【作者】 李炎；

【导师】 寇纲；

【作者基本信息】 西南财经大学 ， 工商管理， 2014， 硕士

【摘要】 “大数据”时代悄然来到。麦肯锡认为：“数据,已经渗透到当今社会的各个公共和私有领域,成为重要的生产因素。对于大数据的挖掘和运用,预示着新一波生产率提升和消费者盈余浪潮的来到。”哈佛大学伽里·金教授认为：“这是一场革命,庞大的数据资源使量化过程在各个领域开始发酵,无论学界、工商界还是政府,所有领域都将开始。”不光是谷歌、百度、阿里巴巴、腾讯这样的互联网公司,华尔街的股市、银行、对冲基金,美国疾病控制和预防中心,美国总统竞选,棱镜计划都有使用大数据的案例。“数据是新的石油。”亚马逊前任首席科学家安德撸斯·韦恩德说。在互联网时代前,银行业是储存数据最多的行业之一。到了互联网和物联网的时代,一般来说大数据的特点之一就是价值密度低。但是银行业拥有的海量的本行业的数据以及可以扩展的外部数据,这些数据的价值相对较高,有更好的挖掘价值。目前,国内的全国性大型银行普遍拥有100TB (1T=1024G)的数据存量。在我省法人机构(不含新型农村金融机构)中,信息化水平相对高的银行,其数据的存量和年增量也都发展到了TB级。随着移动互联网在金融领域的不断渗透,银行数据的发展还将是一个不断加速的趋势。以国内外银行的实践经验来看,对数据的集中整合不完整、质量控制不到位和应用分析不充分的银行,其很难在大数据时代保证其应有的核心竞争力。目前,四川一些银行已经意识到数据的重要性,正在开展或筹划数据工作,但仍有不少银行缺乏应有的认识。随着进一步的改革开放,利率市场化的逐步推进,金融改革的深度试点,互联网金融、第三方支付等新金融业态的冲击,使金融市场竞争格局正在发生深刻变化。同时,银行业资产规模越来越大,机构类型数量越来越多,金融交易越来越快速,金融产品层出不穷,表内外业务风险关联复杂,银证、银保、银信业务交叉融合,风险跨机构、跨市场、跨时空传染的可能性和影响力显著上升。银行业金融机构和监管部门在上述金融新生态的环境下,如何利用大数据时代带来的新机遇来有效应对新挑战,值得深入思考。本文基于四川银监局MAST项目的设计、实施、推广、应用,对大数据时代银行业风险以及监管等方面的问题进行了研究。本文介绍了监管的相关理论,特别是非现场监管的相关实践,为全文的研究起到了理论支撑作用。四川银监局MAST系统的应用现状、数据治理以及对大数据时代监管的影响为本文研究的重点：一是监管手段的发展和探索；二是MAST的设计、建设和监管应用。在大数据时代,面对银行业的快速发展,监管的人力、经费等资源终归有限,如何向科技要生产力,不断创新监管的技术方法,以顺应时代发展的需要,保证监管部门在分析技术方法方面的先进地位；三是MAST的数据共享和风险联防功能探讨；四是数据治理和以MAST为视角的数据成熟度评价模型；五是大数据时代,面对宏观审慎和微观审慎监管的要求越来越高,MAST如何通过满足对海量数据分析的要求,进一步达到持续、及时、全面的风险态势感知,提高监管的前瞻性、针对性和权威性。笔者选择了MAST项目正在推广部署的四川法人银行作为样本,进行四川银监局MAST项目案例研究。通过这个案例可以看得出MAST的设计理念和应用情况,如何去解决一些监管中的难题,向“全天候、全时态、全透明、全功能”的全息监管努力。并结合大数据时代和互联网金融的发展,展望未来的监管方法。更多还原

【Abstract】 The Big Data era has come quietly. McKinsey said:" data has penetrated into various aspects of public and private areas of our society, and it has become an important factor of production. The using of massive data mining heralded the arrival of a new trend of productivity growth and consumer surplus." Harvard University sociology professor Gary King said:"this is a revolution that the quantization process begins in all areas including academic, business and governmental." Examples of the capitalization on massive data mining are legion: they can be found not only in internet companies such as Google, Baidu, Alibaba or Tencent, but also in stocks exchanges, banks, and hedge fund in Wall Street, as well as the CDC (Centers for Disease Control and Prevention), Presidential elections, and the PRISM program."Data is the new oil", Amazon’s former chief scientist Andrews Waynes said.In the pre-Internet era, banking sector was one of the industries that had the largest data storage.In the era of the Internet and the Internet of Things, one of the hallmarks of big data is that it generally has lower proportionate value. However, the banking industry has massive internal, industry-specific and expandable external data, which has comparatively higher proportionate value to be exploited.Currently, the nation’s largest banks generally have100TB-level data storage.In Sichuan province, local banking institutions that have relatively higher level of information technology implementation (other than new rural financial institutions) also have data storage and annual data increment at the TB level. With the advent of mobile internet into the banking industry, the development of banking data will continue to accelerate.Empirical evidence from domestic and foreign banks’practice shows that, banks that focus on data integration, quality control and application analysis are more likely to achieve proper core competitiveness in the era of big data. Currently, some Sichuan banks have realized the importance of data and have begun working on the planning or carrying out of data utilization, although there are still many banks that lack an understanding of the importance of data.With the impacts from further economic reform, the freeing up of interest rate, the pilot in-depth financial reform, online banking, and third-party payment systems, it is not suprising that the competitive dynamics of the financial market is undergoing profound changes. Meanwhile, the continuing growth of the size of bank assets, the number and types of banking institutions, and the instant consummation of financial transactions, as well as the emergence of countless novel financial products, all add up to the overall business risks. Moreover, risks inherent in off-balance sheet transactions and the mixed operation of retail and investment banking will further increase cross-agency, cross-market and cross-space damages when crisis occurs. To use big data to properly respond to these new challenges may worth more attention and thinking.This paper is based on the designing, implementation, promotion and application of CBRC Sichuan Office’s MAST project (Monitoring and Analysis System Technology). This article examines the challenge of banking regulation and risk management in the era of big data and also describes the theory of banking regulation, particularly the practice of off-site supervision, which played a supporting role in the theories of regulation. This article focused on the application of the MSAT system, the data governance and the regulatory impact on banking regulation in the era of big data:Firstly, the development and exploration of regulatory means. Secondly, the design, construction and application of the MAST project--how to innovate technology and methods to ensure that the regulatory authorities may adequately cope with the challenges with limited funds and human resources in the era of big data and in the face of rapid development of banks. Thirdly, discussions of MAST’s features on data sharing and risk management. Fourthly, data governance and the evaluation of data maturity from MAST’s vantage point. Fifthly, in the big data era where macro and micro regulations should be more sensitive and prudential, how to meet the requirements for timely massive data analysis and comprehensive risk situational awareness, so as to improve banking regulation in a proactive and authoritative manner. This article chooses local banking institutions in Sichuan province as samples for MAST project study. This article illustrates the designing concept and application of the MAST program, and tries to apply these concepts to achieve comprehensive banking regulation.更多还原