As the industry continues to evolve, the democratization of generative AI may become a technological trend in 2024.

An increasing number of practices have proven that large models, deeply integrated into various aspects of enterprise operations, can help businesses enhance operational efficiency through rich semantic understanding.

Peking University HSBC Business School Professor Wei Wei and his collaborator, Huang Hongwu, CTO of Shenzhen Super Technology Co., Ltd., jointly wrote in "Peking University Financial Review" that enterprises can utilize the powerful parsing and generation capabilities of large models to more efficiently sort and analyze massive amounts of data.

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However, the current large model technology and centralized business model also bring significant challenges and risks.

To address these issues, Professor Wei Wei's team has proposed a new paradigm for digital finance technology architecture, in which generative large models are combined with discriminative small models, and an Agent system is introduced to provide more accurate and comprehensive data processing and decision support functions.

He stated that as technology continues to develop, businesses need to continuously update their models and algorithms to maintain competitiveness and efficiency.

By collaborating with industry experts, businesses can ensure that the application of their large models complies with the latest technological advancements and ethical and legal standards.

In the financial industry, the emergence of large model technology signifies a new era of intelligent information processing and decision-making.

These advanced models, with their powerful data processing capabilities and complex algorithmic structures, have unique advantages in understanding and generating semantic content.

Whether it's text, audio, images, or video data, large models have demonstrated their formidable parsing and generation capabilities.

Financial institutions can use this to more efficiently sort and analyze massive amounts of data, enhancing the speed and precision of decision-making, thereby gaining an advantage in fierce market competition.

Nevertheless, the current large model technology and centralized business model also bring significant challenges and risks.

The first is the so-called "hallucination" problem, where large models sometimes generate seemingly reasonable but factually incorrect content that cannot be applied to real-world scenarios.

In the financial sector, this could lead to erroneous investment advice and risk assessment, causing unpredictable losses for users.

Secondly, large models often rely on massive historical data for training, but this data is not always updated in real-time or has a clear source, leading to a lack of timeliness and accuracy in the decision support provided by the model's output.

In the fast-paced and volatile financial market, timeliness is crucial.

Models lacking real-time data support are likely to produce analysis results that are outdated by the time they are published.

An even greater challenge is the issue of user data privacy and data property rights.

Under centralized models, large model companies control and process a vast amount of user data, increasing the risk of privacy breaches and potentially infringing on data property rights.

In the financial sector, this approach may face strong opposition from users, as financial data often contains sensitive information.

To address these issues, we propose a new technological architecture and business model.

In this architecture, generative large models are combined with discriminative small models, and an Agent system is introduced to provide more accurate and comprehensive data processing and decision support functions.

Generative large models can create high-quality, highly relevant content, while discriminative small models are responsible for identifying the accuracy and practicality of the content, ensuring that the generated information is consistent with real-world data and scenarios and can be traced.

The Agent system plays a key role here, handling complex business scenarios that cannot be achieved solely by prompt words, thus achieving efficient automated decision-making.

In this system, a decentralized business model is crucial.

This means that the ownership of large models should be dispersed among users, ensuring that data privacy and property rights are respected.

Users can not only control their own data but also train and customize the model according to their needs, thereby better meeting personalized financial service requirements.

This model can encourage users to actively provide and create new data and knowledge because they know they are contributing for their own benefit, not just for the benefit of large model companies.

In summary, the application prospects of large models in the financial industry are broad, but to realize their potential, existing limitations must be overcome, and new technological architectures and business models must be developed.

In this way, we can not only maximize the advantages of large models but also effectively reduce their potential risks.

The new paradigm of digital finance technology architecture: the integration of generative large models, discriminative small models, and AI Agent systems.

Generative large models can process and analyze massive historical transaction data, news reports, and market dynamics, and generate detailed investment reports and market forecasts based on this.

However, the content generated by these models needs to be rigorously filtered and verified to ensure it is consistent with the logic and accuracy of the real world.

At this point, discriminative small models can play a key role.

These models, although smaller in scale, focus on specific tasks and can perform high-precision text parsing, sentiment analysis, knowledge graph construction, etc.

They can efficiently filter useful information from the content generated by large models, categorize and trace the sources of data, and make necessary corrections.

For example, by using pre-trained models like BERT or T5 for sentiment analysis of user feedback, financial institutions can better understand the market acceptance of their products, and thus carry out precise market positioning and product improvement.

However, the generation and identification capabilities of the models alone are not enough.

