Associated transaction information management method and device based on large model, equipment and medium

Abstract

The invention relates to the field of data analysis and the field of application and financial business science and technology, and discloses an associated transaction information management method and device based on a large model, equipment and a medium, and the method comprises the steps: determining target associated transaction information; determining a risk level of the target associated transaction information through a target risk assessment model; and determining a target associated transaction monitoring strategy according to the risk level, and performing monitoring management on the target associated transaction information. According to the method, the preset relevance large model is used to identify the potential target relevance transaction related to the at least one type of initial external transaction information, so that the capability of identifying hidden risks in a complex transaction network is improved. Risk assessment is performed on the target associated transaction information through the pre-constructed target risk assessment model, so that risk management is more flexible and quick in response, the corresponding monitoring strategy is determined according to the assessed risk level, and the security of associated transaction risk control is improved.

Description

Technical Field

[0001] The present application relates to the field of Internet technology, and in particular to a method, device, equipment and medium for managing related transaction information based on a large model. Background Art

[0002] Related-party transaction risk control products are key systems or tools used by financial institutions or enterprises to monitor and manage the risks of internal related-party transactions. Although these products are essential for maintaining financial stability and compliance, they may encounter various challenges during their design and implementation.

[0003] With the rapid development of financial technology, some risk control products may not be updated in time to adapt to new transaction models and technologies. Related-party transaction risk control products fail to fully integrate data from different departments or subsidiaries, and the products may not fully meet the requirements of regulators, posing compliance risks. In addition, related-party transaction risk control products lack an effective early warning mechanism and cannot promptly remind users of potential risks. Related-party transaction risk control products lack sufficient flexibility and adaptability when dealing with transactions of different sizes or types, especially when dealing with large amounts of sensitive data. The compliance risks that exist lead to the risk of leakage of users' sensitive data. Therefore, how to improve the security of related-party transaction risk control has become a technical problem that needs to be solved urgently. Summary of the invention

[0004] The present application provides a method, device, equipment and medium for managing related transaction information based on a large model to improve the security of related transaction risk control.

[0005] In a first aspect, the present application provides a method for managing related transaction information based on a large model, the method comprising:

[0006] Identify at least one type of initial external transaction information through a preset correlation model to determine target related transaction information;

[0007] Performing risk assessment on the target associated transaction information through a pre-built target risk assessment model to determine the risk level of the target associated transaction information;

[0008] According to the risk level, a target related transaction monitoring strategy is determined, and the target related transaction information is monitored and managed according to the target related transaction monitoring strategy.

[0009] In a second aspect, the present application further provides a large-model-based associated transaction information management device, the device comprising:

[0010] A target associated transaction information determination module, used to identify at least one type of initial external transaction information through a preset association model, and determine the target associated transaction information;

[0011] A risk level determination module, used to perform risk assessment on the target associated transaction information through a pre-built target risk assessment model to determine the risk level of the target associated transaction information;

[0012] The monitoring management module is used to determine a target-related transaction monitoring strategy according to the risk level, and monitor and manage the target-related transaction information according to the target-related transaction monitoring strategy.

[0013] In a third aspect, the present application also provides a computer device, comprising a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program and implement the above-mentioned large model-based related transaction information management method when executing the computer program.

[0014] In a fourth aspect, the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor implements the above-mentioned large-model-based related transaction information management method.

[0015] The present application discloses a method, device, equipment and medium for managing related transaction information based on a large model. The method for managing related transaction information based on a large model includes identifying at least one type of initial external transaction information through a preset relevance large model to determine the target related transaction information; performing risk assessment on the target related transaction information through a pre-constructed target risk assessment model to determine the risk level of the target related transaction information; determining the target related transaction monitoring strategy according to the risk level, and monitoring and managing the target related transaction information according to the target related transaction monitoring strategy. In the above manner, the present application identifies potential target related transactions related to at least one type of initial external transaction information by using a preset relevance large model, thereby improving the ability to identify hidden risks in a complex transaction network. Performing risk assessment on the target related transaction information through a pre-constructed target risk assessment model makes risk management more flexible and responsive, and determining the corresponding monitoring strategy according to the assessed risk level improves the security of related transaction risk control. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1is a schematic flow chart of a method for managing related transaction information based on a large model provided in the first embodiment of the present application;

