Trustee investment risk monitoring method and device, storage medium and program product

Abstract

This application provides a method, apparatus, storage medium, and program product for monitoring entrusted investment risks, relating to the fintech field or other related fields. The method includes: responding to a trigger for entrusted investment risk monitoring, invoking multiple pre-generated structured query statements to extract indicator data restricted by contractual clauses in the investment supervision contract from the entrusted investment database. The structured query statements are generated based on the contractual clauses using retrieval enhancement generation technology. Based on the indicator data, it is determined whether there are any entrusted investments in the entrusted investment database that do not comply with the contractual clauses. If such entrusted investments exist, an entrusted investment risk warning report is automatically generated based on these investments. This report serves to indicate the existence of entrusted investment risks. This application can improve the processing efficiency of entrusted investment risk monitoring methods.

Description

Technical Field

[0001] This application relates to the field of financial technology or other related fields, and in particular to a method, device, storage medium and program product for monitoring entrusted investment risks. Background Technology

[0002] In entrusted investment business, the entrusted management institution, as the asset manager, must conduct full-process market risk monitoring of the investment portfolio in accordance with laws, regulations and investment contract provisions to ensure that investment operations are compliant and avoid potential risks.

[0003] In related technologies, entrusted investment risk monitoring includes the following stages: In the pre-investment rule configuration stage, the entrusted business personnel first read the investment supervision contract, break down the contract terms, and provide the contract terms to the developers. The developers then write a Structured Query Language (SQL) to extract the indicators to be restricted by the contract terms from the database, and configure the SQL into a scheduled batch scanning task. In the in-process risk scanning stage, the scheduled task configured in the previous stage will be executed, scanning out investment portfolios that do not comply with the contract terms and reporting them to the entrusted business personnel. In the post-investment report generation stage, the system extracts investment portfolios that do not comply with the contract terms and reports them to the investment manager. At the same time, the entrusted business personnel will also manually analyze external data sources and write risk warning reports for the investment manager.

[0004] The aforementioned methods for monitoring entrusted investment risks suffer from low processing efficiency. Summary of the Invention

[0005] This application provides a method, apparatus, storage medium, and program product for monitoring entrusted investment risks, in order to improve the processing efficiency of entrusted investment risk monitoring methods.

[0006] Firstly, this application provides a method for monitoring the risks of entrusted investments, including:

[0007] The response triggers entrusted investment risk monitoring and calls multiple pre-generated structured query statements to extract the indicator data to be restricted by the contract terms in the investment supervision contract from the entrusted investment database. The structured query statements are generated based on the contract terms using retrieval enhancement generation technology.

[0008] Based on the indicator data, determine whether there are any entrusted investments in the entrusted investment database that do not comply with the contract terms;

[0009] If there are entrusted investments that do not comply with the contract terms, an entrusted investment risk warning report will be automatically generated based on the entrusted investments that do not comply with the contract terms. The entrusted investment risk warning report is used to indicate the existence of entrusted investment risks.

[0010] In one possible implementation, the structured query statement is generated in the following way:

[0011] Automated parsing of investment supervision contract data to obtain the contract text contained in the investment supervision contract;

[0012] By applying retrieval-enhanced generation technology, the contract text is segmented into contract clauses to obtain the contract clauses contained in the investment supervision contract;

[0013] The application uses retrieval enhancement generation technology to call the indicator knowledge base and generate structured query statements corresponding to the contract terms based on the contract terms. The indicator knowledge base is used to store investment risk monitoring indicators.

[0014] In one possible implementation, a retrieval-enhanced generation technique is applied to segment the contract text into contract clauses, obtaining the contract clauses contained in the investment supervision contract, including:

[0015] The application of retrieval enhancement generation technology calls an external knowledge base to segment the contract text into contract clauses, thereby obtaining the contract clauses contained in the investment supervision contract. The external knowledge base is used to store clause segmentation prompts and professional knowledge related to clause identification.

[0016] In one possible implementation, a retrieval-enhanced generation technique is applied to invoke an indicator knowledge base. Based on the contract terms, a structured query statement corresponding to the contract terms is generated. The indicator knowledge base is used to store investment risk monitoring indicators, including:

[0017] The application of retrieval enhancement generation technology calls the indicator knowledge base to identify the matching of contract terms with investment risk monitoring indicators in the indicator knowledge base, and determines the indicator matching result corresponding to the contract terms;

[0018] If the indicator matching result indicates that there is an investment risk monitoring indicator in the indicator knowledge base that matches the contract terms, then the parameters related to the contract terms are embedded into the parameters of the matched investment risk monitoring indicator to obtain the investment risk monitoring indicator with embedded parameters. Based on the investment risk monitoring indicator with embedded parameters, a structured query statement corresponding to the contract terms is generated.

[0019] If the indicator matching result indicates that there is no investment risk monitoring indicator in the indicator knowledge base that matches the contract terms, the retrieval enhancement generation technology is applied to call the table structure knowledge base. Based on the contract terms, structured query prompts are generated by combining the text. The contract terms and the structured query prompts generated by the text are then input into the large language model to generate the structured query statement corresponding to the contract terms.

[0020] In one possible implementation, determining whether there are any entrusted investments in the entrusted investment database that do not comply with the contract terms, based on indicator data, includes:

[0021] Obtain external risk information, which is determined based on current events information and company reports;

[0022] The response indicator data and / or external risk information trigger the risk entrusted investment conditions, and determine that there are entrusted investments in the entrusted investment database that do not comply with the contract terms;

[0023] The response indicator data and external risk information did not trigger the risky entrusted investment conditions, and it was determined that there were no entrusted investments in the entrusted investment database that did not comply with the contract terms.

[0024] In one possible implementation, external risk information is determined based on current events information and company reports:

[0025] Current events information is input into the sentiment analysis model to obtain the corresponding public opinion sentiment analysis results, which include positive, neutral, and negative public opinion; and the public opinion sentiment analysis results are numerically mapped to obtain the corresponding mapping values.

[0026] The enterprise report is input into the risk scoring model to obtain the enterprise risk score corresponding to the enterprise report. The enterprise risk score is then normalized to obtain the normalized enterprise risk score corresponding to the enterprise report.

[0027] The external risk score is obtained by weighted fusion of the mapped value and the normalized enterprise risk score.

[0028] By inputting current events information, corporate reports, and external risk scores into the risk analysis model, external risk information is obtained.

[0029] In one possible implementation, a risk warning report for entrusted investments that do not comply with the contract terms is automatically generated, including:

[0030] Based on the indicator data and external risk information corresponding to entrusted investments that do not comply with the contract terms, the system uses prompt word technology to automatically generate and output entrusted investment risk warning reports.

[0031] Secondly, this application provides a risk monitoring device for entrusted investment, comprising:

[0032] The extraction module is used to respond to the triggering of entrusted investment risk monitoring by calling multiple pre-generated structured query statements to extract the indicator data to be restricted by the contract terms in the investment supervision contract from the entrusted investment database. The structured query statements are generated based on the contract terms using retrieval enhancement generation technology.

[0033] The determination module is used to determine, based on indicator data, whether there are any entrusted investments in the entrusted investment database that do not comply with the contract terms;

[0034] The processing module is used to automatically generate a risk warning report for entrusted investments that do not comply with the contract terms. The risk warning report is used to indicate the existence of risks associated with entrusted investments.

