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论文编号：	9911
作者编号：	2220160664
上传时间：	2018/5/29 14:53:38
中文题目：	深微征信公司大数据技术应用研究
英文题目：	The study on application of big data technology in SHENWEI Credit Investigation Co.,Ltd
指导老师：	古志辉
中文关键字：	大数据；征信；银行；信息不对称；风控模型
英文关键字：	Big data ; Credit investigation ; Bank ; Information asymmetry ; Risk management model
中文摘要：	征信主要是为了解决商业金融交易中，由于交易双方的信息不对称，而带来的信用风险。征信机构通过信息共享和整合，减少信息不对称，利用经验主义和大数定律手段，帮助信用风险管理决策。随着征信体系全面走进信用交易的过程中，它的地位被不断提高，在外部性的影响方面越来越大。 大数据技术的发展极大促进了征信业的进步，为征信企业提供了很多便捷。征信业早期没有定量描述，在电子信息技术得到发展后，数据收集变得更多，数据库数据量大大增加，开始使用数据模型来进行信用评价，改变了原来的分析方法，促进了行业快速发展。互联网信息化发展趋势变得更加明显，数据量变得非常庞大，通过对数据相关度进行分析，进一步得出更可靠的信用数据。除此之外，数据模型不仅可以通过现有数据对现在的情况分析，它还可以对未来进行估计，从而更加全面描述信用状况。 本论文通过引入征信行业的基本理论，分析大量国内外征信行业的文献内容，结合目前征信行业的发展趋势，对发达国家的征信行业现状及国际征信巨头的成熟商业模式进行梳理，总结征信行业技术方法，进而结合目前国内征信行业的政策法规、发展现状，探索深微征信公司大数据技术应用研究。力图为国内大数据征信业的发展，提供一个专业和严谨的角度，对大数据征信行业具体的问题思考和解决有所启发。 作为本文的结论和成果，在论文后半部分，以深微征信公司大数据征信的具体项目为例，详实的论述了从背景介绍、可行性分析、如何创建大数据征信平台，并且以大数据征信平台为基础数据，税务信息为核心数据，创建风控模型和系统，输出给银行等金融机构，从而形成商业闭环，实现社会效益和经济效益，并对结论、未来展望和不足进行了论述。
英文摘要：	Credit investigation is used broadly to manage credit risk which is caused by information asymmetry. Credit investigation institutions decrease information asymmetry by experience of experts and statistical methods and information integration for better credit risk management in commercial activities. Credit investigation system is more and more important and will make more influence as it penetrate into more commercial activities. The technology of big data promote the credit investigation industry deeply and will make it more convenient to investigate enterprises. In the former stage, quantify method was used little to estimate risk of enterprises. As more and more data is gathering with an increasing speed for the progress of electronic information industry, we have use more data driving modelsto analyse enterprise and make more development and changes in credit investigation industry. With the trend of informatization, there will be more reliable information to be extracted from massive data which related to the credit investigation. The models will be used to analyse data to predict the future credit risk and to describe enterprises more accurately. Based on the credit investigation theory and the analysis of massive domestic and foreign literatures, this paper will summarize mature business models of big credit investigation companies and discuss credit investigation industry in developed countries and explore big data methods for credit investigation industry with considering present situation of domestic credit investigation industry. This paper try to consider the challenges and opportunities of the industry from a careful perspective. This paper supply a real big data credit investigation project as an example to illustrate how to make feasibility analysis, how to create a big data platform, how to use taxation data as primary data to integrate other data to make a risk management model and system for financial institution as banks to make a close business circle to create more social and business benefit.
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