大数据对中国银行适用情况(法语)

I. Introduction of non-performing loans in China

1. Status analysis

China's commercial banks' asset quality continues to deteriorate. The balance of non-performing loans of commercial banks since September 2011 (407.8 billion yuan) has been rising in December 2015 has reached 1,274 billion yuan, the current trend continues. While lending is growing, the bank also increased the write-off of bad loans, but with poor balance corresponding NPL ratio also continued to grow: by the end of 2013 non-performing rate of 1%, by the end of 2014 to 1.25% by the end of 2015 reached 1.67%

On August 26, Agricultural Bank of China released 2016 semi-annual report. Due to the rise of non-performingratio, its semi-annual report shows that the first half of the Agricultural Bank operating income of 260.385 billion yuan, down 5% year-on-year.

The high non-performing assets ratio of commercial Banks, from the source is behind due to the bank credit risk management. In the past on the system, due to the distorted relationship between the government, enterprises and Banks, the bank is lack of effective constraint mechanism. On the business, Banks' risk awareness is weak, and they are lack of effective internal control mechanism and risk prevention measures, so the credit risk piled up, but the latent crisis factors cannot be ignored. Rising non-performing assets, in particular, has become the current bank to solve the problem of the most prominent.

In order to change the backward of bank risk management, the first step is to establish an effective credit rating system.

The credit rating is an important and effective market supervision mechanism, is an organic part of the risk management system. Rating is to point to by certain methods and standards for the evaluation of the object's solvency or debt service comprehensive rating and distinguish between process, the goal is to uncover the risk level of evaluation object. Its role is to provide financial markets with a reference of the risk information, in order to meet the needs of the managers, operators and investors. For commercial Banks, credit rating as a result the determine the loan risk degree based on the basis of assets and risk management, help the subdividing customer, select the appropriate market positioning, formulate correct marketing strategy, so as to avoid or reduce the credit risk. Therefore, in the commercial Banks face the risk of growing conditions, it is necessary to develop and perfect our country commercial bank credit rating system.

However, at present, our country commercial bank's credit rating system has many problems. Credit rating system of commercial Banks in China started in 1988 to build, today, the credit rating system of commercial Banks in China has developed for nearly 20 years, but there are many gaps compared with western developed countries and the insufficiency.

The current credit rating system in China mainly exist the following disadvantages: 1. There is no perfect unified credit rating market and the management of the organization.2. Commercial Banks have their own credit rating system, and their internal rating system is not reasonable. China's four major

state-owned commercial Banks on credit ratings closed to each other, and other commercial Banks in their own way also.3. Commercial Banks are lack of professional talents of credit rating.

In order to change these weaknesses, we felt that we apply the technology of data in the bank credit rating system.

2.Suggestion to the credit rating system

It is urgent to improve the credit rating system because of many problems that we have already mentioned. And with the development of international financial integration and opening financial policy of China, commercial banks need to improve competitiveness on the international platform, and also to promote the financial system and the economy market of China.

So, now we would like to offer our bank four suggestions to improve about its credit rating system.

First, given the present circumstances, we must break the impasse of the monopoly and the partition of the credit and market. So we must first develop some activities with regard to the credit rating market in several large and important developed economic regions. As our target bank---the agricultural bank, as one of the leader bank, do shoulder the responsibility to break the monopoly in the first time. Then gradually popularize the credit rating services in the whole country, and finally form a unified market of the credit rating. At the same time, it is necessary to establish an association to monitor the transparency and fairness of the system to ensure the regulations and the principles. On the one hand, an independent system of the government can ensure the interests of all the parties, and on the other hand, it is important to cooperate with the foreign countries which we could set as example to absorb their advantages.

Second, each bank need to have an internal rating system which should be independent of the mortgage sector and decision makers in order to ensure the objectivity. And besides, the process and the unified standards of internal rating are also important. At the same time, we can’t ignore the database of the credit risk rating, including the customers’ financial i nformation and non-financial information which we could analyze them roundly. To have a comprehensive analysis of each customer, we need the big data. Collect the resource of the information is the first step to establish a complete credit rating system. In addition, we can collect the information not only by the bank itself, but also the official sectors and the public media. But we should recognize, select and analyze them carefully by ourselves.

Third, the commercial banks should train professional and versatile talents. The scientific credit rating system requires commercial banks must have the high quality talents and the team equipped the legal knowledge, mastering the financial knowledge and having the accurate ability of estimating and judging of the credit risk. Apart from that, the commercial banks should strengthen the education of ethics and the moral qualities and master the credit rating principles, standards, general situation of the macroeconomic environment, accounting treatment, the related laws and regulations, statistical measurement and other knowledge to further enhance the

ability of credit rating. Commercial banks should also introduce the talents from foreign countries who master well the credit rating and use the model to measure the talents and reserve them.

