Applications and challenges of big data in the financial industry

Big data analysis makes it easier for banks to know who their users are, big data analysis makes it easier for the market to get the information they want, and big data analysis will also make it easier for insurance practitioners to find customers.

First of all, we need to know what big data analysis is, big data is a collection of data with high application value, and big data analysis is the analysis of data collection with high application value, so that we can clearly know the overall situation of a certain piece of information.

Secondly, we need to know what finance is? Finance is an economic activity in which banks, ** or insurers receive money from the market and can lend money to other markets. It is the economic activity of raising funds and borrowing funds, mainly banks, **, and insurance are the main bodies of this type of economic activity.

Big data analysis has a great role for the entire industry, especially in the financial industry, who has the biggest need in the financial industry? Accurately find customers and close deals efficiently, this is what big data analysis does, there is a lot of data, and then through analysis to find out that the people in that place need funds the most, which people need funds the most, and then find these people accordingly. This is one of the applications, and more importantly, the focus on the future is derived from a large amount of data analysis, which is an important part of the development of the industry.

When the information analyzed by big data is enough and comprehensive enough, it plays a prophetic role for the company's decision-making and has a profound impact on the future development layout, which is the commanding heights of information that occupies the lead. From history to the present, big data analysis has a very positive effect on the financial industry by integrating and analyzing value information from all walks of life, saving time, improving efficiency, and making accurate decisions.

The combination of big data analysis and finance is the combination of applications with banking, insurance, insurance and other industries, at this stage is to find the people who need the most effective help, and the same big data analysis can obtain information on the future layout, so that the company's decision-making is accurate and effective.

It is necessary to carry out multi-dimensional analysis capabilities, analyze data, integrate artificial intelligence and big data for research and development, and make corresponding changes, use big data to analyze finance, and promote economic development with each other.

The two are mutually influential, and the combination of big data will have a certain impact on the financing market or the financial market, and can also determine the financial market, and the value evaluation of different industries in terms of technology is also completely different.

After the combination, the characteristics are very obvious, big data analysis becomes easier, all financial points will be listed, and you can also check the information of the past period, more at a glance, you can do a larger data analysis.

The big data industry chain covers a wide range, and the upstream is the basic support layer, mainly including network equipment, computer equipment, storage equipment and other hardware.

Based on massive data resources, the midstream of the big data industry provides auxiliary services around various applications and market needs, including data transactions, data asset management, data collection, data processing and analysis, data security, and data-based IT operation and maintenance.

With the continuous improvement of China's big data research technology, at present, China's big data has been widely used in government affairs, industry, finance, transportation, telecommunications and spatial geography industries.

The applications of big data in the financial field are as follows:

1.Overview.

In recent years, with the rapid development of new technologies such as big data, cloud computing, blockchain, and artificial intelligence, these new technologies have been deeply integrated with financial business, releasing the vitality and application potential of financial innovation, which has greatly promoted the transformation and upgrading of China's financial industry, helped finance better serve the real economy, and effectively promoted the overall development of the financial industry.

In this process of development, big data technology is the most mature and widely used.

From the perspective of development characteristics and trends, the rapid construction and implementation of "financial cloud" has laid the foundation for the application of financial big data, the integration and application of financial data and other cross-domain data has been continuously strengthened, artificial intelligence is becoming a new direction for the application of financial big data, and the integration, sharing and opening of data in the financial industry are becoming a trend, bringing new development opportunities and huge development momentum to the financial industry.

2.Typical application of big data technology in the financial industry.

Big data technology has a wide range of applications in the financial industry, and the following will introduce the application of big data technology in financial subdivisions such as banking, insurance, etc.

3.Challenges and countermeasures of financial big data application.

Big data technology has brought fission-like innovation vitality to the financial industry, and its application potential is obvious to all, but it is important in data application management, business scenario integration, and standard system.

First, the bottlenecks in top-level design and other aspects also need to be broken.

The application of big data in finance includes customer portrait application, precision marketing, risk management and control, and operation optimization.