[0018] Figure 2 is a schematic flow chart of a method for managing related transaction information based on a large model provided in the second embodiment of the present application;

[0019] Figure 3 is a schematic flow chart of a method for managing related transaction information based on a large model provided in the third embodiment of the present application;

[0020] Figure 4 A schematic block diagram of a large-model-based associated transaction information management device provided in an embodiment of the present application;

[0021] Figure 5 A schematic block diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0023] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0024] It should be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0025] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0026] The embodiments of the present application provide a method, device, equipment and medium for managing related transaction information based on a large model. Among them, the method for managing related transaction information based on a large model can be applied to a server, and by using a preset large model of relevance, potential target related transactions related to at least one type of initial external transaction information are identified, thereby improving the ability to identify hidden risks in complex transaction networks. Risk assessment of target related transaction information is performed through a pre-constructed target risk assessment model, making risk management more flexible and responsive, and determining the corresponding monitoring strategy based on the assessed risk level, thereby improving the security of related transaction risk control. Among them, the server can be an independent server or a server cluster.

[0027] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0028] See also Figure 1 , Figure 1 This is a schematic flow chart of a method for managing related transaction information based on a large model provided by the first embodiment of the present application. The method for managing related transaction information based on a large model can be applied to a server, and is used to identify potential target related transactions related to at least one type of initial external transaction information by using a preset large model of relevance, thereby improving the ability to identify hidden risks in a complex transaction network. Risk assessment of target related transaction information is performed through a pre-built target risk assessment model, making risk management more flexible and responsive, and determining the corresponding monitoring strategy based on the assessed risk level, thereby improving the security of risk control of related transactions.

[0029] like Figure 1 As shown, the related transaction information management method based on the big model specifically includes steps S10 to S30.

[0030] Step S10: Identify at least one type of initial external transaction information through a preset correlation model to determine target correlated transaction information;

[0031] Specifically, a reasonable database structure is designed to store various types of data. Data fusion can use ETL (Extract-Transform-Load) tools to merge data from different sources into a unified data warehouse, and then use algorithms to analyze the relationships between enterprises and identify potential related transactions.

[0032] Step S20: performing risk assessment on the target associated transaction information through a pre-built target risk assessment model to determine the risk level of the target associated transaction information;

[0033] Specifically, based on historical data, a target risk assessment model is constructed to assess the risk level of related transactions. The accuracy and reliability of the model are verified through test data sets, and the relationship and transaction data between enterprises are intuitively displayed through charts, network diagrams, etc. The target risk assessment model provides flexible query and screening functions, which facilitates users to obtain specific data as needed and allows users to generate customized reports based on query results.

[0034] Step S30: Determine a target-related transaction monitoring strategy according to the risk level, and monitor and manage the target-related transaction information according to the target-related transaction monitoring strategy.

[0035] In one embodiment, this embodiment can update data in real time, monitor the dynamic changes of related transactions, and the system can automatically issue an early warning when abnormal transactions or risk signals are detected. It is necessary to clarify the definition of related parties, including individuals or entities that directly or indirectly control the enterprise. Regularly update the list of related parties to ensure the accuracy of information, and formulate clear related transaction policies, including the approval process and disclosure requirements for transactions. Ensure that all related transactions are properly approved and disclosed in financial statements. Conduct regular risk assessments on related parties, including financial status, credit risk, legal risk, etc. Assess the impact of related transactions on the financial status of the enterprise and whether there is a conflict of interest.

[0036] Establish early warning indicators, such as automatically triggering an alarm when the transaction amount exceeds a certain threshold. Regularly check the financial status and credit rating of related parties to promptly identify potential problems. Enterprises can customize the risk level corresponding to related party risk events based on their own risk preferences and risk tolerance, such as severe warning, warning, attention, reminder, and no attention. This helps to control related party risks in all aspects, while avoiding missing the identification of key risk points due to excessive early warning information.

[0037] Monitor and manage the target related transaction information based on the determined risk level. This may include real-time monitoring of transaction behavior, analysis of transaction patterns, and in-depth investigation of abnormal transactions.