Finally, improve the legal system of the commercial bank credit rating system. First of all, to establish a legislative basis and frame which can be adapted to market changing rapidly. Second, the credit rating agencies and procedures should accord with legal procedures and the access principles. Third, we must strengthen the construction of information disclosure system to promote the quality and the transparence of the information. Last but not least, the commercial banks should strengthen the management of credit rating system of with the legal punishments. But the government must clearly know its own functions and provide institutional guarantees in time to promote the establishment of the legislative framework and the construction of the credit system.

Credit is the basis of the economic market, personal credit is the "identity card" in the economic and social field. It provides a reference of risk information for financial market which meets the needs of managers, operators and investors. For commercial banks, the credit rating is the basis for determining the degree of the risk management in aspect of the loan and asset, which helps them to segment the customer, determine the market position and formulate the marketing strategy, so as to avoid or reduce the credit risk before or after the loan. Therefore, it is necessary to develop and perfect the credit rating system of commercial banks in China because strengthen the credit rating of commercial banks in China can avoid not only the low evaluation issued by foreign countries but also over-optimistic by the domestic rating agencies.

II. Introduce of the big data

As the fast development of Internet, mobile Internet, Internet of things and social network, people are moving into an age of information explosion. Report from the USA Internet data center, every year the data volume on the Internet rose by about 50%. In order to gain value from these huge amounts of data, big data technology was born. Now the size of the big data and its storage capacity are growing fast, big data has penetrated into various industries, various business, become an important production factor, so that the development of the big data linked to the productivity.

Big data is defined as a huge data sets whose size often beyond human within acceptable time of collection, storage, management and processing capacity. Big data has four characteristics called 4V, Volume (the size) , Velocity(speed) , Value and Variety(type).Big data arises at the historic moment of disruptive technology era, the concept of big data has brought a revolutionary value concept, that is business operation can be based on data processing and analysis for obtaining the required value. In the era of big data, how to collect and store the data is not the most critical, the most important is the professional and efficient analysis of the unstructured data for get the users’ requirements, which are the most worth. Businesses to provide services from the original simple single service

customers into personality Differentiation of customer private ordering. In the era of big data, people can through the media, the Internet and other media real-time full access to all of the information you need. Big data changes only servicing the customers to give the private custom set by knowing what they want.

In the era of big data, people can get all of the information they need through the media, the Internet or other media. Big data will change the company who makes the information processing, due to the big data can use in various industries, currently it multiples the industries including finance, retail, medical and other industries to bring new vitality. In the change produced by big data, the innovation of business model innovation will be the core competitiveness in the wave of the future business.

The influence of the large data of some areas of the economy: the following table

table 1: The influence of the large data of some areas of the economy

some interrelated areas of the economy the main influence

medical and health service in the United States is the $300 billion per year as a growth rate of 0.7%.

Global personal location data service provider revenue of $100 billion or above $700 billion the final value of users.

Retail industry of US the growth level of the net profit is 60% and increase as 0.5% - 1.0% of productivity.

Manufacturing、product research and development 、assemble cost reduce by 9.4% ，running costs by 7%.

Public administration department in European is 2500 billion per year as a growth rate of 0,5%.

The use of big data is of great significance for various industries, in some industries will have a greater impact. Such as The IT industry, IT industry will produce amounts of data, ex. the Internet company deal with kinds of users request online every day, the data can be stored by the Internet company. The electronic product update quickly, thus produced Raw data will be change very quickly, so it is quite valuable for this type industry to use big data technology. And the others industries and costumer concentrated industry, such as securities, banking, insurance… … the large amount of behavior data of customer is also a fortune to the enterprise. as long as the enterprise use the techniques of a certain data analysis and algorithm processor. and classify the customers, we can get very considerable value.

The use of big data is of great significance for various industries, in some industries will have a greater impact. Such as The IT industry, IT industry can produce vast amounts of data, such as the requests from the online users can be stored by the Internet company; And electronic product update quickly, thus data changes very, very quickly, so it is quite

suitable for the industry to use big data technology in mining value. And that some customer intensive services, such as securities, banking, insurance, customer produce the large amount of data and behavior witch are a fortune for the company, as long as the adoption of a certain data analysis techniques and algorithms processing the data, digging out the value, and apply Segment with the customers, we can get very considerable value.

III. Suggestion of five steps for implyingbig data in ABC Step I Determine is if Big Data is the right answer

The first thing we should determine is if Big Data is the right answer for the bank we have chosen- Agricultural Bank of China (using ABC instead after). To make sure that the Big data is the right answer for us to solve the problem, we should ask ourselves 3 questions. Does your organization have business issues or opportunities that point to Big Data? Is Big Data the right way to solve your problems? and is Big Data a good fit for your organization?

