What is the difference between big data credit reporting and traditional credit reporting?

Traditional credit reporting plays a key role in facilitating personal credit reporting, assisting financial credit decision-making, preventing credit risks and improving financial availability, but its limitations in the field of Internet finance cannot be ignored. First, there are about 500 million people in China who have no credit activities in licensed financial institutions, so they are not covered by them. Second, with the development of "internet plus", a large amount of data related to personal credit investigation has been generated and precipitated on the Internet, which is still difficult to be adopted by it [1]. The emergence of big data credit reporting helps to solve the above problems and has achieved rapid development to some extent. According to our research, the basic conditions for the development of big data credit reporting are: First, a good signal of China's policy support and deployment release; Second, the huge long-tail demand of internet finance represented by "financial online"; The third is the strong support of big data technology.

I. Policy support

Since 20 13, China has promulgated a series of laws and regulations, which has built a legal system framework for the healthy development of the credit reporting industry. On March 20 13, the State Council issued "Regulations on the Management of Credit Information Industry" (hereinafter referred to as "Regulations"), which became the first credit information industry regulation in China and the cornerstone of the legal system construction of credit information in China. 20 13 12 in order to cooperate with the implementation of the regulations, the people's bank of China issued the measures for the administration of credit reporting institutions, which fulfilled the requirements of establishing and improving the social credit reporting system and established the institutional norms and regulatory basis for credit reporting business activities.

In addition, in order to improve the level of personal credit information service and introduce market competition, China has made legislative preparations for gradually opening the credit information market. 20 15 15 The People's Bank of China issued the Notice on Preparing for Personal Credit Information Service, and approved eight institutions to prepare for personal credit information service. From July 2065438 to July 2005, the People's Bank of China and other ten departments issued the Guiding Opinions on Promoting the Healthy Development of Internet Finance (hereinafter referred to as the Guiding Opinions), proposing to promote the construction of credit infrastructure, cultivate the supporting service system of Internet finance, and encourage qualified institutions to apply for credit investigation business licenses according to law. Regulatory reform measures have created a good external environment for the development of big data credit reporting.

It is worth noting that in order to speed up the deployment of big data, deepen the application of big data and promote the implementation of the national strategy of "internet plus", in July 20 15, the State Council issued the Action Plan for Promoting the Development of Big Data, and in September 20 15, the State Council General Office issued the Opinions on Strengthening the Service and Supervision of Market Subjects by Using Big Data. The most striking thing in the Action Plan for Promoting Big Data Development is to open government data and promote industrial innovation, and encourage the application and development of big data in the credit information industry. Relevant experts believe that big data is an important "mineral resource" for credit information construction. Credit information construction must rely on and support big data, use big data to establish a credit system in breadth and depth, and improve the comprehensiveness, timeliness and credit efficiency of credit evaluation.

In the era of big data, data has become a strategic resource equivalent to energy, and information disclosure and data openness have become the themes of today's era. In the process of performing administrative management and public service duties, administrative organs have mastered a lot of information. How to manage and revitalize these data assets through information disclosure has become an urgent problem for administrative organs. The Fourth Plenary Session of the 18th CPC Central Committee "Decision of the Central Committee on Comprehensively Advancing Several Major Issues of Governing the Country by Law" clearly stated that it is necessary to comprehensively promote the openness of government affairs, promote the informationization of government affairs, and strengthen the construction of Internet government information data service platform. The gradual establishment of data disclosure system provides institutional guarantee for the opening, sharing and service of social information resources.

The formulation of these laws, regulations, rules and systems is conducive to strengthening the management of the entire credit information market, standardizing the behaviors of information providers, information users and credit information agencies, and safeguarding the rights and interests of information subjects. At the same time, other supporting systems are gradually being formulated and improved, which will form a legal system of credit information with the regulations, promote the healthy and sustainable development of China's credit information industry, and better meet the financing needs of individuals and enterprises.

Second, market demand.

