Method for integrating financial application scene of AI assistant

Abstract

The invention discloses a method for integrating a financial application scene of an AI assistant, which comprises a dialogue module, a policy interpretation module, an intelligent agent and an intelligent financial application formed by an intelligent knowledge base, and comprises the following steps: S100, carrying out copywriting interpretation analysis on a policy text through the policy interpretation module; s200, performing information interaction with a user through a dialogue module; s300, the intelligent agent performs big data analysis, business analysis, model training and model verification and optimization; s400, the intelligent knowledge base is formed through updating and accumulation and provides answers and suggestions for the dialogue module; through strong data processing and analysis capability of an AI assistant, matching of actual demands and preferences of a user is realized, policies are accurately interpreted, the user can accurately acquire and understand the policies conveniently, and on the basis of rapid retrieval and application capability of an intelligent knowledge base, the method is suitable for popularization and application. And the user management level, convenience and efficiency in a financial scene are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent application scenarios, and specifically to a method for integrating a financial application scenario with an AI assistant. Background Art

[0002] In today's era of rapid development of digitalization and information technology, enterprises are facing unprecedented competitive pressure and market challenges. In order to remain invincible in the fierce market competition, it is required that corporate financial personnel keep up with the pace of the times, transform into management-oriented and decision-making support-oriented personnel, and have efficient and accurate financial management capabilities to create more value for the enterprise. When analyzing the daily management work of financial personnel, the following difficulties are summarized:

[0003] 1. Financial data extraction is difficult and error-prone. In their daily work, financial personnel often need to spend a lot of time and energy shuttling between various financial systems and reports to collect and organize the required financial data, and then complete various financial tasks and work. This traditional data retrieval method is not only inefficient, but may also cause data errors or omissions due to human factors, bringing unnecessary risks and hidden dangers to the company's financial management. Specifically, financial personnel often need to manually flip through a large number of financial statements to filter out data related to the current work. This process is not only cumbersome and complicated, but also easily affected by personal experience and subjective judgment, making it difficult to ensure the accuracy and completeness of data extraction.

[0004] 2. Policy documents are obscure and inaccurately understood. In the fierce market competition, enterprises need to continuously optimize their financial management processes and improve the efficiency and accuracy of financial decision-making. This requires financial personnel to know and accurately understand financial policy documents in a timely manner. Financial documents usually contain a large number of professional terms and complex data structures, which is a very challenging task for financial personnel.

[0005] 3. Low work efficiency and insufficient decision support. With the expansion of enterprise scale and business scope, the amount of financial data has shown explosive growth. The traditional manual search method can no longer meet the needs of financial personnel for efficient and accurate data acquisition. Financial personnel spend a lot of time searching and screening knowledge inefficiently, and the results may not be satisfactory, which not only reduces work efficiency, but also may affect the timeliness of decision-making.

[0006] Therefore, it is necessary to design a method to integrate AI assistants into financial application scenarios. Summary of the invention

[0007] The purpose of the present invention is to provide a method for financial application scenarios integrating AI assistants to solve the problems raised in the above background technology.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0009] A method for integrating financial application scenarios of AI assistants, including a dialogue module, a policy interpretation module, an intelligent agent and an intelligent financial application composed of an intelligent knowledge base, includes the following steps:

[0010] S100, interpret and analyze the policy text through the policy interpretation module;

[0011] S200, interacting with the user through a dialogue module;

[0012] S300, the intelligent agent performs big data analysis, business analysis, model training, and model verification and optimization;

[0013] S400, the intelligent knowledge base is formed through updating and accumulation, providing answers and suggestions for the dialogue module.

[0014] According to the above technical solution, the specific processing method of the policy interpretation module in S100 for interpreting and analyzing the policy text is as follows:

[0015] Upload the policy text into the system, extract key information from the policy text, interpret and analyze the key information, gain an accurate understanding of the policy text, and optimize financial decisions based on the understanding of the policy text.

[0016] According to the above technical solution, the specific method of the dialogue module in S200 interacting with the user information is:

[0017] After the user inputs information, the conversation module identifies the frequency of the information, thereby obtaining whether the current information is a daily hot conversation or an unpopular demand conversation;

[0018] If it is a daily hot conversation, the results are quickly presented based on the conversation history content temporarily stored in the conversation module;

[0019] If the conversation is about unpopular demands, the conversation module will understand and parse the conversation information. After obtaining the key information of the conversation information, it will obtain related information from the database or network, and filter it based on financial management rules to finally present the results.

[0020] According to the above technical solution, the specific processing method of the intelligent agent in S300 is:

[0021] Big data analysis is to obtain financial data and user data in real time through the Internet and manual input, and integrate and analyze the above data;

[0022] Business analysis is to continue to analyze the needs input by users to obtain the required business services;

[0023] Model training is based on the correlation between big data analysis and business analysis, and the model of a mutually invoked correlation network is constructed through the correlation relationship;

[0024] Model verification and optimization is to repeatedly verify through the self-calling model within the model to obtain the best business service and financial related calling path.

[0025] According to the above technical solution, the specific method for forming the intelligent knowledge base in S400 is:

[0026] Internal accumulation of data is achieved by searching results on the Internet based on user needs or by business personnel manually inputting answers and suggestions;

[0027] If better answers and suggestions are obtained based on the same needs, the existing information will be updated.

