Business data processing method, device, equipment, medium and program product

Abstract

The present disclosure provides a business data processing method, apparatus, device, medium, and program product that can be applied to the fields of big data and financial technology. The business data processing method includes: in response to receiving a risk identification request from a terminal device, determining a business data group that matches the terminal device, wherein the business data group includes multiple business data of multiple data types; calling a risk identification model that matches the business data, performing risk identification on the multiple business data, and obtaining a risk identification result; returning the risk identification result and the model identifier of the risk identification model to the terminal device; in response to receiving a risk processing request from the terminal device, determining a risk processing strategy for the business data group based on the processing authority of the terminal device and the processing parameters in the risk processing request.

Description

Technical Field

[0001] The present disclosure relates to the fields of big data and financial technology counting, and specifically to a business data processing method, device, equipment, medium and program product. Background Art

[0002] With the development of business diversification, the areas covered by the risk identification systems operated by bank branch staff are becoming increasingly broad, and the traditional risk identification model has become difficult to adapt to the increasingly complex internal and external operating environment.

[0003] For example, risk management and control of business data lacks direct and efficient risk identification methods. The identification hierarchy is overly simplistic, resulting in repeated verification of the same incident and a lack of prompt response to risk hotspots, difficulties, and pain points. Furthermore, the existing risk identification system lacks the necessary data storage and processing centers, and relevant data sources are fragmented and unsystematic, making it difficult to achieve economies of scale and effectively identifying and addressing risks.

[0004] In summary, the existing technologies have technical problems such as low risk identification accuracy, low risk identification efficiency, and imperfect risk handling methods. Summary of the Invention

[0005] In view of the above problems, the present disclosure provides a business data processing method, apparatus, device, medium and program product.

[0006] According to a first aspect of the present disclosure, a business data processing method is provided, comprising: in response to receiving a risk identification request from a terminal device, determining a business data group that matches the terminal device, wherein the business data group includes multiple business data of multiple data types; calling a risk identification model that matches the business data, performing risk identification on the multiple business data, and obtaining a risk identification result; returning the risk identification result and a model identifier of the risk identification model to the terminal device; and in response to receiving a risk handling request from the terminal device, determining a risk handling strategy for the business data group according to the processing authority of the terminal device and processing parameters in the risk handling request.

[0007] According to an embodiment of the present disclosure, in response to receiving a risk identification request from a terminal device, determining a business data group that matches the terminal device includes: in response to receiving a risk identification request from the terminal device, determining multiple business data newly added on the day from the data lake; calling a business volume allocation model to allocate the multiple business data newly added on the day to each operating user in the daily schedule according to groups; determining a target operating user that matches the terminal device based on the device information of the terminal device; and determining the business data group allocated to the target operating user as the business data group that matches the terminal device.

[0008] According to an embodiment of the present disclosure, calling a risk identification model that matches business data, performing risk identification on multiple business data, and obtaining a risk identification result includes: determining at least one risk identification model that matches the business data based on the data type of the business data; calling each risk identification model, performing risk identification on multiple business data, and obtaining a risk identification sub-result output by each risk identification model; and determining a risk identification result based on the risk identification sub-result output by each risk identification model.

[0009] According to an embodiment of the present disclosure, at least one risk identification model that matches the business data is determined based on the data type of the business data, including: when it is determined that the data type of the business data is a reconciliation type, multiple reconciliation risk identification models that match the business data are determined, wherein the reconciliation risk identification model includes at least one of the following: a first reconciliation risk identification model for risk identification in the reconciliation time dimension, a second reconciliation risk identification model for risk identification in the reconciliation method dimension, a third reconciliation risk identification model for risk identification in the reconciliation result dimension, and a fourth reconciliation risk identification model for risk identification in the reconciliation location dimension; when it is determined that the data type of the business data is an account opening type, an account opening risk identification model that matches the business data is determined.

