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Author: Jianping Li, Yongjie Zhang, Dengsheng Wu, and Wei Zhang
Big Data are now viewed as the core asset of major Chinese financial institutions, central to the innovation of financial products and services as well as risk management.
With the rise of the concept of Big Data in 2011, practitioners in the Chinese financial industry began in 2012 to explore ways to gather the best Big Data, and in 2013 launched numerous Big Data projects (2013 is the first year of both Big Data and Internet finance in China). Since then there have been dramatic changes in the Chinese financial industry, which has been actively addressing the challenges and enjoying the opportunities associated with Big Data.
Development associated with Big Data is considered the future direction of the Chinese financial industry. According to a survey of trends published by the China Computer Federation (CCF), use of Big Data in the financial industry is in second place, after e-commerce (CCF 2013a). The sheer size of the Chinese economy and population implies the largest databases in the world, which means more channels and a greater possibility to develop and use Big Data.
China’s economic transformation is at a critical crossroad. Information consumption is expected to become the new engine for domestic demand, and the financial industry will play a key role in integration and innovation in network information technology and service patterns (SCPRC 2013). The Chinese financial industry is rapidly moving toward Big Data as almost every sector (e.g., banking, securities, and insurance) is undertaking Big Data projects, and the industry is improving operational and structural efficiency to fully realize China’s economic transformation.
The Chinese government has shown great interest in and support for the development and innovative use of Big Data in the financial industry. The 18th National Congress of the Communist Party of China (CPC) has proposed to promote financial innovation and improve the competitiveness of banking, securities, insurance, and other industries (Hu 2012), and in August 2013 the State Council approved the Interministerial Joint Coordination Conference System led by the People’s Bank of China for financial regulations.
In November 2013 the third Plenary Session of the 18th CPC Central Committee decided to boost financial innovation and construction of multilayer financial markets (NPC 2013). Also that month, China’s National Bureau of Statistics signed a strategic cooperation agreement with eleven domestic companies—in e-commerce, the Internet, telecommunications, and other areas—for jointly developing and utilizing Big Data. Most recently, the China Government Work Report proposes that Big Data be a leading industry in the country’s national economic development (Li 2014).
Online Financial and Insurance Services and Data
Large banks in China are transforming into Big Data companies, using data mining technology to explore the business value of the data and develop a data value chain. The highly regulated Chinese financial industry, with assets worth more than 150 trillion yuan ($24 trillion1), has more than 100 terabytes (TB) (IDC 2012) of both structured and, increasingly, unstructured data.
As of March 2014 the Industrial and Commercial Bank of China (ICBC) had more than 4.9 petabytes (PB) of stored data. The annual structured and unstructured data of the Agricultural Bank of China (ABC) are 100 TB and 1 PB, respectively (ABC 2014). The Bank of Communications (BOCOM) handles about 600 gigabytes (GB) of data daily, with a storage capacity of more than 70 TB (BOCOM 2014).
The Chinese insurance industry also has massive amounts of information, which have radically transformed the industry’s business model. For example, Zhongan Online Inc., an insurance and Internet finance company founded in September 2013, bypasses traditional branch offices to sell on the Internet. Many companies have established their own process and data centers, and some have achieved interoperability of customer databases.
The Chinese securities market was among the earliest to adopt electronic trading, and more than 200 million investors now generate more than 60,000 orders per minute on average. The online system of the China Securities and Regulatory Commission (CSRC) and stock exchanges performs semantic analysis to detect more than 100 million social media data daily (SSE 2014; SZSE 2013)—and in December 2013 detected “rat trading” in Shanghai through this online monitoring system. Some brokers have begun to study the relationship between Internet information, social media (e.g., Weibo) activity, and stock market performance to identify stock market trends through public opinion analysis.