The introduction of the AI Agent system is a key link in enhancing the level of intelligent decision-making in various aspects of financial decision-making.

Users can use AI Agents to output controllable text that they want, and they also have the ability to learn autonomously and make decisions, able to dynamically adjust the parameters and behaviors of large and small models according to real-time market conditions and investor needs.

This is particularly important in automated financial trading, where AI Agents can analyze market trends and automatically execute trading strategies, thereby ensuring the rationality of investment decisions while improving the speed and efficiency of transactions.

At a higher level, these autonomous agents can act as coordinators for virtual financial investment advisory platforms, integrating data analysis, algorithm design, and user interface inputs from different agents to achieve efficient team collaboration.

AI Agents are not just automated tools; they can also simulate human behavior, understand and predict customer needs, provide personalized customer service, and manage and maintain customer relationships.

In summary, by combining the data generation capabilities of generative large models, the classification and filtering capabilities of discriminative small models, and the dynamic decision-making capabilities of AI Agent systems, services in the digital finance field will become more intelligent, personalized, and automated.

This comprehensive technological architecture provides new possibilities for the future development of financial services, not only improving operational efficiency and reducing error rates but also providing users with richer and more accurate financial solutions.

To ensure the best use of the capabilities of large models, we can hierarchically classify their capabilities and select or customize appropriate model levels according to different business needs.

Question and Answer Capability (Level 1): In financial services, customer inquiries are a common form of interaction.

Large models at this level can quickly respond to various standard questions, such as product information and account inquiries.

Through domain-specific training, these models can achieve high precision and reliability in answering questions.

Question and Answer and Intelligent Search Capability (Level 2): To provide more in-depth and specific information, large models need to be combined with intelligent search functions.

At this level, models can filter out the most relevant information from a vast knowledge base or online data, such as searching for the latest market reports and financial news when providing investment advice, providing more in-depth answers.

Question and Answer, Intelligent Search, and Correction and Tracing Capability (Level 3): The accuracy of information is crucial for financial decision-making.

At this level, large models not only provide question and answer and search functions but also carry out information correction and tracing.

This capability ensures that the information output by the model is not only the most relevant but also the most credible.

For example, in automated investment analysis, the model can verify information from different data sources to ensure its accuracy.

Question and Answer, Intelligent Search, Correction and Tracing, and Task-Oriented Automated Agent Iteration (Level 4): The introduction of automated agents can achieve automation and optimization of tasks.

In areas such as financial risk assessment or investment decision-making, automated agents can continuously learn and optimize, improving the accuracy of decision-making through the iterative process and achieving a more efficient workflow.

Question and Answer, Intelligent Search, Correction and Tracing, Task-Oriented Automated Agent Iteration, and Enhanced Logical Reasoning Capability/Human-Level AI (Level 5): At this level, large models not only have all the aforementioned capabilities but also include predicates such as predicate logic and causal logic, thereby achieving logical reasoning capabilities close to human levels.

This allows large models to make comprehensive judgments when dealing with complex financial decisions such as mergers and acquisitions and asset allocation, considering market trends, regulatory compliance, and other multi-dimensional factors, providing users with comprehensive decision support.

Six business models for applying large model technology in the financial industry can exist in the financial industry, all of which can solve the user data security issues and user data interest ownership issues caused by the centralized model of large models.

The first one: Integrated large model business model.

In this model, companies license pre-trained and fine-tuned general large models to customers, who can deploy them privately and customize their own domain-specific large models according to their needs.

The advantage of this model is that it can ensure high security through standardized infrastructure construction while meeting customers' needs for personalized development.

However, this also means that both companies and customers need to have strong data processing, computing power, and programming capabilities, and the initial capital investment is large, which poses higher requirements for both parties.Here is the translation of the provided content into English: **Second type: Platform-based business model.

** In this model, companies not only develop their own general-purpose large models but also provide a platform that allows third-party companies to develop their own general-purpose large models based on it.

Compared to the first model, this business model reduces the development capability requirements for institutions and provides a more flexible business environment.

Third-party companies (customers) can develop models more quickly and achieve private deployment.

The core of this model lies in balancing the needs for openness and security, providing a flexible and secure model development environment for the financial industry.

**Third type: Domain-specific fine-tuning model.

** In this model, companies start with existing open-source or closed-source general-purpose large models that can be privately deployed and perform adaptive fine-tuning for specific domains to meet their own specific needs.

This approach reduces the development threshold for customers but requires them to continuously perform autonomous fine-tuning after the model is privately deployed to adapt to the constantly changing business needs.

