Associated party supervision identifier analysis method and device

Abstract

The invention discloses an associated party supervision identifier analysis method and device, and relates to the technical field of financial supervision, and the method comprises the steps: obtaining a plurality of associated party identity parameters corresponding to a target associated party; wherein each associated party identity parameter corresponds to one or more supervision identification parameters; the supervision identifier parameters comprise timeliness parameters and validity threshold parameters corresponding to different existing supervision identifiers; inputting a plurality of associated party identity parameters corresponding to the target associated party into an identity parameter conduction supervision algorithm model to obtain target supervision identifiers corresponding to different time periods of the target associated party; the target supervision identifier is used for describing an existing supervision identifier which takes effect in each time period; the identity parameter conduction supervision algorithm model is obtained by training a preset machine learning model by using an associated party supervision identifier training sample set based on a gradient descent algorithm. The method is used for improving the accuracy, timeliness and analysis efficiency of supervision identifier analysis of the related party.

Description

Technical Field

[0001] The present invention relates to the field of financial supervision technology, and in particular to a method and device for analyzing supervision identification of related parties. Background Art

[0002] This section is intended to provide a background or context for the presented embodiments of the invention. No description herein is admitted to be prior art by virtue of its inclusion in this section.

[0003] In the operation and management of banks, accurate and timely management of related party regulatory identification is crucial. Since banks are subject to a variety of regulatory calibers, and the definitions of related parties under each caliber are partially different, coupled with the continuous adjustment of regulatory rules, and the related party's identity is constantly changing due to changes in information such as resignation and shareholding, banks face huge challenges in the refined management of related parties.

[0004] At present, the existing regulatory identification analysis solutions for related parties generally achieve relevant functions by establishing a customer identity relationship set based on identity parameters, establishing the connection between all customer groups layer by layer to form a relationship map, and completing the calculation and transmission of the regulatory identification group value of each node in the map based on the relationship map. However, this existing technology has certain limitations.

[0005] The existing automatic configuration function of related party regulatory identification does not take into account the timeliness of regulatory rules. Adjustments to changes in regulatory rules rely on developers to promptly adjust the system's fixed model. If the model is not adjusted in time on the day the rules take effect, the regulatory identification of the related parties under the regulatory caliber cannot be updated in time, affecting the integrity and accuracy of the data submitted by the related parties under the relevant regulatory caliber. A lot of manpower will be required for subsequent maintenance and it is prone to errors.

[0006] There are differences in the scope of related parties defined by different regulatory rules, and the judgment criteria for some specific identities are different. The existing related party calculation model does not distinguish the differences under different regulatory rules, resulting in inaccurate calculated related party regulatory identification.

[0007] The existing automatic configuration function of related party supervision identification is dependent on the timely and accurate update of related party supervision identification due to changes in related party identity. The timely and accurate update of related party supervision identification due to changes in related party identity depends on the timely maintenance of related party identity by users in the system. If the maintenance is delayed, the supervision identification will be incorrect during that period and the update of the supervision identification will be delayed. Summary of the invention

[0008] The embodiment of the present invention provides a method for analyzing the supervision identification of an associated party, which is used to improve the accuracy, timeliness and analysis efficiency of the supervision identification analysis of the associated party. The method includes:

[0009] Acquire multiple related party identity parameters corresponding to the target related party; wherein each related party identity parameter corresponds to one or more regulatory identification parameters; the regulatory identification parameters include time validity parameters and validity threshold parameters corresponding to different existing regulatory identifications; the time validity parameters are used to describe the effective time and expiration time of the existing regulatory identification; the validity threshold parameters are used to describe the regulatory conditions and condition thresholds for the effectiveness of the existing regulatory identification;

[0010] The multiple related party identity parameters corresponding to the target related party are input into the identity parameter transmission supervision algorithm model to obtain the target supervision identifier corresponding to the target related party in different time periods; the target supervision identifier is used to describe the existing supervision identifier effective in each time period; the identity parameter transmission supervision algorithm model is based on the gradient descent algorithm, and is obtained by training a preset machine learning model with a related party supervision identifier training sample set; the related party supervision identifier training sample set includes: historical related party identity parameters of different historical related parties, and historical target supervision identifiers corresponding to different historical related parties.

[0011] The embodiment of the present invention further provides a device for analyzing the supervision mark of an associated party, which is used to improve the accuracy, timeliness and analysis efficiency of the supervision mark analysis of the associated party, and the device includes:

[0012] A related party identity parameter acquisition module is used to acquire multiple related party identity parameters corresponding to the target related party; wherein each related party identity parameter corresponds to one or more regulatory identification parameters; the regulatory identification parameters include time validity parameters and validity threshold parameters corresponding to different existing regulatory identifications; the time validity parameters are used to describe the effective time and expiration time of the existing regulatory identification; the validity threshold parameters are used to describe the regulatory conditions and condition thresholds for the effectiveness of the existing regulatory identification;

[0013] The target supervision identification output module is used to input multiple related party identity parameters corresponding to the target related party into the identity parameter transmission supervision algorithm model to obtain the target supervision identification corresponding to the target related party in different time periods; the target supervision identification is used to describe the existing supervision identification effective in each time period; the identity parameter transmission supervision algorithm model is based on the gradient descent algorithm, and is obtained by training a preset machine learning model with a related party supervision identification training sample set; the related party supervision identification training sample set includes: historical related party identity parameters of different historical related parties, and historical target supervision identifications corresponding to different historical related parties.

[0014] An embodiment of the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method for analyzing the regulatory identification of related parties when executing the computer program.

[0015] An embodiment of the present invention 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 method for analyzing the regulatory identification of related parties is implemented.

[0016] An embodiment of the present invention further provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the above-mentioned method for analyzing the regulatory identification of related parties is implemented.

[0017] In an embodiment of the present invention, multiple associated party identity parameters corresponding to the target associated party are obtained; wherein each associated party identity parameter corresponds to one or more regulatory identification parameters; the regulatory identification parameters include time validity parameters and validity threshold parameters corresponding to different existing regulatory identifications; the time validity parameters are used to describe the effective time and the invalid time of the existing regulatory identification; the validity threshold parameters are used to describe the regulatory conditions and condition thresholds for the effectiveness of the existing regulatory identification;

[0018] The multiple related party identity parameters corresponding to the target related party are input into the identity parameter transmission supervision algorithm model to obtain the target supervision identifier corresponding to the target related party in different time periods; the target supervision identifier is used to describe the existing supervision identifier that is effective in each time period; the identity parameter transmission supervision algorithm model is based on the gradient descent algorithm, and is obtained by training the preset machine learning model with the related party supervision identifier training sample set; the related party supervision identifier training sample set includes: historical related party identity parameters of different historical related parties, and historical target supervision identifiers corresponding to different historical related parties. The embodiment of the present invention clarifies the effective time and expiration time of the existing supervision identifier by setting the timeliness parameter. When the supervision rules change, it can be timely judged whether the supervision identifier needs to be updated based on the timeliness parameter instead of relying on manual intervention. The identity parameter transmission supervision algorithm model can flexibly adapt to the changes in supervision rules, ensuring that the supervision identifier of the target related party can be quickly updated when the supervision rules change, thereby solving the problem of untimely update of the supervision identifier; by setting the validity threshold parameter, the supervision conditions and condition thresholds for the effectiveness of the existing supervision identifier are limited, so that when calculating the target supervision identifier, it can accurately judge the differentiated regulations of different regulators, and the machine The machine learning model can learn the accurate calculation logic under different regulatory rules, thereby improving the accuracy of regulatory identification calculation and overcoming the defects of inaccurate calculation in existing technologies. In addition, by constructing an identity parameter transmission regulatory algorithm model, it can automatically calculate the target regulatory identification corresponding to different time periods based on the input related party identity parameters, reducing the reliance on users to maintain the related party identities in the system in a timely manner. Even if the related party identity changes, the regulatory identification can be quickly recalculated based on the new related party identity parameters and algorithm model, avoiding regulatory identification errors or untimely updates caused by user maintenance delays, and effectively solving the problem of related party identity maintenance affecting the accuracy of regulatory identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0020] Figure 1 A flowchart of a method for analyzing a supervisory identification of an associated party in an embodiment of the present invention;

[0021] Figure 2 This is an example diagram of the relationship between an associated party identity parameter and a regulatory identifier in an embodiment of the present invention;

[0022] Figure 3This is a specific example diagram of the conduction logic of an identity parameter conduction supervision algorithm model in an embodiment of the present invention;

[0023] Figure 4 This is a specific example diagram of obtaining multiple associated party identity parameters corresponding to a target associated party in an embodiment of the present invention;

[0024] Figure 5 A specific schematic diagram of an identity parameter conduction supervision algorithm model obtained by training in an embodiment of the present invention;

[0025] Figure 6 It is a structural schematic diagram of a supervisory identification analysis device of an associated party in an embodiment of the present invention;

[0026] Figure 7 A schematic diagram of a computer device used for analyzing the regulatory identification of an associated party in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] To make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0028] The term "and / or" herein only describes an association relationship, indicating that three relationships may exist. For example, A and / or B may represent the following three situations: A exists alone, A and B exist at the same time, and B exists alone. In addition, the term "at least one" herein represents any combination of at least two of any one or more of a plurality of. For example, including at least one of A, B, and C may represent including any one or more elements selected from the set consisting of A, B, and C.

[0029] In the description of this specification, the terms "include", "including", "have", "contain", etc. are all open terms, which mean including but not limited to. The descriptions with reference to the terms "one embodiment", "a specific embodiment", "some embodiments", "for example", etc. mean that the specific features, structures or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures or characteristics described may be combined in a suitable manner in any one or more embodiments or examples. The order of steps involved in each embodiment is used to schematically illustrate the implementation of the present application, and the order of steps is not limited and can be appropriately adjusted as needed.

[0030] The acquisition, storage, use, and processing of data in the technical solution of this application are in compliance with the relevant provisions of national laws and regulations. The information collected in this application is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure, and application of relevant data are in compliance with the relevant laws, regulations, and standards of relevant countries and regions, and necessary confidentiality measures are taken, which do not violate public order and good customs, and provide corresponding operation entrances for users to choose to authorize or refuse. In addition, this application provides users with corresponding operation entrances for users to choose to agree or refuse the results of automated decision-making. If the user chooses to refuse, they can enter the expert decision-making process.

[0031] It should be noted that in the embodiments of the present application, some existing solutions in the industry such as certain software, components, models, etc. may be mentioned. For example, some existing software tools, components, algorithm models or other well-known solutions in other technical fields may be cited. They should be considered as exemplary, and their purpose is only to illustrate the feasibility of the implementation of the technical solution of the present application. These references should be understood as typical examples, and their core purpose is to explain and verify the rationality and feasibility of the implementation of the technical solution proposed in the present application. However, it does not mean that the applicant has or will inevitably use the solution. Such references do not imply that the applicant has actually adopted these existing solutions, or will inevitably adopt these methods in the future during its technical implementation. In other words, these references only serve to illustrate the nature of the description, to help understand the connection and transcendence between the innovation of the present application and the prior art, and do not constitute an endorsement or reliance statement on a specific prior art product.

[0032] The embodiments of the present invention involve the following terms, which are explained as follows:

[0033] Related party: a natural person, legal person or unincorporated organization that has a relationship with a bank as defined in the regulatory rules that the bank needs to comply with;

[0034] Transactions involving related parties: refers to the transfer of resources, services, obligations or other interests between a bank and its related parties;

[0035] Related party identity: The relationship between superior and subordinate related parties as defined by the regulator.

[0036] Since banks are subject to multiple regulatory calibers and there are some differences in the definition of related parties under each caliber, and the rules are constantly adjusted, in addition, the identities of related parties are constantly changing due to changes in information such as resignation or shareholding, which requires a high level of refined management and timeliness of user maintenance. Therefore, there is a problem of untimely and inaccurate updating of regulatory identification due to delays in related party maintenance or changes in regulatory rules.