[0010] According to an embodiment of the present disclosure, the first reconciliation risk identification model outputs a first risk identification sub-result, the second reconciliation risk identification model outputs a second risk identification sub-result, the third reconciliation risk identification model outputs a third risk identification sub-result, and the fourth reconciliation risk identification model outputs a fourth risk identification sub-result; determining the risk identification result based on the risk identification sub-results output by each risk identification model includes: determining the number of risk results based on the first risk identification sub-result, the second risk identification sub-result, the third risk identification sub-result, and the fourth risk identification sub-result; and determining the risk identification result based on the number of risk results, the first risk identification sub-result, the second risk identification sub-result, the third risk identification sub-result, and the fourth risk identification sub-result.

[0011] According to an embodiment of the present disclosure, each risk identification model is called to perform risk identification on multiple business data, and a risk identification sub-result output by each risk identification model is obtained, including: for the second reconciliation risk identification model, obtaining the reconciliation track information of the business users related to the business data; and inputting the reconciliation track information, the generation time of the business data and the reconciliation duration into the second reconciliation risk identification model, and outputting the second risk identification sub-result.

[0012] According to an embodiment of the present disclosure, the processing parameters include institutional parameters and policy parameters; the risk handling strategy for the business data group is determined based on the processing authority of the terminal device and the processing parameters in the risk handling request, including: when it is determined that the processing authority is the same as the institutional parameter, the risk handling strategy for the business data group is determined based on the policy parameters; when it is determined that the processing authority is different from the institutional parameter, the risk handling strategy for the business data group is determined based on the policy parameters and the institutional parameters.

[0013] According to a second aspect of the present disclosure, a business data processing device is provided, comprising: a determination module for determining a business data group that matches a terminal device in response to a risk identification request received from a terminal device, wherein the business data group includes multiple business data of multiple data types; a calling module for calling a risk identification model that matches the business data, performing risk identification on the multiple business data, and obtaining a risk identification result; a return module for returning the risk identification result and a model identifier of the risk identification model to the terminal device; and a processing module for determining a risk handling strategy for the business data group according to the processing authority of the terminal device and the processing parameters in the risk handling request in response to a risk handling request received from the terminal device.

[0014] A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the above method.

[0015] The fourth aspect of the present disclosure further provides a computer-readable storage medium having a computer program or instructions stored thereon, which implements the steps of the above method when the computer program or instructions are executed by a processor.

[0016] The fifth aspect of the present disclosure further provides a computer program product, comprising a computer program or instructions, which implement the steps of the above method when executed by a processor.

[0017] In an embodiment of the present disclosure, a smart risk control platform based on big data is constructed. The smart risk control platform can respond to a risk identification request received from a terminal device, determine a business data group that matches the terminal device, automatically match and call a risk identification model to perform risk identification on multiple business data in the assigned business data group, and generate a corresponding risk treatment strategy based on the user's risk treatment request to perform risk treatment. As a result, the embodiment of the present disclosure integrates multi-dimensional and multi-stage risk identification such as data allocation, model matching, and policy processing, and can perform multi-granular risk treatment based on processing parameters and processing permissions, thereby solving the technical problems of low risk identification accuracy, low risk identification efficiency, and imperfect risk treatment means, and achieving the technical effect of more accurate and faster risk identification and risk treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The above contents and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0019] Figure 1 A diagram showing an application scenario of the business data processing method and device according to an embodiment of the present disclosure is shown.

[0020] Figure 2 A flowchart of a business data processing method according to an embodiment of the present disclosure is shown.

[0021] Figure 3 A flowchart of a method for determining a service data group matching a terminal device according to an embodiment of the present disclosure is shown.

[0022] Figure 4 A flow chart of a method for determining risk identification results according to an embodiment of the present disclosure is shown.

[0023] Figure 5 A schematic diagram of a scenario for determining risk identification results according to an embodiment of the present disclosure is shown.

[0024] Figure 6 A scenario diagram of a business data processing method according to a specific embodiment of the present disclosure is shown.

