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论文编号：	12049
作者编号：	2320180678
上传时间：	2020/12/11 10:07:19
中文题目：	基于社会网络分析的XL银行小微企业信贷风险分析及防控对策研究
英文题目：	Research on Small and Micro Enterprises Credit Risk Analysis and Control Countermeasures of XL Bank
指导老师：	徐曼
中文关键字：	信贷风险管理；社会网络分析；小微企业；商业银行；词频分析
英文关键字：	Credit risk management; Social network analysis; Small and micro Enterprises; Commercial banks; Word frequency analysis
中文摘要：	近年来，小微企业因其灵活的组织形式、较低的管理成本，得到了迅速发展，在增加就业、促进科技发展、调整经济结构和稳定经济增长方面都具有重要作用。小微企业的可持续、健康发展离不开商业银行信贷资金的支持。小微企业信贷风险管理一直是商业银行风险管理领域一个重要的前沿问题和实践问题。以往的研究主要倾向于对风险点抽象的归纳、概括，本文旨在利用商业银行自身产生的关于信贷业务存在问题相关的文本出发，提取相关风险点，寻找风险点之间潜在的关联。在研究过程中，本文主要采取词频统计法和社会网络分析法。本文首先通过分析XL银行小微企业信贷风险管理的现状出发，揭示其在信贷风险管理活动中存在的问题及其所反映出的准确识别信贷风险点及其关联关系的必要性。其次，从XL银行收集汇总的2015-2020年之间的110份小微企业信贷业务检查结果相关的文本，使用分词法和词频统计法揭示了风险点及其特征。第三，基于风险点关联的前提，本文将提取出的风险点利用社会网络分析方法进行共词分析，揭示风险控制点之间的关系，并进行了风险网络可视化展示。有效识别风险关系可以辅助未来的风险排查工作，可以帮助小微企业信贷业务人员加强风险隐患的识别、加强知识共享，进而有效防控小微企业信贷业务风险。最后，本文提出了针对XL银行进一步完善小微企业信贷业务风险管理方面的对策。本文探索性地利用词频分析法和社会网络分析方法研究商业银行小微企业信贷业务风险防控网络图，并提出管理对策，可以增加信贷风险管理领域对于风险点识别的认知，并拓宽了对风险点之间关系的认知。实践意义方面，不仅对商业银行信贷业务风险防控知识管理实践者提供参考建议，还可以对商业银行非结构化数据应用提供参考建议。
英文摘要：	In recent years, small and micro enterprises have developed rapidly due to their flexible organizational forms and low management costs. They have played an essential role in increasing employment, promoting technological development, adjusting economic structure and stabilizing economic growth. The sustainable and healthy development of small and micro enterprises is inseparable from the support of commercial bank credit funds. Credit risk management for small and micro enterprises has always been an important frontier and practical issue in the field of commercial bank risk management. Previous research mainly focuses on generalizing risk points in an abstract way. This thesis aims to utilize the texts related to credit business problems generated from commercial banks to extract relevant risk points and find potential connections between risk points. In the research process, this thesis mainly adopts word frequency analysis and social network analysis. The research first starts by analyzing the current status of XL Bank’s credit risk management for small and micro enterprises, and exposes the problems in its credit risk management activities and accordingly the necessity of accurately identifying credit risk points and their relationships. Secondly, based on the 110 texts that related to the inspection results of small and micro enterprise credit business collected from XL Bank from 2015 to 2020, the risk points and their characteristics are revealed using word segmentation and word frequency statistics. Thirdly, based on the premise of risk point correlation, this dissertation uses social network analysis to analyze the extracted risk points to reveal the relationship between risk points, and visualize the risk network. Effective identification of risk relationships can assist future risk investigation work, and can help employees to better identify the underlying credit risks, promote knowledge sharing, and effectively prevent and control small and micro enterprise credit business risks. Finally, this research puts forward the countermeasures for XL Bank to further strengthen the credit risk management of small and micro enterprises. This dissertation employs the word frequency analysis and social network analysis to look into the risk prevention and control network diagram of commercial banks' small and micro enterprises credit business, and proposes management countermeasures, which can increase the recognition of risk points in the field of credit risk management, and broaden the understanding of the relationship between risk points. In terms of practical significance, the research not only provides reference suggestions for practitioners of risk prevention and control management of commercial banks’ credit business, but also provides reference suggestions for applications of unstructured data of commercial banks.
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