The difference between big data credit and traditional credit?

Traditional credit plays a key role in facilitating personal credit, assisting financial credit decision-making, preventing credit risk and enhancing financial accessibility, but its limitations in the field of Internet finance cannot be ignored. One is that there are still about 500 million people in the country who do not have credit activities with licensed financial institutions and thus are not covered by them. Secondly, with the development of "Internet +", a large amount of data related to personal credit has been generated and deposited on the Internet, which is still difficult to be adopted by it [1]. The emergence of big data credit collection helps to solve the above problems, and to a certain extent has achieved rapid development. According to our research, the basic conditions for the development of big data credit have the following three points: first, the good signals released by China's policy support and deployment; second, the "financial online" as the representative of the Internet finance more huge long-tail demand; third, the strong support of big data technology.

I. Policy Support

Since 2013, China has promulgated a series of laws and regulations to build a legal framework for the healthy development of the credit collection industry.

The State Council issued the Regulations on the Administration of the Credit Collection Industry (hereinafter referred to as the "Regulations") in March 2013, which became China's first regulation on the credit collection industry and is the cornerstone of the legal system for the construction of China's credit collection industry.

December 2013, China issued the Regulations on the Administration of the Credit Collection Industry (hereinafter referred to as the "Regulations") to complement the implementation of the Regulations. In conjunction with the implementation of the Regulations, the People's Bank of China issued the Measures for the Administration of Credit Collection Institutions to implement the requirements for the establishment of a sound social credit collection system, and to establish the institutional norms and supervisory basis for credit collection business activities.

In addition, in order to improve the level of personal credit services and introduce market competition, China has made legislative preparations for the gradual liberalization of the credit market.In January 2015, the People's Bank of China issued the Notice on Making Preparations for the Personal Credit Business, approving eight institutions to make preparations for launching the personal credit business.In July 2015, the People's Bank of China and ten other departments issued the Guiding Opinions on Promoting the Healthy Development of Internet Finance" (hereinafter referred to as the "Guiding Opinions"), which proposed to promote the construction of credit infrastructure, cultivate the supporting service system of Internet finance, and encourage conditional institutions to apply for licenses for credit collection business in accordance with the law. The regulatory reform measures have created a favorable external environment for the development of big data credit.

It is worth noting that in order to accelerate the deployment of big data, deepen the application of big data, and promote the implementation of the "Internet Plus" national strategy, the State Council issued the "Outline of Actions for Promoting the Development of Big Data" in July 2015, and the General Office of the State Council issued the "Several Opinions on the Utilization of Big Data to Enhance the Service and Supervision of Market Entities" in September 2015. Several Opinions on Utilizing Big Data to Strengthen the Services and Supervision of Market Entities. The most notable aspects of the Outline of Action for Promoting the Development of Big Data are the opening up of government data and the promotion of industrial innovation, and the encouragement of the application and development of big data in the credit collection industry. Relevant experts believe that big data is an important "mineral resource" for credit construction, which must be based on and supported by big data, and that big data can be used to establish a credit system in terms of breadth and depth, and to improve the comprehensiveness, real-time nature and credit-granting efficiency of credit evaluation.

In the era of big data, data has become a strategic resource equivalent to energy, and the disclosure of information and the opening up of data have become the theme of the development of the current era. Administrative organs in the performance of administrative management and public **** service duties in the process of mastering a huge amount of information, how to open information management, revitalization of these data assets, the administrative organs have become an urgent problem to be solved. The Fourth Plenary Session of the 18th CPC Central Committee, "The Decision of the Central Committee of the People's Republic of China on Several Major Issues on Comprehensively Promoting the Rule of Law", explicitly put forward the need to comprehensively promote the disclosure of government affairs, promote the informatization of government affairs, and strengthen the construction of the Internet-based data service platform for government information. The gradual establishment of the data disclosure system for the opening of social information resources, **** enjoyment and services to provide institutional safeguards.

The formulation of these laws, regulations, ordinances and systems is conducive to strengthening the management of the entire credit collection market, regulating the behavior of information providers, information users and credit collection agencies, and safeguarding the rights and interests of information subjects. Meanwhile, other supporting systems are being gradually developed and improved, which will constitute the legal system of credit collection together with the Regulations*** to promote the healthy and sustainable development of China's credit collection industry and better meet the financing needs of individuals and enterprises.

