Sky Cloud Data Feng Dazhi: Big Data and financial innovation

Source: Internet
Author: User
Keywords We the big data the very this the bank

In "2013 Zhongguancun Big Data Day" Big Data Internet Financial Forum, the sky Cloud Big Data Feng Dazhi brings "big data and financial innovation" keynote speech.

Feng Dazhi: Good afternoon! I am very glad to have the opportunity to discuss with you the big data to help our financial industry. I very much agree with director Liu said that only the industry's big data, not that I pass a big data to create a new industry. Therefore, we look from the sky cloud this angle of view, before our product release we all understand, we hope to help enterprises grow together to solve problems encountered by enterprises.

According to our large data in the time found that the big data from our one idea to how we put it to the ground, facing a lot of problems. I came to the sky before, did 6-7 years of enterprise-class warehouse construction, which encountered a lot of problems and challenges, I think today from another perspective, we say that large data can not only play its platform advantages, help us to solve some problems, At the same time its algorithm lets the cost advantage how to manifest in the finance, below is the main way which I speak.

Big data includes some of the current developments in the Internet, one of the biggest changes to us is personalization, and now it's getting stronger. Finance is a traditional information industry, how can I from this information better for customer service, this is our previous years, including a financial information, Internet, including electronic banking, online payment development after such a long time gradually accumulated, and formed so many effects.

Now the Internet, especially the continuous development of mobile interconnection today, we found that the financial threshold to a certain extent reduced, a lot of enterprises when he has a certain threshold, he can in a very long time to cut down a piece. If we do a very simple transfer transaction now, it includes a lot of data in the industry chain before, during, and after the transaction, including the transfer. Therefore, we in the financial inside said another thing, just now Liu Director also mentioned, how can in such a big chain inside quickly realize the fusion of information, and feedback to each front-end transaction or even operation, can realize our whole financial quick feedback and construction. The third challenge is to connect, why do we focus on the mobile internet, why the popularity of the mobile internet far more than the traditional internet, in fact, the person is an emotional, he is a social animal, he hopes that at any time, any process in which everyone is connected to meet his emotional needs. Now in many financial sectors, including insurance companies, including the traditional financial industry, it is constantly building their own communication with the customer pipeline, this pipeline is built more in the form of mobile phone app. So, from the point of view of large data now, of course, the big data behind a lot of technology, I personally think that the future development of our financial industry is personalized and feedback.

The big data just now tells a lot of directions and trends, but I think the big data for the traditional financial sector is actually two very important direction. Of course, I made a list of these, which is called the scalability of analysis, if you do traditional data mining must have a deep understanding, the traditional mining how to do? We need a very, very good doctor, including some algorithm engineers, I need to sample some data from the traditional data warehouse, to the continuous optimization algorithm, feedback to the Data Warehouse validation algorithm, so the entire algorithm cycle and accuracy depends largely on the ability of these engineers, and rely on your sampling, It's the traditional way to figure out how much the sample data represents the contribution of your business customers.

When your data analysis extends from 1 million to 10 million, it is possible that the complexity of the algorithm will rise from a simple 1 to 2 difference. In this way, we turn to VI, through a number of statistical analysis, through the development of hundreds of indicators to measure the size of the customer. But large data, through its computational power, this distributed computing power can achieve a certain amount of distributed computing power, as I mentioned here, when we are measuring a company, we have 36 million of the stock of customers, on our system about 20 minutes run. But under the traditional architecture, 10 million of customers are already having a hard time analyzing, so what's the biggest benefit to customers? I know that the result of any analysis is the fact that it can be used directly to guide the response, which is the benefit of large data scalability analysis capabilities. Another, is the large data for traditional data cleaning, data quality conversion, data governance, in many levels, large data can do a lot of things. What are the benefits of a traditional business? Improve the efficiency of our time processing.

We see in this picture, we have a very simple experimental algorithm, in the traditional analysis, we need to experiment with this algorithm, from here we can see that a simple analysis, we know that the annual revenue is probably the best contribution rate for the enterprise, Not the higher the annual income to the enterprise's contribution will be greater, but the annual income of more than 20,000, more than 40,000, there may be two or three-line city of ordinary employees, ordinary city residents may prefer to my insurance company to buy. These regulations are in fact large data through an algorithm to find out some of the rules, and then we do not have the prior intervention of the law feedback to our actual data warehouse as a guide. Because I know that the result of this thing is the result of a fact, not that I predicted through the algorithm, nor did I use a lot of means to simulate it, but it exists in real time, so it has a quick analysis to bring a very fast feedback.

