V 第一章 绪论 ............... 1 1.1 研究背景.... 1 1.2 研究目的与意义..................... 2 1.2.1 研究的目的 ............... 2 1.2.2 研究的意义 ............... 2 1.3 文献综述.... 3 1.3.1 国内研究文献综述 . 3 1.3.2 国外研究文献综述 . 4 1.3.3债券市场研究评述 .. 5 1.4 研究思路与方法..................... 6 1.5 研究的内容 .............................. 7 1.6 研究的创新点 ......................... 8 第二章 相关理论综述............................ 9 2.1 信用风险相关理论 ................ 9 2.1.1 信用风险理论 .......... 9 2.1.2 信用风险管理理论 . 9 2.1.3 债券信用风险 ........10 2.2 内部评级理论 .......................12 2.3 全面风险管理理论 ..............14 2.3.1 理论概述..................14 2.3.2 全面风险管理的机制..........................15 广东工业大学硕士学位论文 VI 2.3.3 全面风险管理的目标..........................15 2.4 大数据应用理论...................16 2.4.1数据收集和整合化 17 2.4.2数据分析和多维化 17 2.4.3数据模型化 ..............18 2.4.4数据场景化 ..............19 第三章 A银行债券投资环境分析...20 3.1 国内债券市场概览 ..............20 3.1.1 债券概览..................20 3.1.2 参与主体..................20 3.1.3 市场结构与投资品种..........................21 3.2 宏观环境分析 .......................22 3.2.1 政治环境..................22 3.2.2 经济环境..................22 3.2.3 社会环境..................23 3.2.4 技术分析..................23 3.3 内部环境分析 .......................24 3.3.1 企业概况..................24 3