Expert Perspectives | Mining the Value of Data and Empowering "AI + Finance"

The deep application of artificial intelligence based on financial data mining is a typical new type of productivity. Both analytical and generative artificial intelligence rely heavily on data training, and data is one of the most important foundational elements of artificial intelligence. The financial industry produces a large amount of data per unit. The scale of data and materials in Chinese financial institutions has exceeded 100 billion yuan, with high data quality. Data is becoming the core competitiveness of Chinese financial institutions, with a potential value exceeding trillions of yuan. By utilizing machine deep learning algorithms, financial institutions can extract deeper industry insights from structured and unstructured data, accurately formulate investment strategies, optimize customer service, and implement personalized marketing strategies. It can be said that the effective application of data not only improves the accuracy of decision-making, but also greatly promotes innovation in financial business models.

Analyze user behavior and innovate financial services

AI continues to conduct in-depth research on user behavior habits, hobbies, demands, and backgrounds, and based on data technology, can provide appropriate service content at accurate times and locations. Traditional customer behavior analysis models rely on structured data, such as bank account transactions and credit history, but this method cannot evaluate customers with insufficient bank records. The introduction of artificial intelligence has changed this situation. Through data mining, structured and unstructured data (such as social media activities, online behavior, and user generated content) can be integrated and analyzed, providing a more comprehensive customer profile. By conducting in-depth analysis of customer transaction records, browsing habits, purchasing patterns, and feedback data, financial institutions can gain precise insights into individual customer preferences and behaviors.

Bloomberg's Bloomberg GPT has significantly changed the way data mining and analysis are conducted. As a large-scale language model with 50 billion parameters, its main advantage lies in its ability to quickly analyze and mine financial data, supporting key financial functions such as risk assessment, sentiment analysis, and Q&A. This model combines 345 billion public datasets and 363 billion professional financial datasets for training, giving it the ability to process highly complex financial information. Practical applications include generating query languages, providing title suggestions, and executing financial Q&A functions, which have been successfully implemented in its internal system. The language processing ability of this model enables it to mine deeper conclusions from massive financial data, opening up new perspectives for financial professional analysis.

Through the case of Bloomberg GPT, we can see that the application of artificial intelligence in the financial field is not limited to simple automated tasks, but delves into complex decision support and predictive analysis. Bloomberg utilizes its massive parameter scale and vast dataset to ensure the accuracy and depth of its models in handling highly specialized financial data.

Building new institutions and expanding development ideas

In today's era of data explosion, banks have generally abandoned the "pawnshop" model and turned to innovative models based on big data technology to algorithmic transactions, truly achieving a historical leap from "technical banks" to "banks that understand technology" and then to "financial comprehensive service providers in the new digital era". This strengthens the connectivity between financial data, shortens the calculation time of massive data, and enhances the coordination, upgrading, and transformation of the entire industry chain.

Bank of Beijing has demonstrated advanced practices in financial data mining and artificial intelligence technology applications through its "Jingzhi Brain" project. This project achieves efficient mining and utilization of financial data by building a centralized and unified AI platform, covering multiple aspects such as data processing, model delivery, algorithm deployment, and model management. This project not only improves the efficiency of data processing and analysis, but also optimizes customer service and product promotion strategies, significantly enhancing the bank's market competitiveness. At the same time, "Jingzhi Brain" effectively enhances the data governance capability of the entire organization through integrated and systematic data mining.

Precision wealth management enhances service connotation

In the financial industry, utilizing data mining and artificial intelligence to develop precise investment strategies has become an increasingly common practice. By analyzing a large amount of historical transaction data, market conditions, news reports, and macroeconomic indicators, financial institutions can establish complex predictive models to optimize their investment decision-making process. Advanced analytical tools also enable financial institutions to better manage and diversify investment risks. Through in-depth mining and analysis of market data, investors can more accurately identify potential risk factors and adjust their investment portfolios accordingly to achieve the optimal balance between risk and return. This data-driven approach not only improves the scientificity and accuracy of investment decisions, but also greatly enhances the efficiency of resource allocation.

BlackRock, as one of the world's largest asset management companies, uses its proprietary technology platform - Aladdin - to enhance investment strategies and effectively manage assets. Aladdin is a comprehensive investment management system that integrates advanced risk analysis and portfolio management tools, utilizing big data and artificial intelligence technology to process, analyze, and mine massive amounts of data from global financial markets. Enable BlackRock to provide real-time insights and predictive analysis, helping analysts predict price trends of various asset classes and make informed investment decisions. These tools surpass traditional charts and graphics in terms of information representation, introducing interactive elements, real-time updates, and predictive analysis capabilities, enabling users to extract deeper insights from complex datasets and more easily discover opportunities for financial investment.

Strengthening the Management Foundation and Identifying Financial Risks

Artificial intelligence can identify fraudulent behavior in the financial industry through data mining and analysis tools, thereby more comprehensively and timely protecting the interests of financial institutions and customers. By implementing machine learning models, financial institutions can monitor and analyze transaction patterns in real-time, automatically identifying abnormal transactions that do not conform to conventional behavior. By utilizing natural language processing (NLP) technology to analyze customer communication and public data, financial institutions can obtain more contextual information about customer behavior, further enhancing their anti fraud capabilities. This technology integration enables financial institutions to not only respond to traditional fraud threats, but also effectively prevent emerging complex fraud strategies. Data mining plays a very important role in the financial industry. Through data mining, it not only helps financial institutions identify potential business relationships between transaction data and segment customer groups, but also effectively identifies abnormal behaviors in the data, providing support for risk management.

As a leading global online payment platform, PayPal actively applies machine learning technology to strengthen its anti fraud system. The platform processes each transaction through a complex risk management engine that utilizes multiple machine learning models to analyze and evaluate hundreds of transaction related variables in real-time. This technology allows PayPal to monitor various details of transactions, such as time, location, amount, and account history, effectively identifying unusual transaction patterns and potential fraudulent behavior. With its powerful computing and data analysis capabilities, artificial intelligence is continuously unleashing new productivity in the financial field. By fully utilizing AI technology in various deep vertical scenarios, financial institutions can achieve customer demands and expectations faster and safer.

In the future, with the implementation of the three-year action plan for "Data Elements X" (2024-2026) issued by the National Data Administration, data will play a "multiplier effect" in various scenarios, and financial institutions are expected to further strengthen the integration of cross industry data. The application scope of financial data mining is expanding, which has also driven the progress of data visualization technology. The collaborative innovation of the "AI+Financial Big Model" reveals a trend of transformation from quantitative management to qualitative decision-making, indicating that the financial industry has entered a new stage centered on data. It is also a strategic choice made by the financial industry to respond to changes in the global economic environment and enhance competitiveness. By integrating a wider range of data sources, such as geographic information and publicly available government data, combined with existing financial data, not only can more accurate predictive models be provided, but it also helps financial institutions in China to better understand market dynamics.

The above content is sourced from China Urban and Rural Financial News