[0037] (1) The existing automatic transmission function of the regulatory identification of related parties does not take into account the timeliness of regulatory rules. If the rules of a certain regulatory party change, if the system does not promptly amend the existing regulatory identification calculation model when the new regulatory rules take effect, the regulatory identification of the related parties in the system will not be updated in time. The automatic configuration function of the regulatory identification of related parties does not take into account the impact of three factors on the calculation of regulatory identification: the timeliness of regulatory rules, the differences in the definitions of related parties with the same identity under different regulatory standards, and the timeliness of related party identity maintenance. There will be problems such as inaccurate calculation of regulatory identification and untimely updates;

[0038] (2) The existing automatic configuration function of the related party supervision mark does not make special treatment for the differentiated provisions of different regulators. When different regulatory calibers have different definitions of related parties with the same identity, the calculation of the supervision mark on the related party is not accurate. There are differences in the scope of related parties defined by different regulatory rules, and the judgment standards for some specific identities are different. For example, regulator "A" stipulates that only natural persons with a shareholding ratio of more than 5% are considered related parties, but regulator "B" stipulates that only natural persons with a shareholding ratio of more than 10% are considered related parties. Under the existing calculation model of related party regulators, the differences under different regulatory rules are not distinguished, resulting in inaccurate calculation of related party supervision marks.

[0039] (3) The existing automatic configuration function of the regulatory identification of related parties is dependent on the timely and accurate update of the regulatory identification of related parties due to changes in the identity of related parties. The timely and accurate update of the regulatory identification of related parties due to changes in the identity of related parties depends on the timely maintenance of the identity of related parties in the system by users. If the maintenance is delayed, the regulatory identification will be incorrect during that period and the update of the regulatory identification will be delayed. The relationship between an associated party and its superior associated party is constantly changing, and different relationships may transmit different regulatory identifications. Under the existing technology, it is impossible to set the validity period of the relationship. There are only two processing methods for the existing and entered relationships: retain or delete. If retained, the regulatory identification transmitted by the relationship will always exist; if deleted, the regulatory identification that should actually be possessed in the corresponding historical stage will be lost. Both processing will cause errors in the calculation of the regulatory identification, which is inconsistent with the actual situation.

[0040] In order to solve the above problems, firstly, the time dimension and regulatory-specific judgment rules are added to the identity parameters, and when entering the related parties, the user is required to enter the valid time interval of the related party identity to increase the judgment factor. At the same time, a set of identity parameter regulatory identification transmission model calculation is built to realize the measurement of regulatory identity parameter transmission, thereby improving the accuracy and timeliness of the calculation of the related party regulatory identification, reducing the dependence on the timeliness of user operations, further improving the refined management of related parties, and improving the quality of regulatory data reported by banks. Specifically, the embodiment of the present invention provides a method for analyzing the regulatory identification of related parties to improve the accuracy, timeliness and analysis efficiency of the regulatory identification analysis of related parties, see Figure 1, the method may include:

[0041] Step 101: Acquire multiple related party identity parameters corresponding to the target related party; wherein each related party identity parameter corresponds to one or more regulatory identification parameters; the regulatory identification parameters include time validity parameters and validity threshold parameters corresponding to different existing regulatory identifications; the time validity parameters are used to describe the effective time and expiration time of the existing regulatory identification; the validity threshold parameters are used to describe the regulatory conditions and condition thresholds for the effectiveness of the existing regulatory identification;

[0042] Step 102: Input multiple affiliate identity parameters corresponding to the target affiliate into the identity parameter transmission supervision algorithm model to obtain the target supervision identifier corresponding to the target affiliate in different time periods; the target supervision identifier is used to describe the existing supervision identifier effective in each time period; the identity parameter transmission supervision algorithm model is based on the gradient descent algorithm, and is obtained by training a preset machine learning model with an affiliate supervision identifier training sample set; the affiliate supervision identifier training sample set includes: historical affiliate identity parameters of different historical affiliates, and historical target supervision identifiers corresponding to different historical affiliates.

[0043] In an embodiment of the present invention, multiple associated party identity parameters corresponding to the target associated party are obtained; wherein each associated party identity parameter corresponds to one or more regulatory identification parameters; the regulatory identification parameters include time validity parameters and validity threshold parameters corresponding to different existing regulatory identifications; the time validity parameters are used to describe the effective time and the invalid time of the existing regulatory identification; the validity threshold parameters are used to describe the regulatory conditions and condition thresholds for the effectiveness of the existing regulatory identification;

[0044] The multiple related party identity parameters corresponding to the target related party are input into the identity parameter transmission supervision algorithm model to obtain the target supervision identifier corresponding to the target related party in different time periods; the target supervision identifier is used to describe the existing supervision identifier that is effective in each time period; the identity parameter transmission supervision algorithm model is based on the gradient descent algorithm, and is obtained by training the preset machine learning model with the related party supervision identifier training sample set; the related party supervision identifier training sample set includes: historical related party identity parameters of different historical related parties, and historical target supervision identifiers corresponding to different historical related parties. The embodiment of the present invention clarifies the effective time and expiration time of the existing supervision identifier by setting the timeliness parameter. When the supervision rules change, it can be timely judged whether the supervision identifier needs to be updated based on the timeliness parameter instead of relying on manual intervention. The identity parameter transmission supervision algorithm model can flexibly adapt to the changes in supervision rules, ensuring that the supervision identifier of the target related party can be quickly updated when the supervision rules change, thereby solving the problem of untimely update of the supervision identifier; by setting the validity threshold parameter, the supervision conditions and condition thresholds for the effectiveness of the existing supervision identifier are limited, so that when calculating the target supervision identifier, it can accurately judge the differentiated regulations of different regulators, and the machine The machine learning model can learn the accurate calculation logic under different regulatory rules, thereby improving the accuracy of regulatory identification calculation and overcoming the defects of inaccurate calculation in existing technologies. In addition, by constructing an identity parameter transmission regulatory algorithm model, it can automatically calculate the target regulatory identification corresponding to different time periods based on the input related party identity parameters, reducing the reliance on users to maintain the related party identities in the system in a timely manner. Even if the related party identity changes, the regulatory identification can be quickly recalculated based on the new related party identity parameters and algorithm model, avoiding regulatory identification errors or untimely updates caused by user maintenance delays, and effectively solving the problem of related party identity maintenance affecting the accuracy of regulatory identification.

[0045] During specific implementation, step 101 is first performed: multiple related party identity parameters corresponding to the target related party are obtained; wherein each related party identity parameter corresponds to one or more regulatory identification parameters; the regulatory identification parameters include time validity parameters and validity threshold parameters corresponding to different existing regulatory identifications; the time validity parameters are used to describe the effective time and expiration time of the existing regulatory identification; the validity threshold parameters are used to describe the regulatory conditions and condition thresholds for the effectiveness of the existing regulatory identification.

[0046] In the embodiment, first, it is necessary to comprehensively and accurately extract the definition information formulated by each supervisory party for the related party from the preset supervision rule library.

[0047] The definitions of related parties extracted from various regulatory parties are broken down in detail to obtain the identity parameters of each related party. These related party identity parameters include but are not limited to shareholder identity, job relationship identity, and kinship identity. For example, shareholder identity parameters can reflect the shareholding of related parties in the enterprise, including information such as shareholding ratio and shareholding duration; job relationship identity parameters can reflect the characteristics of the related party's job level, scope of responsibilities, etc. within the enterprise or between related enterprises; kinship identity parameters clarify the relationship between the related party and other related parties based on kinship.

[0048] For each of the related party identity parameters obtained through splitting, it is necessary to further determine the timeliness parameters in the corresponding regulatory identification parameters based on the effective time of the transaction management-related systems of the related parties of each regulatory party. The timeliness parameters are specifically used to accurately describe the effective time and expiration time of the existing regulatory identification. Taking a certain regulatory identification as an example, its timeliness parameters may clearly stipulate that it will take effect from X / X / XX and expire on AA / AA / AA / AA. Through such a clear timeliness definition, it can ensure that at different time points, the regulatory identification applicable to the related parties can maintain a high degree of consistency with the timeliness of the regulatory rules, avoiding the incorrect or outdated application of the regulatory identification due to time factors.

[0049] In the processing of the correspondence between the related party identity parameters and the regulatory identification, if it is found that the name and meaning of a certain related party identity parameter defined by different regulators are the same, it is necessary to determine the timeliness parameters and validity threshold parameters corresponding to multiple regulatory identifications for the related party identity parameter. For example, for the related party identity parameter of "major shareholder", different regulatory identifications may correspond to different regulatory identifications under the regulations of different regulators, and each regulatory identification has its own timeliness parameters and validity threshold parameters. Among them, the validity threshold parameters are used to describe in detail the regulatory conditions and condition thresholds for the effectiveness of existing regulatory identifications. For the shareholder identity parameter, its validity threshold parameter may include the shareholding ratio threshold, such as a regulatory identification that stipulates that the regulatory identification takes effect when the shareholding ratio reaches 5% or more; for the position relationship identity parameter, the validity threshold parameter may be reflected as the position level threshold, such as stipulating that a specific regulatory identification is applicable to senior management level and above; the validity threshold parameter of the kinship identity parameter may be the kinship type, such as the kinship is applicable to a certain regulatory identification, etc.

[0050] In one embodiment, the related party identity parameters include but are not limited to shareholder identity, job relationship identity and kinship identity; the validity threshold parameters of each related party identity parameter include but are not limited to shareholding ratio threshold, job level threshold, and kinship type.

[0051] In a specific embodiment of the related party regulatory identification analysis method of the present invention, the related party identity parameters cover multiple types, including but not limited to shareholder identity, position relationship identity and kinship relationship identity. These different types of related party identity parameters each have unique characteristics, and their corresponding validity threshold parameters also have clear definitions and diverse expressions.

[0052] The shareholder identity parameter is mainly used to characterize the shareholding of related parties in the enterprise. This parameter is of key significance in the process of determining the regulatory identification of related parties. Its effectiveness threshold parameter includes the shareholding ratio threshold, and different shareholding ratio thresholds correspond to different regulatory identifications. For example, under certain regulatory requirements, when the shareholding ratio reaches or exceeds 5%, the related party may be regarded as a shareholder with a specific influence, thereby triggering a series of corresponding regulatory identifications. This means that the shareholder may have a certain say in corporate decision-making, related party-related transactions, etc., and needs to be subject to stricter supervision. In other regulatory scenarios, it may be stipulated that a specific regulatory identification is applicable only when the shareholding ratio reaches 10% or higher, which reflects the differentiated judgment criteria of different regulators on the influence of shareholders based on factors such as enterprise scale and industry characteristics. By using a clear shareholding ratio threshold as the effectiveness threshold parameter, it is possible to accurately define the regulatory identification corresponding to the shareholder identity parameter in different regulatory environments, thereby realizing refined supervision of shareholder related parties.

[0053] The position relationship identity parameter focuses on reflecting the position hierarchy and scope of responsibilities of the related party within the enterprise or with the related enterprises. Its effectiveness threshold parameter is reflected in the position level threshold. For example, in financial institutions, senior managers (such as presidents, vice presidents, department general managers, etc.) have higher position levels, and their decisions and behaviors may have a significant impact on the operation of the enterprise and transactions involving related parties. When the position level of the related party reaches the level of senior management, according to regulatory regulations, a specific regulatory mark will be triggered, requiring more stringent review and information disclosure of transactions involving related parties involved. For middle-level managers or grassroots employees, their position levels are lower, and the applicable regulatory marks and regulatory requirements are relatively loose. This effectiveness threshold parameter setting based on the position level threshold can reasonably allocate regulatory resources according to the position influence of the related party in the enterprise, ensure the pertinence and effectiveness of supervision, and prevent potential interest transfer or risk hazards caused by position relationships.

[0054] The kinship identity parameter focuses on the relationship between the related party and other related parties based on kinship. Its effectiveness threshold parameter is mainly the type of kinship. In business operations, certain kinship relationships may lead to potential conflicts of interest or transaction risks involving related parties. For example, kinship relationships (such as parents, children, spouses, etc.) are usually regarded as a relatively close type of kinship and may be the focus of supervision. When there is a kinship between related parties, once the related parties are involved in transactions, more stringent approval procedures and information disclosure requirements may need to be met, and specific regulatory identification will be given accordingly. For other kinship relationships (such as brothers and sisters, uncles and nephews, etc.), the regulatory requirements and corresponding regulatory identification will also be different depending on the distance of their relationship and the degree of possible impact. By clarifying the type of kinship as the effectiveness threshold parameter, it is possible to effectively identify and manage the transaction risks involving related parties caused by kinship, and maintain market fairness and transparency.