[0035] In one possible implementation, the structured query statement is generated in the following way:

[0036] Automated parsing of investment supervision contract data to obtain the contract text contained in the investment supervision contract;

[0037] By applying retrieval-enhanced generation technology, the contract text is segmented into contract clauses to obtain the contract clauses contained in the investment supervision contract;

[0038] The application uses retrieval enhancement generation technology to call the indicator knowledge base and generate structured query statements corresponding to the contract terms based on the contract terms. The indicator knowledge base is used to store investment risk monitoring indicators.

[0039] In one possible implementation, a retrieval-enhanced generation technique is applied to segment the contract text into contract clauses, obtaining the contract clauses contained in the investment supervision contract, including:

[0040] The application of retrieval enhancement generation technology calls an external knowledge base to segment the contract text into contract clauses, thereby obtaining the contract clauses contained in the investment supervision contract. The external knowledge base is used to store clause segmentation prompts and professional knowledge related to clause identification.

[0041] In one possible implementation, a retrieval-enhanced generation technique is applied to invoke an indicator knowledge base. Based on the contract terms, a structured query statement corresponding to the contract terms is generated. The indicator knowledge base is used to store investment risk monitoring indicators, including:

[0042] The application of retrieval enhancement generation technology calls the indicator knowledge base to identify the matching of contract terms with investment risk monitoring indicators in the indicator knowledge base, and determines the indicator matching result corresponding to the contract terms;

[0043] If the indicator matching result indicates that there is an investment risk monitoring indicator in the indicator knowledge base that matches the contract terms, then the parameters related to the contract terms are embedded into the parameters of the matched investment risk monitoring indicator to obtain the investment risk monitoring indicator with embedded parameters. Based on the investment risk monitoring indicator with embedded parameters, a structured query statement corresponding to the contract terms is generated.

[0044] If the indicator matching result indicates that there is no investment risk monitoring indicator in the indicator knowledge base that matches the contract terms, the retrieval enhancement generation technology is applied to call the table structure knowledge base. Based on the contract terms, structured query prompts are generated by combining the text. The contract terms and the structured query prompts generated by the text are then input into the large language model to generate the structured query statement corresponding to the contract terms.

[0045] In one possible implementation, the determining module is specifically used for:

[0046] Obtain external risk information, which is determined based on current events information and company reports;

[0047] The response indicator data and / or external risk information trigger the risk entrusted investment conditions, and determine that there are entrusted investments in the entrusted investment database that do not comply with the contract terms;

[0048] The response indicator data and external risk information did not trigger the risky entrusted investment conditions, and it was determined that there were no entrusted investments in the entrusted investment database that did not comply with the contract terms.

[0049] In one possible implementation, external risk information is determined based on current events information and company reports:

[0050] Current events information is input into the sentiment analysis model to obtain the corresponding public opinion sentiment analysis results, which include positive, neutral, and negative public opinion; and the public opinion sentiment analysis results are numerically mapped to obtain the corresponding mapping values.

[0051] The enterprise report is input into the risk scoring model to obtain the enterprise risk score corresponding to the enterprise report. The enterprise risk score is then normalized to obtain the normalized enterprise risk score corresponding to the enterprise report.

[0052] The external risk score is obtained by weighted fusion of the mapped value and the normalized enterprise risk score.

[0053] By inputting current events information, corporate reports, and external risk scores into the risk analysis model, external risk information is obtained.

[0054] In one possible implementation, the processing module is specifically used for:

[0055] Based on the indicator data and external risk information corresponding to entrusted investments that do not comply with the contract terms, the system uses prompt word technology to automatically generate and output entrusted investment risk warning reports.

[0056] Thirdly, this application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0057] The memory stores the instructions that the computer executes;

[0058] The processor executes computer execution instructions stored in memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0059] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a device such as a processor, are used to implement the first aspect and / or various possible embodiments of the first aspect.

[0060] Fifthly, this application provides a computer program product, including a computer program that, when executed by a device such as a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0061] The entrusted investment risk monitoring method, device, storage medium, and program product provided in this application, by responding to the trigger of entrusted investment risk monitoring, invokes multiple pre-generated structured query statements to extract indicator data restricted by the contract terms in the investment supervision contract from the entrusted investment database, significantly shortening the preparation time for indicator data extraction. Specifically, the structured query statements are pre-generated based on the contract terms using retrieval enhancement generation technology. When risk monitoring is triggered, the pre-generated structured query statements can be directly invoked, compressing the preparation time for a single monitoring session to the second level, significantly improving efficiency from the start of the process. Based on the indicator data, the system automatically compares the indicator data with the restrictions in the contract terms to determine whether there are entrusted investments in the entrusted investment database that do not comply with the contract terms, quickly locating non-compliant entrusted investments, shortening the processing time for a single batch of monitoring, and ensuring the accuracy of monitoring results while improving efficiency. If non-compliant entrusted investments are found, an entrusted investment risk warning report is automatically generated based on the non-compliant entrusted investments, eliminating the need for manual writing and formatting. The generated entrusted investment risk warning report can be directly pushed to investment management personnel, shortening report production time. Among them, the entrusted investment risk warning report is used to alert users to the existence of entrusted investment risks, significantly improving the efficiency of risk information transmission and handling. Through pre-generated structured query statements, automated indicator data extraction, automated violation judgment, and automated report generation, the processing efficiency of entrusted investment risk monitoring methods can be improved. Attached Figure Description

[0062] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0063] Figure 1 A flowchart illustrating the entrusted investment risk monitoring method provided in this application embodiment;

[0064] Figure 2 A schematic diagram illustrating an application of the entrusted investment risk monitoring method provided in this application embodiment;

[0065] Figure 3 A schematic diagram of the structure of the entrusted investment risk monitoring device provided in the embodiments of this application;

[0066] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0067] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0068] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0069] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of the relevant data all comply with the relevant laws, regulations, and standards of the relevant countries and regions, have taken necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation access points for users to choose to authorize or refuse.

[0070] Furthermore, the technical solution involved in this application, which involves big data analysis of user information (including but not limited to personal biometrics, identity data, consumption data, asset data, electronic terminal operation data, etc.) and the use of artificial intelligence technology for automated decision-making, and makes decisions that have a significant impact on personal rights based on the results of automated decision-making, provides users with corresponding operation entry points for users to choose to agree to or reject the results of automated decision-making; if the user chooses to reject, the process will proceed to the expert decision-making process.

[0071] It should be noted that the entrusted investment risk monitoring method, device, storage medium and program product provided in this application can be used in the financial technology field, or in any field other than the financial technology field. The application field of the entrusted investment risk monitoring method, device, storage medium and program product in this application is not limited.

[0072] First, let me explain the terms used in this application:

[0073] Structured query statements, also known as SQL statements, are complete instructions or commands written in SQL and conforming to its syntax rules. A structured query statement is the basic unit for issuing operation requests to a relational database management system, used to execute specific data queries or operations.

[0074] Retrieval-Augmented Generation (RAG) is an AI technology that combines information retrieval and text generation. It aims to supplement the generative model by dynamically introducing external knowledge bases, such as Large Language Models (LLMs), to improve the accuracy, timeliness, and interpretability of the generated content. Its core idea is to retrieve relevant documents / information from structured or unstructured data sources before generating the answer, and then generate the final output based on the retrieval results.