1. Customer portrait application.

Customer portrait applications are mainly divided into individual customer portraits and enterprise customer portraits. Individual customer portraits include demographic characteristics, spending power data, interest data, risk appetite, etc.; Enterprise customer portraits include the production, circulation, operation, finance, sales and customer data of the enterprise, as well as the upstream and downstream data of the relevant industrial chain. It is important to note that the customer information that banks have is not comprehensive, and it is sometimes difficult to draw desirable results or even wrong conclusions based on the data that banks have themselves.

2. Precision marketing.

On the basis of customer portraits, banks can effectively develop precision marketing, including real-time marketing. Real-time marketing is based on the real-time status of the customer, such as the customer's location at the time, the customer's latest consumption and other information to carry out targeted marketing (a customer uses a credit card to purchase maternity products, can speculate the probability of pregnancy through modeling and recommend the business that pregnant women like); Or think of life-changing events (changing jobs, changing marital status, changing homes, etc.) as marketing opportunities.

3. Risk management and control.

This includes SME loan risk assessment and fraudulent transaction identification. SME Loan Risk Assessment. Banks can conduct loan risk analysis through the relevant information of the enterprise's production, circulation, sales, and finance combined with big data mining methods, quantify the credit limit of the enterprise, and carry out SME loans more effectively.

Real-time fraudulent transaction identification and anti-money laundering analysis. Banks can use cardholder basic information, card basic information, transaction history, customer historical behavior patterns, ongoing behavior patterns (such as transfers), etc., combined with intelligent rule engines (such as transferring money from an infrequent country for a unique user or from an unfamiliar location) for real-time transaction anti-fraud analysis.

4. Operational optimization.

Market and channel analysis optimization. Through big data, banks can monitor the quality of different marketing channels, especially online channels, so as to adjust and optimize cooperation channels. At the same time, it can also analyze which channels are more suitable for promoting which types of banking products or services, so as to optimize the channel promotion strategy.

Pros and cons of big data

From ancient times to the present, the ability to analyze is one of the abilities that everyone looks forward to, and big data is the most important use of data. Today's big data is the analysis and application of recorded historical records, the integration of mathematical analysis models, predicting the future and then speculating the results.

In the era of big data, everyone will inadvertently perceive that their personal privacy is threatened: big data technology service providers monitor people's personal privacy, buy things to monitor people's consumption habits, search engines monitor people's browsing habits, dating software monitors people's interpersonal relationships, investment and financial products monitor people's wealth, and so on.

Big data has changed the traditional way financial institutions operate data in four ways, thereby realizing huge business value. These four aspects (the "four Cs") include: compatibility of data quality, connectedness of data utilization, cost of data analysis, and capitalization of data value.

The application scenarios of big data in the financial industry are gradually expanding. Overseas, big data has been comprehensively tried in the fields of risk control, operation management, sales support and business model innovation in the financial industry. In China, the application of big data by financial institutions is still in its infancy.

Challenges in key areas, such as data integration and departmental coordination, remain the main bottlenecks preventing financial institutions from turning data into value. The development of data technology and data economy is the support for the continuous realization of the value of big data. Deep applications are pushing traditional IT from the "back to the front", and the effective integration of stock architecture and innovation modules is the main challenge faced by traditional financial institutions at the level of technology talk.

In addition, the development and evolution of data ecology has its significant social characteristics. As one of them, financial institutions have a long way to go in promoting the development of the data economy. Whether in financial enterprises or non-financial enterprises, the life cycle of data application and business innovation consists of five stages:

Business defines requirements; IT departments capture and integrate data; Data scientists build and refine algorithms and models; IT publishes new insights; Apply business and measure the real-world impact of your insights. In today's big data environment, the lifecycle remains the same, and the only thing that has changed is the role of the "data scientist" in the lifecycle. Big data will allow it to use a variety of new algorithms and technologies to help IT continuously uncover new relevant insights to better meet business needs.