[0025] Figure 7 A schematic diagram of the system framework of an intelligent wind control system according to a specific embodiment of the present disclosure is shown.

[0026] Figure 8 A structural block diagram of a business data processing device according to an embodiment of the present disclosure is shown.

[0027] Figure 9 A block diagram of an electronic device suitable for implementing a business data processing method according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0028] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present disclosure. In the detailed description below, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0029] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise," "include," etc. used herein indicate the presence of the features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0030] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0031] In the technical solutions disclosed herein, the user information (including but not limited to user personal information, user image information, user device information, such as location information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved are all 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 the relevant data comply with relevant laws, regulations and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0032] In scenarios where personal information is used for automated decision-making, the methods, devices, and systems provided by the embodiments of the present disclosure all provide users with corresponding operation portals for them to choose to agree or reject the automated decision-making results; if the user chooses to reject, the expert decision-making process will be entered. The expression "automated decision-making" here refers to the activity of automatically analyzing and evaluating an individual's behavioral habits, interests and hobbies, or economic, health, credit status, etc. through computer programs and making decisions. The expression "expert decision-making" here refers to the activity of making decisions by people who specialize in a certain field, have specialized experience, knowledge, and skills, and have reached a certain level of professionalism.

[0033] In order to at least partially solve the technical problems in the existing technology of low risk identification accuracy, low risk identification efficiency, and imperfect risk handling means, the embodiments of the present disclosure are based on data analysis, with risk models and management models as engines. By deeply exploring risk characteristics and management difficulties, dynamic management and utilization of various data, a highly integrated intelligent risk control system based on big data is created.

[0034] An embodiment of the present disclosure provides a business data processing method, which can be applied to the above-mentioned intelligent risk control system. The method includes: in response to receiving a risk identification request from a terminal device, determining a business data group that matches the terminal device, wherein the business data group includes multiple business data of multiple data types; calling a risk identification model that matches the business data, performing risk identification on the multiple business data, and obtaining a risk identification result; returning the risk identification result and the model identifier of the risk identification model to the terminal device; and in response to receiving a risk processing request from the terminal device, determining a risk processing strategy for the business data group according to the processing authority of the terminal device and the processing parameters in the risk processing request.

[0035] Figure 1 The application scenario diagram of the business data processing method and device according to the embodiment of the present disclosure is shown. It should be understood that Figure 1 The number of terminal devices, networks, and servers in the embodiment is for illustration only. Any number of terminal devices, networks, and servers may be provided as required.

[0036] like Figure 1 As shown, the application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is used as a medium for providing a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired or wireless communication links or optical fiber cables.

[0037] Users can use a first terminal device 101, a second terminal device 102, and a third terminal device 103 to interact with a server 105 via a network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (for example only). The first terminal device 101, the second terminal device 102, and the third terminal device 103 can be various electronic devices with display screens and support web browsing, including but not limited to smartphones, tablet computers, laptop computers, and desktop computers.

[0038] The server 105 may be a server that provides various services, such as a background management server (for example only) that supports websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process received data such as user requests, and feed back processing results (e.g., web pages, information, or data obtained or generated based on user requests) to the terminal devices.

[0039] It should be noted that the business data processing method provided in the embodiment of the present disclosure can generally be executed by the server 105 or a server cluster. Accordingly, the business data processing device provided in the embodiment of the present disclosure can generally be set in the server 105 or a server cluster.

[0040] For example, a smart risk control system may be deployed in server 105. A user may use a terminal device such as first terminal device 101, second terminal device 102, or third terminal device 103 to send a risk identification request. In response to receiving the risk identification request from a terminal device, the smart risk control system in server 105 may determine a business data group that matches the terminal device, where the business data group includes multiple business data of multiple data types; invoke a risk identification model that matches the business data to perform risk identification on the multiple business data, obtaining a risk identification result; and return the risk identification result and the model identifier of the risk identification model to first terminal device 101, second terminal device 102, and third terminal device 103. Server 105 may also, in response to receiving a risk handling request from first terminal device 101, second terminal device 102, or third terminal device 103, determine a risk handling strategy for the business data group based on the terminal device's processing permissions and processing parameters in the risk handling request.