[0075] In related technologies, entrusted investment risk monitoring includes the following stages: In the pre-investment rule configuration stage, the entrusted personnel first read the investment supervision contract, break down the contract terms, and provide these terms to the developers. The developers then write SQL to extract the indicators to be restricted by the contract terms from the database and configure the SQL into a scheduled batch scanning task. In the in-process risk scanning stage, the scheduled task configured in the previous stage will be executed, scanning for investment portfolios that do not comply with the contract terms and reporting them to the entrusted personnel. In the post-investment report generation stage, the system extracts investment portfolios that do not comply with the contract terms and reports them to the investment manager. Simultaneously, the entrusted personnel will manually analyze external data sources and write risk warning reports for the investment manager. The above entrusted investment risk monitoring method suffers from low processing efficiency.

[0076] The entrusted investment risk monitoring method provided in this application aims to solve the aforementioned technical problems of existing technologies. Specifically, by calling multiple SQL statements pre-generated based on RAG technology, it automatically extracts indicator data that must be restricted by the investment supervision contract terms. Based on the indicator data, it automatically identifies entrusted investments that do not comply with the contract terms and automatically generates investment risk warning reports for these non-compliant investments to indicate the existence of entrusted investment risks. Through this series of automated processes, the processing efficiency of entrusted investment risk monitoring methods can be improved.

[0077] This application applies to risk monitoring scenarios in entrusted investment businesses, such as asset management institutions, trust units, fund units, and banks, for compliance and risk control of investment portfolios including bonds and derivatives. The system is deployed within the internal network environment of the financial industry, using a distributed architecture to handle the entire process from contract text parsing and external data access (such as news and public opinion, corporate credit scoring) to risk analysis and report generation. Users upload contract text or trigger scanning tasks through the front-end interface. The system calls the large model module to parse clauses and generate rules, while simultaneously accessing external data sources (such as news APIs and corporate databases) for real-time risk scoring. Intelligent reports are then pushed to investment management personnel via email or API interfaces.

[0078] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0079] Figure 1 This is a flowchart illustrating a method for monitoring entrusted investment risks provided in an embodiment of this application. Figure 1 As shown, the entrusted investment risk monitoring method includes:

[0080] S101. In response to the trigger of entrusted investment risk monitoring, multiple pre-generated structured query statements are invoked to extract the indicator data to be restricted by the contract terms in the investment supervision contract from the entrusted investment database. The structured query statements are generated based on the contract terms using retrieval enhancement generation technology.

[0081] For example, the system responds to preset risk monitoring trigger conditions, such as the arrival of a scheduled monitoring period, price fluctuations in investment targets, or manual initiation of monitoring commands. In other words, when the preset risk monitoring trigger conditions are met, the system triggers entrusted investment risk monitoring. Specifically, it automatically retrieves multiple sets of SQL statements pre-generated based on the contract terms in the investment supervision contract. These SQL statements are not manually written but generated using RAG technology based on the contract terms. By batch executing these pre-generated SQL statements, the system can efficiently extract indicator data corresponding one-to-one with the restrictions in the contract terms from core data tables such as the holdings information table, net asset value change table, and target information table in the entrusted investment database. This provides an accurate and reliable data foundation for subsequent violation determination and risk warning.

[0082] It is important to note that the pre-generated SQL statements can be stored in a batch task library for centralized management and scheduling.

[0083] S102. Based on the indicator data, determine whether there are any entrusted investments in the entrusted investment database that do not comply with the contract terms.

[0084] For example, based on a preset rule engine, various indicator data extracted from the entrusted investment database can be compared and verified one by one with the risk control restrictions explicitly stipulated in the investment supervision contract. Optionally, the comparison dimensions can cover all core risk control indicators stipulated in the contract, including but not limited to whether the holding ratio of a single investment target exceeds the agreed upper limit, whether the investment target is within the scope permitted by the contract, whether the net value of the investment portfolio falls below the preset stop-loss threshold, and whether the proportion of liquid assets meets the minimum requirements.

[0085] By using precise numerical comparison, range verification, and logical judgment, the compliance of each indicator data is determined. Once it is determined that the actual indicator data of an investment target or portfolio deviates from the restrictions of the contract terms, and the deviation reaches the preset violation judgment standard, the entrusted investment will be automatically marked as an entrusted investment that does not comply with the contract terms. At the same time, key information such as the name of the non-compliant target, the type of non-compliant indicator, the actual value, the contractual threshold, and the deviation range will be recorded, providing a clear and explicit judgment basis for subsequent risk warning and disposal.

[0086] S103. If there are entrusted investments that do not comply with the contract terms, an entrusted investment risk warning report will be automatically generated based on the entrusted investments that do not comply with the contract terms. The entrusted investment risk warning report is used to indicate the existence of entrusted investment risks.

[0087] For example, if a comparison of indicator data determines that a entrusted investment does not comply with the contract terms, an automated risk warning report generation process will be immediately triggered. Specifically, all previously marked violation information will be integrated, including the name of the violation investment target, the corresponding contractual binding clauses, the deviation of the actual indicator value from the contractually agreed threshold, the duration of the violation, the investment amount involved, and the holding ratio, etc., and a risk warning report for the entrusted investment will be automatically generated based on the above violation information.

[0088] The generated entrusted investment risk warning report clearly lists each violation and supporting data, and also makes a preliminary judgment on the risk level of the violation, such as distinguishing between different levels like "minor overreach," "moderate violation," and "serious breach," while also including a brief analysis of the corresponding risk impact. The core purpose of the entrusted investment risk warning report is to promptly alert investment managers, risk control officers, and other relevant parties to the compliance risks existing in entrusted investment business, providing them with accurate and intuitive decision-making basis for quickly grasping the risk overview and formulating targeted disposal measures (such as position adjustment, target reduction, compliance rectification, etc.), ensuring that risks can be identified and dealt with in a timely manner.

[0089] Optionally, if there are no entrusted investments that do not comply with the contract terms, it is not necessary to generate an entrusted investment risk warning report.

[0090] The entrusted investment risk monitoring method provided in this embodiment, by responding to the trigger of entrusted investment risk monitoring, calls multiple pre-generated structured query statements to extract indicator data restricted by the contract terms in the investment supervision contract from the entrusted investment database, significantly shortening the preparation time for indicator data extraction. The structured query statements are pre-generated based on the contract terms using retrieval enhancement generation technology. When risk monitoring is triggered, the pre-generated structured query statements can be directly called, compressing the preparation time for a single monitoring session to the second level, significantly improving efficiency from the start of the process. Based on the indicator data, the method automatically compares the indicator data with the restrictions in the contract terms to determine whether there are entrusted investments in the entrusted investment database that do not comply with the contract terms, quickly locating such investments and shortening the processing time for a single batch of monitoring, improving efficiency while ensuring the accuracy of monitoring results. If entrusted investments that do not comply with the contract terms are found, an entrusted investment risk warning report is automatically generated based on these investments, eliminating the need for manual writing and formatting. The generated entrusted investment risk warning report can be directly pushed to investment management personnel, shortening report production time. The entrusted investment risk warning report is used to indicate the existence of entrusted investment risks, significantly improving the efficiency of risk information transmission and handling. By using pre-generated structured query statements, automated indicator data extraction, automated violation judgment, and automated report generation, the processing efficiency of entrusted investment risk monitoring methods can be improved.

