P2P biggest problem, credit system off the net

Now China's P2P industry with "two days of ice and snow" to describe is no longer suitable. On the one hand, P2P boss ran the news has not stopped; the other hand, there are a lot of people and money into the industry.

From July 19 to July 20, the "2014 Shanghai New Financial Annual Meeting and Internet Finance Bund Summit" will be held in Shanghai. The venue was crowded with people. The corridor outside the "Big Data and Internet Credit Service" was crowded with audiences. From this we can know the fiery industry.

And such an atmosphere of academic forum, the Internet finance, P2P industry is also very different view. Xie Ping, vice chairman of China Finance 40 Forum Standing Committee, pointed out that the P2P industry may be the market with the most efficient allocation of credit resources. Downing, the largest P2P company in China, believes that P2P industry is a supplement to traditional financial institutions.

Wang Xiaolei, deputy director of the Central Credit Information Center, directly pointed out that China's credit system currently covers 800 million people, but 500 million of whom have never borrowed from banks. In other words, these 500 million people are strangers to the financial sector, All lenders are opportunities.

Lin Zhaoyi, Invited Expert, Shanghai New Finance Research Institute, Chen Huan, Chief Strategy Officer, Ernst & Young, and Wang Zhengyu, Founder and CEO, Shinefocus: Internet finance companies need to step out of these 300 million people and develop them in 500 million "strangers." New users; and big data provides a relatively low-cost, efficient technology that will complement the central bank's credit bureau in an attempt to address the issue of credit to 500 million "strangers."

P2P biggest problem is the credit system off the net

Wang Xiaolei: Personal understanding, credit should be divided into at least two parts: general credit, narrow credit. In a narrow sense, credit collection is actually a platform for information sharing among lenders. Broad credit reference refers to all lending institutions' pre-loan investigation activities. I think this should be the core competitiveness of the P2P platform. The credit system in China currently covers 800 million people, but among the 800 million people there are only 300 million people who have genuine credit ties with banks. In other words, 500 million people have never had credit transactions with banks. These 500 million people are strangers to the financial sector. This is both a challenge and an opportunity for all lending institutions, including P2P. In the medium to long term, P2P has found its niche among the 500 million people and found its unique technology for managing credit risk in this segment of the market, a core part of P2P's long-term competitiveness.

Credit regulations clearly stipulate that all lending institutions (not lending financial institutions) should access the credit information system. If a regulators can determine P2P is a lending institution, or is engaged in lending business institutions, then in accordance with the requirements of the credit regulations, access to these agencies credit information system is the legal responsibility of the credit information center. Prior to this, Credit Information Center acquired Shanghai Credit Information Co., Ltd. (full name "Shanghai Credit Information Co., Ltd."). Shanghai Credit Information Co., Ltd. set up a credit information platform for Internet finance based on the unified deployment of credit reference centers. Now, over 200 institutions Access to the Shanghai credit information platform for Internet credit to achieve the information sharing between P2P agencies. For credit reference centers, the future of the background is centralized and unified, but only foreground service customers in this regard may be done by Shanghai Credit.

Lam Choi: Internet lending must have a "networked" system support is efficient, that is, you enter a platform, lose a property of identity into this identity-related information, including income, occupational assets have come out. At present, one of the biggest problems with Internet lending is that its credit reporting system is offline. Every lending platform, users have to personal information to enter, by the platform to complete the verification of information. The information between each platform is fragmented, so the cost of verifying information is high, which determines the efficiency of internet credits and the extent of bad debts.

The fragmentation of this internet personal reputation data led to the failure of the current credit model because all evaluations of a person's credit rating are based on real, reliable personal data, which is structural data such as occupation, income, etc. Instead of "big data" refers to the non-structural, such as a personal circle of friends, a person's usual chat history.

Without a widely accepted objective and reliable rating, lenders can get debit credit at a very high cost. Recently, P2P industry bad news came every day, the way out, that bad debts, I think the most fundamental problem is that the entire network credit information system lack of an integrated and rapid sharing mechanism. So how to open up the sharing mechanism is one of the toughest and most urgent problems that China P2P may have to solve.

Does the credit need big data?

Wang Xiaolei: How to understand these 500 million strangers is a challenge to all lending institutions, but I think it is more opportunity. The development of the Internet has led many individuals and small businesses to have Internet access on the Internet and recorded a great deal of information, however, to what extent can these recorded information satisfy the borrower's willingness to repay and repay the loan to borrowers Ability to review, but also rely on the industry to research and exploration. Personally think that not only P2P, including microfinance institutions, including even the current banks, do not clearly differentiate the so-called online and offline channels when conducting risk management prior to credit investigation. It should be that under the existing conditions, Significantly.

Chen Huan: With the increasing popularity of the Internet and mobile Internet, our behavior is being digitized more and more. Through digital behavior, a lot of information can be collected, regardless of our identity information, location information, transaction information, social information, and behavioral habits, are collected and processed. Under such circumstances, I think that all the data as proposed by some foreign institutions are credit data. Unlike the traditional viewpoints, only the behavior of financial transactions, some social public information and personal identification information are the credit information. This is the opportunity offered by the so-called Big Data and Internet developments.

Wang Zhengyu: The U.S. Bureau of Credit Bureaus answered three questions. Big data, combined with Internet financial credit, also answered these three questions: First, who are you? Second, where do you work? What is the difference? To extend these two issues to the third issue is the willingness to repay, repayment ability and stability.

Internet finance and credit information, the most important thing is that access to credit information through technical means now not available to the Chinese Internet financial credit to solve the problem is covered. The collection of these data does not have to go knocking on door to door, but rather cost-effectively and efficiently through the Internet.

How to score data on the technical means and how to evaluate this person after passing the score have been solved many years ago. Now the question is not whether there is a score, but rather: First, the data in the end to solve any problem? Second, what technical means to extract information from the data? Third, after getting these information, what scale should be used to characterize a person's risk behavior and risk characteristics? Fourth, after determining the person's risk characteristics, how to adopt an effective credit strategy for him, and of course, post-loan management and so on. These may be big data to help us.

Lam Choi: I think, credit does not require big data. The expression of willingness to repay first long-term credit history, the central bank credit system to solve this; then your repayment ability, stability, there is your occupation and your income. These data will play a central role in whether a person will repay. In fact, these three data acquisition needs big data? I do not think so. What it needs is the acquisition and sharing mechanism of our personal reputational data.

Second, unstructured data like a person's trading behavior on the web, purchasing behavior, and credit checking are not positively correlated. I have seen in life, someone driving an Audi car, living a very good life, eating Hao meal, but he borrowed my 50,000 yuan is not back. You can see from his credit history that he not only did not return my money nor did he repay my friend's money.

China and the United States are a bit different. Americans basically do not borrow money from friends. And if the Chinese people have good credit, they can borrow money from colleagues, friends and relatives. If they do not lend you, you are basically a bankrupt , So you go to the network by the high interest money.

Wang Zhengyu: The reason why big data makes sense in the issue of credit information, the main meaning is to supplement rather than replace. The 300 million people covered by the credit bureau, the best data is the central bank credit information center, you no longer find the data that 300 million data, that 300 million people borrow money is by that data, no other . The problem is that except for those 300 million people, the central bank's credit reference center can not help you either. Your own data can only do a blacklist of exclusions. How to solve the credit problem of 300 million people? Internet big data provides a low-cost technical means.