A method for asset-liability application scenarios integrating AI assistants

Abstract

The present invention discloses a method for asset-liability application scenarios integrating AI assistants, deeply integrating artificial intelligence technology with asset-liability management, providing enterprises and individual users with efficient and intelligent solutions, utilizing the powerful data processing and analysis capabilities of AI assistants to improve processing efficiency and accuracy, and realizing rapid parsing and accurate interpretation of financial documents through natural language processing technology, thereby helping enterprises to grasp their financial status in a timely manner, using intelligent recommendation algorithms and multimodal large model technology to tap potential financing needs and business opportunities, and at the same time building a risk identification and analysis system, supporting functions such as automated writing and intelligent summarization, improving work efficiency and decision-making accuracy, bringing enterprises a more efficient and accurate financial management experience, reducing financial risks caused by human misjudgment, improving the scientific nature of decision-making, helping enterprises to quickly capture market trends, making decisions faster, and avoiding operating risks caused by information lags.

Description

Technical Field

[0001] The present invention relates to the technical field of asset-liability management, and specifically to a method for asset-liability application scenarios integrating an AI assistant. Background Art

[0002] In today's era of rapid development of digitalization and informatization, enterprises are facing unprecedented competitive pressure and market challenges. In order to maintain a leading position in this fierce market competition, corporate financial personnel must keep pace with the times and achieve a transformation from traditional record-keeping to management and decision-making support. They need to have efficient and accurate financial management capabilities to create more value and provide solid financial support for the steady development of the enterprise. When analyzing the daily management work of financial personnel, it mainly includes the following aspects:

[0003] 1. Difficulty in data integration and analysis: During the digital transformation process, enterprises are faced with the challenge of effectively integrating and analyzing large amounts of data from different systems and departments. Enterprises need to transform data into valuable information to support decision making.

[0004] 2. Risk management and forecasting: In the context of market volatility and economic uncertainty, it has become more difficult to accurately predict and assess the risks associated with assets and liabilities. Enterprises need to use advanced tools and technologies, such as big data analysis and artificial intelligence, to improve the accuracy and foresight of risk management.

[0005] 3. Compliance and policy adaptation: With the continuous changes in financial regulations and tax policies, enterprises must ensure that asset and liability management meets the latest compliance requirements. This requires financial personnel to not only have professional knowledge, but also the ability to quickly learn and adapt to new policies.

[0006] 4. Technology update and digital transformation: Financial personnel need to constantly update their skills to adapt to new digital tools and platforms. How to effectively use these tools to optimize the asset-liability management process and improve decision-making efficiency is a problem that financial personnel need to face.

[0007] 5. Decision support and strategic planning: The role of financial personnel is changing from traditional accounting and reporting to decision support and strategic planning. This requires them to not only understand financial data, but also be able to transform this data into insights and suggestions that have practical guiding significance for corporate strategy.

[0008] 6. Resource allocation and cost control: Under the dual influence of cost pressure and market competition, how to effectively allocate resources and control costs to maintain the competitiveness of the enterprise is another problem that financial personnel need to solve. This involves the optimization of the asset-liability structure and the precise management of capital flow.

[0009] 7. Talent cultivation and teamwork: As the technical content of financial work increases, the quality requirements for the financial team also increase accordingly. Financial personnel need to have stronger analytical skills, strategic thinking and cross-departmental collaboration capabilities. At the same time, how to cultivate and retain these talents is also an issue that companies need to consider.

[0010] 8. Asset-liability ratio management: Establishing and improving a scientific and orderly asset-liability operation system is the core of commercial bank management. However, there are many difficulties and challenges in the implementation of this management process, and reform is imperative.

[0011] 9. Liability management challenges: Against the backdrop of slowing economic growth, diversification of residents’ asset allocation, and the continuous advancement of interest rate marketization, commercial banks’ competition for deposits is intensifying, and the growth of high-quality deposits is structurally weak.

[0012] 10. Changes in the regulatory environment: Banks face more and more mandatory regulatory constraints, which requires them to meet many different regulatory requirements simultaneously in asset-liability management.

[0013] Therefore, it is necessary to design a method to integrate AI assistants into asset-liability application scenarios. Summary of the invention

[0014] The purpose of the present invention is to provide a digital customer management system based on multi-terminal collaboration and an implementation method to solve the problems raised in the above background technology.

[0015] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0016] A method for asset-liability application scenarios integrating an AI assistant comprises the following steps:

[0017] S100, Voice Interaction and Intelligent Approval: Access system functions based on voice commands input by users. System functions automatically approve or provide transaction suggestions related to asset and liability approval based on preset financial rules and logic;

[0018] S200, Attribution Analysis and Peer Analysis: Analyze the reasons for changes in assets and liabilities, provide data support, compare assets and liabilities in the same industry, and formulate financial strategies;

[0019] S300, building a knowledge base and intelligent assistant assistance: building a knowledge base based on financial regulations and industry practice materials, and the intelligent assistant generates content and scripts based on semantic logic and logical reasoning to assist users in making decisions;

[0020] S400, data service and digital screen interaction: provide services based on internal application database and external information database, and interact with users through digital screen.