[0091] Based on the above embodiments, the structured query statement is generated as follows: Contract data of the investment supervision contract is automatically parsed to obtain the contract text contained in the investment supervision contract. Search-enhanced generation technology is applied to segment the contract text into contract clauses, obtaining the contract clauses contained in the investment supervision contract. Search-enhanced generation technology is then applied to call an indicator knowledge base to generate a structured query statement corresponding to the contract clauses, based on the contract clauses. The indicator knowledge base is used to store investment risk monitoring indicators.

[0092] For example, the contract data for investment supervision contracts includes contract documents in different formats such as PDF and DOC.

[0093] Optionally, contract documents of different formats are automatically parsed to obtain the contract text contained in the investment supervision contract. For editable text contracts, the complete text data is directly extracted. For non-text formats such as scanned copies, optical character recognition (OCR) technology is used to convert image information into machine-recognizable text content, and finally, the data is integrated to form a complete and parsable investment supervision contract text.

[0094] As we can understand it, OCR technology refers to the technology of converting text in an image into editable text. For example, extracting contract text from a PDF scan into plain text.

[0095] Furthermore, RAG technology is applied to segment the contract text into contract clauses, yielding the contract text contained in the investment supervision contract. Through a deep understanding of the text's semantics and contextual association, RAG technology can accurately identify and segment the independent obligations, rights, and risk clauses in the contract, thereby deconstructing the complete contract text into a series of semantically complete and clearly defined standardized contract clause units.

[0096] Subsequently, RAG technology is applied and integrated with a dedicated indicator knowledge base. This knowledge base stores definitions, calculation logic, and data association rules for various indicators related to investment risk monitoring. Based on the segmented specific contract terms, standardized and logically accurate SQL statements are automatically generated that can be directly used to query or calculate the database.

[0097] By combining OCR and RAG technologies, automated parsing and rule generation of contract texts are achieved. By converting contract terms into executable SQL statements, the process of converting natural language contract terms into executable data query commands is automated and precise, providing solid technical support for efficient monitoring of investment risks.

[0098] Optionally, applying retrieval enhancement generation technology to segment the contract text into contract terms to obtain the contract terms contained in the investment supervision contract may include: applying retrieval enhancement generation technology to call an external knowledge base to segment the contract text into contract terms to obtain the contract terms contained in the investment supervision contract. The external knowledge base is used to store clause segmentation prompts and professional knowledge related to clause identification.

[0099] For example, the external knowledge base is not a simple text storage, but a structured professional knowledge system that systematically stores two types of key information: one is customized prompt word templates for clause segmentation tasks, and the other is clause recognition professional knowledge covering legal semantic features, clause structure paradigms, industry-specific expressions, etc.

[0100] Optionally, a lightweight clause segmentation model can be employed, combined with RAG technology to access an external knowledge base, to achieve efficient and accurate clause segmentation of investment supervision contracts. This clause segmentation model, through targeted training, possesses the basic ability to identify clause boundaries and classify clause types, enabling rapid preliminary structured parsing of the contract text. Furthermore, based on the preliminary parsing results, the clause segmentation model performs secondary analysis and correction using prompts and professional knowledge retrieved from the external knowledge base, resulting in the contract clauses contained in the investment supervision contract. This improves the semantic accuracy and structural integrity of clause segmentation while controlling computational resource consumption.

[0101] By applying RAG technology to access external knowledge bases and employing a clause segmentation model to divide contract clauses, the boundaries of clauses are accurately identified, thus providing high-quality data input for indicator matching and SQL generation. For example, in complex contract scenarios, multiple clauses are broken down into independent information, and indicators are matched separately to generate corresponding SQL statements, which can significantly improve the accuracy and efficiency of rule generation.

[0102] Furthermore, the retrieval-enhanced generation technology is applied to access the indicator knowledge base. Based on the contract terms, a structured query statement corresponding to the contract terms is generated. The indicator knowledge base stores investment risk monitoring indicators and may include: applying the retrieval-enhanced generation technology to access the indicator knowledge base, identifying the matching situation between the contract terms and the investment risk monitoring indicators in the indicator knowledge base, and determining the indicator matching result corresponding to the contract terms. If the indicator matching result indicates that there is an investment risk monitoring indicator in the indicator knowledge base that matches the contract terms, the parameters related to the contract terms are embedded into the parameters of the matched investment risk monitoring indicator to obtain the embedded parameter investment risk monitoring indicator. Based on the embedded parameter investment risk monitoring indicator, a structured query statement corresponding to the contract terms is generated. If the indicator matching result indicates that there is no investment risk monitoring indicator in the indicator knowledge base that matches the contract terms, the retrieval-enhanced generation technology is applied to access the table structure knowledge base. Based on the contract terms, structured query prompts are generated by combining the text, and the contract terms and the text-generated structured query prompts are input into the large language model to generate a structured query statement corresponding to the contract terms.

[0103] For example, the segmented contract terms are input into a lightweight matching and recognition model. This model, in conjunction with RAG technology, calls upon a dedicated indicator knowledge base for storing investment risk monitoring metrics to perform deep semantic analysis and matching recognition on the segmented contract terms. The indicator knowledge base encompasses various standardized risk control indicators, statistical definitions, and data mapping rules accumulated over time. The segmented contract terms are then precisely compared and identified against the investment risk monitoring metrics in the indicator knowledge base to determine the matching results. These matching results indicate whether a matching investment risk monitoring metric exists in the indicator knowledge base.

[0104] In one example, if the indicator matching result indicates that there is an investment risk monitoring indicator in the indicator knowledge base that matches the contract terms, a specialized parameter embedding model automatically extracts business parameters from the contract terms, such as numerical values, thresholds, and time conditions, and dynamically embeds them into a preset parameter template for the matching indicator, generating an investment risk monitoring indicator adapted to the current contract. Furthermore, this parameter embedding model automatically converts the embedded investment risk monitoring indicator into grammatically compliant and logically precise SQL statements.

[0105] For example, if the indicator in the indicator knowledge base is: "The holding ratio of a single target is less than or equal to X%" (X is a variable parameter), and the contract terms state: "The holding ratio of a single investment target shall not exceed 8% of net assets", then the parameter embedding result can replace X of the original indicator with 8 to form a dedicated monitoring indicator for this contract: "The holding ratio of a single target is ≤8%".

[0106] In another example, if the indicator matching result indicates that there is no investment risk monitoring indicator in the indicator knowledge base that matches the contract terms, the RAG technology is used to call the table structure knowledge base to retrieve the database table names, field names, data types, and inter-table relationships (such as the related fields between the holdings table and the target information table) related to the risk control requirements of the contract terms. Simultaneously, predefined text-generated SQL prompts are used. The segmented contract terms, the table structure information retrieved by RAG, and the prompts are input into the LLM. Leveraging its powerful semantic understanding and logical reasoning capabilities, the LLM accurately analyzes the personalized risk control requirements of the contract terms. Combining the table structure information and prompt constraints, it generates SQL statements that are adapted to specific business scenarios and can be directly executed, ensuring efficient and compliant query instructions even when dealing with non-standardized contract terms.

