The banks ' coping way in the Big Data era

One big thing that hit the tech industry in 2012 was the large data program unveiled by the Obama administration. A major US technology development deployment was the 1993 information Superhighway program, which changed the way information is produced and transmitted around the world, triggering the Internet revolution. Statistics show that ATM, internet banking, mobile banking, such as the main network of electronic banks to China's traditional channels of the replacement rate of more than 60%, the upcoming large data revolution may be the bank's ideas and business model again subversion.

A case study of large data application and analysis of bank competitor

With the advent of the Internet and related networks, banks are no longer the main builders of the rules in the field, and the traditional relationships of banks have been undermined, and in the face of this shift, the bank's use of large data to improve its service model is to recreate and shape new social relationships. The foreign advanced Bank has carried on the certain exploration in this aspect, mainly manifests the thought has:

Increase the sensing device to enhance the ability of perceiving the objective world. For example, the US Zestcash uses thousands of of information clues to pick out customers whose credit is low, but the real credit for the environment is limited. Zestcash uses so many sensors to understand real-world differences and to understand how the Web mirrors the real world.

Provide in-depth data Analysis Services, become a consumer information center, improve customer recognition. A case in which Singapore Citibank is based on consumer credit card transactions is targeted to provide them with business and restaurant concessions. This behavior is the contention and display of a clearing house--albeit from an economic information center to a consumer information center--but the client feels that the ability to access and process the bank's information is as strong as ever, and thus recognizes that the bank is reliable and safe.

The use of large data for non-traditional, non-accounting nature of the possibility of clues to the investigation. In the case of anti-money laundering, the suspect, though directly related to the bank, is not intentionally or unintentionally taking the bank as its information centre, as the suspect would have to do to conceal the bank, which is not the traditional bank's strength to deal with standard documents. Watson, the computer star who defeated human opponents in the "brink" of the television quiz show, was used by Citigroup to tackle the challenge of money-laundering because he was adept at analyzing the unstructured data more consistent with the objective world relationship. So big data is an effective tool to deal with the normal business of banking.

Use large data to find those who are suitable for their own enterprise model of customer groups, to build, strengthen the enterprise-specific business model. The business philosophy of ING Direct online banking is that it is simple and attractive to customers pursuing high returns. For this they pay an average of 4% of the high interest on the checking account, as they can be paid by calculating the material and labor costs that are saved. ING Direct also proactively dismisses customers who waste their costs, such as customers who make too many calls to call centers, saving millions of dollars. The way to Jane's ING Direct and the miraculous zestcash seem to have succeeded in two extremes: no matter how different business models are, they all have the ability to analyze real social relationships, which are often manifested through large data, because of the complexity of today's society. It can analyze the social relationship which is the best match with their mode of operation, so as to select the same quality customers.

In addition, a number of new credit intermediaries have emerged, and the banks ' main rivals are finance companies with large customer services from traditional banks and network service/content providers that serve small micro clients of traditional banks. For finance companies, the higher the standardization of business and data is, the better it is to reduce costs. However, in order to control the risk between financial capital and industry, a firewall needs to be established, which deviates from the extreme standardization of the business and data of financial companies. In this respect, banks can observe more samples than finance companies, and the biggest advantage of banks is to become the control center of the risk relationship, the big data becomes the magic weapon of the bank competition. In contrast, banks have been challenged by Web services/content providers, represented by search engine companies, e-commerce companies, social networking companies, and courier companies in the retail sector, which are the first producers and rule makers of network technology rules. They are trying to create an area of information that they can adapt to changes in the data brought about by changes in behaviour, rather than imposing standardized requirements on customers--which is considered offensive in the big data age and a prelude to obsolescence.

Suggestions for banking industry to deal with big data challenges

The beginning of the change is often a prelude to the revolution of measuring means, in order to deal with the Big Data challenge, the bank should configure the perceptual device in the corresponding domain according to the business purpose and characteristic.

The age of large data is the age of analysis. In the past, China's banking industry too stressed the integration of resources, the boundaries of enterprises and enterprises blurred, the era of high monopoly of financial resources this model can be successful. But the times are changing, with the reform of financial market, banks no longer have unique resources, they integrate what? How dare you say that your integration is stronger than your competitors? Our future direction only by strengthening the analytical ability to find resources to the market, more importantly through the analysis to find a unique business model. In setting up their own unique business model must admit that not all customers are suitable, only a part of the customer and the bank's choice of business model to match, those most worthy of service of the homogeneous customers are often covered under different data, need to use data analysis to find.

The essence of large data analysis is to expose customers to a larger social background, accurately positioning the location of the customer in the environment in accordance with what kind of business model; So far no technology has been able to surpass people (for the bank is the account manager) more to grasp the analysis of living customers, The client manager is the best social perception device, and it also has the responsibility of importing the familiar relational model of bank reservation into the real social network and expanding the relationship, which is the biggest advantage of the bank.

Bankers should try to describe the complex world as objectively as possible. We are not sure whether big data will be able to dominate the future, but the idea that it describes the objective world is what we seek to effectively improve the level of banking.

(Responsible editor: The good of the Legacy)