[0021] According to the above technical solution, the voice interaction of S100 is specifically as follows:

[0022] S111, converting the user's voice command into feature text by integrating voice recognition technology;

[0023] S112, parsing the feature text by using the TF-IDF algorithm to obtain key information of the voice command;

[0024] S113, performing interactive processing based on key information of the voice command.

[0025] According to the above technical solution, the smart approval of S100 is specifically as follows:

[0026] S121. Preset financial rules and logic in the system;

[0027] S122, parsing and processing the converted text based on financial rules and logic;

[0028] S123. The system analyzes historical approval data and combines analysis and processing results to automatically process approval.

[0029] According to the above technical solution, the attribution analysis in S200 is specifically as follows:

[0030] Through data analysis tools, we analyze the reasons for changes in assets and liabilities, provide data support, and use industry data to generate asset and liability data.

[0031] According to the above technical solution, the industry analysis of S200 is specifically as follows:

[0032] By accessing industry data on a daily basis and formulating batch running strategies, we can achieve automated data collection and integration, and analyze the assets and liabilities of the same industry based on the collected and integrated industry data.

[0033] According to the above technical solution, the specific method of constructing the knowledge base in S300 is:

[0034] Through the LinkAi platform, a personalized knowledge base and digital human assistant are built based on financial regulations and industry practice materials. The system searches the knowledge base based on the key text or phrases in the input instructions, and displays the searched information results through an interactive interface.

[0035] According to the above technical solution, the smart assistant assistance of S300 is specifically as follows:

[0036] Through the PhiData framework, large language models are integrated to pre-train text data, build intelligent agents with memory, knowledge, tools and reasoning capabilities, and implement natural language processing through semantic logic and logical reasoning, including but not limited to web search and financial data analysis, and can work collaboratively in the form of a team.

[0037] According to the above technical solution, the data service of S400 is specifically:

[0038] Provides integration services for internal application databases and external information databases. The internal application database accesses industry data daily and forms a standardized data set after batch processing. The external information is accessed from external interfaces daily and collected and integrated into the data set, and the corresponding charts that can be viewed and analyzed are formed through the data set.

[0039] According to the above technical solution, the specific method of digital large screen interaction in S400 is:

[0040] The digital interactive large screen displays the charts formed by the data set. Users can use touch operations to select details in the chart for viewing and analysis, and drill down to the corresponding vertical statistical data.

[0041] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0042] The present invention deeply integrates artificial intelligence technology with asset-liability management to provide enterprises and individual users with efficient and intelligent solutions. It uses the powerful data processing and analysis capabilities of AI assistants to improve processing efficiency and accuracy. Through natural language processing technology, it can realize rapid parsing and accurate interpretation of financial documents, help enterprises grasp their financial status in a timely manner, and use intelligent recommendation algorithms and multimodal large model technology to explore potential financing needs and business opportunities. At the same time, it builds a risk identification and analysis system, supports functions such as automated writing and intelligent summarization, improves work efficiency and decision-making accuracy, and brings enterprises a more efficient and accurate financial management experience, reduces financial risks caused by human misjudgment, improves the scientific nature of decision-making, and helps enterprises quickly capture market trends, make decisions faster, and avoid operating risks caused by information lags. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a flowchart of the present invention. DETAILED DESCRIPTION

[0044] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0045] like Figure 1 As shown, the present invention provides a technical solution: a method for asset-liability application scenarios integrating an AI assistant, comprising the following steps:

[0046] S100, Voice Interaction and Intelligent Approval: Access system functions based on voice commands input by users. System functions automatically approve or provide transaction suggestions related to asset and liability approval based on preset financial rules and logic;

[0047] S200, Attribution Analysis and Peer Analysis: Analyze the reasons for changes in assets and liabilities, provide data support, compare assets and liabilities in the same industry, and formulate financial strategies;

[0048] S300, building a knowledge base and intelligent assistant assistance: building a knowledge base based on financial regulations and industry practice materials, and the intelligent assistant generates content and scripts based on semantic logic and logical reasoning to assist users in making decisions;

[0049] S400, data service and digital screen interaction: provide services based on internal application database and external information database, and interact with users through digital screen.

[0050] Specifically, the voice interaction of S100 is as follows:

[0051] S111, converting the user's voice command into feature text by integrating voice recognition technology;

[0052] S112, parsing the feature text by using the TF-IDF algorithm to obtain key information of the voice command;

[0053] S113, performing interactive processing based on key information of the voice command.

[0054] Specifically, the smart approval of S100 is as follows:

[0055] S121. Preset financial rules and logic in the system;

[0056] S122, parsing and processing the converted text based on financial rules and logic;

[0057] S123. The system analyzes historical approval data and combines analysis and processing results to automatically process approval.