[0055] In practical applications, by accurately identifying and applying these diverse related party identity parameters and their validity threshold parameters, combined with the subsequent identity parameter transmission supervision algorithm model, it is possible to achieve accurate analysis and dynamic management of related party supervision identification, adapt to the complex and changing regulatory environment and business conditions, and improve the scientificity and effectiveness of related party supervision.

[0056] Figure 4 FIG. 1 is a specific example diagram of obtaining multiple associated party identity parameters corresponding to a target associated party in an embodiment of the present invention. In one embodiment, Figure 4 As shown, multiple associated party identity parameters corresponding to the target associated party are obtained, including:

[0057] Step 401: Select corresponding affiliate identity parameters for the target affiliate according to the actual relationship between the target affiliate and the affiliate management entity or the superior affiliate;

[0058] Step 402: extracting association-related information of different supervisors to the target associated party from a preset supervisory rule library;

[0059] Step 403: splitting the association-related information of each supervisory party to the target associated party into associated party identity parameters corresponding to the supervisory structure;

[0060] Step 404: for each existing supervisory identifier in the identity parameter of the associated party, determine the timeliness parameters and validity threshold parameters of different existing supervisory identifiers according to the associated related information of the supervisor.

[0061] In a specific embodiment of the method for analyzing the associated party supervision identification of the present invention, the process of obtaining multiple associated party identity parameters corresponding to the target associated party involves multiple key steps, which will be described in detail below.

[0062] First, it is necessary to deeply explore the actual relationship between the target related party and the related party management entity or the superior related party. This actual relationship covers a variety of possible forms, such as equity relationship, business cooperation relationship, management control relationship, etc.

[0063] Based on the accurate understanding of these relationships, select the corresponding related party identity parameters for the target related party. If the target related party is a shareholder of a certain enterprise, its shareholding ratio reaches a certain level, and it has a certain influence on the enterprise decision-making, then according to this actual situation, the related party identity parameters related to the shareholder identity should be selected, such as the shareholder identity parameters. If the target related party is a manager within the enterprise and plays an important role in the business process or resource allocation, it is necessary to select the position relationship identity parameters to accurately reflect its position and role in the enterprise structure. If the target related party has a family relationship with the enterprise, and this relationship may affect the enterprise's business decisions or the related party is involved in transactions, the family relationship identity parameters become a suitable choice. By carefully selecting the related party identity parameters based on the actual relationship, the accuracy and pertinence of the subsequent analysis can be ensured.

[0064] Next, comprehensively extract the association-related information of different regulators on the target related parties from the preset regulatory rule base. The regulatory rule base integrates various rules, regulations and guidelines issued by many authoritative regulators, such as various rules and regulations issued by different financial regulators. These information sources provide a solid basis for accurately defining the identity parameters of the target related parties. The information extracted from the regulatory rule base includes but is not limited to the regulator's definition of related parties, classification standards, regulatory requirements, and condition settings related to specific identities.

[0065] The information on the target related parties obtained from each regulator is carefully split to obtain the related party identity parameters corresponding to the regulatory structure. Taking the regulations of the Financial Regulatory Bureau on the related parties of banking and insurance institutions as an example, its definition of related parties may include multiple situations, such as shareholders holding a certain proportion of shares, legal persons or natural persons with controlling relations, key managers and their close relatives, etc. In the splitting process, these different situations are converted into specific related party identity parameters, such as shareholder identity parameters (corresponding to shareholders holding a certain proportion of shares), position relationship identity parameters (corresponding to key managers) and kinship relationship identity parameters (corresponding to close relatives), etc. Through this precise splitting operation, the abstract regulatory provisions are converted into operational related party identity parameters, providing a clear basis for subsequent analysis and calculation.

[0066] For the existing regulatory identifiers in each related party identity parameter, the timeliness parameters and validity threshold parameters of different existing regulatory identifiers are determined in depth based on the related information of the regulator. For the timeliness parameters, it is necessary to strictly refer to the effective and invalid time points specified by the regulator. For example, if a regulatory identifier is specified to be effective from a specific date and expire on another date, this information will be accurately recorded as the timeliness parameter of the regulatory identifier. As for the validity threshold parameters, they are determined according to the conditions set by the regulator for different identity parameters. For example, for the shareholder identity parameter, the regulator may specify that the shareholding ratio reaches 8% as a threshold. When the shareholding ratio exceeds this threshold, the specific regulatory identifier takes effect. At this time, 8% is the validity threshold parameter (shareholding ratio threshold) of the regulatory identifier; for the position relationship identity parameter, if the regulator stipulates that department managers and above are subject to a certain regulatory identifier, the "department manager" level becomes the corresponding validity threshold parameter (position level threshold); for the kinship identity parameter, if it is specified that kinship triggers a specific regulatory identifier, "kinship" is its validity threshold parameter (kinship type). Through such a rigorous determination process, it is ensured that the regulatory identification corresponding to each related party's identity parameters can be accurately defined in terms of time validity and conditional applicability, thereby providing strong support for accurate regulatory identification analysis.

[0067] In the entire process of obtaining multiple related party identity parameters corresponding to the target related parties, each step is closely linked and interdependent, together forming a complete and rigorous system, which lays a solid foundation for the subsequent use of the identity parameter transmission supervision algorithm model to accurately calculate the target supervision identification corresponding to the target related parties in different time periods, and helps to achieve refined and scientific management of related party supervision, and effectively respond to complex and changeable regulatory environments and business conditions.

[0068] During specific implementation, after performing step 101: obtaining multiple related party identity parameters corresponding to the target related party; wherein each related party identity parameter corresponds to one or more regulatory identification parameters, performing step 102: inputting the multiple related party identity parameters corresponding to the target related party into the identity parameter transmission regulatory algorithm model to obtain the target regulatory identification corresponding to the target related party in different time periods; the target regulatory identification is used to describe the existing regulatory identification effective in each time period; the identity parameter transmission regulatory algorithm model is based on the gradient descent algorithm, and is obtained by training a preset machine learning model with a related party regulatory identification training sample set; the related party regulatory identification training sample set includes: historical related party identity parameters of different historical related parties, and historical target regulatory identifications corresponding to different historical related parties.

[0069] In the embodiment, the identity parameter transmission supervision algorithm model is constructed based on the gradient descent algorithm, which plays an important role in optimizing the model parameters. By training the pre-set machine learning model with the related party supervision identification training sample set, the model can learn the complex relationship between the related party identity parameters and the supervision identification. The related party supervision identification training sample set contains rich historical information, among which the historical related party identity parameters of different historical related parties and the historical target supervision identification corresponding to different historical related parties are key components. These historical data are derived from the actual supervision scenarios and the related party management records in the operation process of the enterprise, and are highly authentic and representative. For example, the historical related party identity parameters may cover the detailed identity information of various related parties (such as major shareholders, senior managers, relatives, etc.) in different types of enterprises (such as financial institutions, manufacturing enterprises, service enterprises, etc.), including specific data such as shareholding ratio, job level, and type of relatives. The historical target supervision identification clearly records the accurate supervision identification applicable to these related parties in the corresponding historical period. By integrating and analyzing a large amount of such historical data, a solid data foundation is provided for model training, enabling it to accurately predict the corresponding target regulatory identification based on the input related party identity parameters when facing new target related parties.

[0070] Figure 5 FIG. 1 is a specific schematic diagram of an algorithm model for obtaining identity parameter conduction supervision through training in an embodiment of the present invention. In one embodiment, Figure 5 As shown, it also includes: obtaining the identity parameter conduction supervision algorithm model by training as follows:

[0071] Step 501: Obtain a training sample set of related party supervision identification; randomly initialize the weight and bias parameters of a preset machine learning model;

[0072] Step 502: presetting a loss function of the machine learning model; the loss function is used to measure the difference between the target regulatory identifier predicted by the model and the actual regulatory identifier in the sample set;

[0073] Step 503: Using the training sample set of related party supervision identification, adopting the gradient descent algorithm as the optimization algorithm, and iteratively updating the randomly initialized machine learning model to gradually reduce the value of the loss function;

[0074] Step 504: Repeat the above iterative update operation until the iteratively updated model converges or reaches a predetermined number of iterations; the model obtained by stopping the iterative update is used as the trained identity parameter conduction supervision algorithm model.

[0075] For example, the training process of the identity parameter transmission supervision algorithm model includes the following steps:

[0076] 1. Acquisition of training sample set of related party supervision identification

[0077] In order to build an effective identity parameter transmission supervision algorithm model, the first task is to obtain a training sample set of related party supervision identification. The data source of this sample set is mainly historical supervision data accumulated over a long period of time. These historical data involve enterprises in many industries, including but not limited to various corporate entities in the fields of finance, manufacturing, and services. From the supervision records of these enterprises, we deeply explore and screen out detailed information of different historical related parties, and then form historical related party identity parameters. These parameters comprehensively cover the various relationship characteristics of related parties in the enterprise, such as the identity of shareholders, accurately record the shareholding ratio, nature of shares, and length of shareholding; in terms of job relationship identity, clearly record key elements such as job level, department, and scope of responsibilities; in terms of kinship identity, detail the type of kinship, the way relatives are related to the enterprise, and the potential influence on the enterprise decision-making. At the same time, for each historical related party, accurately obtain the historical target supervision identification that was actually applicable at the time. These identifications reflect the judgment made by the regulator at the time based on the actual situation of the enterprise and the regulatory rules. By systematically integrating and carefully organizing a large amount of multi-dimensional historical data, a rich and widely representative training sample set of related party supervision identification is constructed, providing a solid data foundation for subsequent model training.

[0078] 2. Initialization of machine learning model parameters

[0079] After obtaining the training sample set, start to initialize the preset machine learning model. Random initialization is used for the weights and bias parameters in the model. The random initialization process assigns initial values ​​to these parameters within a specific numerical range. The significance of this operation is to break the symmetry of the initial state of the model and avoid the model falling into the dilemma of local optimal solution at the beginning of training. By introducing randomness, it is ensured that the model has sufficient exploration space in the subsequent training process and can fully learn the complex relationships and potential patterns contained in the data. Different initial weights and bias values ​​enable the model to learn and adjust in a variety of ways when facing the same training data, thereby increasing the possibility of finding the global optimal solution or a solution close to the optimal solution, laying the foundation for subsequent accurate model training.

[0080] 3. Presetting of loss function

[0081] Next, the loss function of the machine learning model is preset. This loss function plays a vital role in model training. Its core function is to measure the degree of difference between the target regulatory identification predicted by the model and the actual regulatory identification in the sample set. Specifically, the loss function quantifies this difference through precise mathematical calculations. The common construction method is based on the combination of the mean square error term and the weight decay term. The calculation formula of the mean square error term usually involves summing and averaging the square of the difference between the model prediction value and the actual value, which intuitively reflects the degree of deviation between the prediction result and the actual situation. During the training process, the model continuously adjusts the parameters to minimize the value of the mean square error term, thereby improving the accuracy of the prediction. The weight decay term is a regularization method introduced to prevent the model from overfitting. Its role is to constrain the model weights to avoid excessive weights that cause the model to be too complex and lose the ability to generalize to new data. By reasonably setting the coefficient of the weight decay term, a balance can be achieved between model complexity and generalization ability, ensuring that the model not only performs well on training samples, but also accurately predicts regulatory identification when facing new related party data.

[0082] 4. Model iterative update based on gradient descent algorithm

[0083] Based on the obtained training sample set of related party regulatory identification, the gradient descent algorithm is used as the optimization algorithm to iteratively update the randomly initialized machine learning model. In each iteration, the model first calculates the related party identity parameters in the training sample set according to the current weights and bias parameters to obtain the predicted target regulatory identification. Then, these predicted values ​​and the actual regulatory identification in the sample set are substituted into the loss function to calculate the value of the loss function. Next, the direction and amplitude of parameter adjustment are determined by calculating the gradient of the loss function to the model weights and bias parameters. The gradient descent algorithm updates the model parameters at a pre-set learning rate based on the reverse direction of the gradient. The choice of learning rate is crucial. If it is too large, the model may fail to converge or even diverge, while if it is too small, the training process will become lengthy. In this way, the model gradually adjusts the parameters in each iteration to gradually reduce the value of the loss function. By repeating this iterative update operation, the model continuously optimizes its own parameters to better fit the relationship between the related party identity parameters and regulatory identification in the training data.