II. Market demand

In recent years, Internet finance has emerged as an emerging force in China's economic development. Internet finance in the prosperity of the development of the same time, due to the establishment of a shorter period of time, their own risk prevention and control capabilities are weak, credit assessment, risk pricing and risk management are imperfect, problematic events continue to emerge. On the one hand, most of the users of Internet finance are network users with "long-tail characteristics", which are difficult to be covered by the traditional credit report, and due to the lack of communication and exchange of information and data among industry organizations, the phenomenon of "one person with multiple loans" and duplicate borrowing is prominent. The whole industry is facing a huge credit risk. On the other hand, due to the unsound credit system, Internet finance companies generally focus on offline risk control, and a large number of due diligence investigations are time-consuming and labor-intensive, which not only increases their own operating costs, but also makes it easy for them to bias the assessment of the borrower's credit level, which indirectly raises the cost of financing. Inadequate traditional credit collection mechanism has become a major factor restricting the development of Internet finance. The development of Internet finance provides a huge application prospect for the development of big data credit, forcing credit to keep up with the pace of the times and promoting changes in the credit mechanism.

Third, technical support

The reason for the rise of big data credit, in addition to the above two factors, technical support is indispensable. The progress of big data and cloud computing technology provides support and convenience for the development of big data credit, and artificial intelligence algorithmic modeling provides a powerful complement to the comprehensive portrayal of user default probability and credit status. On the one hand, with the development of "Internet+", the people's clothing, food, housing, transportation, social interaction and the Internet tend to be closely integrated, and a large amount of data related to personal credit has been generated and deposited on the Internet. With the help of big data crawling and mining technology and cloud computing technology, it becomes easier to collect, record, store and analyze these data. On the other hand, artificial intelligence technologies represented by machine learning have been adopted one after another, which can not only analyze, summarize and aggregate structured and unstructured data obtained from various channels, but also design a variety of predictive models (fraud model, identity verification model, repayment willingness model and stability model, etc.) to predict the credit subject's willingness to perform and the ability to perform, and to reduce the risk of default and the rate of bad debts.

[1]Xie Ping,Zou Chuanwei. The way to develop an independent third-party credit agency. Caixin Weekly,2017-02.

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South Lake Fintech Research 100 Series of (XXXIII)

--Big Data Credit Collection vs. Traditional Credit Collection

In recent years, along with the development of Internet finance and big data technology, big data credit collection has begun to rise. Big data credit collection has four innovative features: wide coverage of people, diversified information dimensions, rich application scenarios and comprehensive credit assessment. However, compared with traditional credit collection, big data credit collection still has many problems in terms of the utility of the data scope and connotation, the independence of the credit collection agency and the protection of privacy, etc., which need to be emphasized.

I. The basic concept of credit collection

Traditional credit collection is a specialized credit management service in which a professional organization collects financial and financial transaction information through a fixed model and processes, handles and reports the information. Traditional credit collection emerged in foreign countries, in the United States, represented by Dun & Bradstreet established in 1933, and in China, mainly represented by the Central People's Bank credit collection system, which is the prevailing credit collection industry in our country and even globally. The establishment of China's credit agencies and the conduct of credit business is governed by the Regulations on the Administration of the Credit Collection Industry and requires the application of appropriate licenses.

Big data credit collection refers to the collection, organization, analysis and mining of massive, diversified, real-time and valuable data, and the use of big data technology to re-design the algorithms of credit evaluation model, multi-dimensional portrayal of the credit subject's "portrait", and presenting to the information users the default rate and credit status of the credit subject. The company's business model is based on the following principles

Big data credit collection activities are within the scope of credit collection business defined by the Regulations on Administration of Credit Collection Industry, and its essence is still the collection, arrangement, preservation, processing and publication of credit subject information, but compared with traditional credit collection, it highlights the application of big data technology in credit collection activities, emphasizes the characteristics of large data volume, wide portrayal dimension and dynamic interaction of credit status, and can be a useful supplement to the credit collection system.

The innovative features of big data credit collection

On the surface, big data credit collection and traditional credit collection seem to be only different data acquisition channels, the former is mainly from the Internet, the latter is mainly from the traditional offline channels, but there are big differences between the two. The innovation of big data credit collection is mainly manifested in the four aspects of extensive coverage of the population, diversified information dimensions, rich application scenarios and comprehensive credit assessment, which brings about the reduction of the cost of credit collection and the improvement of the efficiency of credit collection.