**Fourth type: Customer fine-tuning model of domain-specific large models.

** This model simplifies the development process for customers by providing them with domain-specific large models (model files) and allowing them to fine-tune based on private data.

Although this model can speed up the iteration speed of general models, its relatively low security and the low barrier to replication are its disadvantages.

**Fifth type: Combination model of domain-specific large models and professional knowledge graphs.

** This model empowers customers to participate in the application of large models with a smaller initial investment, lower business risk, and cost by providing domain-specific large models and professional knowledge graphs.

The challenge of this model lies in building and maintaining a professional knowledge graph, which requires companies to have an accumulation of professional knowledge and support from high-quality data.

**Sixth type: Industry vertical integration model.

** Under this model, companies not only provide domain-specific large models but also combine data from professional data companies to build professional knowledge graphs, thereby providing customized services for different industry enterprise customers and individual users.

The advantage of this model lies in its ability to build a high industry barrier and meet the needs of diversified terminal industries, but it is highly dependent on the professional knowledge graph, requiring companies to have an in-depth understanding and a large accumulation of knowledge in professional fields.

Overall, these business models demonstrate the flexibility and diversity of large models in the application of the financial industry, and also point out the technical and commercial challenges under different models.

For financial institutions, choosing a business model that suits their own development stage and business needs can more effectively utilize the capabilities of large models to promote business innovation and enhance competitiveness.

**Value-added applications and future prospects of large model technology in the financial industry.

** In the rapid development of the current financial industry, big data and artificial intelligence technology have become the key driving force for industry innovation.

As a pioneer in these changes, large model technology has not only shone in traditional business activities and management activities but has also shown great potential in solving past intractable problems and creating new application scenarios.

Financial companies are moving towards the fast track of value-added through the application of large models.

In the field of marketing, financial institutions use large models for intelligent marketing, achieving a transformation from simple product promotion to precise personalized marketing.

Large models, by analyzing a vast amount of customer data, not only optimize marketing strategies but also improve the efficiency of generating marketing copy, making financial products and services more in line with the unique needs of customers.

Qifutech's practice is a typical case, which has improved the efficiency of its telesales system by building large models, thereby achieving a faster customer response.

The Fourth Paradigm has significantly increased the marketing response rate and transaction amount of banks through its accurate financial product recommendation model.

In the field of risk management, the application of large models is also remarkable.

Financial institutions use large models for financial risk assessment and optimize financial forecasts to support decision-making.

These models can perform deep learning and analysis on a large amount of financial data, quickly identifying potential risk points.

For example, Bairong Jinfu's integrated risk control solution can efficiently monitor and respond to risks in the loan process, significantly improving market stability.

In the field of investment management, intelligent investment advisors are leveraging the power of large models to provide customers with tailored financial plans.

Using machine learning algorithms, large models can provide personalized investment advice based on customer profiles and risk preferences.

Hengsheng Electronics' intelligent investment research platform helps investors make wiser investment decisions by improving their work efficiency and investment capabilities.

Wen Yin Interconnection's TalentGPT uses large model technology to quickly identify the highlights and anomalies of talents, enhancing the efficiency of talent recruitment and management.

Back-office management is an important part of the operation of financial enterprises, and the application of large models in this field is also very important.

Intelligent recruitment systems, with the help of large models, have improved accuracy and efficiency in screening candidates.

Intelligent training systems use large models to customize training content and optimize it based on employees' learning progress and effectiveness, significantly improving the quality and effectiveness of training.

Overall, the application of large model technology in the financial industry is not limited to improving existing operational processes; it is also a gateway to new possibilities.

Financial institutions are no longer limited to traditional analytical tools and methods but can perform deeper data mining through large models, extract more accurate business insights, and gain a competitive edge in fierce market competition.

As large model technology continues to advance and mature, we can expect it to play an increasingly important role in various fields of the financial industry, helping financial institutions achieve higher performance goals.

For example, large models can complete tasks that were difficult or impossible to achieve before, and even create new application scenarios that were unimaginable before.

The application of large models in enterprise internal activities shows great potential, with significant improvements in intelligence analysis and business automation.

Especially in intelligence analysis, large models can process and analyze a large number of complex data sets, such as financial transaction data, customer information, market trends, and risk management data.

With natural language processing capabilities, these models can identify and understand complex patterns of customer behavior, providing more personalized sales and service solutions.