[0084] 5. Model convergence judgment and final determination

[0085] In the process of continuous iterative updates, it is necessary to constantly judge the state of the model. Repeat the above iterative update operation until one of the following two conditions is met: First, the model converges after iterative updates, that is, the loss function value of the model no longer changes significantly in multiple consecutive iterations, or the change amplitude is less than the preset threshold, indicating that the model has learned the stable pattern in the data and reached a relatively optimal state; second, the predetermined number of iterations is reached. Even if the model has not yet fully converged, in order to avoid over-training or too long training time, the training is stopped when the predetermined number of iterations is reached. When one of the above conditions is met, the model obtained by stopping the iterative update is determined as the trained identity parameter conduction supervision algorithm model. After a rigorous training process, the model has the ability to accurately predict the target regulatory identification based on the input related party identity parameters, and can provide reliable technical support for subsequent related party regulatory identification analysis, and effectively respond to complex and changeable related party supervision scenarios.

[0086] In one embodiment, the identity parameter conduction supervision algorithm model includes:

[0087] An input layer, for receiving multiple associated party identity parameters corresponding to the target associated party;

[0088] The transmission layer is used to perform nonlinear transformation on the inputted associated party identity parameters, and to map and translate the changes through weights and offsets, so as to transmit the regulatory identification of the associated party identity parameters to the target associated party, and to output the target regulatory identification corresponding to the target associated party in different time periods through the output layer; the transmission layer includes a plurality of transmission layer nodes, and each transmission layer node represents a state in the transmission process of the regulatory identification of the associated party identity parameters;

[0089] The output layer is used to output the target supervision identification corresponding to the target related party in different time periods.

[0090] In the above embodiment, in the method for analyzing the associated party regulatory identification of the present invention, the identity parameter transmission regulatory algorithm model is the core component for realizing accurate regulatory identification analysis, and its specific structure and function show a high degree of systematicity and logic in one embodiment.

[0091] The input layer in the model undertakes the key task of receiving multiple related party identity parameters corresponding to the target related party. These related party identity parameters are the basic data for subsequent analysis and cover a wealth of information.

[0092] For example, when the target related party is a shareholder of a certain enterprise, the related party identity parameters received by the input layer may include shareholding ratio, nature of shares (such as common stock, preferred stock, etc.), shareholding duration and other parameters related to the identity of the shareholder. If the target related party is an internal manager of the enterprise, the input layer will receive job relationship identity parameters such as job level, department, and scope of responsibilities. For related parties with kinship relationships, the parameters received by the input layer include kinship identity parameters such as kinship type (such as relatives, other relatives, etc.), and the way relatives are related in the enterprise (such as indirect connection through equity, direct participation in business management, etc.). The input layer has a high degree of compatibility and data processing capabilities, and can accurately identify and receive related party identity parameters of various types and formats to ensure the integrity and accuracy of the data, providing a basis for subsequent processing in the transmission layer.

[0093] The transmission layer is the core operation area of ​​the model, which is responsible for complex nonlinear transformation of the input related party identity parameters, and mapping and translation changes through weights and offsets, so as to achieve the key function of transmitting the regulatory identification of the related party identity parameters to the target related party. The transmission layer contains multiple transmission layer nodes, each of which represents a state in the transmission process of the regulatory identification of a related party identity parameter. During the transmission process, the related party identity parameters received from the input layer first enter the first node of the transmission layer. Each node performs weighted calculation on the input parameters according to the preset weights, and the weight value reflects the relative importance of the related party identity parameters represented by the node in the overall regulatory identification transmission process.

[0094] For example, in some regulatory scenarios, the shareholding ratio may have a higher weight in determining the regulatory identification, while the weight of the kinship type is relatively low. After weighted calculation, the node uses an activation function (such as the sigmoid function, etc.) to perform a nonlinear transformation on the result. This nonlinear transformation capability enables the model to handle the nonlinear relationship between complex related party identity parameters and regulatory identification, and enhances the model's adaptability to actual regulatory conditions. The result after nonlinear transformation is then translated according to the offset to further adjust the distribution and characteristics of the data. Then, the processed data is passed to the next node through the connection weights between the nodes, and so on, layer by layer, with multiple rounds of calculation and transmission in the transmission layer. When processing data, each node not only considers its own input and parameters, but is also affected by the output of the previous node, and also affects the input of the subsequent node, forming a dynamic, interconnected transmission network. In this way, the transmission layer gradually integrates and optimizes the information in the related party identity parameters, converts it into intermediate results closely related to the regulatory identification, and finally transmits these results to the output layer.

[0095] The main function of the output layer is to output the target regulatory identification corresponding to the target related party in different time periods. After the complex processing of the transmission layer, the regulatory identification information related to the target related party is transmitted to the output layer in a highly refined and accurate form. Based on the final result of the transmission layer, the output layer outputs the regulatory identification that should be applied to the target related party in different time periods according to the predetermined rules and format.

[0096] For example, the output result may be presented in a timetable format, clearly listing the regulatory identification corresponding to the target related parties in each time period. These target regulatory identifications provide clear regulatory basis for regulators and enterprises, which helps to accurately judge the regulatory status of target related parties in different periods, so as to implement corresponding regulatory measures and internal management strategies of enterprises, ensure the compliance and risk controllability of related party transactions, and promote fair and orderly operation of the market. The entire identity parameter transmission supervision algorithm model realizes the accurate transmission and analysis from related party identity parameters to target regulatory identification through the collaborative work of input layer, transmission layer and output layer, providing an efficient and reliable technical solution for related party supervision.

[0097] In one embodiment, the objective function of the identity parameter conduction supervision algorithm model is constructed based on the mean square error term and the weight decay term; the mean square error term is used to measure the difference between the predicted value and the true value; the weight decay term is used to avoid overfitting caused by excessive weights, and the gradient descent method is used to solve the objective function to optimize the model parameters.

[0098] In an embodiment, the objective function of the identity parameter conduction supervision algorithm model is constructed based on the mean square error term and the weight decay term. The mean square error term is a key indicator to measure the difference between the model prediction value and the true value. In the context of related party supervision identification analysis, the predicted value is the target supervision identification predicted by the model based on the input related party identity parameters, and the true value is the supervision identification actually applicable to the corresponding related party in a specific time period obtained from historical supervision data. The calculation formula for the mean square error term is usually to sum the squares of the difference between the predicted supervision identification and the true supervision identification in each sample data, and then divide it by the number of samples.

[0099] The constructed objective function is solved by the gradient descent method to optimize the model parameters. The basic principle of the gradient descent method is to gradually adjust the parameter value according to the gradient direction of the objective function at the current parameter value, so that the objective function value decreases towards the minimum value. In each iteration, the gradient of the objective function to each parameter of the model (including weights and bias parameters) is first calculated. For the mean square error term, the gradient calculation of the weight parameter involves complex mathematical derivation based on the difference between the predicted value and the true value and the characteristics of the input data.

[0100] In one embodiment, it further includes:

[0101] Update related party identity parameters and corresponding regulatory identification parameters in real time;

[0102] Retrain or fine-tune the identity parameter transmission supervision algorithm model based on the updated related party identity parameters and regulatory identification parameters.

[0103] In one embodiment of the related party regulatory identification analysis method of the present invention, in order to ensure the timeliness and accuracy of the regulatory identification analysis, it involves real-time updating of the related party identity parameters and the corresponding regulatory identification parameters, and corresponding retraining or fine-tuning of the identity parameter transmission regulatory algorithm model based on the updated data.

[0104] As the business activities of an enterprise continue and the regulatory environment changes dynamically, the actual situation of related parties may change, which requires real-time updating of related party identity parameters and corresponding regulatory identification parameters. The sources of updates to related party identity parameters are wide-ranging. For example, changes in the equity structure of an enterprise will lead to changes in the identity parameters of shareholders, including increases or decreases in shareholding ratios, the addition of new shareholders or the withdrawal of old shareholders. When an enterprise makes internal personnel adjustments, the position relationship identity parameters need to be updated accordingly, such as the promotion of management personnel, the redivision of responsibilities, etc. The kinship identity parameters may also change due to changes in the kinship of related parties, such as the establishment or termination of kinship relationships. These changes directly affect the status and potential influence of related parties in the enterprise, and are then related to their corresponding regulatory identification parameters.

[0105] The update of regulatory identification parameters closely follows the adjustment of regulatory rules and changes in the actual situation of related parties. Regulators may revise regulatory rules according to the needs of market development and risk management, such as adjusting the shareholding ratio threshold, job level definition standards, or regulatory focus on kinship types, which will prompt changes in the corresponding regulatory identification parameters of related parties. At the same time, the optimization of the company's internal risk assessment mechanism may also lead to changes in regulatory requirements for related parties, thereby triggering the update of regulatory identification parameters. For example, if an enterprise finds that the business transaction risk of a related party has increased, it may take the initiative to adjust its regulatory identification parameters and strengthen its supervision. In order to achieve real-time updates, enterprises need to establish an efficient information monitoring and collection mechanism to obtain various information changes related to related parties in a timely manner, and accurately convert them into updateable related party identity parameters and regulatory identification parameter formats to ensure the timeliness and accuracy of data.

[0106] After the related party identity parameters and regulatory identification parameters are updated, the identity parameter transmission regulatory algorithm model needs to be retrained or fine-tuned based on the new data to ensure that the model's predictive ability matches the actual situation. If the amount of updated data is large and has a significant impact on the judgment logic of the related party regulatory identification, such as major changes in regulatory rules leading to a comprehensive change in the calculation rules of regulatory identification, or large-scale equity restructuring or business transformation of the enterprise, the model needs to be retrained at this time. The retraining process is similar to the initial training process of the model, that is, the training sample set containing the updated related party identity parameters and regulatory identification parameters is re-obtained, the weights and bias parameters of the preset machine learning model are randomly initialized again, and a new loss function is preset (taking into account the updated regulatory environment and data characteristics). Then, based on the new training sample set, the gradient descent algorithm is used for iterative updates until the model converges or reaches the predetermined number of iterations, thereby obtaining a brand new identity parameter transmission regulatory algorithm model that adapts to new data characteristics and regulatory requirements.

[0107] However, if the amount of updated data is relatively small, or the impact on the regulatory identification judgment logic is relatively local, such as a slight adjustment of the shareholding ratio of individual related parties, a small-scale change in the position of a certain manager, etc., the model can be optimized by fine-tuning. The fine-tuning process mainly uses a small amount of updated data to locally adjust the model parameters based on the existing model. First, the updated related party identity parameters are input into the existing identity parameter transmission supervision algorithm model to calculate the model's prediction results. Then, the loss function is calculated based on the difference between the updated regulatory identification parameters (true values) and the prediction results. Next, the gradient of the loss function to the model parameters is calculated by the gradient descent algorithm, and the model parameters are fine-tuned and updated at a smaller learning rate. Repeat this process until the model's prediction performance on the updated data reaches a satisfactory effect, such as the loss function value is reduced to an acceptable range. In this way, while avoiding the high computational cost and time consumption caused by large-scale retraining, it ensures that the model can adapt to small changes in related party identity parameters and regulatory identification parameters in a timely manner, and maintain the accuracy and timeliness of related party regulatory identification analysis. This dynamic update and model adjustment mechanism enables the related party regulatory identification analysis method of the present invention to flexibly respond to complex and changing business operations and regulatory environments, and continuously provide reliable regulatory identification analysis services.

[0108] In one embodiment, it further includes:

[0109] Verifying and reviewing the calculated target regulatory identification; the verification and review are used to determine that the target regulatory identification complies with regulatory requirements and business logic;

[0110] If verification and review reveal errors or inconsistencies in the target regulatory identification, the input process of the related party identity parameters and regulatory identification parameters will be traced back to locate and correct the errors.

[0111] In one embodiment of the related party regulatory identification analysis method of the present invention, in order to ensure the accuracy and reliability of the regulatory identification analysis results, a strict verification and review process is implemented after the target regulatory identification is calculated, and an effective backtracking and correction mechanism is established for possible errors or inconsistencies.

