Big data can mine and analyze the deep content of financial information, so that decision makers can grasp the key points and guide the strategic direction.
in the coming era of big data, the competition among financial institutions will be fully launched on the network information platform. In the final analysis, data is king. Whoever has the data will have the risk pricing ability, who will get high risk returns and finally win the competitive advantage.
China's financial industry is entering the primary stage of the era of big data. After years of development and accumulation, the data volume of domestic financial institutions has reached more than 1TB, and the unstructured data volume is increasing at a faster speed. Financial institutions have natural advantages in the application of big data: on the one hand, financial enterprises have accumulated a large number of high-value-intensive data including customer identity, assets and liabilities, fund receipt and payment transactions, etc. In the course of business development, these data will have great commercial value after being mined and analyzed by professional technology; On the other hand, financial institutions have sufficient budgets, which can attract high-end talents who implement big data and have the ability to adopt the latest technology of big data. Overall, the emerging big data technology will show a trend of rapid integration with financial business, which will bring important opportunities for the future development of the financial industry.
? First, big data promotes the strategic transformation of financial institutions. Under the macro-economic structural adjustment and the gradual marketization of interest rates, domestic financial institutions are increasingly affected by financial disintermediation, which is manifested in the loss of core liabilities, the narrowing of profit space and the urgent need to adjust their business positioning. The key to business transformation lies in innovation, but at this stage, the innovation of domestic financial institutions is often reduced to regulatory arbitrage, which fails to provide more valuable services based on tapping the internal needs of customers. Big data technology is an important tool for financial institutions to dig deep into existing data, identify market positioning, clarify the direction of resource allocation and promote business innovation.
? Secondly, big data technology can reduce the management and operation costs of financial institutions. Through the application and analysis of big data, financial institutions can accurately locate internal management defects, formulate targeted improvement measures, and implement management models that meet their own characteristics, thereby reducing management and operation costs. In addition, big data also provides brand-new communication channels and marketing methods, which can better understand customers' consumption habits and behavior characteristics and grasp the marketing effect in a timely and accurate manner.
? Third, big data technology helps to reduce the degree of information asymmetry and enhance risk control capabilities. Financial institutions can abandon the original business mode of relying too much on customers to provide financial statements to obtain information, and instead monitor and analyze their liquidity data such as asset prices, accounting flow and related business activities dynamically and in the whole process, thus effectively improving the transparency of customer information. At present, advanced banks have been able to integrate customers' assets and liabilities, transaction payment, liquidity status, tax payment and credit records based on big data, evaluate customers' behaviors in an all-round way, calculate dynamic default probability and loss rate, and improve the reliability of loan decisions.
Of course, it must be noted that financial institutions also face many challenges and risks in the process of integrating with big data technology.
first, the application of big data technology may lead to the reconstruction of the competitive landscape of the financial industry. The progress of information technology, the opening of the financial industry and the changes of regulatory policies have objectively lowered the entry threshold of the industry, and non-financial institutions have cut into the financial service chain more, and taken advantage of their own technological advantages and regulatory blind spots to occupy a place. However, traditional financial institutions are limited by the original organizational structure and management mode, and cannot give full play to their potential, but may be in a competitive disadvantage.
Second, the infrastructure and security management of big data need to be strengthened. In the era of big data, in addition to the traditional financial statements, financial institutions have also added unstructured data such as images, pictures and audio. Traditional analysis methods have not adapted to the management needs of big data, and the construction of software and hardware infrastructure needs to be strengthened urgently. At the same time, the security problem of financial big data has become increasingly prominent, and it may suffer devastating losses if it is not handled properly. In recent years, domestic financial enterprises have been increasing their investment in data security, but the extension of business chain, the popularization of cloud computing model and the improvement of their own system complexity have further increased the hidden risks of big data.
Third, there are decision-making risks in the technology selection of big data. At present, big data is still in the exploration and growth stage of operation mode, and the analytical database is not mature compared with the traditional transactional database, and it still lacks high scalability support for the analysis and processing of big data, and it is still mainly oriented to structured data and lacks the processing ability for unstructured data. In this case, there is a risk of wrong choice, too advanced or lagging behind in the technical decision-making related to financial enterprises. Big data is an overall trend, but investing a lot too early, choosing software and hardware that are not suitable for their own reality, or being too conservative and doing nothing may have an adverse impact on the development of financial institutions.
how should big data be applied to financial enterprises?
although the application of big data in financial enterprises has just started, the impact is still relatively small, but from the development trend, we should fully understand the far-reaching impact brought by big data. When formulating the development strategy, the board of directors and management should not only consider the traditional elements such as scale, capital, outlets, personnel and customers, but also pay more attention to the ability to possess and use big data, as well as the research and development capabilities of the Internet, mobile communication and electronic channels; It is necessary to introduce and practice the concepts and methods of big data in the development strategy and promote the transformation of decision-making from "experience dependence" to "data dependence"; It is necessary to ensure the investment in big data resources, and take channel integration, information networking, data mining, etc. as an important basis for providing financial services and innovative products to customers.