[0038] This embodiment discloses a method, device, equipment and medium for managing related transaction information based on a large model. The method for managing related transaction information based on a large model includes identifying at least one type of initial external transaction information through a preset relevance large model to determine the target related transaction information; performing risk assessment on the target related transaction information through a pre-constructed target risk assessment model to determine the risk level of the target related transaction information; determining a target related transaction monitoring strategy based on the risk level, and monitoring and managing the target related transaction information based on the target related transaction monitoring strategy. In the above manner, the present application identifies potential target related transactions related to at least one type of initial external transaction information by using a preset relevance large model, thereby improving the ability to identify hidden risks in a complex transaction network. Performing risk assessment on the target related transaction information through a pre-constructed target risk assessment model makes risk management more flexible and responsive, and determining the corresponding monitoring strategy based on the assessed risk level improves the security of related transaction risk control.

[0039] See also Figure 2 , Figure 2 This is a schematic flow chart of a method for managing related transaction information based on a large model provided in the second embodiment of the present application. The method for managing related transaction information based on a large model can be applied to a server, and is used to automatically extract data from different sources through an ETL tool, perform necessary conversions and cleaning, and then load it into a preset database. The fused initial external transaction information is more complete and consistent, which is convenient for in-depth analysis and decision support. The pre-built target risk assessment model can accurately assess the risk of the target related transaction information, determine the risk level, and improve the security of risk control of related transactions.

[0040] based on Figure 1 The embodiment shown, this embodiment Figure 2 As shown, step S10 includes step S101 to step S102.

[0041] Step S101: fusing the initial external transaction information through an extract-transform-load tool (ETL), and storing the fusing initial external transaction information in a preset database;

[0042] Specifically, ETL (Extract-Transform-Load) tools are used to describe the process of extracting, transforming, and loading data from the source to the destination. ETL is the process of extracting, cleaning, and transforming the data of the business system and loading it into the data warehouse. The purpose is to integrate the scattered, messy, and non-standard data in the enterprise and provide an analytical basis for the enterprise's decision-making.

[0043] Step S102: Determine the relevance of each of the initial external transaction information in the preset database through the built-in relevance algorithm of the preset relevance macromodel, and determine the target related transaction information.

[0044] In one embodiment, built-in correlation algorithms, such as April, FP-Growth, etc., are run to identify frequent item sets and association rules in the data. The transaction data is analyzed by the built-in correlation algorithm to find out the item combinations that often appear together. Association rules are extracted from the frequent item sets. These rules will show the correlation between different transaction features. Meaningful association rules are screened out according to the preset minimum confidence threshold. The business significance and practical application value of the extracted association rules are evaluated to determine whether these rules can provide useful information for monitoring management.

[0045] This embodiment discloses a method, device, equipment and medium for managing related transaction information based on a large model. The method for managing related transaction information based on a large model includes fusing the initial external transaction information through an extraction-transformation-loading tool ETL, and storing the fused initial external transaction information in a preset database; determining the relevance of each initial external transaction information in the preset database through the built-in relevance algorithm of the preset relevance large model, and determining the target related transaction information; performing risk assessment on the target related transaction information through a pre-constructed target risk assessment model, and determining the risk level of the target related transaction information; determining the target related transaction monitoring strategy according to the risk level, and monitoring and managing the target related transaction information according to the target related transaction monitoring strategy. Through the above-mentioned method, the present application automatically extracts data from different sources through the ETL tool, performs necessary conversion and cleaning, and then loads it into the preset database. The fused initial external transaction information is more complete and consistent, which is convenient for in-depth analysis and decision support. The pre-constructed target risk assessment model can accurately assess the risk of the target related transaction information, determine the risk level, and improve the security of related transaction risk control.

[0046] See also Figure 3 , Figure 3This is a schematic flow chart of a method for managing related transaction information based on a large model provided in the third embodiment of the present application. The method for managing related transaction information based on a large model can be applied to a server, and is used to use a preset large model of correlation and historical data to more accurately identify and determine target related transaction information, thereby improving the accuracy of risk assessment. Through iterative training of training sets and validation sets, the initial risk assessment model can be adjusted and optimized, the model calculation error can be reduced, and a more accurate target risk assessment model can be generated. According to the monitoring strategy determined by the risk level, the target related transaction information can be more targeted and managed, the monitoring efficiency and effect can be improved, and the security of related transaction risk control can be improved.