First, we answer the question-does ABC have business issues or opportunities that point to Big Data?

The ABC lend the loans to the farmers as it is the bank that provides the aides particular on the field of the agriculture. However, most of them have become the permanent bad debts for the bank, considering as the loans cannot be withdrawn. Although China gives a hand for this one of the national bank called "write-off bad debts", the bank has sufferedthe loss a lot and arrives at the first rang in the field of non-performing loans.

Non-performing loans have exposed at the high speed.As the ABC takesstrategy of reclaiming arrearages in advance, the write off and the transfer ratio is small. The annual write-off rate is just 33% and the rate of non-performing loans has increased by 37bp, to 2.39%. From the perspective of the industry, non-performing loans exposedand concentrated on the manufacturing sector and on the retail, which its increase accounted for 31% and 41% in 2015, and by the end of 2015 the rate of non-performing loans were 5.45% and 12.31%. While from the perspective of the region, the western region of China is the place where grew the fastest,the non-performing rate increased the 1.55pc to 3.23% in the end of last year, account for an increase of 38%.

Nowadays, most of the banks solve the problems par the asset management company which auctions the non-performing. So, it is a good chance for us to use the big data to improve the quality of the loans and to withdraw them. Using the Big Data, in return, the bank can at the ahead of dealing with the non-performing loans, promote the sales according to the customers’ habits, identify the good quality customers form a large number of customers and use the big data to avoid the frauds.

Besides, if Big Data the right way to solve your problems is also the question that we should answer. Of course, it’s indeed a right way. With the development of the

technology, the significance of Big Data technology lies not only in gathering the huge amount of the data, but also in dealing with the data collected with specialization knowledge and technology. In other words, if we describe the big data as an industry, the key to the profitability to this industry is to improve the capacity of processing the data which we can get the increase of the value. We have mentioned that the non-performing loans are existed widely in the ABC and the farmers account for 50,32% of the whole amount of people in China. So, using the Big Data, we can collect the various of the information both about the customers and the non-performing loans to resolve this problem ranking the first place. And the Big Data can solve unstructured data problems which is the weakness for the bank.

Finally, I would answer the question- is Big Data a good fit for Agricultural Bank of China? Actually,

ABC has already practiced successful with the use of Big Data in many different fields, take one named Accurate Marketing Mode of the Customer Credit Card as the example. Based on the comprehensive data collected from the system inside and outside and the data mining algorithms such as clustering analysis, association rule discovery, decision-making tree and so on, the bank can identify the potential demands of the customers who have not hold ABC credit cards and divide them into the different groups to develop different products and to adopt the different strategies according to the customer s’ consumption behaviors, the natural attributes, the loyalty, etc.

At the same time, the ABC has an advanced hardware facilities and infrastructure to analyze its large amount of data.The basic hardware, basic software, data model, tool platform and system management are autonomous and controllable.

In the perspective of Hardware, the ABC usesthe series of Huawei's RH2288 such as 2C, docore, 256 memories,4T hard drive which is different in differentperiod.

For the basic software, we introduced the domestic MPP database expanded to 56 nodes in 2013. Unstructured and structured data are using with the MPV database which makes it more efficient to use.

In the aspect of the data model, the ABC use the advanced model theory which integrates the canonicalform and the dimension’s ideas.

We have developeda set of more complete norms, systems, methods and standards and have developed the tools such as ETL, the batching and dispatching and the management of the data source by ourselves.

Finally, the first introduction of the domestic high-performance database applications can prove that the ABC has the taste to the innovation.

Step II Design and establish a Big Data organization

Big Data adoption can be likened to a perfect storm of data, technology, business management, and economics. Adopting a systematic approach is critical for Big Data initiatives, as is building a centralized data organization to foster data innovation and agility.

It is critical that the Big Data organization is led by an Executive Council, has a core solution team, and is governed by strong guiding principles. This team should have a vision of how Big Data can transform the organization. The Executive Council would help to formalize a strategy and facilitate business decisions. In other to apply big data to credit rating system, Agricultural Bank of China should set up a new apartment which possess necessary equipment, IT specialists and technical system.

What Agricultural Bank of China needs is not only the change in structure and system, but also the supply of human resources. It needs a core development team, composed of business strategists, data scientists, and data architects, should focus on delivering solutions.

The Guiding principles of this team is:

? Insulated, but not isolated.

? Emphasize experimentation and learning.

? Ideas should be based on quali ed business values.

? Test fast, adjust fast.

There is aexample of big data talent organization:

Step III Establish business case evolution

When we have established a big data organisation, then we need to build a standard business case model with which to evaluate Big Data pilot opportunities.