[0058] Specifically, the attribution analysis of S200 is as follows:

[0059] Through data analysis tools, we analyze the reasons for changes in assets and liabilities, provide data support, and use industry data to generate asset and liability data.

[0060] Specifically, the S200 peer analysis is as follows:

[0061] By accessing industry data on a daily basis and formulating batch running strategies, we can achieve automated data collection and integration, and analyze the assets and liabilities of the same industry based on the collected and integrated industry data.

[0062] Specifically, the specific method of constructing the knowledge base in S300 is:

[0063] Through the LinkAi platform, a personalized knowledge base and digital human assistant are built based on financial regulations and industry practice materials. The system searches the knowledge base based on the key text or phrases in the input instructions, and displays the searched information results through an interactive interface.

[0064] Specifically, the S300's smart assistant assists:

[0065] Through the PhiData framework, large language models are integrated to pre-train text data, build intelligent agents with memory, knowledge, tools and reasoning capabilities, and implement natural language processing through semantic logic and logical reasoning, including but not limited to web search and financial data analysis, and can work collaboratively in the form of a team.

[0066] Specifically, the data service of S400 is:

[0067] Provides integration services for internal application databases and external information databases. The internal application database accesses industry data daily and forms a standardized data set after batch processing. The external information is accessed from external interfaces daily and collected and integrated into the data set, and the corresponding charts that can be viewed and analyzed are formed through the data set.

[0068] Specifically, the specific method of digital large screen interaction in S400 is:

[0069] The digital interactive large screen displays the charts formed by the data set. Users can use touch operations to select details in the chart for viewing and analysis, and drill down to the corresponding vertical statistical data.

[0070] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0071] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for asset-liability application scenarios integrating AI assistants, characterized in that: The steps include: S100, Voice Interaction and Intelligent Approval: Access system functions based on voice commands input by users. System functions automatically approve or provide transaction suggestions related to asset and liability approval based on preset financial rules and logic; S200, Attribution Analysis and Peer Analysis: Analyze the reasons for changes in assets and liabilities, provide data support, compare assets and liabilities in the same industry, and formulate financial strategies; S300, building a knowledge base and intelligent assistant assistance: building a knowledge base based on financial regulations and industry practice materials, and the intelligent assistant generates content and scripts based on semantic logic and logical reasoning to assist users in making decisions; S400, data service and digital screen interaction: provide services based on internal application database and external information database, and interact with users through digital screen.

2. The method for asset-liability application scenarios integrating AI assistants according to claim 1, characterized in that: The voice interaction of S100 is specifically as follows: S111, converting the user's voice command into feature text by integrating voice recognition technology; S112, parsing the feature text by using the TF-IDF algorithm to obtain key information of the voice command; S113, performing interactive processing based on key information of the voice command.

3. The method for asset-liability application scenarios integrating AI assistants according to claim 1, characterized in that: The smart approval of S100 is specifically as follows: S121. Preset financial rules and logic in the system; S122, parsing and processing the converted text based on financial rules and logic; S123. The system analyzes historical approval data and combines analysis and processing results to automatically process approval.

4. The method for asset-liability application scenarios integrating AI assistants according to claim 1, characterized in that: The attribution analysis of S200 is specifically as follows: Through data analysis tools, we analyze the reasons for changes in assets and liabilities, provide data support, and use industry data to generate asset and liability data.

5. The method for asset-liability application scenarios integrating AI assistants according to claim 1, characterized in that: The specific analysis of the S200 industry is as follows: By accessing industry data on a daily basis and formulating batch running strategies, we can achieve automated data collection and integration, and analyze the assets and liabilities of the same industry based on the collected and integrated industry data.

6. The method for asset-liability application scenarios integrating AI assistants according to claim 1, characterized in that: The specific method of constructing the knowledge base in S300 is: Through the LinkAi platform, a personalized knowledge base and digital human assistant are built based on financial regulations and industry practice materials. The system searches the knowledge base based on the key text or phrases in the input instructions, and displays the searched information results through an interactive interface.

7. The method for asset-liability application scenarios integrating AI assistants according to claim 1, characterized in that: The smart assistant assistance of the S300 is specifically: Through the PhiData framework, large language models are integrated to pre-train text data, build intelligent agents with memory, knowledge, tools and reasoning capabilities, and implement natural language processing through semantic logic and logical reasoning, including but not limited to web search and financial data analysis, and can work collaboratively in the form of a team.

8. The method for asset-liability application scenarios integrating AI assistants according to claim 1, characterized in that: The data service of S400 is specifically: Provides integration services for internal application databases and external information databases. The internal application database accesses industry data daily and forms a standardized data set after batch processing. The external information is accessed from external interfaces daily and collected and integrated into the data set, and the corresponding charts that can be viewed and analyzed are formed through the data set.

9. The method for asset-liability application scenarios integrating AI assistants according to claim 1, characterized in that: The specific method of digital large screen interaction in S400 is: The digital interactive large screen displays the charts formed by the data set. Users can use touch operations to select details in the chart for viewing and analysis, and drill down to the corresponding vertical statistical data.

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