First of all, it covers a wide range of people. Traditional credit collection mainly covers people with credit records in licensed financial institutions. Big data credit collection captures the people not covered by traditional credit collection through big data technology, and utilizes the Internet to leave traces to assist in the judgment of credit, and meets the needs of identification, anti-fraud, credit assessment and other aspects of credit collection of the new Internet financial industry, such as P2P network lending, third-party payment and Internet insurance.

Secondly, the information dimension is diversified. In the Internet era, the information and data sources of big data credit are wider and more diverse. Big data credit data is no longer limited to the basic personal information, billing information, credit records, overdue records, etc. provided by financial institutions, government agencies and telecommunications, but also introduces data such as Internet behavior track records, social and customer evaluation. To a certain extent, these data can reflect the behavioral habits, consumption preferences and social relationships of the information subject, which is conducive to a comprehensive assessment of the credit risk of the information subject.

Once again, the application scene is rich. Big data credit will no longer be used purely for economic and financial activities, but also to expand the application scene from the economic and financial fields to the daily life, every aspect of life, such as renting an apartment or car, booking hotels, visas, marriage, job hunting, employment, insurance, and other needs of the credit performance of the life scene, marketing support, anti-fraud, post-loan risk monitoring and early warning, and collection of accounts, etc., has a good performance of the application.

Finally, credit assessment is comprehensive. The credit assessment model of big data credit collection not only pays attention to the in-depth excavation of the historical information of the credit subject, but also pays more attention to the real-time, dynamic and interactive information of the credit subject. Based on the research of the behavioral trajectory of the credit subject, it can accurately predict its willingness to perform, its ability to perform, and the stability of its performance to a certain extent. In addition, big data credit collection uses big data technology, adopts machine learning modeling technology on the basis of comprehensive traditional modeling technology, and evaluates the credit status of credit subjects from multiple assessment dimensions.

Three, big data credit problems

Big data credit with the help of big data technology can be more comprehensive understanding of the credit object, reduce information asymmetry, increase anti-fraud ability, while more accurate risk pricing, from the data dimension and analysis perspective to enhance the level of the traditional credit, which can make credit more scientific and rigorous, it is a necessary supplement. However, from the utility of the data scope and connotation, the independence of the credit agency and privacy protection, there are still many problems with big data credit, which need to be emphasized.

First, the scope and connotation of data breaks through the "financial attributes", and the utility has yet to be verified. Traditional credit data mainly comes from financial institutions and public **** departments constitute the data cycle, with bank credit information as the core, including social security, provident fund, environmental protection, tax arrears, civil rulings and implementation of public **** information, data is relatively complete and authoritative. The scope of data collected by big data credit collection breaks through the "financial attributes", and the data mainly comes from e-commerce platforms, social platforms and life service platforms, covering online transaction data, social data and behavioral data generated in the process of Internet services, which are mostly of little relevance to the borrowing and lending behaviors, with weak authority, and the data completeness varies among platforms, thus whether the data can be used for the purpose of credit collection or not. The integrity of the data varies from platform to platform, so whether it can be used as the main indicator for judging the credit status of credit subjects has yet to be verified by the market.

Secondly, the collection and use of data does not follow the basic principle of "independent third party". Traditional credit collection adheres to the principle of independent third-party credit collection, and credit bureaus are "market-neutral"-neither having direct commercial competition with information providers or information users, nor intervening in or influencing the competition of information providers or information users in their respective market segments. While big data credit collection breaks through the boundary of "independent third party", the collection and use of data by credit collection agencies mostly originates from and is applied to their own business, so that the validity of the credit report is not guaranteed and its credibility is questioned. Moreover, if the information provider or the information user controls the credit bureau, it is difficult to restrain it from abusing the credit data or damaging the rights and interests of the individual credit bureau. In addition, credit bureaus will invariably gain certain market influence, which may distort the behavior of information providers and information users and have manipulative power over fees. Therefore, the development of big data credit should adhere to the basic principles of independent third-party credit, to maintain "market neutrality".

Thirdly, the situation of privacy protection is getting more and more serious. In the era of big data, data mining and crawling technologies are widely used, and the full range of information and data of credit subjects can be included, and the collection of huge amounts of information and data has brought great challenges to the privacy of credit subjects, and the protection of privacy has become more difficult. For example, information data used for specific occasions is used for other commercial purposes, and the risk of privacy infringement is greatly increased by the cross-validation of information data between different organizations.

(By Xueding Li, South Lake Internet Finance Institute)