[0112] The verification process is mainly carried out based on pre-set regulatory requirements and business logic rules. Regulatory requirements are derived from laws, regulations and guidelines issued by various regulators, such as the detailed regulations formulated by the Financial Regulatory Administration and the China Securities Regulatory Commission for related party supervision, which clarify the regulatory identification standards that should be applied to different types of related parties in different situations. Business logic is a set of logical systems for determining related party regulatory identification based on factors such as the company's own operating characteristics, internal management rules, and industry practices. For example, in some industries, there may be special regulatory identification requirements for related party transactions in specific business areas, which are formulated based on industry risk characteristics and business process specifications.

[0113] During the verification process, we first check whether the target regulatory identification conforms to the definition and classification standards of the related party identity by the regulator. For example, for related parties with shareholder identities, we verify whether their regulatory identification is consistent with factors such as shareholding ratio and nature of shares, and whether they meet the regulatory identification requirements set by the regulator for different shareholding levels. For related parties with job relationship identities, we review whether their regulatory identification is consistent with factors such as job level and scope of responsibilities, and whether they comply with the relevant regulations of the regulator on the supervision of internal management personnel of the enterprise.

[0114] At the same time, according to the business logic, check the rationality of the target regulatory identification in the internal related party management system of the enterprise. For example, within the enterprise group, transactions between related parties may be subject to internal risk control rules. The target regulatory identification should match these rules to ensure the compliance and risk controllability of related party transactions within the enterprise. Through comprehensive verification and review of the target regulatory identification, ensure its accuracy and consistency at the regulatory requirements and business logic levels.

[0115] If errors or inconsistencies are found in the target regulatory identification after verification and review, the input process of the related party identity parameters and regulatory identification parameters will be immediately traced back to accurately locate and correct the errors. The traceback process starts from the calculation results of the target regulatory identification and reversely traces the processing flow of the data in the identity parameter transmission regulatory algorithm model. First, check whether the output results of the output layer are consistent with the transmission logic within the model, and confirm whether there are problems such as data conversion or output format errors in the output stage. Then, go deep into the transmission layer and check the calculation process and parameter settings of each transmission layer node one by one. In the transmission layer, focus on whether the weight connections between nodes are correct, whether the application of activation functions meets expectations, and whether the offset settings are reasonable. By comparing the input data and expected output data of each node, combined with regulatory requirements and business logic, determine whether there is a deviation in the transmission of regulatory identification caused by node calculation errors or parameter anomalies.

[0116] Continue to trace back to the input layer, and carefully review the accuracy and completeness of the input related party identity parameters and regulatory identification parameters. Check whether the source of the related party identity parameters is reliable, and whether there are omissions, errors, or data format mismatches during the data entry process. For regulatory identification parameters, confirm whether they are correctly set according to the latest regulatory rules and internal corporate regulations. Once the error is located, make targeted corrections based on the error type and specific circumstances. If it is a data entry error, correct the erroneous data in a timely manner; if the parameter setting is improper, readjust the parameter value; if it involves a logical error within the algorithm model, repair or optimize the relevant part of the model. After correcting the error, re-run the identity parameter transmission regulatory algorithm model, recalculate and verify the target regulatory identification to ensure that the corrected results are accurate and meet regulatory requirements and business logic. Through this rigorous verification, review and error handling mechanism, the related party regulatory identification analysis method of the present invention can effectively improve the accuracy and reliability of regulatory identification analysis, promptly discover and correct potential errors, provide solid technical support for related party supervision, ensure that enterprises operate in compliance under a complex regulatory environment, and reduce the risk of related party transactions.

[0117] In one embodiment, it further includes:

[0118] Monitor and manage transactions involving related parties of target related parties according to the target regulatory identification corresponding to the target related parties in different time periods;

[0119] Based on the target regulatory identification, mark the regulatory scope to which the related party is subject to in transactions on different transaction dates.

[0120] In the above embodiment, a dynamic transaction monitoring mechanism is established based on the target regulatory identification of the target related party in different time periods. For each time period covered by the target regulatory identification, all transaction activities involving related parties in which the target related party participates are closely monitored. In the real-time or near real-time stage of the transaction, detailed information on the transaction is collected, including key elements such as the transaction amount, transaction type (such as equity transactions, fund borrowing, commodity trading, etc.), and counterparty. The regulatory requirements and risk warnings contained in the target regulatory identification are used to conduct preliminary screening and risk assessment of transactions. For example, transactions within the time period marked as high-risk regulatory identification are subject to more stringent review, focusing on whether there is potential for interest transfer, insider trading, or violation of regulatory provisions in the transaction.

[0121] Formulate corresponding transaction management strategies based on the regulatory rules set by the target regulatory identification. If the target regulatory identification indicates that the related party is subject to strict fund flow supervision within a certain period of time, then in transaction management, the related party’s large-scale fund transfers will be strictly restricted, or it will be required to obtain additional approval before conducting specific types of transactions. At the same time, according to the changing trend of the target regulatory identification, the transaction behavior of the related party will be dynamically adjusted and intervened. If it is found that the regulatory identification of the related party changes from low risk to high risk, the monitoring of its transaction activities will be strengthened in a timely manner. It may be required to increase the frequency and detail of transaction information disclosure, or suspend certain high-risk transaction types to prevent the further expansion of potential risks and ensure the stability and compliance of the overall operation of the enterprise.

[0122] Based on the target regulatory identification, accurately mark the regulatory scope to which the transactions involving the related party fall on different transaction dates. This marking process is based on the detailed definition of regulatory rules and requirements corresponding to different regulatory identifications by the regulator. For example, a regulator stipulates that transactions of a certain type of related party within a certain shareholding ratio should be classified as a certain regulatory scope, which involves specific reporting requirements, approval processes and information disclosure standards. When the target related party conducts transactions within this shareholding ratio, according to its corresponding target regulatory identification, it is clearly marked that the transaction should follow the relevant provisions of this specific regulatory scope.

[0123] In the company's transaction record system, for each transaction involving related parties, the regulatory scope information is clearly marked in its transaction record. This not only helps the company's internal management and statistical analysis of related-party transactions, but also facilitates the rapid and accurate provision of transaction information that meets regulatory requirements when responding to regulatory inspections. At the same time, by accurately marking the regulatory scope, companies can better communicate and collaborate with regulators to ensure that their related-party transaction activities are carried out in an orderly manner within the regulatory framework and avoid compliance risks caused by unclear regulatory scope. This transaction monitoring, management and regulatory scope marking mechanism based on target regulatory identification has formed a complete closed-loop management system, which effectively improves the company's ability to control related-party transactions and enhances the company's adaptability and compliance level in a complex regulatory environment.

[0124] Two specific embodiments are given below to illustrate the specific application of the method of the present invention.

[0125] The first specific embodiment is as follows:

[0126] Assume that there is a large enterprise group with many subsidiaries. There are many related-party transactions involved in the operation of the enterprise. It is necessary to conduct effective regulatory identification analysis on the related parties, which involves the following steps:

[0127] 1. Obtain the identity parameters of the target related party

[0128] First, identify the target related party, for example, A, an important shareholder of a subsidiary. According to the actual relationship between A and the enterprise group (the management entity of the related party), A, as a shareholder, holds 12% of the shares and has a certain influence on some major decisions of the enterprise group, so the shareholder identity parameter is selected. At the same time, A serves as a senior consultant in a subsidiary of the group, and there is also a position relationship identity parameter.

[0129] Extract the related information of A from different regulators (such as the Financial Regulatory Authority, China Securities Regulatory Commission, etc.) from the preset regulatory rule library. According to the regulations of the Financial Regulatory Authority, shareholders with a shareholding ratio of more than 10% may be regarded as important related parties, and related parties holding senior advisory positions have specific regulatory requirements in related party transactions. These regulations are divided into corresponding related party identity parameters. For the shareholder identity parameters of A, according to the regulations of the Financial Regulatory Authority, it belongs to the category of important related parties.

[0130] For the existing regulatory identification in the identity parameters of A's shareholder, according to the regulations of the Financial Regulatory Administration, the validity parameter is determined to be effective from the date when A's shareholding ratio reaches 10%, and there is no clear expiration time (unless there is a major change in the shareholding ratio, etc.). The validity threshold parameter is the shareholding ratio threshold, that is, holding more than 10% triggers the regulatory identification. For his position relationship identity parameters, the validity parameter is effective from the date he assumes the position of senior consultant, and there is also no clear expiration time (unless the position changes). The validity threshold parameter is the position level threshold, that is, senior consultant and above positions trigger the corresponding regulatory identification.

[0131] 2. Training Identity Parameter Transmission Supervision Algorithm Model

[0132] In order to build an identity parameter transmission supervision algorithm model, a training sample set of related party supervision identification is obtained. Information on different historical related parties is screened out from the historical supervision data of the enterprise group over the years, such as the historical related party identity parameters of various shareholders, managers and their relatives involved in the past ten years, as well as their historical target supervision identification actually applied at that time. For example, there was once a shareholder B who held 8% of the shares and did not hold any position. The supervision identification applicable to him under the regulatory environment at that time was the ordinary related party supervision identification. This information constitutes part of the training sample set.

[0133] The weights and bias parameters of the preset machine learning model (such as the neural network model) are randomly initialized and assigned initial values. The loss function of the machine learning model is preset and constructed using the mean square error term and the weight decay term. The mean square error term is used to measure the difference between the target regulatory identification predicted by the model and the actual regulatory identification in the sample set, such as the difference between the regulatory identification that should be applied to B under the current circumstances and the actual ordinary related party regulatory identification. The weight decay term is used to avoid overfitting of the model and prevent the model from being too complex due to excessive weights and losing its generalization ability.

[0134] Based on the training sample set of related party regulatory identification, the gradient descent algorithm is used as the optimization algorithm. In each iteration, the historical related party identity parameters in the sample set are input into the model to obtain the predicted target regulatory identification and calculate the loss function value. Then, according to the gradient of the loss function to the model parameters, the model parameters are updated at a certain learning rate so that the loss function value gradually decreases. Repeat the iterative update operation until the model converges (such as the loss function value no longer changes significantly) or reaches the predetermined number of iterations (such as 1000 times). Finally, the trained identity parameter conduction regulatory algorithm model is obtained.

[0135] 3. Calculate the target supervision mark

[0136] Input A's shareholder identity parameters (holding 12%, effective time, etc.) and position relationship identity parameters (senior advisor position, effective time, etc.) into the trained identity parameter transmission supervision algorithm model. The input layer of the model receives these parameters, and multiple transmission layer nodes in the transmission layer start working. Each node performs a nonlinear transformation on the input parameters, mapping and translating changes through weights and offsets. For example, for the shareholder identity parameters, according to the weights learned during the model training process, information is exchanged and calculated with other nodes, and the relevant information of its regulatory identification is gradually transmitted.

[0137] After complex calculations at the transmission layer, the output layer finally outputs the target regulatory identification corresponding to A in different time periods. Assume that the model output results show that in the current time period, since A holds more than 10% of the shares and serves as a senior consultant, the target regulatory identification applicable to him is the high-risk related party regulatory identification, indicating that the transactions involving his related parties need to be subject to stricter supervision and review.

[0138] 4. Real-time update and model adjustment

[0139] Assume that the enterprise group has made an equity structure adjustment, and A's shareholding ratio has increased to 15%, and his position has been changed to a member of the board of supervisors. At this time, A's related party identity parameters are updated in real time, the new shareholding ratio and position information are updated to the system, and the corresponding regulatory identification parameter changes are determined according to the latest regulatory provisions in the regulatory rule library. For example, according to the new regulations, related parties with a shareholding ratio of more than 15% and serving as members of the board of supervisors are subject to key supervision, and the validity parameters of the regulatory identification parameters are updated to take effect from the date of the change, and the validity threshold parameters are adjusted accordingly.

[0140] According to the updated related party identity parameters and regulatory identification parameters, due to the large data changes, it is decided to retrain the identity parameter transmission regulatory algorithm model. Re-acquire the related party regulatory identification training sample set containing the latest information of A and other related party updated information, and perform model initialization, loss function setting, gradient descent iterative update and other operations again until a new optimization model is obtained to adapt to the new regulatory requirements and the actual situation of the enterprise.

[0141] 5. Verification, review and error handling

[0142] Verify and review the calculated target regulatory identification of A. Check whether the regulatory identification of high-risk related parties meets the requirements based on the latest regulations of the Financial Regulatory Administration and other regulatory authorities and the business logic within the enterprise group. For example, check whether the application of the regulatory identification to related parties with a shareholding ratio of 15% and serving as members of the Supervisory Board complies with the principles of risk management and control of such related parties in the regulatory rules, and whether it is consistent with other related party management rules within the enterprise group.