In 2013 the financial division of Alibaba Group Holding Ltd. created an entirely new model for tapping into the financial industry as an e-commerce giant, offering services involving the mining of Big Data. Yu’e Bao, for example, is a monetary fund colaunched by Alipay under Alibaba and Beijing-based Tianhong Asset Management Co.; it now has more than 81 million customers and total funds of more than 350 billion yuan (Tianhong Asset Management Co. 2014). As of December 31, 2013, Yu’e Bao had revenue of 1.79 billion yuan (Tianhong Asset Management Co. 2014). Similarly, Sina, Inc. obtained a third-party payment license and has launched Weibo Wallet; JD.com has announced plans to establish a financial conglomerate; and Rong360.com Inc. has launched a financing package of $3 billion. Internet service platforms for Big Data can be divided into the business model (represented by Alibaba Financial and 360buy) and the financial supply chain model (represented by Suning Appliance Company Limited). Chinese mobile Internet finance promises greater action, including the launch of creative services and other financial uses of Big Data, and more advances in 2014.
With Big Data, companies at all levels have opportunities to develop themselves in the Chinese financial industry. Industry leaders expect to make full use of this resource to compete in the worldwide market. While small and medium enterprises (SMEs) aim to achieve greater breakthroughs, Internet-based companies will surmount financial barriers by relying on their own technology. Thus the competition for Big Data in the Chinese financial industry is growing.
Innovations in the Chinese Financial Industry
In the context of intensified competition, market-based reform of interest rates, increased regulation, and growing financial pressure, Big Data have brought an innovation boom to the Chinese financial industry.
Financial institutions are using Big Data technology to improve products and services, and Internet companies are using disruptive innovations to challenge traditional financial institutions.
Banks are funding companies through online banking, third-party payment companies, and e-commerce platforms. They expect to realize seamless docking among consumption, working capital management, and financial value-added services, thereby connecting consumers, investment, and credit card payments.
According to the latest data of the China Banking Regulatory Commission (CBRC), in 2013 mobile payment service accounted for 1.668 billion transactions, aggregate mobile payments amounted to 9.64 trillion yuan, and mobile banking had 458 million individual customers, with annual growth of 55.5 percent (CBRC 2014).
At the end of 2013 microchannel payments of Tencent, Inc. were extended from online to offline. On the first day of a financial product of Baidu, Inc., sales volume was 1 billion yuan, three times that of Yu’e Bao on its first day (Baidu 2014). NetEase, Inc. launched a financial product with an annual interest rate as high as 10 percent, and in the first quarter of 2014 new Internet finance products known as Bao-Baos also did well2; WeiChat Li-cai-tong of Tencent, Inc. topped the list with 152.667 yuan revenue per 10,000 yuan and Yu’e Bao ranked second with 145.58 yuan (Sina Finance 2014).
With updated regulations, the Chinese government is working to improve the information disclosure system and formulate new regulatory norms for the financial industry. Companies are required, for example, to state the goal of their financial innovation, the probability distribution of risk exposure, measures of risk avoidance, and rights and liabilities.
Improvements in Risk Management
The CCF holds the view that Big Data will have a profound impact on two principal concerns of the Chinese financial industry: risk and credit (CCF 2013b). Deep data mining and value analysis based on Big Data can enhance the ability to manage financial risk.
The Credit Reference Center of the People’s Bank of China (CRCPBC) collects data reported by commercial banks and other social agencies (and verified by the Identity Authentication Center). As of end-November 2013 its data represented more than 830 million individuals and nearly 20 million businesses or other organizations; of these, approximately 300 million people have borrowing records in banks or other financial institutions (CRCPBC 2013). The credit system is fully built and accessible to the public, greatly contributing to transparency and the availability of credit information.
From January to November 2013 the trading volume of the ICBC was 335 trillion yuan, 80 percent of which was accounted for by online trading. The ICBC applied these data for risk control and significantly reduced the daily workload of manual data entry from 8.99 million to 54,000 transactions (ICBC 2014).
As the pioneer of Internet finance, Alibaba has been constantly improving its risk control through Big Data analysis. It has its own closed credit ratings and risk control models based on payment and e-commerce transaction information, and eight approved patents on risk control. In 2013 Alibaba purchased 18 percent of Sina Weibo shares to acquire social Big Data, further enriching its database and strengthening its risk control. As of mid-February 2014 the company had made loans worth more than 170 billion yuan and served more than 700,000 SMEs. Its nonperforming loan ratio is less than 1 percent (Paidai 2014).