[0107] It's understandable that text-generated SQL prompts are precise instruction sets designed for the task of "converting natural language into structured SQL statements." Their core function is to convey clear and specific requirements to the large model, guiding it to transform unstructured business needs, such as entrusted investment contract terms, into grammatically compliant, logically correct, and directly interfaceable SQL statements that can be directly connected to the business database. The table structure knowledge base stores the table names, field names, data types, and inter-table relationships of the business database, such as "the holdings table (t_hold) and the target information table (t_target) are linked through target_id."

[0108] Alternatively, the LLM model can be trained in the following ways:

[0109] The LLM model is based on a large-scale foundational model with general natural language understanding, logical reasoning, and text generation capabilities, and is formed through scenario-based adaptation and targeted optimization. Specifically, it is fine-tuned based on financial risk control-specific data such as investment supervision contract texts, historical standardized risk monitoring indicator data, database table structure information, and compliant SQL statement samples, enabling it to master professional terminology in entrusted investment business (such as "position ratio" and "stop-loss threshold"), the risk control logic of contract terms, and SQL syntax specifications. Secondly, combined with the engineered design of SQL prompts for text generation, rules such as SQL output format, syntax requirements, and business constraints are embedded in instructions to guide the model to form standardized output habits. At the same time, the model deeply collaborates with RAG technology, obtaining real-time scenario data support by calling indicator knowledge bases and table structure knowledge bases, and can adapt to the personalized needs of different contract terms without continuous large-scale training. The final LLM model can accurately parse contract risk control requirements and generate compliant and usable SQL statements.

[0110] By leveraging RAG technology to precisely access the indicator knowledge base, intelligent matching of contract terms with existing investment risk monitoring indicators is rapidly achieved. For scenarios with matching indicators, SQL statements are generated through parameter embedding, reducing development costs and ensuring consistency between the SQL statements and contract terms. For scenarios without matching indicators, RAG technology accesses the table structure knowledge base to obtain data structure support. Combined with the standardized guidance of text-generated SQL prompts, and utilizing the semantic understanding and logical reasoning capabilities of the LLM model, SQL statements corresponding to the contract terms are generated, covering various contract term scenarios. The overall process achieves automated SQL statement generation, full scenario coverage, and high accuracy, providing efficient and reliable technical support for the extraction of indicator data for entrusted investment risk monitoring.

[0111] In some embodiments, S102, determining whether there are entrusted investments in the entrusted investment database that do not comply with the contract terms based on indicator data, may further include: obtaining external risk information, which is determined based on current information and corporate reports. If the indicator data and / or external risk information trigger risky entrusted investment conditions, it is determined that there are entrusted investments in the entrusted investment database that do not comply with the contract terms. If the indicator data and external risk information do not trigger risky entrusted investment conditions, it is determined that there are no entrusted investments in the entrusted investment database that do not comply with the contract terms.

[0112] For example, current affairs information includes macroeconomic strategy adjustments, the release of new industry regulations, market sentiment hotspots (such as negative news about the target company and sudden industry events), and dynamic content such as financial market fluctuations; corporate reports include the target company's regularly released financial statements, business briefings, and compliance disclosure documents.

[0113] Optionally, the conditions for risk-based entrusted investment include indicator data trigger conditions and external risk information trigger conditions.

[0114] It is understandable that the triggering conditions for indicator data refer to actual indicator data extracted from the entrusted investment database that directly corresponds to the restrictions in the contract terms and does not comply with the contract. For example, the holding ratio of a single target exceeds the upper limit stipulated in the contract, the net value of the investment portfolio falls below the pre-set stop-loss threshold in the contract, the proportion of liquid assets does not meet the minimum standard required by the contract, and the investment targets exceed the investment scope permitted by the contract. As long as one or more actual indicator data deviate from the restrictions in the contract terms (and meet the standards for determining violations), the risky entrusted investment conditions are triggered.

[0115] It is understandable that external risk information triggering conditions refer to risk signals that may lead to violations of entrusted investment regulations, identified through current events and corporate financial statements. Triggers based on current events include: macroeconomic policy adjustments (such as industry lending restrictions or regulatory bans), industry-specific unforeseen events (such as overcapacity or policy tightening), and market sentiment hotspots (such as negative scandals involving the target company or compliance penalty announcements). Triggers based on corporate financial statements include: abnormal fluctuations in the target company's financial indicators (such as a significant decline in net profit or excessive debt-to-equity ratio), flaws in operating data (such as revenue fraud or related-party transaction violations), and compliance disclosure issues (such as failure to disclose annual reports on time or false statements). If any of the above risk signals appear in the external risk information, and that signal triggers the implicit compliance requirements of the contract terms, such as "no investment in companies with significant financial risks," then the risky entrusted investment conditions are triggered.

[0116] Specifically, when only the indicator data meets the conditions for risky entrusted investment, or only external risk information triggers the conditions for risky entrusted investment, or both trigger the conditions for risky entrusted investment, it will be determined that there are illegal entrusted investments in the entrusted investment database that do not comply with the terms of the investment supervision contract.

[0117] Optionally, if the indicator data fully comply with the contractual agreement and no abnormalities in external risk information may affect the compliance of the entrusted investment, and the risky entrusted investment conditions are not triggered, then it is determined that there are no entrusted investments in the entrusted investment database that do not comply with the contractual terms. In other words, all entrusted investments comply with the contractual terms.

[0118] By integrating current events information and corporate reports to form external risk information input, risk assessment becomes more closely aligned with the dynamics over time and the actual situation of the target company. Employing a two-dimensional risk assessment using indicator data and external risk information enables precise identification of whether entrusted investments comply with contractual terms, improving the efficiency and accuracy of risk identification, and providing efficient and reliable technical support for the compliant and stable operation of entrusted investment business.

[0119] As one possible implementation, external risk information is determined based on current events and corporate reports in the following ways: Current events are input into a sentiment analysis model to obtain the corresponding public opinion sentiment analysis results, which include positive, neutral, and negative sentiment; and the sentiment analysis results are numerically mapped to obtain the corresponding mapped values. Corporate reports are input into a risk scoring model to obtain the corresponding corporate risk scores, which are then normalized to obtain normalized corporate risk scores. The mapped values ​​and normalized corporate risk scores are weighted and merged to obtain the external risk score. Finally, current events, corporate reports, and the external risk score are input into the risk analysis model to obtain external risk information.

[0120] In some embodiments, current affairs information is input into a sentiment analysis model, which uses deep learning algorithms to extract semantic features, sentiment tendencies and risk-related elements from the current affairs information, and accurately outputs the public opinion sentiment analysis results corresponding to the current affairs information, namely positive public opinion, neutral public opinion or negative public opinion.

[0121] Furthermore, based on preset numerical mapping rules, the sentiment analysis results are numerically mapped, such as 0.0 for positive sentiment, 0.5 for neutral sentiment, and 1.0 for negative sentiment. This numerical mapping yields the mapped values ​​corresponding to the sentiment analysis results, intuitively reflecting the degree of impact of current events information on the potential risks of the entrusted investment. It should be noted that when sentiment data is missing, the mapped value corresponding to the sentiment analysis results can be set to 0.5.

[0122] Optionally, the enterprise's financial statements can be input into a risk scoring model. This risk scoring model constructs a multi-indicator evaluation system around multiple key dimensions such as the enterprise's financing ability, management ability, operational ability, and financial ability. It converts unstructured / semi-structured information such as financial, operational, and compliance information into corresponding risk scores, and obtains the enterprise risk score corresponding to the enterprise's financial statements through weighted calculation.