In recent years, Internet finance has sprung up as a new force in China's economic development. While Internet finance is booming, due to its short establishment time, its own risk prevention and control ability is weak, credit evaluation, risk pricing and risk management are imperfect, and problem events are constantly emerging. On the one hand, most users of internet finance are network users with "long tail characteristics" and it is difficult to be covered by traditional credit reporting. Moreover, due to the lack of communication and exchange of information and data between industry organizations, the phenomenon of "one person lends more" is prominent, and the whole industry faces huge credit risks. On the other hand, due to the imperfect credit information system, Internet finance companies generally rely on offline risk control, and a lot of due diligence is time-consuming and labor-intensive, which not only increases their own operating costs, but also tends to bias the evaluation of borrowers' credit level, which indirectly increases financing costs. The imperfection of the traditional credit reporting mechanism has become the main factor restricting the development of Internet finance. The development of internet finance provides a huge application prospect for the development of big data credit reporting, which forces credit reporting to keep pace with the times and promotes the reform of credit reporting mechanism.

Third, technical support.

In addition to the above two factors, the rise of big data credit reporting is also inseparable from technical support. The progress of big data and cloud computing technology provides support and convenience for the development of big data credit reporting, and the artificial intelligence algorithm model provides a powerful supplement for comprehensively depicting the default probability and credit status of users. On the one hand, with the development of "internet plus", people's basic necessities of life, social interaction and the Internet tend to be closely integrated, and a large number of data related to personal credit information are generated and precipitated on the Internet. With the help of big data capture and mining technology and cloud computing technology, it is easier to collect, record, store and analyze these data. On the other hand, artificial intelligence technology represented by machine learning has been adopted one after another, which can not only analyze, summarize and summarize the structured and unstructured data obtained from various channels, but also design various prediction models (fraud model, identity verification model, repayment willingness model and stability model). ) to predict the willingness and ability of credit subjects to perform, and reduce the default risk and bad debt rate.

Xie Ping, Zou Chuanwei. Ways to develop independent third-party credit reporting agencies. Caixin Weekly, 20 17-02.

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-Comparison between big data credit reporting and traditional credit reporting.

In recent years, with the development of internet finance and big data technology, big data credit reporting has begun to rise. Big data credit reporting has four innovative features: wide coverage, multi-dimensional information, rich application scenarios and comprehensive credit evaluation. However, compared with traditional credit reporting, there are still many problems in the utility of data categories and connotations, the independence of credit reporting institutions, privacy protection and so on, which need to be paid attention to.

First, the basic concept of credit investigation

Traditional credit investigation is a professional credit management service that collects financial and financial transaction information in a fixed mode, and processes, processes and reports the information. The traditional credit investigation rose abroad. In the United States, represented by Dunham & Company, it was established in 1933. In China, the Central People's Bank Credit Information System is the representative, which is a common credit information format in China and even in the world. The establishment of a credit reporting agency in China and the development of credit reporting business are bound by the Regulations on the Administration of Credit Reporting Industry, and it is necessary to apply for a corresponding license.

Credit investigation with big data refers to the collection, collation, analysis and mining of massive, diversified, real-time and valuable data, the redesign of credit evaluation model algorithm by using big data technology, the multi-dimensional portrayal of credit subjects, and the presentation of default rate and credit status of credit subjects to information users.

The essence of big data credit reporting activities is still the collection, collation, preservation, processing and release of credit subject information within the credit reporting business scope defined in the Regulations on the Administration of Credit Reporting Industry. However, compared with traditional credit reporting, it highlights the application of big data technology in credit reporting activities, and emphasizes the characteristics of large amount of data, wide representation dimension and dynamic interaction of credit status, which can be used as a useful supplement to the credit reporting system.

Second, the innovative characteristics of big data credit reporting

On the surface, big data credit reporting and traditional credit reporting are just different data acquisition channels. The former mainly comes from the Internet, and the latter mainly comes from traditional offline channels, but there are great differences between them. The innovation of big data credit reporting is mainly manifested in four aspects: wide coverage, multi-dimensional information, rich application scenarios and comprehensive credit evaluation, which will reduce the cost of credit reporting and improve the efficiency of credit reporting.

First of all, it covers a wide range of people. Traditional credit investigation mainly covers people with credit records in licensed financial institutions. Big data credit information captures people that cannot be covered by traditional credit information through big data technology, and uses Internet traces to assist credit judgment, so as to meet the credit information needs of new Internet finance formats such as P2P peer-to-peer lending, third-party payment and Internet insurance, such as identity identification, anti-fraud and credit evaluation.