[0143] If problems are found during the verification and audit process, such as finding that according to the new regulations, related parties with a shareholding ratio of 15% and serving as members of the board of supervisors should be subject to a higher level of regulatory identification, but the model output is a high-risk related party regulatory identification, which is inconsistent. At this time, trace back the input process of A's related party identity parameters and regulatory identification parameters. Starting from the output layer, check whether the output result has errors in the model transmission process, and then check the calculation process and weight setting of the transmission layer nodes in turn, and finally check whether the parameter input of the input layer is accurate. After detailed investigation, it was found that a small error occurred in the weight update process of a node in the transmission layer, resulting in a deviation in the calculation result. Correct the error, recalculate A's target regulatory identification, and conduct verification and audit again to ensure that the results are accurate.

[0144] 6. Monitoring and management of related-party transactions and regulatory caliber marking

[0145] According to the target supervision mark corresponding to A in different time periods (currently the high-risk related party supervision mark), the related party transactions involving A are monitored and managed. When each related party transaction in which A participates occurs, such as the fund borrowing transaction between him and another subsidiary in the enterprise group, the transaction amount (such as 5 million yuan), transaction type (fund borrowing), counterparty (subsidiary name) and other information are recorded in detail. As it is a high-risk related party, the transaction is strictly reviewed and a detailed description of the use of funds and a risk assessment report are required.

[0146] Based on A's target regulatory identification, mark the regulatory scope to which the fund lending transaction belongs on the date of occurrence. According to regulatory regulations, such high-risk related-party fund lending transactions should be classified into specific regulatory scopes, involving regular reporting of transaction details to the regulator, and meeting certain capital adequacy requirements. In the enterprise group's transaction record system, clearly mark that the transaction belongs to this regulatory scope to facilitate subsequent statistical analysis, regulatory reporting and internal management, ensure that A's related-party transaction activities are carried out within the correct regulatory framework, effectively reduce the enterprise group's related-party transaction risks, and improve the compliance and stability of overall operations.

[0147] The second embodiment first adds a time dimension and specific regulatory judgment rules to the identity parameters, and requires users to enter the valid time period of the related party identity when entering the related party, adding judgment factors. At the same time, a set of identity parameter regulatory identification transmission model calculation is established to realize the measurement of regulatory identity parameter transmission, thereby improving the accuracy and timeliness of related party regulatory identification calculation, reducing dependence on user operation timeliness, further enhancing the refined management of related parties, and improving the quality of regulatory data submitted by banks.

[0148] The second embodiment involves the following steps:

[0149] (1) Related party identity parameter settings:

[0150] (1.1) Split each regulator’s definition of related parties into related party identity parameters A (each related party identity parameter A represents a set of related parties), and assign corresponding regulatory identifiers to each of them; if it is found that the name and meaning of a related party identity parameter defined by different regulators are the same, then assign multiple corresponding regulatory identifiers to this related party identity parameter.

[0151] (1.2) According to the effective time of the transaction management related systems of each regulatory party's affiliates, the effective and expiration time information is further added to each regulatory identifier attached to the affiliate identity parameter A in (1.1). Thus, the affiliate identity parameter A(T) is established (the affiliate identity parameter A(T) represents a set of affiliate parameters).

[0152] (2) Identity threshold parameter setting corresponding to the related party identity:

[0153] (2.1) If the regulator specifies comparison conditions for some of the related party identity parameters A(T) in (1), threshold items and thresholds are further set under the related party identity parameters; there can be multiple combinations of threshold items and thresholds. If different regulators define the same related party identity but specify different comparison conditions, multiple combinations of threshold items and thresholds are set under the related party identity parameter A(T), and corresponding regulatory identifiers are assigned to each. Thus, the related party identity parameter A(T)B is established (the related party identity parameter A(T)B represents a set of related party parameters).

[0154] (2.2) According to the effective time of the transaction management related systems of each regulatory party's affiliates, the effective and invalid time are further added to each regulatory identifier corresponding to the affiliate identity parameter A(T)B in (2.1). In this way, the affiliate identity parameter A(T)B(T) is established (the affiliate identity parameter A(T)B(T) represents a set of affiliate parameters), and the above affiliate identity parameter A(T)B(T) is referred to as the affiliate identity parameter E (the affiliate identity parameter E represents a set of affiliate parameters).

[0155] (3) Related party entry:

[0156] (3.1) In the course of daily operations and management, enterprises shall proactively check and confirm related parties and enter them into the IT system for management. When entering, it is necessary to select the relationship between the related party and the related party management entity, or select a related party that has been stored as its superior related party and select the relationship with the superior related party, that is, a related party identity parameter e in the related party identity parameter E. A group of information composed of the above-mentioned related parties, related party identity parameters e, related party management entities or superior related parties, etc., constitutes the related party identity F, which represents the relationship between the related party and the related party management entity or superior related party; according to the actual situation, set the effective and expiration time for the selected related party identity F. If there are multiple related party identities, select and enter the effective and expiration time respectively according to the above steps; the effective and expiration times of different related party identities are allowed to overlap. The above-mentioned related party identity F with the effective and expiration time attached can be named related party identity N. From this, it can be seen that if an affiliate has multiple superior affiliates, or has multiple association relationships with the affiliate management entity or superior affiliate, then the affiliate can have multiple affiliate identities N, which can be named affiliate identity N1, affiliate identity N2, to affiliate identity NM.

[0157] (3.2) If the associated party identity N selected in (3.1) has the corresponding identity threshold parameter, then further select the threshold item and enter the actual value of the threshold, as well as the corresponding effective and ineffective time. According to the actual situation, multiple threshold items can be selected and the corresponding actual values ​​of the threshold can be entered, and the corresponding effective and ineffective time can be entered at the same time; the effective and ineffective time of different threshold items are allowed to have overlapping parts.

[0158] (4) Constructing an identity parameter regulatory transmission model

[0159] (4.1) Figure 2 FIG. 1 is an example diagram of the relationship between an associated party identity parameter and a regulatory identifier in an embodiment of the present invention. Figure 2 As shown, the establishment of identity parameter associated party supervision identification training sample set involves the following parameters:

[0160] {(x 1 ,y 1 ),(x 2 ,y 2 ),L,(xn,y n )}, construct a nonlinear model M w,b (x), parameters w, b, through which y is fitted.

[0161] (4.2) The construction and transmission method in the identity parameter transmission supervision algorithm model is as follows:

[0162]

[0163] Among them, the Lth layer has kl nodes, conduction layer is the activation value of the i-th node in the l-th layer, is the input value of the lth layer. From the J node of the lth layer to the i node of the l+1th layer through the weight connection, f(q) is the activation function, and nonlinear transformation is performed through the activation function. The result of the transformation is passed through the weight Mapped to the new space, and passed the offset The leftmost layer of the transmission model is the parameter input of the transaction supervision identity of the related party, the rightmost layer is the final output result after the supervision identity transmission, and the middle layer is the transmission process layer. This application uses the sigmoid function as the activation function, as follows:

[0164]

[0165] Figure 3 FIG. 1 is a specific example diagram of the conduction logic of an identity parameter conduction supervision algorithm model in an embodiment of the present invention. The construction conduction mode in the identity parameter conduction supervision algorithm model is as follows: Figure 3 shown.

[0166] (4.3) Constructing the objective function

[0167] If there is only one sample, the cost function is:

[0168]

[0169] There are n data samples, and the entire cost function is:

[0170]

[0171] The first part of the above formula J(w,b) is the mean square error term; the second part is the weight attenuation term, which is used to avoid overfitting caused by excessive weights.

[0172] The entire conduction model algorithm is ultimately transformed into solving the minimum value of the target cost function:

[0173]

[0174] In this regard, the objective function of the entire conduction model can be solved by the gradient descent method:

[0175]

[0176] In the above formula, α is the iterative learning rate.

[0177] (5) According to the identity parameter transmission supervision algorithm model, the supervision identification of the related party and the identity parameter transmission relationship are established to achieve the measurement of the supervision identity identification of the related party. The implementation example is as follows:

[0178] Table 1 shows the correspondence between the related party identity parameters and various information such as regulatory identification. Table 1 lists in detail the related party identity parameters, their corresponding regulatory identification, regulatory identification validity period (including effective date and expiration date), threshold items, thresholds, and threshold combination validity period (including effective date and expiration date). For example, it shows the various regulatory identifications corresponding to different related party identity parameters (such as related party identity parameters, related party identity parameters, etc.) and their related time limits and threshold conditions, which helps to understand the complex correspondence between related party identity parameters and regulatory identifications and the specific settings of time limits and threshold parameters. The example table of related party identity basic parameters E is shown in Table 1:

[0179] Table 1

[0180]

[0181] Table 2 shows the basic information table of the reported related parties, presenting the related party identity, validity period of the related party identity, threshold situation, and corresponding validity period of the threshold. Through the example of specific related parties, the changes in the related party identity and the corresponding threshold situation in different time periods are shown, which provides a reference for understanding how to determine the identity parameters and threshold parameters according to the actual situation of the related party, and also provides a basic data example for the subsequent calculation of the target regulatory identification. Table 2 shows the identity, validity period, threshold, etc. of the related parties to be reported:

[0182] Table 2

[0183]

[0184] Table 3 is an example table of target regulatory identifications corresponding to target related parties in different time periods, and clearly gives the corresponding regulatory identifications of related parties at different start and end dates. Through the division of specific time intervals, the regulatory identifications that should be effective in each time period calculated by the related party based on its related party identity parameters, threshold conditions, and the regulatory identification transmission algorithm model are displayed. It intuitively reflects the application results of the method of the present invention in determining the regulatory identification of related parties in different time periods, which helps to understand the final output and actual effect of the entire regulatory identification analysis method. The final regulatory identification calculation results of related party Y calculated by the gradient descent transmission model algorithm are shown in Table 3:

[0185] Table 3

[0186]

[0187]

[0188] The above-mentioned related party Y can obtain the regulatory identification required at each time point in time through the transmission model. At the same time, the system can automatically monitor the validity of each regulatory identification of related party Y every day, ensure timely and effective capture of the corresponding related party-related transactions, and accurately mark the regulatory caliber of the related party-related transactions on the day the transaction occurs, participate in the calculation of the regulatory business model of related party-related transactions, various report statistics, and accurately report to the regulator, quickly improve the quality of related party-related transactions, and meet regulatory requirements. This invention not only optimizes the configuration of the new time function parameters of the related party regulatory identification, but also constructs an overall transmission model method to improve the accuracy, flexibility and scalability of the regulatory identity identification of related party-related transactions.

[0189] According to the changes in regulatory rules, this specific embodiment adds a time element to the identity parameters as the basic configuration of the identity parameters, which improves the timeliness of the control of the time element by the transmission model's automatic transmission of its regulatory identification calculation function through the identity relationship of the associated party;

[0190] According to the changes in regulatory rules, an identity transmission network model algorithm is constructed to effectively transmit regulatory identity parameters to the identities of related parties. This improves the flexibility and scalability of the system's automatic transmission of regulatory identification functions through identity relationships;

[0191] The gradient descent algorithm is used to update and adjust the parameters of the related-party regulatory identification transmission model in the same direction to achieve convergence of the transmission model process and rapid model fitting, thereby quickly completing the measurement of regulatory identity parameters and achieving timeliness and accuracy in the measurement of subsequent related-party transactions.

[0192] Of course, it is understandable that the above detailed process may have other variations, and the relevant variations should all fall within the protection scope of the present invention.