For example, through deep learning algorithms, large models can predict individual customers' future purchasing behavior, thereby promoting targeted sales strategies, optimizing inventory management, and reducing the risk of overstock or shortage.

In the field of Business Intelligence (BI), large models provide a new solution by reducing the technical threshold through natural language processing and advanced data analysis capabilities.

Traditional BI tools rely on data scientists and analysts to write complex queries and maintain reports, while large models can automatically generate queries and even provide intuitive data visualization, enabling business personnel without a technical background to participate in data-driven decision-making processes.

In addition, the application of large models in business process automation is also changing the way companies operate.

By using natural language generation prompts (Prompt Engineering), companies can write complex rules and scripts to let the model automatically perform tasks such as inventory management, network planning, and customer support.

This not only improves work efficiency and reduces human errors but also allows employees to focus on more creative and strategic work.

Entering the financial field, the application of large model tools is gradually reshaping business analysis and decision-making processes.

In this data-intensive industry, models can overcome the limitations of traditional data processing methods, such as inconsistent data quality, information omissions, and complexity.

By delegating complex data processing tasks to models, business analysts can focus on more in-depth analysis and strategic planning.

Data analysis using large models is not only more efficient but also more accurate, providing a solid data support for developing complex financial products and services.

In the field of supply chain finance, large models have shown their powerful potential.

Models can act on behalf of both supply and demand parties, not only automatically matching buyers and sellers but also making decisions and adjustments based on market dynamics.

This improvement in automation is crucial for controlling costs and risk management.

For example, through algorithmic analysis, large models can optimize the credit assessment process in supply chain finance, predict financial risks, and provide real-time data support for supply chain financing decisions.

Although large models show great potential in enterprise applications, there are still challenges, such as model transparency, bias, privacy, and security issues.

Therefore, companies must carefully integrate these tools into their operations and ensure regulatory compliance.

In addition, with the continuous development of technology, companies need to continuously update their models and algorithms to maintain competitiveness and efficiency.

By collaborating with industry experts, companies can ensure that the application of their large models complies with the latest technological advancements and ethical and legal standards.

Creating new application scenarios, large model technology can not only optimize existing processes and significantly improve the efficiency of existing work methods and complete tasks that were difficult or impossible to achieve before, but it can also create entirely new application scenarios, bringing unprecedented efficiency improvements and cost optimization for companies.

Innovation in human resource management, in the field of human resource management, traditional methods face the dual challenges of low matching efficiency and inaccurate talent cultivation.

The application of large model technology makes the matching of new projects and talents faster and more accurate.

This technology can quickly identify the most suitable candidates by analyzing a large amount of employee data and project requirements, effectively connecting talents with project needs, and significantly improving the efficiency and quality of human resource allocation.Additionally, large model technology has also propelled the development of personalized training.

By analyzing employees' job performance, career development paths, and personal interests, these models can provide customized learning plans and resources, thereby more accurately meeting the development needs of employees, improving training effectiveness, and increasing employee satisfaction.

Breakthroughs in Knowledge Management: Large model technology is fundamentally changing the way information is stored, searched, and edited.

With automated classification and indexing systems, enterprises can quickly organize and retrieve a vast amount of text, images, audio, and video materials.

Automated parsing tools can not only extract text information but also recognize elements in images, audio, and video, and even analyze their relationships, achieving in-depth integration and efficient management of information.

Integration of Large Models with Blockchain: Combining large model technology with blockchain opens up new horizons for the rights confirmation and application of corpora.

By storing private corpora on blockchain, enterprises can ensure data security and traceability while utilizing large models for efficient data analysis and utilization.

This model can also bring new revenue sources to enterprises through a billing system based on the frequency of corpus usage.

Application of Model Packages and Their Economic Models: More forward-looking is the application of model packages.

Enterprises can create specialized model packages by fine-tuning existing data and model definitions into large models.

These model packages can be directly called upon in subsequent business processes, providing targeted data analysis and decision support.

The use of model packages not only enhances the business capabilities of enterprises but also brings a new economic model, that is, charging based on the number of times the model package is called, providing enterprises with a flexible revenue model.

These new application scenarios not only enhance the internal operational efficiency of enterprises but also improve the quality of customer service.

In the future, as large model technology continues to develop, its application in various industries will become more extensive and in-depth.

Enterprises will be able to develop more innovative service models and business models to adapt to the ever-changing market demands.

Large model technology is becoming an important engine for driving the digital transformation of enterprises, injecting new momentum into the sustainable development of various industries.