Secondly, the information dimension is diverse. In the Internet era, the sources of information and data for big data credit reporting are more extensive and diverse. Big data credit data is no longer limited to personal basic information, billing information, credit records, overdue records and so on. It is provided by financial institutions, government agencies and telecommunications, but it also introduces data such as Internet behavior tracking records, social interaction and customer evaluation. These data can reflect the behavior habits, consumption preferences and social relations of information subjects to a certain extent, which is conducive to comprehensively evaluating the credit risk of information subjects.

Thirdly, the application scenarios are rich. Big data credit investigation will no longer be simply used for economic and financial activities, but also can expand the application scenarios from the economic and financial fields to all aspects of daily life, such as renting a house and renting a car, booking a hotel, visa, getting married, applying for a job, handling insurance and other life scenarios that require credit performance. It has good application performance in marketing support, anti-fraud, post-loan risk monitoring and early warning, and collection of accounts.

Finally, the credit evaluation is comprehensive. The credit evaluation model of big data credit investigation not only pays attention to the deep mining of the historical information of credit subjects, but also pays more attention to the real-time, dynamic and interactive information of credit subjects. Based on the research on the behavior track of credit subjects, we can accurately predict their willingness, ability and stability to perform their duties to some extent. In addition, on the basis of integrating traditional modeling technology, Big Data Credit Information uses big data technology and machine learning modeling technology to evaluate the credit status of credit subjects from multiple evaluation dimensions.

Third, the issue of big data credit reporting.

With the help of big data technology, big data credit investigation can understand the credit investigation object more comprehensively, reduce information asymmetry, increase anti-fraud ability, price risks more accurately, and improve the traditional credit investigation level from the perspective of data dimension and analysis, which can make credit investigation more scientific and rigorous and is a necessary supplement. However, from the aspects of the utility of data categories and connotations, the independence of credit reporting agencies and privacy protection, there are still many problems in big data credit reporting, which need to be paid attention to.

First, the category and connotation of data have broken through the "financial attribute", and its utility has yet to be verified. The data of traditional credit investigation mainly comes from the data cycle composed of financial institutions and public departments, with bank credit information as the core, including public information such as social security, provident fund, environmental protection, tax arrears, civil trial and execution, and the data is relatively complete and authoritative. The scope of big data credit data has broken through the "financial attribute". The data mainly comes from e-commerce platform, social platform and life service platform, covering online transaction data, social data and behavior data generated in the process of Internet service. Most of these data have little to do with lending behavior, and their authority is weak, and the data integrity of each platform is also different. Therefore, whether it can be used as the main index to judge the credit status of credit subjects remains to be verified by the market.

Second, data collection and use did not follow the basic principle of "independent third party". Traditional credit reporting adheres to the principle of independent third-party credit reporting, and credit reporting agencies are "market-neutral"-they do not have direct commercial competition with information providers or information users, nor do they interfere with or affect the competition of information providers or information users in their respective market segments. However, big data credit reporting broke through the boundary of "independent third party", and the collection and use of data by credit reporting agencies mostly came from and applied to their own businesses, so the effectiveness of credit reporting could not be guaranteed and the credibility was questioned. Moreover, if the information provider or information user controls the credit reporting agency, it is difficult to restrain it from abusing the credit reporting data or damaging the personal credit reporting rights. In addition, credit reporting agencies will gain a certain market influence invisibly, which may distort the behavior of information providers and information users and control fees. Therefore, the development of big data credit reporting should adhere to the basic principle of independent third-party credit reporting and maintain "market neutrality".

Third, the situation of privacy protection is becoming increasingly severe. In the era of big data, data mining and crawler technology are widely used, and all-round information data of credit subjects can be collected completely. The collection of massive information data brings great challenges to the privacy of credit subjects, and privacy protection becomes more difficult. For example, the information data used in specific occasions are used for other commercial purposes, and the cross-validation of information data between different institutions greatly increases the risk of invasion of privacy.

(Author: Li Xueting, Nanhu Internet Finance College)