[0193] In an embodiment of the present invention, multiple associated party identity parameters corresponding to the target associated party are obtained; wherein each associated party identity parameter corresponds to one or more regulatory identification parameters; the regulatory identification parameters include timeliness parameters and validity threshold parameters corresponding to different existing regulatory identifications; the timeliness parameters are used to describe the effective time and expiration time of the existing regulatory identification; the validity threshold parameters are used to describe the regulatory conditions and condition thresholds for the effectiveness of the existing regulatory identification; the multiple associated party identity parameters corresponding to the target associated party are input into the identity parameter transmission supervision algorithm model to obtain the target regulatory identification corresponding to the target associated party in different time periods; the target regulatory identification is used to describe the existing regulatory identification effective in each time period; the identity parameter transmission supervision algorithm model is based on the gradient descent algorithm, and a preset machine learning model is trained with an associated party regulatory identification training sample set; the associated party regulatory identification training sample set includes: historical associated party identity parameters of different historical associated parties, and historical target regulatory identifications corresponding to different historical associated parties. The embodiment of the present invention clarifies the effective time and expiration time of the existing regulatory identification by setting the timeliness parameter. When the regulatory rules change, it can be timely judged whether the regulatory identification needs to be updated based on the timeliness parameter, rather than relying on manual intervention. The regulatory algorithm model can flexibly adapt to the changes in regulatory rules through the identity parameter transmission, ensuring that the regulatory identification of the target associated party can be quickly updated when the regulatory rules change, thereby solving the problem of untimely update of the regulatory identification; by setting the validity threshold parameter, the regulatory conditions and condition thresholds for the effectiveness of the existing regulatory identification are limited, so that when calculating the target regulatory identification, accurate judgment can be made based on the differentiated regulations of different regulators, and the machine The machine learning model can learn the accurate calculation logic under different regulatory rules, thereby improving the accuracy of regulatory identification calculation and overcoming the defects of inaccurate calculation in existing technologies. In addition, by constructing an identity parameter transmission regulatory algorithm model, it can automatically calculate the target regulatory identification corresponding to different time periods based on the input related party identity parameters, reducing the reliance on users to maintain the related party identities in the system in a timely manner. Even if the related party identity changes, the regulatory identification can be quickly recalculated based on the new related party identity parameters and algorithm model, avoiding regulatory identification errors or untimely updates caused by user maintenance delays, and effectively solving the problem of related party identity maintenance affecting the accuracy of regulatory identification.

[0194] As mentioned above, the embodiment of the present invention adds a time dimension element to the identity parameter to avoid the time dimension not being transmitted, resulting in the calculation of the related party supervision identification not updating the time identity parameter in a timely manner, and failing to quickly respond to the time change of the related party supervision identification. This improves the timeliness of the transmission model's automatic transmission of its supervision identification calculation function through the related party identity relationship to control the time element.

[0195] The embodiment of the present invention has established a set of identity transmission network model algorithms, which instantiate and measure the transaction identity parameters of the entire related party and the final regulatory identity mark through an effective transmission model.

[0196] The embodiment of the present invention updates and adjusts the parameters of the related party regulatory identification transmission model in the same direction through the gradient descent algorithm to achieve the minimization of the cost function; at the same time, the iterative learning rate is adjusted to control the step size of the parameter update, so as to achieve rapid convergence of the transmission model process, and then achieve rapid model fitting, thereby quickly completing the measurement of regulatory identity parameters and achieving better regulatory identity identification transmission effect.

[0197] The embodiment of the present invention adds time and regulatory specific judgment rule elements to the identity parameters, builds a related party regulatory identification transmission model, and can promptly and quickly calculate the regulatory identification information of the related party through the transmission algorithm model to meet the requirements of supervision and internal control management, which not only improves the refinement of related party management, but also improves the quality of the submitted regulatory data.

[0198] In the prior art, since the automatic configuration function of the regulatory identification of related parties does not take into account the timeliness of regulatory rules, it relies on developers to manually adjust the system's fixed model, which easily leads to delayed updates of regulatory identification. In the embodiment of the present invention, when obtaining multiple related party identity parameters corresponding to the target related party, the regulatory identification parameters corresponding to each related party identity parameter include a timeliness parameter, which specifies the effective time and expiration time of the existing regulatory identification. When the regulatory rules change, the system can promptly determine whether the regulatory identification needs to be updated based on these timeliness parameters, rather than relying on manual intervention. At the same time, the identity parameter transmission supervision algorithm model is trained based on the gradient descent algorithm and the related party regulatory identification training sample set containing rich historical information. It can adapt to changes in regulatory rules more flexibly, adjust the calculation logic in time, and ensure that the regulatory identification of the target related party can be quickly updated when the rules change, thereby solving the problem of untimely updates of regulatory identification.

[0199] Different regulatory rules have different definitions of related parties and specific identity judgment standards. The existing related party regulator calculation model does not effectively distinguish these differences, resulting in inaccurate calculations. In an embodiment of the present invention, the corresponding validity threshold parameter is determined when the related party identity parameter is obtained. The parameter describes in detail the regulatory conditions and condition thresholds for the effectiveness of the existing regulatory identification, such as shareholding ratio thresholds, position level thresholds, etc. This makes it possible to make accurate judgments based on the differentiated regulations of different regulators when calculating the target regulatory identification. In addition, the identity parameter transmission supervision algorithm model has been trained with a large number of historical related party identity parameters and historical target regulatory identifications of different historical related parties, and can learn the accurate calculation logic under different regulatory rules, thereby improving the accuracy of regulatory identification calculations and overcoming the defects of inaccurate calculations in the prior art.

[0200] The existing automatic configuration function of related party regulatory identification relies on timely maintenance by users for regulatory identification updates caused by changes in related party identities, which poses a risk of delay and error. The method in the embodiment of the present invention constructs a complete identity parameter transmission regulatory algorithm model, which is trained based on historical data and can automatically calculate the target regulatory identification corresponding to different time periods based on the input related party identity parameters. This reduces the reliance on users to maintain related party identities in the system in a timely manner. Even if the related party identity changes, such as changes in shareholding ratios, job adjustments, etc., the system can quickly recalculate the regulatory identification based on the new related party identity parameters and algorithm model, avoiding regulatory identification errors or untimely updates due to user maintenance delays, and effectively solving the problem of related party identity maintenance affecting the accuracy of regulatory identification.

[0201] The present invention also provides a device for analyzing the supervision mark of an associated party, as described in the following embodiments. Since the principle of solving the problem by the device is similar to that of the method for analyzing the supervision mark of an associated party, the implementation of the device can refer to the implementation of the method for analyzing the supervision mark of an associated party, and the repeated parts will not be repeated.

[0202] Figure 6 is a schematic diagram of the structure of a regulatory identification analysis device for an associated party in an embodiment of the present invention. The present invention also provides a regulatory identification analysis device for an associated party, which is used to improve the accuracy, timeliness and analysis efficiency of regulatory identification analysis of an associated party, such as Figure 6 As shown, the device comprises:

[0203] The associated party identity parameter acquisition module 601 is used to acquire multiple associated party identity parameters corresponding to the target associated party; wherein each associated party identity parameter corresponds to one or more regulatory identification parameters; the regulatory identification parameters include time validity parameters and validity threshold parameters corresponding to different existing regulatory identifications; the time validity parameters are used to describe the effective time and expiration time of the existing regulatory identification; the validity threshold parameters are used to describe the regulatory conditions and condition thresholds for the effectiveness of the existing regulatory identification;

[0204] The target regulatory identification output module 602 is used to input multiple associated party identity parameters corresponding to the target associated party into the identity parameter transmission regulatory algorithm model to obtain the target regulatory identification corresponding to the target associated party in different time periods; the target regulatory identification is used to describe the existing regulatory identification effective in each time period; the identity parameter transmission regulatory algorithm model is based on the gradient descent algorithm, and is obtained by training a pre-set machine learning model with an associated party regulatory identification training sample set; the associated party regulatory identification training sample set includes: historical associated party identity parameters of different historical associated parties, and historical target regulatory identifications corresponding to different historical associated parties.

[0205] In one embodiment, the related party identity parameters include but are not limited to shareholder identity, job relationship identity and kinship identity; the validity threshold parameters of each related party identity parameter include but are not limited to shareholding ratio threshold, job level threshold, and kinship type.

[0206] In one embodiment, obtaining multiple associated party identity parameters corresponding to the target associated party includes:

[0207] Select corresponding affiliate identity parameters for the target affiliate according to the actual relationship between the target affiliate and the affiliate management entity or the superior affiliate;

[0208] Extract relevant information about the association of different regulators with the target related party from the preset regulatory rule library;

[0209] Split the information related to each supervisory party's relationship with the target related party into related party identity parameters corresponding to the supervisory structure;

[0210] For each existing regulatory identifier in the identity parameter of the associated party, the timeliness parameters and validity threshold parameters of different existing regulatory identifiers are determined according to the associated relevant information of the regulator.

[0211] In one embodiment, it further includes: obtaining an identity parameter conduction supervision algorithm model by training as follows:

[0212] Obtain training sample sets of related party regulatory identification; randomly initialize the weights and bias parameters of the preset machine learning model;

[0213] A loss function of the machine learning model is pre-set; the loss function is used to measure the difference between the target regulatory identifier predicted by the model and the actual regulatory identifier in the sample set;

[0214] Using the training sample set of related party regulatory identification, the gradient descent algorithm is used as the optimization algorithm to iteratively update the randomly initialized machine learning model to gradually reduce the value of the loss function;

[0215] The above-mentioned iterative update operation is repeatedly performed until the iteratively updated model converges or reaches a predetermined number of iterations; the model obtained by stopping the iterative update is used as the trained identity parameter conduction supervision algorithm model.

[0216] In one embodiment, the identity parameter conduction supervision algorithm model includes:

[0217] An input layer, for receiving multiple associated party identity parameters corresponding to the target associated party;

[0218] The transmission layer is used to perform nonlinear transformation on the inputted associated party identity parameters, and to map and translate the changes through weights and offsets, so as to transmit the regulatory identification of the associated party identity parameters to the target associated party, and to output the target regulatory identification corresponding to the target associated party in different time periods through the output layer; the transmission layer includes a plurality of transmission layer nodes, and each transmission layer node represents a state in the transmission process of the regulatory identification of the associated party identity parameters;

[0219] The output layer is used to output the target supervision identification corresponding to the target related party in different time periods.

[0220] In one embodiment, it further includes:

[0221] Update related party identity parameters and corresponding regulatory identification parameters in real time;

[0222] Retrain or fine-tune the identity parameter transmission supervision algorithm model based on the updated related party identity parameters and regulatory identification parameters.

[0223] In one embodiment, it further includes:

[0224] Verifying and reviewing the calculated target regulatory identification; the verification and review are used to determine that the target regulatory identification complies with regulatory requirements and business logic;

[0225] If verification and review reveal errors or inconsistencies in the target regulatory identification, the input process of the related party identity parameters and regulatory identification parameters will be traced back to locate and correct the errors.

[0226] In one embodiment, the objective function of the identity parameter conduction supervision algorithm model is constructed based on the mean square error term and the weight decay term; the mean square error term is used to measure the difference between the predicted value and the true value; the weight decay term is used to avoid overfitting caused by excessive weights, and the gradient descent method is used to solve the objective function to optimize the model parameters.

[0227] In one embodiment, it further includes:

[0228] Monitor and manage transactions involving related parties of target related parties according to the target regulatory identification corresponding to the target related parties in different time periods;

[0229] Based on the target regulatory identification, mark the regulatory scope to which the related party is subject to in transactions on different transaction dates.

[0230] An embodiment of the present invention provides an embodiment of a computer device for implementing all or part of the contents of the above-mentioned method for analyzing the supervision mark of an associated party. The computer device specifically includes the following contents:

[0231] Processor, memory, communication interface and bus; wherein the processor, memory and communication interface communicate with each other through the bus; the communication interface is used to realize information transmission between related devices; the computer device can be a desktop computer, a tablet computer and a mobile terminal, etc., but the present embodiment is not limited thereto. In the present embodiment, the computer device can be implemented with reference to the embodiment of the method for implementing the regulatory identification analysis of the associated party and the embodiment of the apparatus for implementing the regulatory identification analysis of the associated party, and the contents thereof are incorporated herein and the repeated parts are not repeated.

[0232] Figure 7 1 is a schematic block diagram of the system structure of the computer device 1000 according to an embodiment of the present application. Figure 7 As shown, the computer device 1000 may include a central processor 1001 and a memory 1002; the memory 1002 is coupled to the central processor 1001. It is worth noting that Figure 7 is exemplary; other types of structures may also be used to supplement or replace this structure to implement telecommunication functions or other functions.

