Internet finance is on fire, and big data risk control is also on fire. As a result, companies are constantly jumping out to say that they want to do big data and provide big data risk control for Internet finance companies. Can people who want to do big data risk control do it well?

Risk control big data mining requires professionalism and craftsmanship.

The primary problem that big data risk control cannot avoid is the problem of big data sources, that is, the acquisition, storage, management and analysis of big data. In China, at present, the public credit bureau has not been built, and the credit data cannot be completely obtained in the form of public services. Moreover, the credit rating of domestic people and enterprises has no historical tradition, and the construction of credit society is in its infancy. Therefore, multi-channel, diversified, dynamic and real-time big data mining for credit risk control data sources is the only way for big data risk control. Can big data risk control really only be achieved by data capture technology?

"Wind control big data mining requires professionalism and craftsmanship. For many professional technicians, it is not difficult to capture data online through technical means, but what is really difficult is how to identify, filter and analyze information that is really valuable to risk control. Without deep experience in the field of financial risk control, it is impossible to focus on and combine the top data mining technology in the industry. In the end, what you may get from big data is just more error signals and noise. " Chris, director of data operation of Shenzhen Big Bee, said. It is understood that the Big Bee data team has been immersed in the financial risk control industry for many years, and has been focusing on data analysis, enterprise CRM and risk control system construction in the financial field. Its customers are all over major banks, insurance institutions and government agencies in Australia, Southeast Asia, Hong Kong, Macao and Chinese mainland. For example, HSBC, Moody's Asia-Pacific Headquarters, Bank of China, China Merchants Bank, China CITIC Bank, Guangdong Development Bank, China Ping An, China Customs, China Mobile, China Unicom, Peng Yuanzheng Letter, Standard Chartered Bank, Dah Sing Bank, Wing Hang Bank, Singapore Development Bank and Thailand Dacheng Bank. Just like Huawei a few years ago, the Big Bee team has quietly adhered to the duty of "artisans" for many years, and intensively cultivated behind the scenes to promote the development of the industry.

"Many project managers and R&D leaders of the Big Bee team have many years of working experience in well-known institutions such as HSBC, Chase Bank, IBM, SAP, SAS, Huawei and Pengyuanzheng Letter. For big data risk control, there is a deep technical understanding and industry awareness. For Internet financial risk control, the most efficient and convenient way should be big data risk control, which is the core business that the whole company is currently concentrating on. " Chris is calm and confident about the problem of mining big data sources. He said: "Big data risk control is still in the stage of industry exploration, but over the years, the Big Bee team is proud of not only massive data collection and processing analysis technology, but also multi-dimensional core big data resources in the financial industry, and other related data can be obtained quickly and accurately through technology. Of course, there is no end to big data and data mining. At present, what we need to do in big data mining is to continuously enrich big data sources and explore the openness and effectiveness of big data applications with the industry. " Chris concocted the article "Any Data Source for Big Data Risk Control", which attracted wide attention from the industry. "In the field of big data credit risk control, big bees have a' craftsman spirit'. We are professional enough, we are more focused and continue to expand our cross-industry experience and resources. Big bee data is most likely to truly realize the perfect combination of big data technology and risk control applications. "

The credit risk control system is more attractive than the technology itself.

It is understood that Dick Cheung, founder of Big Bee Data, is a famous expert in data analysis and financial risk control. As a chartered statistician of the Royal Statistical Society, he was engaged in data analysis and financial risk control research in well-known institutions such as Europe and Australia. After returning to China, Huace Company was established to build risk control systems for well-known banks, insurance companies and government agencies. I am familiar with the construction of foreign financial risk control systems and the domestic credit risk control market. Personally, he thinks that financial risk control must be a systematic project. In addition to the macro environment, the internal risk control of credit institutions can never be achieved only with the support of big data sources, credit data and information in some dimensions of BAT. To do a good job in credit risk control, especially mutual financial risk control, we need to consider the risk control process, risk control personnel management, risk control efficiency, risk control quality, risk control cost and other dimensions. Effective and efficient credit risk control system construction, breaking through the technical barriers of big data is the foundation, and more importantly, it needs in-depth understanding and research in the field of financial risk control and mutual gold industry.