[0047] based on Figure 1 The embodiment shown, this embodiment Figure 3 As shown, step S20 includes steps S11 to S14.

[0048] Step S11, obtaining historical related transaction information as a training set and a verification set;

[0049] Step S12: training the training set by using an initial risk assessment model to determine a training set risk parameter for each of the historical related transaction information in the training set;

[0050] Step S13, determining a validation set risk parameter according to the validation set, and determining a model calculation error according to the validation set risk parameter and the training set risk parameter;

[0051] Step S14: correct the model calculation error and generate the pre-built target risk assessment model.

[0052] In one embodiment, a risk assessment model is constructed based on historical data to assess the risk level of related transactions, and the accuracy and reliability of the model are verified through a test data set.

[0053] Obtain historical related transaction information, which will be used as training sets and validation sets. You can download it through various data platforms, such as the data download platform provided by NetEase Finance, use the initial risk assessment model to train the training set, and determine the training set risk parameters for each historical related transaction information in the training set.

[0054] You can build a deep learning model, such as using TensorFlow and Keras libraries to build a neural network model for training. Use the validation set to determine the validation set risk parameter, compare the validation set risk parameter with the training set risk parameter, and determine the model calculation error.

[0055] According to the calculated error, the model parameters are corrected. An error correction model can be used to introduce an error correction term (ECM) to ensure the accuracy of the model.

[0056] The present embodiment discloses a method, apparatus, device and medium for managing related transaction information based on a big model. The method for managing related transaction information based on a big model includes identifying initial external transaction information of at least one type through a preset correlation big model to determine target related transaction information; obtaining historical related transaction information as a training set and a verification set; training the training set through an initial risk assessment model to determine the training set risk parameter of each of the historical related transaction information in the training set; determining the verification set risk parameter according to the verification set, and determining the model calculation error according to the verification set risk parameter and the training set risk parameter; correcting the model calculation error to generate the pre-constructed target risk assessment model; performing risk assessment on the target related transaction information through the pre-constructed target risk assessment model to determine the risk level of the target related transaction information; determining a target related transaction monitoring strategy according to the risk level, and monitoring and managing the target related transaction information according to the target related transaction monitoring strategy. Through the above method, the present application utilizes the preset correlation large model and historical data to more accurately identify and determine the target related transaction information, thereby improving the accuracy of risk assessment. Through iterative training of training sets and validation sets, the initial risk assessment model can be adjusted and optimized, the model calculation error can be reduced, and a more accurate target risk assessment model can be generated. The monitoring strategy determined according to the risk level can more specifically monitor and manage the target related transaction information, improve monitoring efficiency and effectiveness, and thereby improve the security of related transaction risk control.

[0057] based on Figure 3 In the embodiment shown, in this embodiment, step S20 includes:

[0058] Determining a risk parameter threshold by using the pre-built target risk assessment model and the target associated transaction information;

[0059] Calculating the target risk parameter of the target associated transaction information by using the pre-built target risk assessment model;

[0060] The risk level is determined based on the target risk parameter and the risk parameter threshold.

[0061] In one embodiment, historical data and expert experience are used to determine the threshold of the risk parameter. This can be achieved through comparison method, fluctuation method and expert consultation method. For example, the median principle method, the mean principle method and the majority principle method can be used to determine the threshold of a single risk indicator. Using a pre-built target risk assessment model, the target associated transaction information is input, and the risk parameter of each transaction information is calculated. This may involve the algorithm of the model, such as a machine learning algorithm, to assess the risk level of the transaction, and determine the risk score or level of each transaction based on the output of the model.

[0062] Compare the calculated target risk parameters with the pre-set risk parameter thresholds. Based on the comparison results, the risks are divided into different levels, such as extremely high risk, high risk, medium risk, low risk, etc. For example, the risk matrix method can be used to evaluate the risk level, where the risk value R is the product of the possibility L and the severity S (R = L × S). According to the risk assessment results, determine the corresponding control measures and response strategies. For example, extremely high risks require immediate measures, while low risks may only require regular monitoring.