Another piece I mentioned just now, we have two broad categories of data in the financial sector, the first of which is the financial sector's internal data. With the improvement of the bank's it construction, every business in the bank has been done very fine, but also very perfect, but this also brings another problem, that is, the data distribution in different departments and different corners of the enterprise, and even some data exist, but many people do not know that the data is there. What does that bring? How to realize the integration of data within the financial enterprise, to play the value of this data to a greater extent, this piece in a lot of enterprises, whether it is finance, just now director Liu also mentioned, we in the radio and television industry also realized the integration of business data, structured and unstructured, this is I speak of the internal data fusion problem.

What's the other big thing about the data? The data storage mode of this kind of database is more suitable to unify the structured and unstructured data together for the requirement analysis. When we were down there, a client talked to me about one of his particular concerns about how to structure unstructured data. He believes that there is a lot of unstructured data in the enterprise and he needs to analyze it. I told him that unstructured data is structured, we can provide an algorithm capabilities and tools, really how to unstructured data structure, so that it does not distort, the real reflection of the situation of the enterprise, this is also the need to work together, this is our big data in the hope that partners with us to big this industry and common development.

The third piece, I think big data in the enterprise the most important feature is brand building. We are now doing more of the public opinion monitoring, including brand image, this brand image contains such as Everbright, we talked about the most money, because it is based on the financing of a special professional to attract customers a bank. However, the same is a corporate image of the country in different provinces, the performance is different, you are in the Southwest border of a Chongqing city, may be the impression of Everbright and corporate propaganda image is completely different, how can I let a headquarters decision directly Shidi inserted into the grassroots, Let everyone know what your impressions are in this area and what you think of your competitors. So, in this case, we need to use large data, through other technologies, we can achieve this kind of public opinion monitoring, the original we just feel a feeling of people to quantify it, which is the big data in the financial application of another way.

The third block is business development, one is perfect, the other is introduced. Perfection is how to improve the existing system, Ray always mentioned, now our credit evaluation system has been very shaped, no one can subvert, disruptive innovation such a system, but we can on this basis, through the customer's approach to introduce a circumference, to be able to improve the system. Another idea is to introduce data, what is the introduction of data? Now many banks are doing the internet finance, in the supply chain finance, why does he do this? Because he found that when you introduce a new industry, it has industry upstream and downstream data, you can do more data, you can accurately portray a person, more accurate to describe a problem, or better to look at a trend, this is the financial industry now faces many problems, here, Everyone they talked about a lot of internet finance, supply chain finance, now also talk about the bank, in fact, the essence is how to introduce a reasonable data source, better solve our future banking system development.

What we're talking about is how the banks are doing better for our customers in the financial industry, in this case, there are a lot of ways, let us have a new data source, we can know in the bank, online banking, each operation can be analyzed to better solve what customers want, What purpose he could achieve, he wants to buy what kind of product, including Liu Chu also talked about with many enterprises in cooperation, in fact, is to solve how to better customer service, financial competition is more and more fierce, I think the image of the bank is no longer a all-inclusive bank, but focus on a certain industry bank.

The next thing we're talking about is that we're trying to capture some fragmented bits of information scattered over the Internet and be able to quantify it to achieve an analysis of enterprise value. In this case, in fact, for the bank, he has a lot of controllable operational negative control, for example, when the financial crisis, the enterprise has closed, but the company's credit status in the bank is intact. In the wake of the financial crisis, a bank in Europe made a low-level mistake, and his client was close to bankruptcy, but he repaid the client money. We hope that in the next year, some of our projects will help some companies to achieve the financial industry's landing.

The above is what I talked about from the sky cloud angle, from our algorithm angle to the understanding of financial innovation, thank you very much, thank you!

(Responsible editor: The good of the Legacy)

Related Article

Contact Us

The content source of this page is from Internet, which doesn't represent Alibaba Cloud's opinion; products and services mentioned on that page don't have any relationship with Alibaba Cloud. If the content of the page makes you feel confusing, please write us an email, we will handle the problem within 5 days after receiving your email.

If you find any instances of plagiarism from the community, please send an email to: info-contact@alibabacloud.com and provide relevant evidence. A staff member will contact you within 5 working days.

A Free Trial That Lets You Build Big!

Start building with 50+ products and up to 12 months usage for Elastic Compute Service

  • Sales Support

    1 on 1 presale consultation

  • After-Sales Support

    24/7 Technical Support 6 Free Tickets per Quarter Faster Response

  • Alibaba Cloud offers highly flexible support services tailored to meet your exact needs.