Typical applications of big data technology in the financial industry
In recent years, my country's financial technology has developed rapidly and has been at the forefront of the world in many fields. The deep integration of technologies such as big data, artificial intelligence, cloud computing, and mobile Internet with financial services has greatly promoted the transformation and upgrading of my country's financial industry, helped finance better serve the real economy, and effectively promoted the overall development of the financial industry. In this development process, 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-field data have been continuously strengthened. Artificial intelligence is becoming a new direction for financial big data applications. Financial The integration, sharing and openness of industry data are becoming a trend, bringing new development opportunities and huge development momentum to the financial industry.
Typical application scenarios of big data in the financial industry
Big data involves a wide range of industries. In addition to finance, it also includes politics, education, media, medicine, commerce, industry and agriculture, In many aspects such as the Internet, the definition of big data in various industries has not yet been unified. The characteristics of big data can be summarized as “4V”. First, the data volume is large (Volume). Massiveness may be the most relevant feature of big data. Second, there are many types of data (Variety). Big data includes not only traditional structured data represented by transactions, but also semi-structured data represented by web pages and unstructured data represented by video and voice information. Third, the value density is low (Value). The volume of big data is huge, but the value density in the data is very low. For example, in several hours or even days of surveillance video, valuable clues may only be a few seconds. Fourth, the processing speed (Velocity) is fast. Big data requires fast processing, strong timeliness, and real-time or quasi-real-time processing.
The financial industry has always attached greater importance to the development of big data technology. Compared with conventional business analysis methods, big data can make business decisions forward-looking, make the corporate strategy formulation process more rational, optimize the allocation of production resources, quickly adjust business strategies according to market changes, improve user experience and capital turnover rate, and reduce costs. The risk of overstocked inventory leads to higher profits.
Currently, the typical application scenarios of big data in the financial industry include the following aspects:
The application in the banking industry is mainly reflected in two aspects: First, credit risk assessment. In the past, banks' default risk assessments for corporate customers were mostly based on static data such as past credit data and transaction data. Big data integrated with internal and external data resources can provide forward-looking predictions. The second is supply chain finance. Using big data technology, banks can form a relationship map between enterprises based on their investments, holdings, loans, guarantees, and the relationships between shareholders and legal persons, which is beneficial to enterprise analysis and risk control.
The main applications in the securities industry are as follows: First, stock market forecasting. Big data can effectively expand the dimensions of quantitative investment data for securities companies and help companies understand market conditions more accurately. By constructing more diversified quantitative factors, investment research models will be more complete. The second is stock price prediction. Big data technology collects and analyzes structured and unstructured data on social networks such as Weibo, Moments, professional forums and other channels to form subjective market judgment factors and investor sentiment scores, thereby quantifying the expected changes in human factors in stock prices. . The third is intelligent investment advisor. The intelligent investment advisory business provides online investment advisory services, which are based on personalized data such as customers' risk preferences and trading behaviors, and rely on big data quantitative models to provide customers with personalized wealth management solutions with low thresholds and low rates.
Applications in the Internet financial industry include precision marketing. Big data classifies and filters customer preferences through multi-dimensional portraits of users, thereby achieving the purpose of precision marketing. The second is consumer credit. Automatic scoring models, automatic approval systems and collection systems based on big data can reduce the risk of default in consumer credit business.
Typical case analysis of financial big data
It is to receive electronic channel transaction data in real time and integrate business data within the bank's system.
China Bank of Communications intends to implement functions such as rapid modeling, real-time alarms and online intelligent monitoring reports through rules to achieve the purpose of receiving official website business data in real time and integrating customer information, device portraits, location information, official website transaction logs, browsing records and other data.
By building an anti-fraud model, real-time calculation, and real-time decision-making system for the Bank of Communications Card Center, this system helps the bank card center, which has massive historical data and an average daily growth of more than 20 million logs, to form an electronic Channel real-time anti-fraud transaction monitoring capabilities. Utilize distributed real-time data collection technology and real-time decision-making engine to help credit card centers efficiently integrate multi-system business data, process massive amounts of highly concurrent online behavior data, identify malicious users and fraudulent behaviors, and provide real-time warning and disposal; by introducing a machine learning framework, Analyze, mine, construct and periodically update anti-fraud rules and anti-fraud models on a small amount of data.
After the system went online, the bank quickly monitored new risks and fraudulent behaviors such as false accounts, disguised accounts, abnormal logins, frequent logins, etc. generated through electronic channels; the system ran stably and processed more than 20 million logs every day. Streamlined and identified nearly 10,000 risky behaviors in real time and issued early warnings. The overall processing time of data access, alarm calculation, and case investigation has been reduced from hours to seconds, and the monitoring timeliness has been increased by nearly 3,000 times. It has helped the card center recover risk losses of millions of yuan within 3 months of being online.
Baidu’s search technology is being fully injected into Baidu Finance. The gradient boosted decision tree algorithm used by Baidu Finance can analyze the high-dimensional characteristics of big data. It is unique in many aspects such as knowledge analysis, summary, aggregation, and refining. Its deep learning capabilities can better solve big problems by using data mining algorithms. Issues such as low data value density. Baidu's "Panshi" system is based on 10 billion search behaviors per day, accurately profiles 860 million accounts through more than 200 dimensions, and efficiently divides people. It can provide identity recognition, anti-fraud, information inspection, credit rating, etc. for banks and Internet financial institutions. Serve. This system has intercepted hundreds of thousands of fraudulent users for Baidu's internal credit business, intercepted billions of non-performing assets, reduced millions of labor costs, and cooperated with nearly 500 social financial institutions, helping them improve their overall risk prevention and control levels.
Challenges and countermeasures faced by financial big data applications
Big data technology has brought fission-type innovation vitality to the financial industry, and its application potential is obvious, but in Bottlenecks in data application management, business scenario integration, standard unification, and top-level design also need to be overcome.
First, the level of data asset management still needs to be improved. Mainly reflected in aspects such as low data quality, single acquisition method, and scattered data systems.
Second, breakthroughs are still needed in application technology and business exploration. This is mainly reflected in the fact that the original data system architecture of financial institutions is relatively complex, involving many system platforms and suppliers, and it is very difficult to realize technological transformation of big data applications. At the same time, the big data analysis application model in the financial industry is still in its infancy, with relatively few mature cases and solutions, requiring a lot of time and cost to invest in research and trial and error. The system's misjudgment rate is relatively high.
Third, industry standards and safety regulations still need to be improved. Financial big data lacks unified storage management standards and interoperability and sharing platforms, and a credible security mechanism has not yet been formed to protect personal privacy.
Fourth, top-level design and supporting policies need to be strengthened. The data barriers among financial institutions are relatively obvious, the problem of fragmentation is prominent, and there is a lack of effective integration and coordination. At the same time, industry applications lack overall planning, are scattered, temporary, and stressful, and there is still great potential for information value development.
The above issues require the state to introduce industrial planning and support policies to promote the development of financial big data. At the same time, the industry also needs to promote the opening, sharing and unified platform construction of financial data in stages to strengthen the industry. standards and safety regulations. Only in this way can big data technology be steadily applied and developed in the financial industry and continuously promote the development and improvement of the financial industry.