Challenges and Trends
Changes in the Structure of the Financial Industry
In the Chinese financial industry, Big Data are significantly affecting the needs of consumers and businesses in direct and indirect ways. In the next several years, changes in the structure of the industry will be reflected in the following aspects:
Technological and regulatory changes have prompted financial companies to compete with cross-industry potential competitors as well as other financial companies both domestic and international. Competitive enterprises are expanding rapidly, and other companies are being eliminated or are struggling to differentiate their services and products from those of their competitors. Eventually, these factors will drive greater competition and efficiency in the financial industry.
Need for Big Data Standards and Platforms
The Chinese financial industry has seen the emergence of semi- and unstructured pools of data that are of different types and often fragmented, without any apparent patterns, such that traditional data warehousing is of only limited utility and relevance. The quantity and mix of data create difficulties in both operations and analysis for major commercial banks and insurance companies.
The value of mixed data for business intelligence analysis depends on their unification and standardization (Mayer-Schönberger and Cukier 2013). Standardization can promote the communication, exchange, and comparison of data, facilitating data applications and technology extension. For example, the emergence of the Semantic Web (www.w3.org/standards/semanticweb/) paved the way to combine ontology with standards to enhance understanding of concepts, methods, purposes, and examples of Big Data (Lanier 2013).
Financial data standards can also guide the creation of Big Data platforms to help connect data islands in various areas (Hubbard 2010). Supported by improved standards and platforms, financial institutions will be able to offer the threefold capacity of online payment + Big Data + e-commerce to respond to the tide and challenges of Internet finance. Such a development will be of great strategic significance for innovation, refinement of management, and efficient decision support.
China CITIC Bank, for example, has formed an Internet platform supported by Big Data, with an online “mall” for e-commerce, mobile banking, and online payments. By the end of 2013 the CITIC online bank had more than 10 million customers, 3.4 million of whom used mobile banking. The total trading volume is more than 33 trillion yuan, with annual growth of 33.87 percent (China CITIC Bank 2014). Some banks have built their own Big Data platforms (e.g., E-mall of BOCOM, Cloud Shopping of Bank of China, and Rong e-Shopping of ICBC); others are seeking cooperation with e-commerce companies to support the construction and operation of Big Data platforms.
Further recognizing the utility of Big Data, in January 2014 China Insurance Information Technology Management Co., Ltd. was established to provide a platform for data sharing not only between insurance companies but also between insurance and other sectors.
For online finance and financial data service providers, the Internet has already made their platforms relatively comprehensive. But they need to further integrate data structures and explore the deeper value of standards to ensure and enhance the value of financial data.
Improved Analytical and Processing Techniques
With the increasing integration of e-commerce and social media, the challenges of storing, processing, indexing, and integrating data from different sources and with differing patterns are increasing (Shiller 2009). Furthermore, Chinese financial data analyses are now based largely on offline historical data, as the ability to analyze high-frequency and real-time data remains limited. Such analytical capacity will be an important development direction for Big Data techniques (Mayer-Schönberger and Cukier 2013).
In addition, financial data analyses mainly focus on structured data, which account for only about 15 percent of total data. Improvements to Big Data techniques on both semi- and unstructured data will help to break down data boundaries and enhance the transparency of financial industry operations.
ABC cooperates with Huawei Technologies Co., Ltd. and expects to use Big Data techniques to process annual data greater than 1.1 PB. However, the software it currently uses (and to which it was a significant contributor), Hadoop, is an open source software.3 The creation of a closed commercial version of Hadoop and the addition of peripheral technology applications, particularly migration, are very difficult for commercial banks (Turkington 2013). Huawei therefore undertook an in-depth study of the demand for a Big Data processing platform and provided a distributed parallel computing cluster based on the Huawei RH2288 V2 server for testing. Huawei RH2288 V2 server clusters can complete 85 GB original data loading or 50 million accounts on a regular basis, with batch processing in 10 minutes. In terms of the average response time for 200 million pieces of trading details and 600 concurrent random queries, test results are under 40 ms, far better than expected. ABC recently announced that 200 Huawei RH2288 V2 servers will be used on the Hadoop data processing platform in a formal production environment (ABC 2014).