[0063] The present embodiment discloses a method, device, equipment and medium for managing related transaction information based on a big model. The method for managing related transaction information based on a big model includes identifying at least one type of initial external transaction information through a preset correlation big model to determine target related transaction information; obtaining historical related transaction information as a training set and a verification set; training the training set through an initial risk assessment model to determine a training set risk parameter of each of the historical related transaction information in the training set; determining a verification set risk parameter according to the verification set, and determining a model calculation error according to the verification set risk parameter and the training set risk parameter; correcting the model calculation error to generate the pre-constructed target risk assessment model; determining a risk parameter threshold through the pre-constructed target risk assessment model and the target related transaction information; calculating a target risk parameter of the target related transaction information through the pre-constructed target risk assessment model; determining the risk level based on the target risk parameter and the risk parameter threshold; determining a target related transaction monitoring strategy according to the risk level, and monitoring and managing the target related transaction information according to the target related transaction monitoring strategy. In the above manner, the present application uses a preset large correlation model to identify potential target related transactions related to at least one type of initial external transaction information, thereby improving the ability to identify hidden risks in complex transaction networks. The target related transaction information is risk assessed by a pre-built target risk assessment model, making risk management more flexible and responsive, and determining the corresponding monitoring strategy based on the assessed risk level, thereby improving the security of related transaction risk control.

[0064] based on Figure 1 In the embodiment shown, in this embodiment, step S30 includes:

[0065] When the risk level is the first risk level, determining the target related-party transaction monitoring strategy as an uninterrupted monitoring strategy;

[0066] When the risk level is the second risk level, determining the target related-party transaction monitoring strategy as a regular monitoring strategy;

[0067] When the risk level is the third risk level, the target related-transaction monitoring strategy is determined as a basic monitoring strategy.

[0068] In a more preferred embodiment, step S30 further includes:

[0069] When the target related-transaction monitoring strategy is determined to be the uninterrupted monitoring strategy, performing enhanced monitoring measures on the target related-transaction information;

[0070] When the target related-party transaction monitoring strategy is determined to be the periodic monitoring strategy, performing standard monitoring measures on the target related-party transaction information;

[0071] When the target related-transaction monitoring strategy is determined to be the basic monitoring strategy, basic monitoring measures are performed on the target related-transaction information.

[0072] In one embodiment, the first risk level corresponds to a high risk level, the second risk level corresponds to a medium risk level, and the third risk level corresponds to a low risk level.

[0073] Through charts, network diagrams, etc., it intuitively displays the relationship and transaction data between enterprises, provides flexible query and screening functions, facilitates users to obtain specific data as needed, and allows users to generate customized reports based on query results

[0074] Based on any of the above embodiments, in this embodiment, before step S10, the following steps are included:

[0075] Acquiring initial enterprise information through crawler technology, wherein the initial enterprise information includes public financial information, enterprise registration information, industry information, news report information and / or legal document information;

[0076] Perform data preprocessing operations on each of the initial enterprise information to generate the initial external transaction information, wherein the preprocessing operations include deduplication operations, padding operations and / or format standardization operations.

[0077] Specifically, in modern enterprise operations, related-party transactions are common business practices, but they may also hide risks such as interest transfer and tax avoidance. Therefore, integrating external related-party transaction data is an important part of risk management and compliance inspection for both regulators and enterprises themselves. This solution aims to integrate external related-party transaction data from different sources through a systematic approach to improve data transparency and strengthen risk control. The data sources and types can be public financial reports, corporate registration information, industry reports, news and announcements, or legal documents. Public financial reports can be annual reports and quarterly reports of listed companies; corporate registration information can be industrial and commercial registration information, including equity structure, executive information, etc.; industry reports can be industry analysis reports and market research reports; news and announcements can be announcements of major events and news reports of enterprises; legal documents can be legal proceedings and judgment documents involving enterprises.

[0078] See also Figure 4 , Figure 4 The embodiment of the present application provides a schematic block diagram of a large-model-based related transaction information management device, which is used to execute the aforementioned large-model-based related transaction information management method. The large-model-based related transaction information management device can be configured on a server.