(1) Promoting the integration of financial services and social networks
In order to develop a big data platform, Chinese financial enterprises must break the boundaries of traditional data sources, pay attention to new data sources such as Internet sites and social media, and obtain as much customer and market information as possible through various channels. First of all, we should integrate new customer contact channels, give full play to the role of social networks, enhance understanding and interaction with customers, and establish a good brand image. Secondly, we should pay attention to the development of new media customer service, and use various chat tools and other network tools to build it into a service channel parallel to telephone customer service. The third is to connect the internal data of the enterprise with the external social data to obtain a more complete customer view and conduct more efficient customer relationship management. The fourth is to use social network data and mobile data for product innovation and precision marketing. Fifth, public opinion monitoring, which pays attention to new media channels, promptly and effectively handles the risk events before they break out, so as to minimize the reputation risk.
(II) Dealing with the competition and cooperation with data service providers
At present, there are a large number of transactions on major e-commerce platforms every day, but most of the payment and settlement of these transactions are monopolized by third-party payment institutions, and traditional financial enterprises are at the end of the payment chain, and the value obtained from them is small. To this end, financial institutions can consider building their own data platforms and holding the core discourse power in their own hands. On the other hand, we can also carry out strategic cooperation with big data platforms such as telecommunications, e-commerce, social networks, exchange data and information, fully integrate effective customer information, and integrate financial services with mobile networks, e-commerce and social networks. From the perspective of professional division of labor, it is a realistic choice for financial institutions to carry out strategic cooperation with data service providers; If you run your own e-commerce, there is no professional advantage, which is not only time-consuming and laborious, but also may lose market opportunities.
(3) Enhance the core processing capability of big data
First, strengthen the integration capability of big data. This includes not only data integration within financial enterprises, but also integration with other external data in the big data chain. At present, there are differences in data standards from various industries and channels. It is necessary to unify the standards and formats as soon as possible in order to carry out standardized data fusion and form a complete customer view. At the same time, in view of the massive data requirements brought by big data, it is necessary to re-engineer the traditional data warehouse technology, especially the data transmission mode ETL (extraction, transformation and loading). Secondly, the ability of data mining and analysis should be enhanced, and a business logic model should be established by using big data professional tools to transform a large amount of unstructured data into decision support information. The third is to strengthen the interpretation and application ability of big data analysis conclusions. The key is to build a compound big data professional team. They should not only master the technology of mathematical modeling and data mining, but also have good business understanding and be able to fully communicate and cooperate with internal business lines.
? (IV) Intensify financial innovation and set up big data labs
A big data innovation lab can be set up within financial enterprises to co-ordinate talents and resources in business, management, science and technology and statistics, and establish a special management system and incentive mechanism. The laboratory is responsible for the formulation, experiment, evaluation, promotion and upgrade of big data solutions. Before each big data scheme is implemented, the laboratory should conduct unit test, walk-through test, stress test and return inspection in advance; After the test is passed, make a comprehensive evaluation of the risk and return of the project with data support. Another task of the laboratory is to conduct "big analysis" on "big data" and constantly optimize the model algorithm. In "methodology".
(5) Strengthen risk management and control to ensure the security of big data.
Big data can alleviate the problem of information asymmetry to a great extent and provide a more effective means for risk management of financial enterprises. However, if it is not well managed, "big data" itself may also evolve into "big risk". The application of big data has changed the characteristics of data security risks. It not only needs new management methods, but also must be incorporated into a comprehensive risk management system for unified monitoring and governance. In order to ensure the security of big data, financial institutions must grasp three key links: First, coordinate all institutions in the big data chain, promote data security standards, and strengthen industrial self-monitoring and technology sharing; The second is to strengthen cooperation and exchanges with regulatory agencies and improve their own big data security level with the help of regulatory services; The third is to actively strengthen communication with customers in data security and data use, enhance customers' awareness of data security, and form a synergy effect of big data risk management.