[0028] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0029] Through the powerful data processing and analysis capabilities of the AI ​​assistant, it is possible to match users' actual needs and preferences, and accurately interpret policies to facilitate users to accurately obtain and understand policies. It also has the ability to quickly retrieve and apply based on the intelligent knowledge base, which improves the level of user management, convenience, and efficiency in financial scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a flowchart of the present invention. DETAILED DESCRIPTION

[0031] 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.

[0032] like Figure 1 As shown, the present invention provides a technical solution: a method for integrating financial application scenarios of AI assistants, including a dialogue module, a policy interpretation module, an intelligent agent and an intelligent financial application composed of an intelligent knowledge base, including the following steps:

[0033] S100, interpret and analyze the policy text through the policy interpretation module;

[0034] S200, interacting with the user through a dialogue module;

[0035] S300, the intelligent agent performs big data analysis, business analysis, model training, and model verification and optimization;

[0036] S400, the intelligent knowledge base is formed through updating and accumulation, providing answers and suggestions for the dialogue module.

[0037] Specifically, the specific processing method of the policy interpretation module in S100 for interpreting and analyzing the policy text is as follows:

[0038] Upload the policy text into the system, extract key information from the policy text, interpret and analyze the key information, gain an accurate understanding of the policy text, and optimize financial decisions based on the understanding of the policy text.

[0039] Specifically, the specific method of the dialogue module in S200 interacting with the user information is:

[0040] After the user inputs information, the conversation module identifies the frequency of the information, thereby obtaining whether the current information is a daily hot conversation or an unpopular demand conversation;

[0041] If it is a daily hot conversation, the results are quickly presented based on the conversation history content temporarily stored in the conversation module;

[0042] If the conversation is about unpopular demands, the conversation module will understand and parse the conversation information. After obtaining the key information of the conversation information, it will obtain related information from the database or network, and filter it based on financial management rules to finally present the results.

[0043] Specifically, the specific processing method of the intelligent agent in S300 is:

[0044] Big data analysis is to obtain financial data and user data in real time through the Internet and manual input, and integrate and analyze the above data;

[0045] Business analysis is to continue to analyze the needs input by users to obtain the required business services;

[0046] Model training is based on the correlation between big data analysis and business analysis, and the model of a mutually invoked correlation network is constructed through the correlation relationship;

[0047] Model verification and optimization is to repeatedly verify through the self-calling model within the model to obtain the best business service and financial related calling path.

[0048] Specifically, the specific method for forming the intelligent knowledge base in S400 is:

[0049] Internal accumulation of data is achieved by searching results on the Internet based on user needs or by business personnel manually inputting answers and suggestions;

[0050] If better answers and suggestions are obtained based on the same needs, the existing information will be updated.

[0051] 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.

[0052] 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 integrating financial application scenarios of AI assistants, including a dialogue module, a policy interpretation module, an intelligent agent and an intelligent financial application composed of an intelligent knowledge base, characterized in that: The steps include: S100, interpret and analyze the policy text through the policy interpretation module; S200, interacting with the user through a dialogue module; S300, the intelligent agent performs big data analysis, business analysis, model training, and model verification and optimization; S400, the intelligent knowledge base is formed through updating and accumulation, providing answers and suggestions for the dialogue module.

2. A method for integrating financial application scenarios of AI assistants according to claim 1, characterized in that: The specific processing method of the policy interpretation module in S100 for interpreting and analyzing the policy text is as follows: Upload the policy text into the system, extract key information from the policy text, interpret and analyze the key information, gain an accurate understanding of the policy text, and optimize financial decisions based on the understanding of the policy text.

3. The method for integrating financial application scenarios of AI assistants according to claim 1, characterized in that: The specific method of the dialogue module in S200 interacting with the user information is: After the user inputs information, the conversation module identifies the frequency of the information, thereby obtaining whether the current information is a daily hot conversation or an unpopular demand conversation; If it is a daily hot conversation, the results are quickly presented based on the conversation history content temporarily stored in the conversation module; If the conversation is about unpopular demands, the conversation module will understand and parse the conversation information. After obtaining the key information of the conversation information, it will obtain related information from the database or network, and filter it based on financial management rules to finally present the results.

4. The method for integrating financial application scenarios of AI assistants according to claim 1, characterized in that: The specific processing method of the intelligent agent in S300 is: Big data analysis is to obtain financial data and user data in real time through the Internet and manual input, and integrate and analyze the above data; Business analysis is to continue to analyze the needs input by users to obtain the required business services; Model training is based on the correlation between big data analysis and business analysis, and the model of a mutually invoked correlation network is constructed through the correlation relationship; Model verification and optimization is to repeatedly verify through the self-calling model within the model to obtain the best business service and financial related calling path.

5. The method for integrating financial application scenarios of AI assistants according to claim 1, characterized in that: The specific method for forming the intelligent knowledge base of S400 is: Internal accumulation of data is achieved by searching results on the Internet based on user needs or by business personnel manually inputting answers and suggestions; If better answers and suggestions are obtained based on the same needs, the existing information will be updated.

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