New financial regulatory and compliance requirements place greater emphasis on governance, risk disclosure, and transparent data analysis. New regulations should improve data collection capability, analytical skills, and the ability to translate a large quantity of archived data to accessible regulatory information. It is also critical to implement dynamic monitoring, process regulation, and real-time supervision to improve regulatory effectiveness.
Security and Privacy
With the growth of Big Data in the Chinese financial industry, security and privacy concerns have become more and more prominent. The collection, storage, management, and use of data still lack adequate technologies, software, and programming as well as regulatory supervision. These problems drive the need for more sophisticated data processing algorithms, stricter access control, and significant upgrades to existing systems. Although the Chinese financial industry has invested much in these areas, other factors, such as the elongated business chain, the popularity of cloud computing, the complexity of upgrading systems, and improper use of data, have increased the risk exposure of financial Big Data.
Princeton University computer scientist Arvind Narayanan has observed that, as long as there is a reasonable incentive to promote commercial data mining, any form of privacy is “algorithmically impossible” (Patrick 2013). Indeed, tens of millions of customer information leaks occur at home and abroad, raising doubts about security and privacy among both managers and customers. Financial institutions can and must reduce this number by strengthening their technical and other protective measures to improve data security and customer privacy.
As the role of Big Data in the economic and financial world steadily increases, it is critical for China to improve its data analysis systems and reinforce the security of its financial networks. Huawei has helped the State Administration of Foreign Exchange (SAFE) use virtualization technology to ensure data protection and enhance business stability. The Chinese government and regulatory institutions should further strengthen cooperation—for example, in technology development, information sharing, and system maintenance—between domestic Big Data technology companies and financial institutions to ensure the security of national financial data.
In the Chinese financial industry reliance on Big Data has developed rapidly, together with a boom in financial innovation that has seen the emergence of new financial products and business models. Deep data mining and value analysis based on Big Data have the capacity to enhance financial risk management. Competitive enterprises that can adapt to these changes will expand rapidly, while others will be eliminated or differentiate themselves from the competition.
Similarly, a new pattern will develop in the Chinese financial industry based on the use of Big Data in data processing and regulation. The creation of a platform and unified set of standards for Big Data will bring dramatic changes to data collection and integration. But these advances come with much greater security and privacy challenges that will require financial institutions to strengthen technical and protective measures.
Big Data have promoted the development and structural improvement of the Chinese financial industry, but they also retain imperfections and carry risks. The use of Big Data in the Chinese financial industry is still in the initial stages and requires sustained efforts to improve utility and security. With such improvements China may take advantage of this important opportunity to establish a world-class financial data analysis system.
ABC [Agricultural Bank of China]. 2014. Annual Report of ABC 2013. Beijing. Available at www.abchina.com/en/investor-relations/performance-reports/ annual-reports.
Baidu. 2014. Annual Report 2013. Beijing. Available at http://8.baidu.com.
BOCOM [Bank of Communications]. 2014. Annual Report of BOCOM 2013. Shanghai. Available at www.bankcomm.com/BankCommSite/en/invest_relation/company_ develop.jsp?type=report.
CBRC [China Banking Regulatory Commission]. 2014. Annual Report on Chinese Banking to Improve Service 2013. Released March 15. Beijing. Available at www.cbrc.gov.cn/chinese/home/docViewPage/110009.html.
CCF [China Computer Federation]. 2013a. Predictions of Big Data Trends 2014. Beijing. Available at www.ccf.org.cn/sites/ccf/zlcontnry.jsp?contentId= 27792864436 23.