[0079] like Figure 4 As shown, the large model-based related transaction information management device 400 includes:

[0080] The target associated transaction information determination module 410 is used to identify at least one type of initial external transaction information through a preset association model to determine the target associated transaction information;

[0081] The risk level determination module 420 is used to perform risk assessment on the target associated transaction information through a pre-built target risk assessment model to determine the risk level of the target associated transaction information;

[0082] The monitoring management module 430 is used to determine a target-related transaction monitoring strategy according to the risk level, and monitor and manage the target-related transaction information according to the target-related transaction monitoring strategy.

[0083] Furthermore, the target associated transaction information determination module 410 includes:

[0084] An external transaction information fusion unit, used to fuse the initial external transaction information through an extract-transform-load tool (ETL), and store the fused initial external transaction information in a preset database;

[0085] The target associated transaction information determining unit is used to determine the association of each of the initial external transaction information in the preset database through the built-in association algorithm of the preset association macromodel, and determine the target associated transaction information.

[0086] Furthermore, the large model-based related transaction information management device 400 includes:

[0087] A historical related transaction information acquisition module is used to acquire historical related transaction information as a training set and a verification set;

[0088] A training set risk parameter determination module, used to train the training set through an initial risk assessment model to determine a training set risk parameter for each of the historical associated transaction information in the training set;

[0089] A model calculation error determination module, used to determine a validation set risk parameter according to the validation set, and to determine a model calculation error according to the validation set risk parameter and the training set risk parameter;

[0090] The target risk assessment model generation module is used to correct the model calculation error and generate the pre-built target risk assessment model.

[0091] Furthermore, the risk level determination module 420 includes:

[0092] A risk parameter threshold determination unit, configured to determine a risk parameter threshold by using the pre-built target risk assessment model and the target associated transaction information;

[0093] A target risk parameter determination unit, configured to calculate the target risk parameter of the target associated transaction information by using the pre-built target risk assessment model;

[0094] A risk level determination unit is used to determine the risk level based on the target risk parameter and the risk parameter threshold.

[0095] Furthermore, the monitoring management module 430 includes:

[0096] An uninterrupted monitoring strategy determining unit, configured to determine the target associated transaction monitoring strategy as an uninterrupted monitoring strategy when the risk level is the first risk level;

[0097] a periodic monitoring strategy determining unit, configured to determine the target associated transaction monitoring strategy as a periodic monitoring strategy when the risk level is the second risk level;

[0098] The basic monitoring strategy determining unit is used to determine the target associated transaction monitoring strategy as the basic monitoring strategy when the risk level is the third risk level.

[0099] Furthermore, the monitoring management module 430 includes:

[0100] an enhanced monitoring measure execution unit, configured to execute enhanced monitoring measures on the target associated transaction information when the target associated transaction monitoring strategy is determined to be the uninterrupted monitoring strategy;

[0101] A standard monitoring measure execution unit, configured to execute a standard monitoring measure on the target associated transaction information when the target associated transaction monitoring strategy is determined to be the periodic monitoring strategy;

[0102] The basic monitoring measure execution unit is used to execute basic monitoring measures on the target associated transaction information when the target associated transaction monitoring strategy is determined to be the basic monitoring strategy.

[0103] Furthermore, the large model-based related transaction information management device includes:

[0104] An initial enterprise information acquisition module, used to acquire initial enterprise information through crawler technology, wherein the initial enterprise information includes public financial information, enterprise registration information, industry information, news report information and / or legal document information;

[0105] The initial external transaction information generation module is used to perform data preprocessing operations on each of the initial enterprise information to generate the initial external transaction information, wherein the preprocessing operations include deduplication operations, padding operations and / or format standardization operations.

[0106] It should be noted that those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described device and each module can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0107] The above-mentioned device can be implemented in the form of a computer program. Figure 5 Runs on the computer device shown.

[0108] See also Figure 5 , Figure 5 1 is a schematic block diagram of a computer device provided in an embodiment of the present application. The computer device may be a server.

[0109] See also Figure 5 The computer device includes a processor, a memory and a network interface connected through a system bus, wherein the memory may include a non-volatile storage medium and an internal memory.