[0233] In one embodiment, the monitoring identification analysis function of the associated party may be integrated into the central processor 1001. The central processor 1001 may be configured to perform the following control:

[0234] Acquire multiple related party identity parameters corresponding to the target related party; wherein each related party identity parameter corresponds to one or more regulatory identification parameters; the regulatory identification parameters include time validity parameters and validity threshold parameters corresponding to different existing regulatory identifications; the time validity parameters are used to describe the effective time and expiration time of the existing regulatory identification; the validity threshold parameters are used to describe the regulatory conditions and condition thresholds for the effectiveness of the existing regulatory identification;

[0235] The multiple related party identity parameters corresponding to the target related party are input into the identity parameter transmission supervision algorithm model to obtain the target supervision identifier corresponding to the target related party in different time periods; the target supervision identifier is used to describe the existing supervision identifier effective in each time period; the identity parameter transmission supervision algorithm model is based on the gradient descent algorithm, and is obtained by training a preset machine learning model with a related party supervision identifier training sample set; the related party supervision identifier training sample set includes: historical related party identity parameters of different historical related parties, and historical target supervision identifiers corresponding to different historical related parties.

[0236] In another embodiment, the regulatory identification analysis device of the associated party can be configured separately from the central processor 1001. For example, the regulatory identification analysis device of the associated party can be configured as a chip connected to the central processor 1001, and the regulatory identification analysis function of the associated party is realized through the control of the central processor.

[0237] like Figure 7 As shown, the computer device 1000 may also include: a communication module 1003, an input unit 1004, an audio processor 1005, a display 1006, and a power supply 1007. It is worth noting that the computer device 1000 does not necessarily have to include Figure 7 In addition, the computer device 1000 may also include Figure 7 For components not shown, reference may be made to the prior art.

[0238] like Figure 7 As shown, the central processor 1001 is sometimes also referred to as a controller or an operation control, and may include a microprocessor or other processor devices and / or logic devices. The central processor 1001 receives inputs and controls the operations of various components of the computer device 1000 .

[0239] The memory 1002 may be, for example, a cache, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory or other suitable devices, or one or more thereof. The memory may store the above-mentioned information related to the device, and may also store a program for executing the related information. The CPU 1001 may execute the program stored in the memory 1002 to implement information storage or processing, etc.

[0240] The input unit 1004 provides input to the CPU 1001. The input unit 1004 is, for example, a key or a touch input device. The power supply 1007 is used to provide power to the computer device 1000. The display 1006 is used to display display objects such as images and text. The display may be, for example, an LCD display, but is not limited thereto.

[0241] The memory 1002 may be a solid-state memory, such as a read-only memory (ROM), a random access memory (RAM), a SIM card, etc. It may also be a memory that saves information even when the power is off, can be selectively erased, and is provided with more data, examples of which are sometimes referred to as EPROMs, etc. The memory 1002 may also be some other type of device. The memory 1002 includes a buffer memory 1021 (sometimes referred to as a buffer). The memory 1002 may include an application / function storage unit 1022, which is used to store application programs and function programs or processes for executing the operation of the computer device 1000 through the central processor 1001.

[0242] The memory 1002 may also include a data storage unit 1023 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the computer device. The driver storage unit 1024 of the memory 1002 may include various drivers for the computer device for communication functions and / or for executing other functions of the computer device (such as messaging applications, address book applications, etc.).

[0243] The communication module 1003 is a transmitter / receiver that sends and receives signals via the antenna 1008. The communication module (transmitter / receiver) 1003 is coupled to the central processor 1001 to provide input signals and receive output signals, which may be the same as the case of a conventional mobile communication terminal.

[0244] Based on different communication technologies, multiple communication modules 1003 may be provided in the same computer device, such as a cellular network module, a Bluetooth module and / or a wireless local area network module, etc. The communication module (transmitter / receiver) 1003 is also coupled to a speaker 1009 and a microphone 1010 via an audio processor 1005 to provide an audio output via the speaker 1009 and receive an audio input from the microphone 1010, thereby realizing a common telecommunication function. The audio processor 1005 may include any suitable buffer, decoder, amplifier, etc. In addition, the audio processor 1005 is also coupled to the central processor 1001, so that recording can be performed on the local machine through the microphone 1010, and the sound stored on the local machine can be played through the speaker 1009.

[0245] An embodiment of the present invention 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 method for analyzing the regulatory identification of related parties is implemented.

[0246] An embodiment of the present invention further provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the above-mentioned method for analyzing the regulatory identification of related parties is implemented.

[0247] In an embodiment of the present invention, multiple associated party identity parameters corresponding to the target associated party are obtained; wherein each associated party identity parameter corresponds to one or more regulatory identification parameters; the regulatory identification parameters include time validity parameters and validity threshold parameters corresponding to different existing regulatory identifications; the time validity parameters are used to describe the effective time and the invalid time of the existing regulatory identification; the validity threshold parameters are used to describe the regulatory conditions and condition thresholds for the effectiveness of the existing regulatory identification;

[0248] The multiple related party identity parameters corresponding to the target related party are input into the identity parameter transmission supervision algorithm model to obtain the target supervision identifier corresponding to the target related party in different time periods; the target supervision identifier is used to describe the existing supervision identifier that is effective in each time period; the identity parameter transmission supervision algorithm model is based on the gradient descent algorithm, and is obtained by training the preset machine learning model with the related party supervision identifier training sample set; the related party supervision identifier training sample set includes: historical related party identity parameters of different historical related parties, and historical target supervision identifiers corresponding to different historical related parties. The embodiment of the present invention clarifies the effective time and expiration time of the existing supervision identifier by setting the timeliness parameter. When the supervision rules change, it can be timely judged whether the supervision identifier needs to be updated based on the timeliness parameter instead of relying on manual intervention. The identity parameter transmission supervision algorithm model can flexibly adapt to the changes in supervision rules, ensuring that the supervision identifier of the target related party can be quickly updated when the supervision rules change, thereby solving the problem of untimely update of the supervision identifier; by setting the validity threshold parameter, the supervision conditions and condition thresholds for the effectiveness of the existing supervision identifier are limited, so that when calculating the target supervision identifier, it can accurately judge the differentiated regulations of different regulators, and the machine The machine learning model can learn the accurate calculation logic under different regulatory rules, thereby improving the accuracy of regulatory identification calculation and overcoming the defects of inaccurate calculation in existing technologies. In addition, by constructing an identity parameter transmission regulatory algorithm model, it can automatically calculate the target regulatory identification corresponding to different time periods based on the input related party identity parameters, reducing the reliance on users to maintain the related party identities in the system in a timely manner. Even if the related party identity changes, the regulatory identification can be quickly recalculated based on the new related party identity parameters and algorithm model, avoiding regulatory identification errors or untimely updates caused by user maintenance delays, and effectively solving the problem of related party identity maintenance affecting the accuracy of regulatory identification.

[0249] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0250] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0251] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0252] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0253] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for analyzing the supervision identification of related parties, characterized in that: include: Acquire multiple related party identity parameters corresponding to the target related party; wherein each related party identity parameter corresponds to one or more regulatory identification parameters; the regulatory identification parameters include time validity parameters and validity threshold parameters corresponding to different existing regulatory identifications; the time validity parameters are used to describe the effective time and expiration time of the existing regulatory identification; the validity threshold parameters are used to describe the regulatory conditions and condition thresholds for the effectiveness of the existing regulatory identification; The multiple related party identity parameters corresponding to the target related party are input into the identity parameter transmission supervision algorithm model to obtain the target supervision identifier corresponding to the target related party in different time periods; the target supervision identifier is used to describe the existing supervision identifier effective in each time period; the identity parameter transmission supervision algorithm model is based on the gradient descent algorithm, and is obtained by training a preset machine learning model with a related party supervision identifier training sample set; the related party supervision identifier training sample set includes: historical related party identity parameters of different historical related parties, and historical target supervision identifiers corresponding to different historical related parties.

2. The method according to claim 1, characterized in that The related party identity parameters include but are not limited to shareholder identity, job relationship identity and kinship identity; the validity threshold parameters of each related party identity parameter include but are not limited to shareholding ratio threshold, job level threshold, and kinship type.

3. The method according to claim 1, characterized in that Get multiple related party identity parameters corresponding to the target related party, including: Select corresponding affiliate identity parameters for the target affiliate according to the actual relationship between the target affiliate and the affiliate management entity or the superior affiliate; Extract relevant information about the association of different regulators with the target related party from the preset regulatory rule library; Split the information related to each supervisory party's relationship with the target related party into related party identity parameters corresponding to the supervisory structure; For each existing regulatory identifier in the identity parameter of the associated party, the timeliness parameters and validity threshold parameters of different existing regulatory identifiers are determined according to the associated relevant information of the regulator.

4. The method according to claim 1, characterized in that Also includes: The identity parameter conduction supervision algorithm model is obtained by training as follows: Obtain training sample sets of related party regulatory identification; randomly initialize the weights and bias parameters of the preset machine learning model; A loss function of the machine learning model is pre-set; the loss function is used to measure the difference between the target regulatory identifier predicted by the model and the actual regulatory identifier in the sample set; Using the training sample set of related party regulatory identification, the gradient descent algorithm is used as the optimization algorithm to iteratively update the randomly initialized machine learning model to gradually reduce the value of the loss function; The above-mentioned iterative update operation is repeatedly performed until the iteratively updated model converges or reaches a predetermined number of iterations; the model obtained by stopping the iterative update is used as the trained identity parameter conduction supervision algorithm model.

5. The method according to claim 1, characterized in that The identity parameter transmission supervision algorithm model includes: An input layer, for receiving multiple associated party identity parameters corresponding to the target associated party; The transmission layer is used to perform nonlinear transformation on the inputted associated party identity parameters, and to map and translate the changes through weights and offsets, so as to transmit the regulatory identification of the associated party identity parameters to the target associated party, and to output the target regulatory identification corresponding to the target associated party in different time periods through the output layer; the transmission layer includes a plurality of transmission layer nodes, and each transmission layer node represents a state in the transmission process of the regulatory identification of the associated party identity parameters; The output layer is used to output the target supervision identification corresponding to the target related party in different time periods.

6. The method according to claim 1, characterized in that Also includes: Update related party identity parameters and corresponding regulatory identification parameters in real time; Retrain or fine-tune the identity parameter transmission supervision algorithm model based on the updated related party identity parameters and regulatory identification parameters.

7. The method according to claim 1, characterized in that Also includes: Verify and review the calculated target regulatory identification; The verification and review are used to determine that the target regulatory identification complies with regulatory requirements and business logic; If verification and review reveal errors or inconsistencies in the target regulatory identification, the input process of the related party identity parameters and regulatory identification parameters will be traced back to locate and correct the errors.

8. The method according to claim 1, characterized in that The objective function of the identity parameter conduction supervision algorithm model is constructed based on the mean square error term and the weight decay term; the mean square error term is used to measure the difference between the predicted value and the true value; the weight decay term is used to avoid overfitting caused by excessive weights, and the gradient descent method is used to solve the objective function to optimize the model parameters.

9. The method according to claim 1, characterized in that Also includes: Monitor and manage transactions involving related parties of target related parties according to the target regulatory identification corresponding to the target related parties in different time periods; Based on the target regulatory identification, mark the regulatory scope to which the related party is subject to in transactions on different transaction dates.

10. A device for analyzing the supervision mark of an associated party, characterized in that: include: A related party identity parameter acquisition module is used to acquire multiple related party identity parameters corresponding to the target related party; wherein each related party identity parameter corresponds to one or more regulatory identification parameters; the regulatory identification parameters include time validity parameters and validity threshold parameters corresponding to different existing regulatory identifications; the time validity parameters are used to describe the effective time and expiration time of the existing regulatory identification; the validity threshold parameters are used to describe the regulatory conditions and condition thresholds for the effectiveness of the existing regulatory identification; The target supervision identification output module is used to input multiple related party identity parameters corresponding to the target related party into the identity parameter transmission supervision algorithm model to obtain the target supervision identification corresponding to the target related party in different time periods; the target supervision identification is used to describe the existing supervision identification effective in each time period; the identity parameter transmission supervision algorithm model is based on the gradient descent algorithm, and is obtained by training a preset machine learning model with a related party supervision identification training sample set; the related party supervision identification training sample set includes: historical related party identity parameters of different historical related parties, and historical target supervision identifications corresponding to different historical related parties.

11. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 9 is implemented.

12. 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 method according to any one of claims 1 to 9 is implemented.

13. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 9 is implemented.

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