[0123] The risk scoring model can be trained in the following way:

[0124] Historical financial statements from enterprises across multiple industries and risk levels are collected as input, and risk labels (no risk / risky or low / medium / high risk) are added based on authoritative sources such as regulatory penalties and credit reports. Subsequently, core financial, operational, and compliance features are extracted and optimized. Models adapted to structured data, such as logistic regression and XGBoost, are selected and trained using cross-entropy loss as the objective function. Hyperparameters are fine-tuned through methods such as grid search, and finally, the model's output probability values ​​are scaled to a stable range to form an enterprise risk score. After deployment, new data is regularly incorporated for iterative updates.

[0125] Furthermore, min-max normalization can be used to normalize the enterprise risk score, normalizing it to the (0,100) range to obtain the normalized enterprise risk score corresponding to the enterprise report, thereby eliminating the impact of differences in the data volume of different enterprise reports.

[0126] For example, the external risk score can be obtained by weightedly combining the mapped value and the normalized enterprise risk score using the following formula:

[0127]

[0128] in, Assess external risk levels; Normalized enterprise risk score corresponding to enterprise financial statements; This represents the mapping value corresponding to the public opinion sentiment analysis results; and These are the configurable weights corresponding to the normalized enterprise risk score and the mapping value, respectively. For example, , .

[0129] By inputting current affairs information, corporate reports, and external risk scores into the risk analysis model, the inherent correlation between various types of information is explored, and structured external risk information including risk level, core risk points, and scope of impact is output, providing a direct and actionable basis for verifying the risk triggering conditions of entrusted investments.

[0130] The risk analysis model can be trained in the following ways:

[0131] The input consists of a combination of current affairs information, corporate reports, and external risk scores, labeled with structured risk information (risk level, core risk points, etc.). A pre-trained language model is used to extract textual semantic features, standardize numerical features, construct multi-source related features, and weighted fusion. An architecture using a pre-trained language model, fully connected layers, and a multi-task output head is employed, with a multi-task learning strategy for training. Business rule verification is also incorporated to ensure the output complies with regulatory and contractual requirements, enabling accurate interpretation and judgment of risks.

[0132] By utilizing current events information and corporate reports, corresponding public opinion sentiment analysis results and corporate risk scores are determined. Based on these results, a quantitative score comprehensively reflecting the level of external risk is established—the external risk score. Combining current events information, corporate reports, and the external risk score, a risk analysis model is used to uncover the inherent correlations among multi-dimensional information, resulting in structured external risk information that directly supports risk assessment. This entire process achieves a precise transformation from qualitative to quantitative analysis, while multi-source fusion ensures the comprehensiveness and objectivity of risk assessment, enhancing the standardization and application value of external risk information, and providing high-quality data support for the accurate triggering and judgment of entrusted investment risk monitoring.

[0133] Based on the above embodiments, S103, automatically generating a risk warning report for entrusted investments that do not comply with the contract terms, may further include: automatically generating and outputting a risk warning report for entrusted investments by applying prompt word technology based on the indicator data and external risk information corresponding to the entrusted investments that do not comply with the contract terms.

[0134] For example, upon determining that an entrusted investment violates the terms of the investment supervision contract, an intelligent risk warning report generation process will be initiated. Specifically, the system automatically extracts the corresponding indicator data and external risk information for the non-compliant investment. The indicator data is obtained by periodically executing SQL statements. This indicator data includes key quantitative information such as the name of the non-compliant investment, the amount of investment involved, the percentage of holdings, the deviation between the actual indicator value and the contractually agreed threshold, and the duration of the non-compliance. The external risk information includes details of the public opinion events that triggered the risk (such as strategy adjustments or negative corporate events), the target company's risk score and normalization results, and a combination of qualitative and quantitative information such as the external risk level and core risk points.

[0135] Furthermore, by applying prompt word technology and based on preset professional prompt word templates (containing core elements such as a structured report framework, risk description specifications, data presentation format, and compliance expression requirements), the aforementioned violation indicator data and external risk information are logically embedded into the prompt words, forming precise instructions to guide the Artificial Intelligence (AI) model in generating the report. Under the targeted guidance of the prompt words, the AI ​​model automatically completes data integration, risk logic analysis, and structured report content writing. Specifically, the AI ​​model not only clearly lists the facts of the violation, quantifies the supporting data and external risk factors, but also analyzes the compliance risks and potential impacts that the violation may cause, clarifies the specific contractual clauses violated, and provides preliminary risk management suggestions.

[0136] The AI ​​model can be trained in the following ways:

[0137] Using historical risk warning reports, sample investment supervision contract terms, violation indicator data cases, and external risk information annotation samples from the entrusted investment field as core training data, the model becomes proficient in financial risk control terminology, the structured framework of risk reports (such as modules for violation facts, data support, risk analysis, and disposal suggestions), and compliant expression standards. Secondly, customized prompt word technology is integrated into the training, constructing a professional prompt word template library that includes report format requirements, data presentation rules, and risk logic association methods. This guides the model to learn the mapping relationship between indicator data, external risk information, and report content, mastering the ability to extract core information from heterogeneous data and structure and integrate it into a coherent expression. Simultaneously, entrusted investment business rules and regulatory compliance requirements are embedded to ensure that the reports generated by the model comply with contract terms and industry regulatory standards. Finally, through multiple rounds of human feedback and sample iteration optimization, the model parameters and prompt word guidance logic are continuously adjusted, ultimately forming a dedicated model capable of accurately integrating violation indicator data and external risk information, and automatically generating standardized and compliant entrusted investment risk warning reports.

[0138] Ultimately, the system automatically generates standardized entrusted investment risk warning reports that comply with business norms and regulatory requirements. These reports are then pushed to relevant stakeholders, such as investment managers and risk control officers, in real time through pre-set output channels, including internal risk control systems, emails, and collaborative office platforms. This ensures the rapid flow of risk information and provides an intuitive and comprehensive basis for timely initiation of non-compliance handling and rectification.

[0139] By integrating prompt word technology with multi-source data, intelligent generation of entrusted investment risk warning reports and in-depth expansion of risk analysis can be achieved. Specifically, structured prompt words guide the model to output quantitative analysis and strategy recommendations, enhancing the report's practicality and decision-making reference value. This enables investment managers to make rapid decisions based on quantitative analysis and personalized recommendations, thereby optimizing risk management efficiency and portfolio security.

[0140] Figure 2 This is a schematic diagram illustrating an application of the entrusted investment risk monitoring method provided in this application embodiment. For example... Figure 2 The diagram illustrates the automated process for monitoring entrusted investment risks. Starting with scanned contract documents, the process first verifies the results of clause breakdown and indicator matching. Then, the investment monitoring agent generates SQL statements to add batch tasks to the database. After the database identifies abnormal investment portfolios, the agent combines external public opinion data to conduct intelligent risk analysis, ultimately generating a risk warning report and pushing it to the investment manager. This achieves intelligent monitoring of entrusted investment risks throughout the entire process, from contract analysis to risk warning.

[0141] As can be understood, an intelligent agent refers to an entity capable of perceiving its environment, making autonomous decisions, and executing actions to achieve a specific goal or task. An intelligent agent can be a software program, a robot, or any system possessing autonomous behavior. In this application, it refers to a software program.