[0110] The non-volatile storage medium can store an operating system and a computer program. The computer program includes program instructions, and when the program instructions are executed, the processor can execute any one of the related transaction information management methods based on the big model.

[0111] The processor is used to provide computing and control capabilities and support the operation of the entire computer equipment.

[0112] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor, the processor can execute any one of the related transaction information management methods based on the large model.

[0113] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0114] It should be understood that the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0115] In one embodiment, the processor is used to run a computer program stored in the memory to implement the following steps:

[0116] Identify at least one type of initial external transaction information through a preset correlation model to determine target related transaction information;

[0117] Performing risk assessment on the target associated transaction information through a pre-built target risk assessment model to determine the risk level of the target associated transaction information;

[0118] According to the risk level, a target related transaction monitoring strategy is determined, and the target related transaction information is monitored and managed according to the target related transaction monitoring strategy.

[0119] In one embodiment, the preset correlation model is used to identify at least one type of initial external transaction information and determine the target correlation transaction information, so as to achieve:

[0120] The initial external transaction information is integrated by using an extract-transform-load tool (ETL), and the integrated initial external transaction information is stored in a preset database;

[0121] The built-in correlation algorithm of the preset correlation model is used to determine the correlation of each of the initial external transaction information in the preset database, and to determine the target correlated transaction information.

[0122] In one embodiment, the target-related transaction information is risk assessed by using a pre-built target risk assessment model, and before the risk level of the target-related transaction information is determined, it is used to achieve:

[0123] Obtain historical related transaction information as training sets and validation sets;

[0124] The training set is trained by using an initial risk assessment model to determine a training set risk parameter for each of the historical related transaction information in the training set;

[0125] Determine a validation set risk parameter according to the validation set, and determine a model calculation error according to the validation set risk parameter and the training set risk parameter;

[0126] The model calculation error is corrected to generate the pre-built target risk assessment model.

[0127] In one embodiment, the target-related transaction information is risk assessed by using a pre-built target risk assessment model to determine the risk level of the target-related transaction information, so as to achieve:

[0128] Determining a risk parameter threshold by using the pre-built target risk assessment model and the target associated transaction information;

[0129] Calculating the target risk parameter of the target associated transaction information by using the pre-built target risk assessment model;

[0130] The risk level is determined based on the target risk parameter and the risk parameter threshold.

[0131] In one embodiment, according to the risk level, a target related transaction monitoring strategy is determined to achieve:

[0132] When the risk level is the first risk level, determining the target related-party transaction monitoring strategy as an uninterrupted monitoring strategy;

[0133] When the risk level is the second risk level, determining the target related-party transaction monitoring strategy as a regular monitoring strategy;

[0134] When the risk level is the third risk level, the target related-transaction monitoring strategy is determined as a basic monitoring strategy.

[0135] In one embodiment, the target-related transaction information is monitored and managed according to the target-related transaction monitoring strategy to achieve:

[0136] When the target related-transaction monitoring strategy is determined to be the uninterrupted monitoring strategy, performing enhanced monitoring measures on the target related-transaction information;

[0137] When the target related-party transaction monitoring strategy is determined to be the periodic monitoring strategy, performing standard monitoring measures on the target related-party transaction information;

[0138] When the target related-transaction monitoring strategy is determined to be the basic monitoring strategy, basic monitoring measures are performed on the target related-transaction information.

[0139] In one embodiment, the preset correlation model is used to identify at least one type of initial external transaction information and determine the target correlation transaction information before implementing:

[0140] Acquiring initial enterprise information through crawler technology, wherein the initial enterprise information includes public financial information, enterprise registration information, industry information, news report information and / or legal document information;

[0141] Perform data preprocessing operations on each of the initial enterprise information to generate the initial external transaction information, wherein the preprocessing operations include deduplication operations, padding operations and / or format standardization operations.

[0142] A computer-readable storage medium is also provided in an embodiment of the present application, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and the processor executes the program instructions to implement any one of the large model-based related transaction information management methods provided in the embodiments of the present application.

[0143] The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiment, such as a hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card (Smart Medi a Card, SMC), a secure digital (Secure Digital, SD) card, a flash memory card (Flash Card), etc., equipped on the computer device.