Based on this belief, from the beginning of the research and development of 20 13 big data risk control business, Big Bee team has made great efforts to build a big data risk control solution that runs through the whole life cycle of credit, from obtaining high-quality customers, to pre-lending review, decision-making in lending, and then to post-lending monitoring, providing credit institutions with full-cycle risk control consulting and decision-making assistance services. Through in-depth business combing, data statistics, analysis and modeling, Big Bee team has integrated complex big data risk control solutions into several intelligent and concise operating systems, namely, business application information investigation system, business audit fraud identification system, big data scoring system and post-loan monitoring system, which have been used by a large number of users and will be applied soon. After that, Big Bee also plans to integrate these systems into a unified platform "Bee Control Online". By then, through this platform, ordinary business personnel of all microfinance companies, P2P platforms, personal loan departments of banks, personal credit card centers, loan intermediaries and other institutions can easily handle the risk control business that previously required multiple process departments and professionals.

Chris is always excited when he thinks about what the team is doing and sees the changes in risk control brought by the system to the industry. He said, "This has always been my dream, the dream of the company and the dream of this team for many years. The construction of the credit risk control system is more fascinating than the technology itself. "

Reduce cost, increase efficiency and improve quality, and realize the transformation from "0" to "1"

We know that in the field of Internet finance, the main battlefields are microfinance and P2P platform. Generally speaking, the credit business of microfinance and P2P platform has the characteristics of many customers, small quantity and fast lending, which determines that the mutual fund industry can not control risks through traditional asset mortgage or offline investigation like banks. Therefore, many small and micro mutual fund institutions have almost no risk control departments, or the risk control work is fragmented and unsystematic. Even if a special risk control department is set up, most of them are unlikely to set up a system R&D team, but most of them use crowd tactics and manual audit, and their risk control efficiency is always limited, the cost of risk control is bound to be high, and various problems will arise in the risk control process and personnel management.

From the development trend of technology and application, the characteristics of big data risk control, such as networking, platformization, intelligence and full dimension, will be able to meet the needs of large-scale, high-efficiency, low-cost and high-quality risk control of mutual loan risk control, and can solve these problems from the aspects of survey dimension, credit rating, process management and risk control cost.

Of course, new technologies and applications are always an iterative process, which needs the promotion of the system environment and the constant trial and error, falsification and evolution of the whole industry. For the "blank" or "chaos" of risk control of many mutual financial institutions, big data credit risk control can realize the change of the whole industry from "0" to "1", and then realize the decisive value of "1".

The value of big data risk control can be seen from the application effect of several big data risk control products (systems) that Big Bee Data is currently pushing to the market. From some cooperative customers of Gitzo, Zendai Express Loan, AXA, Jinsheng and other big bees, we know that they have fully cooperated in the application of "Business Application Information Survey System", a big data risk control platform for credit audit business. In terms of saving time and increasing efficiency, it is at least 300% higher than the traditional manual credit audit; In terms of the quality of credit audit, we can flexibly and quickly obtain a broader data dimension, conduct automated big data investigation and verification on online loan applicants more accurately, and quickly generate investigation reports to assist pre-loan access decision-making. At the same time, they are discussing the "business audit fraud identification system" that accesses the big bee data. This is a set of intelligent big data dynamic question bank and interactive examination scoring system, which can effectively solve the anti-fraud problems such as the lender forging personal application information, fraudulently using other people's identity information, the "credit blacklist" being untraceable, and the illegal intermediary tricking others into indirectly implementing credit fraud, making the traditional application form "dynamic" and playing a good role in identifying and preventing those prepared and premeditated frauds. It is reported that a large number of influential credit institutions are negotiating deeper risk control cooperation with Big Bee Data, including the big data scoring system for decision-making in lending and the big data monitoring system after lending.

Credit risk control is an eternal field. Ancient pawn mortgage, modern bank credit mortgage loan and modern bank credit loan are accompanied by corresponding risk control modes in each period. The history of credit risk control is as long as the history of credit, and the credit model is accompanied by the risk control model. We believe that with the development of internet finance in the world, internet finance and credit will surely usher in her brand-new risk control model-big data credit risk control model. The historical mission of Big Bee Data is to be a "craftsman" and a "guardian" at first.