CCF. 2013b. China Big Data Technology and Industrial Development White Paper 2013. Beijing. Available at www.ccf.org.cn/sites/ccf/ccfziliao.jsp?contentId= 27747936491 05.
China CITIC Bank. 2014. The Annual Report of China CITIC Bank 2013. Beijing. Available at http://bank.ecitic.com/download/investorrelation_en/ e2013110 7H_2.pdf.
CRCPBC [Credit Reference Center of the People’s Bank of China]. 2013. The Introduction of CRCPBC. Beijing. Available at www.pbccrc.org.cn/zxzx/zxgk/gywm.shtml.
Hu J. 2012. Report to the 18th National Congress of the Chinese Communist Party: Unswervingly Advance along the Road of Socialism with Chinese Characteristics, for Building a Moderately Prosperous Society in all Respects. Available at http://news.xinhuanet.com/english/special/18cpcnc/2012- 11/17/c_131981259.htm.
Hubbard DW. 2010. How to Measure Anything: Finding the Value of Intangibles in Business. Hoboken, NJ: John Wiley & Sons.
ICBC [Industrial and Commercial Bank of China]. 2014. 2013 Annual Report. Beijing. Available at www.icbc.com.cn/icbc.
IDC [International Data Corporation]. 2012. Chinese Big Data Technology and Services Market Forecast and Analysis 2012–2016 [in Mandarin]. Available at www.idc.com.cn/uploadpic/5-Frank-banking2013.pdf.
Lanier J. 2013. Who Owns the Future? New York: Simon & Schuster Press.
Li K. 2014. Report on the Work of the Government 2014. Available at http://news.xinhuanet.com/english/special/2014-03/14/c_ 133187027.htm.
Mayer-Schönberger V, Cukier K. 2013. Big Data: A Revolution That Will Transform How We Live, Work, and Think. Orlando: Houghton Mifflin Harcourt.
NPC [National People’s Congress]. 2013. The Communiqué of the Third Plenary Session of the 18th Central Committee of the Communist Party of China. Beijing. Available at www.china.org.cn/china/third_plenary_session/2014-01/15/ content_31203056.htm.
Paidai. 2014. The First Quarterly Report of Alibaba Financial. Available at www.paidai.com/labels/èç³å¡.html.
Patrick T. 2013. Has big data made anonymity impossible? MIT Technology Review, May 7.
SCPRC [State Council of the People’s Republic of China]. 2013. The Promotion of Consumer Information on a Number of Opinions to Expand Domestic Demand. Beijing. Available at www.gov.cn/zwgk/2013-08/14/content_2466856.htm.
Shiller RJ. 2009. The New Financial Order: Risks in the 21st Century. Princeton, NJ: Princeton University Press.
Sina Finance. 2014. Quarter inventory of Internet Finance: Yu’e Bao’s earning is 17 times than bank deposit. Shanghai. Available at http://tech.sina.com.cn/i/2014-04-03/14139296458.shtml.
SSE [Shanghai Stock Exchange]. 2013. Statistical Yearbook of the Shanghai Stock Exchange. Available at www.sse.com.cn/researchpublications/publication/yearly/ c/tjnj_2013.pdf.
SZSE [Shenzhen Stock Exchange]. 2013. Statistical Yearbook of the Shenzhen Stock Exchange. Available at www.szse.cn/main/files/2013/05/28/101380619096.pdf.
Tianhong Asset Management Co., Ltd. 2014. First Quarterly Report of Yu’e Bao 2014. Tianjin. Available at www.thfund.
com.cn/column.dohsmode=searchtopic&pageno=0& channeli d=2&categoryid=2435&childcategoryid=2436. htm.
Turkington G. 2013. Hadoop Beginner’s Guide. Birmingham: Packt Publishing Ltd.
1 Converted at the rate of 1 yuan = 0.16 US dollar (October 14, 2014).
2 Bao-Baos are innovative Internet finance products concentrated in microfinance and produced by Internet financial companies. The most representative one is Yu’e Bao of Alipay.
3 Hadoop is a distributed system infrastructure developed by Apache. It is popular for use with Big Data.