[0144] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A method for managing related transaction information based on a large model, characterized in that: include: Identify at least one type of initial external transaction information through a preset correlation model to determine target related transaction information; Performing risk assessment on the target associated transaction information through a pre-built target risk assessment model to determine the risk level of the target associated transaction information; According to the risk level, a target related transaction monitoring strategy is determined, and the target related transaction information is monitored and managed according to the target related transaction monitoring strategy.

2. The method for managing related transaction information based on a large model according to claim 1, characterized in that: The step of identifying at least one type of initial external transaction information through a preset correlation model and determining target correlated transaction information includes: The initial external transaction information is integrated by using an extract-transform-load tool (ETL), and the integrated initial external transaction information is stored in a preset database; The built-in correlation algorithm of the preset correlation model is used to determine the correlation of each of the initial external transaction information in the preset database, and to determine the target correlated transaction information.

3. The method for managing related transaction information based on a large model according to claim 1, characterized in that: Before performing risk assessment on the target associated transaction information by using the pre-built target risk assessment model to determine the risk level of the target associated transaction information, the method includes: Obtain historical related transaction information as training sets and validation sets; The training set is trained by using an initial risk assessment model to determine a training set risk parameter for each of the historical related transaction information in the training set; Determine a validation set risk parameter according to the validation set, and determine a model calculation error according to the validation set risk parameter and the training set risk parameter; The model calculation error is corrected to generate the pre-built target risk assessment model.

4. The method for managing related transaction information based on a large model according to claim 3, characterized in that: The step of performing risk assessment on the target associated transaction information by using a pre-built target risk assessment model to determine the risk level of the target associated transaction information includes: Determining a risk parameter threshold by using the pre-built target risk assessment model and the target associated transaction information; Calculating the target risk parameter of the target associated transaction information by using the pre-built target risk assessment model; The risk level is determined based on the target risk parameter and the risk parameter threshold.

5. The method for managing related transaction information based on a large model according to claim 1, characterized in that: Determining the target related-transaction monitoring strategy according to the risk level includes: When the risk level is the first risk level, determining the target related-party transaction monitoring strategy as an uninterrupted monitoring strategy; When the risk level is the second risk level, determining the target related-party transaction monitoring strategy as a regular monitoring strategy; When the risk level is the third risk level, the target related-transaction monitoring strategy is determined as a basic monitoring strategy.

6. The method for managing related transaction information based on a large model according to claim 5, characterized in that: The monitoring and managing the target associated transaction information according to the target associated transaction monitoring strategy includes: When the target related-transaction monitoring strategy is determined to be the uninterrupted monitoring strategy, performing enhanced monitoring measures on the target related-transaction information; When the target related-party transaction monitoring strategy is determined to be the periodic monitoring strategy, performing standard monitoring measures on the target related-party transaction information; When the target related-transaction monitoring strategy is determined to be the basic monitoring strategy, basic monitoring measures are performed on the target related-transaction information.

7. The method for managing related transaction information based on a large model according to any one of claims 1 to 6, characterized in that: Before the method of identifying at least one type of initial external transaction information through a preset correlation model and determining target correlated transaction information includes: Acquiring initial enterprise information through crawler technology, wherein the initial enterprise information includes public financial information, enterprise registration information, industry information, news report information and / or legal document information; Perform data preprocessing operations on each of the initial enterprise information to generate the initial external transaction information, wherein the preprocessing operations include deduplication operations, padding operations and / or format standardization operations.

8. A related transaction information management device based on a large model, characterized in that: include: A target associated transaction information determination module, used to identify at least one type of initial external transaction information through a preset association model, and determine the target associated transaction information; A risk level determination module, used to perform risk assessment on the target associated transaction information through a pre-built target risk assessment model to determine the risk level of the target associated transaction information; The monitoring management module is used to determine a target-related transaction monitoring strategy according to the risk level, and monitor and manage the target-related transaction information according to the target-related transaction monitoring strategy.

9. A computer device, characterized in that: The computer device includes a memory and a processor; The memory is used to store computer programs; The processor is used to execute the computer program and implement the related transaction information management method based on a large model as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor implements the method for managing related transaction information based on a large model according to any one of claims 1 to 7.

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