[0142] The aforementioned investment supervision intelligent agent can greatly improve the efficiency of rule construction for investment supervision and the ability to identify risks.

[0143] In summary, the entrusted investment risk monitoring method provided in this application has at least the following advantages:

[0144] First, by responding to the risk monitoring of entrusted investments, multiple pre-generated SQL statements are invoked to extract the indicator data to be restricted by the contractual clauses in the investment supervision contract from the entrusted investment database, significantly shortening the preparation time for indicator data extraction. The SQL statements are pre-generated based on the contractual clauses using RAG technology. When risk monitoring is triggered, the pre-generated SQL statements can be directly invoked, reducing the preparation time for a single monitoring session to the second level, greatly improving efficiency from the very beginning of the process.

[0145] Second, the system automatically parses investment supervision contract data to obtain the corresponding contract text. It then applies RAG technology to call an external knowledge base to segment the contract text into clauses, accurately identifying clause boundaries and achieving intelligent parsing of contract supervision requirements. RAG technology precisely calls an indicator knowledge base to quickly determine the indicator matching results for each contract clause. If the indicator matching result indicates that an investment risk monitoring indicator matching the contract clause exists in the indicator knowledge base, SQL statements are generated through parameter embedding, reducing development costs and ensuring consistency between the SQL statements and the contract clauses. If the indicator matching result indicates that no matching investment risk monitoring indicator exists in the indicator knowledge base, RAG technology calls a table structure knowledge base to obtain data structure support. Combined with the standardized guidance of text-generated SQL prompts, and leveraging the semantic understanding and logical reasoning capabilities of the LLM model, SQL statements corresponding to the contract clauses are generated, covering various contract clause scenarios.

[0146] Based on different matching results, corresponding SQL statements are generated to automatically generate scanning rules for contract supervision requirements, reducing the cost of manual intervention, improving the efficiency of rule configuration, and providing solid technical support for efficient monitoring of investment risks.

[0147] Third, by integrating current affairs information and corporate reports to form external risk information input, risk assessment becomes more aligned with the dynamics of the time period and the actual situation of the target company. A two-dimensional risk assessment using indicator data and external risk information enables accurate identification of whether entrusted investments comply with contract terms, improving the efficiency and accuracy of risk identification and providing efficient and reliable technical support for the compliant and stable operation of entrusted investment business. Specifically, sentiment analysis models and risk scoring models are used to obtain public opinion sentiment analysis results and corporate risk scores. Based on these results, an external risk score that comprehensively reflects the level of external risk is determined. Combining current affairs information, corporate reports, and external risk scores, the risk analysis model uncovers the inherent correlations among multi-dimensional information, obtaining structured external risk information that directly supports risk assessment, providing high-quality data support for the accurate triggering and judgment of entrusted investment risk monitoring.

[0148] Fourth, based on the indicator data and external risk information corresponding to entrusted investments that do not comply with contract terms, the system utilizes prompt word technology to guide the model to output quantitative analysis and strategy recommendations. This automatically generates and outputs entrusted investment risk warning reports, constructing a real-time fusion analysis mechanism for internal and external data. This expands the dimensions of risk monitoring and enables proactive early warning of market risks triggered by external events (such as abnormal bond price fluctuations). Specifically, the entrusted investment risk warning report alerts to existing entrusted investment risks. By automatically calculating the quantitative impact of violations on the investment portfolio, it generates personalized response suggestions based on multi-source data, improving decision-making accuracy and the efficiency of risk information transmission and handling, thereby optimizing risk management efficiency and portfolio security.

[0149] In summary, the entrusted investment risk monitoring method provided in this application embodiment can improve the processing efficiency of entrusted investment risk monitoring methods.

[0150] Furthermore, the entrusted investment risk monitoring method provided in this application, by introducing large-scale model capabilities, achieves intelligent innovation across the entire investment supervision process, specifically bringing the following significant benefits: In the pre-supervision stage, the intelligent parsing and rule generation technology of contract terms reduces manual operation time by 60%. In the in-process supervision stage, external risk tags are integrated to build a multi-source data real-time analysis engine, improving risk coverage from the traditional single dimension to composite risks. Post-supervision breaks through the limitations of templated reports, automatically generating intelligent reports containing impact analysis and strategy recommendations.

[0151] Figure 3 This is a schematic diagram of the structure of the entrusted investment risk monitoring device provided in an embodiment of this application. Figure 3 As shown, the entrusted investment risk monitoring device 20 provided in this application embodiment includes:

[0152] The extraction module 201 is used to respond to the triggering of entrusted investment risk monitoring by calling multiple pre-generated structured query statements to extract the indicator data to be restricted by the contract terms in the investment supervision contract from the entrusted investment database. The structured query statements are generated based on the contract terms using retrieval enhancement generation technology.

[0153] The determination module 202 is used to determine, based on the indicator data, whether there are any entrusted investments in the entrusted investment database that do not comply with the contract terms;

[0154] The processing module 203 is used to automatically generate a risk warning report for entrusted investments that do not comply with the contract terms if there are any such investments. The risk warning report is used to indicate the existence of risks associated with the entrusted investments.

[0155] In one possible implementation, structured query statements are generated in the following way:

[0156] Automated parsing of investment supervision contract data to obtain the contract text contained in the investment supervision contract;

[0157] By applying retrieval-enhanced generation technology, the contract text is segmented into contract clauses to obtain the contract clauses contained in the investment supervision contract;

[0158] The application uses retrieval enhancement generation technology to call the indicator knowledge base and generate structured query statements corresponding to the contract terms based on the contract terms. The indicator knowledge base is used to store investment risk monitoring indicators.

[0159] In one possible implementation, retrieval-enhanced generation technology is applied to segment the contract text into contract clauses, obtaining the contract clauses contained in the investment supervision contract, including:

[0160] The application of retrieval enhancement generation technology calls an external knowledge base to segment the contract text into contract clauses, thereby obtaining the contract clauses contained in the investment supervision contract. The external knowledge base is used to store clause segmentation prompts and professional knowledge related to clause identification.

[0161] In one possible implementation, a retrieval-enhanced generation technique is applied to invoke an indicator knowledge base. Based on the contract terms, a structured query statement corresponding to the contract terms is generated. The indicator knowledge base is used to store investment risk monitoring indicators, including:

[0162] The application of retrieval enhancement generation technology calls the indicator knowledge base to identify the matching of contract terms with investment risk monitoring indicators in the indicator knowledge base, and determines the indicator matching result corresponding to the contract terms;

[0163] If the indicator matching result indicates that there is an investment risk monitoring indicator in the indicator knowledge base that matches the contract terms, then the parameters related to the contract terms are embedded into the parameters of the matched investment risk monitoring indicator to obtain the investment risk monitoring indicator with embedded parameters. Based on the investment risk monitoring indicator with embedded parameters, a structured query statement corresponding to the contract terms is generated.

[0164] If the indicator matching result indicates that there is no investment risk monitoring indicator in the indicator knowledge base that matches the contract terms, the retrieval enhancement generation technology is applied to call the table structure knowledge base. Based on the contract terms, structured query prompts are generated by combining the text. The contract terms and the structured query prompts generated by the text are then input into the large language model to generate the structured query statement corresponding to the contract terms.