A standard business case framework defines the sponsors, rationale, risks, investments, benefits, users, stakeholders, resources, and required services.We then use it as a validation tool to test big data pilots until the output data is consistent with our goals. It‘s forassess potential business value，promote strategy alignment andhelp prioritize, in a sustainable way, proposed Big Data initiatives.

We can use the goals, (like Business goals and objectives for this fiscal year / Business opportunities) Strategy, (likegrowth / Revenue and profitability / Managing risk)Customers,

(products / Operational efficiencies / Compliance surveillance) as the inputs to assess and prioritize the opportunity.

The specific value of the initiative may not be known at this time, but the goal is to make sure the direction of the initiative is valuable and aligned with the needs of the organization.

This phase is intended to be a very high-level and quick activity that will determine if moving forward with the opportunity is directionally a good idea. Business executives, IT executives, and architects should be involved in the assessment.

Step IV Pilot, assess and operationalize

After decided to build a database, and the c right now can be solved by it, we now move on to the next step, we call it pilot, asset and operationalize step.in this step, we need to figure out if our sources can achieve our goal and how we make it reel. The first part is called ideate.

Primarily, we need to go through our sources of information,what kind and how much information we can get. Specifically, to our bank, we have a strong connection with government, also the other 3 principal banks and insurance companies. they are good providers. for example, we can contact with taxation bureau to get tax records, now in china, government authorizes the third financial or IT party to use data of government, especially taxation bureau. as our background, the bank controlled by country, that’s not difficult to get one commission .and we can dig deeper, the criminal records, the history of complaints or professional career, the change of habitation, as long as we maintain a good relationship with them, we can upgrade our calculation in the future.also, we join in the network of information among banks to get the records of credit and the history of loans to analyze their behavior pattern. To insurance companies ,on one hand they have already built a net to share information ,on the other hand ,these companies are also state-owned.for these two reasons ,it’s not difficult to get access to insurance records. Then if we want to go further, we can expand our database in two ways: one is to connect to payment or shopping software companies like PayPal, enterprise to assess their consummation information, another is to gather other type of information in life to compare their habits and earnings, like water or electricity bills, which will be hard to get fraud but only target to a small part of people and difficult to build the model (yet in German, a company has already built one, we can buy that).

Then we focus on if our present technology can handle this information. Unfortunately, our bank still uses the system dated to 90s, as we said in the first part, the agency of notation inter the bank is regional and administrative, that’s to say, the system is far behind the times and is very subjective made decisions by un professional employees. so the situation is very severe, inChina, state-owned companies cannot fire employees. we need to consider to buy an analyze system or to build up one from zero, which one is more economic. If we decide to build one ourselves, we have to recruit professional programmers to ask them. if they cannot handle this type of information, we need to reconsider how can we use .

The second part is incubating.

In this part, it is more technical than strategic, we use a famous way to evaluate if our database can run well and assess information. It is called POC (Proof of Concept). there are lots of existed famous consulting companies to sell this service, likepwc etc. we can simply buy one and get the result. we have to test and improve to make the system pass the test.

The third part is operationalizing

If we go into this part, that means our database is near to go into practice .so we need to focus on hardware, how can the database can be maintained and reduce the cost of that.

Lack of professional programmers and engineers, we absolutely need to store this information in the head office, using internet to transfer information. although it’s not very safe and easy to interrupt, comparing with the high cost of training people, this is a good idea .to solve these disadvantages, we can build an encrypted, stable line.

The job in priority is managing employees, including training people to use the software, sending people gathering concerning information, rearranging or simplifying production team to do the maintenance.

After achieving these, our database can run stably and make returns. Now we can turn into the next step.

Step V Evaluate and improve

Measuring the value of big data initiatives keeps the organization focused and aligned with the needs of the enterprise. Without this step, firms run the risk of adopting new technologies without having anything to show for them in the end.

Determine if big data capabilities are delivering value by continually monitoring performance. To continuously improve big data capabilities, focus on improving performance by taking action in response to staff feedback and market trends.

Big data is a rapidly evolving domain that is not as mature as many of the other data management domains. There is significant opportunity to improve the big data capability, as most organizations are at a relatively low level of maturity.

Once in production, the capabilities should be reevaluated on a regular schedule (for example,

quarterly, annually) to measure progress until either full value is realized or it is determined that full value realization is not needed.

Existing implemented architecture should occasionally be reevaluated to determine if the architecture can be improved.

Employees should have the right mix of on-the-job training and formal education, so that their big data knowledge is up to date with the rest of the industry.

As the big data capability matures in an organization, how the capability is enabled should also be assessed to understand steps that should be added, removed, or improved.