[0165] In one possible implementation, the determining module 202 is specifically used for:

[0166] Obtain external risk information, which is determined based on current events information and company reports;

[0167] The response indicator data and / or external risk information trigger the risk entrusted investment conditions, and determine that there are entrusted investments in the entrusted investment database that do not comply with the contract terms;

[0168] The response indicator data and external risk information did not trigger the risky entrusted investment conditions, and it was determined that there were no entrusted investments in the entrusted investment database that did not comply with the contract terms.

[0169] In one possible implementation, external risk information is determined based on current events and company reports:

[0170] Current events information is input into the sentiment analysis model to obtain the corresponding public opinion sentiment analysis results, which include positive, neutral, and negative public opinion; and the public opinion sentiment analysis results are numerically mapped to obtain the corresponding mapping values.

[0171] The enterprise report is input into the risk scoring model to obtain the enterprise risk score corresponding to the enterprise report. The enterprise risk score is then normalized to obtain the normalized enterprise risk score corresponding to the enterprise report.

[0172] The external risk score is obtained by weighted fusion of the mapped value and the normalized enterprise risk score.

[0173] By inputting current events information, corporate reports, and external risk scores into the risk analysis model, external risk information is obtained.

[0174] In one possible implementation, the processing module 203 is specifically used for:

[0175] Based on the indicator data and external risk information corresponding to entrusted investments that do not comply with the contract terms, the system uses prompt word technology to automatically generate and output entrusted investment risk warning reports.

[0176] The entrusted investment risk monitoring device provided in this application embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0177] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 4 As shown, the electronic device 30 provided in this application embodiment includes at least one processor 301 and a memory 302. Optionally, the electronic device 30 further includes a communication component 303. The processor 301, memory 302, and communication component 303 are connected via a bus.

[0178] In a specific implementation, at least one processor 301 executes computer execution instructions stored in memory 302, causing at least one processor 301 to perform the above-described method.

[0179] The specific implementation process of processor 301 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0180] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0181] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0182] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0183] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0184] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0185] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0186] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an application-specific integrated circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0187] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0188] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0189] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0190] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0191] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0192] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A fiduciary investment risk monitoring method, characterized by, include: The response triggers entrusted investment risk monitoring and calls multiple pre-generated structured query statements to extract indicator data that are to be restricted by the contract terms in the investment supervision contract from the entrusted investment database. The structured query statements are generated based on the contract terms using retrieval enhancement generation technology. Based on the aforementioned indicator data, determine whether there are any entrusted investments in the entrusted investment database that do not comply with the contract terms; If there are entrusted investments that do not comply with the contract terms, an entrusted investment risk warning report will be automatically generated based on the entrusted investments that do not comply with the contract terms. The entrusted investment risk warning report is used to indicate the existence of entrusted investment risks.

2. The fiduciary investment risk monitoring method of claim 1, wherein, The structured query statement is generated in the following way: The contract data of the investment supervision contract is automatically parsed to obtain the contract text contained in the investment supervision contract; By applying retrieval enhancement generation technology, the contract text is segmented into contract clauses to obtain the contract clauses contained in the investment supervision contract; The application uses retrieval enhancement generation technology to call the indicator knowledge base and generate a structured query statement corresponding to the contract terms based on the contract terms. The indicator knowledge base is used to store investment risk monitoring indicators.

3. The fiduciary investment risk monitoring method of claim 2, wherein, The application of enhanced retrieval generation technology segments the contract text into contract clauses to obtain the contract clauses contained in the investment supervision contract, including: The application of retrieval enhancement generation technology calls an external knowledge base to segment the contract text into contract clauses, thereby obtaining the contract clauses contained in the investment supervision contract. The external knowledge base is used to store clause segmentation prompts and professional knowledge related to clause identification.

4. The fiduciary investment risk monitoring method of claim 2, wherein, The application retrieval enhancement generation technology invokes an indicator knowledge base to generate a structured query statement corresponding to the contract terms based on the contract terms. The indicator knowledge base stores investment risk monitoring indicators, including: The application of retrieval enhancement generation technology calls the indicator knowledge base to identify the matching situation between the contract terms and the investment risk monitoring indicators in the indicator knowledge base, and determines the indicator matching result corresponding to the contract terms; If the indicator matching result indicates that there is an investment risk monitoring indicator in the indicator knowledge base that matches the contract terms, then the parameters related to the contract terms are embedded into the parameters of the matched investment risk monitoring indicator to obtain the investment risk monitoring indicator with embedded parameters, and a structured query statement corresponding to the contract terms is generated based on the investment risk monitoring indicator with embedded parameters. If the indicator matching result indicates that there is no investment risk monitoring indicator in the indicator knowledge base that matches the contract terms, the retrieval enhancement generation technology is applied to call the table structure knowledge base. Based on the contract terms, combined with the text-generated structured query prompts, the contract terms and the text-generated structured query prompts are input into the large language model to generate the structured query statement corresponding to the contract terms.

5. The method of claim 1 to 4, wherein, The step of determining whether there are any entrusted investments in the entrusted investment database that do not comply with the contract terms based on the indicator data includes: Obtain external risk information, which is determined based on current events information and corporate reports; In response to the indicator data and / or the external risk information triggering the risk entrusted investment conditions, it is determined that there are entrusted investments in the entrusted investment database that do not comply with the contract terms; If the aforementioned indicator data and external risk information do not trigger the conditions for risky entrusted investment, it is determined that there are no entrusted investments in the entrusted investment database that do not comply with the contract terms.

6. The entrusted investment risk monitoring method according to claim 5, characterized in that, The external risk information was determined based on current events information and company reports in the following ways: The current affairs information is input into the sentiment analysis model to obtain the public opinion sentiment analysis results corresponding to the current affairs information. The public opinion sentiment analysis results include positive public opinion, neutral public opinion, and negative public opinion. Furthermore, the public opinion sentiment analysis results are numerically mapped to obtain the mapping values ​​corresponding to the public opinion sentiment analysis results; The enterprise report is input into the risk scoring model to obtain the enterprise risk score corresponding to the enterprise report, and the enterprise risk score is normalized to obtain the normalized enterprise risk score corresponding to the enterprise report. The external risk score is obtained by weighted fusion of the mapped value and the normalized enterprise risk score. The current affairs information, the company reports, and the external risk score are input into the risk analysis model to obtain the external risk information.

7. The entrusted investment risk monitoring method according to any one of claims 1 to 4, characterized in that, The automatic generation of a risk warning report for entrusted investments that do not comply with the contract terms includes: Based on the indicator data and external risk information corresponding to the entrusted investments that do not comply with the contract terms, the system uses prompt word technology to automatically generate and output a risk warning report for the entrusted investments.

8. A risk monitoring device for entrusted investments, comprising: The extraction module is used to respond to the triggering of entrusted investment risk monitoring by calling multiple pre-generated structured query statements to extract the indicator data to be restricted by the contract terms in the investment supervision contract from the entrusted investment database. The structured query statements are generated based on the contract terms using retrieval enhancement generation technology. The determination module is used to determine, based on the indicator data, whether there are any entrusted investments in the entrusted investment database that do not comply with the contract terms; The processing module is used to automatically generate a entrusted investment risk warning report based on the entrusted investment that does not comply with the contract terms if there is such an investment. The entrusted investment risk warning report is used to indicate the existence of entrusted investment risks.

9. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1 to 7.

11. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 7.

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