[0041] Figure 2 A flowchart of a business data processing method according to an embodiment of the present disclosure is shown.

[0042] like Figure 2 As shown, the business data processing method 200 of this embodiment includes operations S210 to S240, and the business data processing method can be executed by an intelligent risk control system.

[0043] In operation S210 , in response to receiving a risk identification request from a terminal device, a business data group matching the terminal device is determined, wherein the business data group includes a plurality of business data of a plurality of data types.

[0044] According to an embodiment of the present disclosure, a terminal device may be a terminal device used by an operator of the intelligent risk control system. The operator can view and operate the intelligent risk control system on the terminal device and initiate a risk identification request. Upon receiving the risk identification request from a terminal device, the server begins executing the risk identification process.

[0045] According to an embodiment of the present disclosure, the service data group that matches the terminal device may be specified by the terminal device, or may be randomly assigned to the terminal device based on daily service data.

[0046] For example, the business data requiring risk identification changes daily, weekly, and monthly. Therefore, upon receiving a risk identification request, the intelligent risk control system's intelligent performance module first determines the corresponding business data group for each terminal device. It should be noted that users operating terminal devices can log into the intelligent risk control system on a daily, weekly, or monthly basis to perform routine risk identification.

[0047] Alternatively, the user can specify multiple business data items for risk identification. In this case, the risk identification request can include the identifier of the specified business data, such as the business data group identifier "XX Group." The intelligent risk control system can then parse the risk identification request, obtain the identifier of the specified business data, and retrieve this specified business data from the head office's data lake.

[0048] According to embodiments of the present disclosure, a business type refers to the type of business being performed, such as reconciliation business and clearing business. A broad business type can include multiple sub-categories. For example, a reconciliation business can include multiple types, such as reconciliation failure, unreconciled, abnormal reconciliation account, and abnormal reconciliation result. The same business data group can include multiple business data, and multiple business data can include multiple data types.

[0049] In operation S220 , a risk identification model matching the business data is called to perform risk identification on the plurality of business data to obtain a risk identification result.

[0050] According to an embodiment of the present disclosure, the risk identification model may be a risk identification model embedded in the intelligent risk control system, which is used to identify risks in business data and obtain risk identification results.

[0051] According to an embodiment of the present disclosure, different risk identification models may be used for different business types to specifically identify risks of business data of a specified business type.

[0052] According to an embodiment of the present disclosure, the risk identification model can be a model constructed based on machine learning, a model constructed based on expert rules, a classification model constructed based on deep learning, a decision tree, etc. The present disclosure does not limit the type of risk identification model.

[0053] In operation S230 , the risk identification result and the model identifier of the risk identification model are returned to the terminal device.

[0054] According to an embodiment of the present disclosure, after the risk identification model identifies a risk identification result, the risk identification result and the model identifier of the risk identification model may be returned to the terminal device and visually displayed on the terminal device.

[0055] According to an embodiment of the present disclosure, the operating user can select corresponding processing parameters on the terminal device based on the risk identification results and the model identifier of the risk identification model, and generate a risk processing request based on the processing parameters, so that the intelligent risk control system can generate a corresponding risk processing strategy based on the risk processing request.

[0056] In operation S240 , in response to receiving the risk handling request from the terminal device, a risk handling strategy for the business data group is determined according to the handling authority of the terminal device and the handling parameters in the risk handling request.

[0057] According to embodiments of the present disclosure, processing authority refers to the authority to perform risk management on the current business data group. Different terminal devices have different processing authorities. For example, processing authority may include: having processing authority for the business data of the affiliated organization, and having no processing authority for the business data of the affiliated organization.

[0058] According to embodiments of the present disclosure, the processing parameters parsed from a risk handling request may include organization parameters and policy parameters. Organization parameters are used to identify the source organization of the business data, while policy parameters may be information about the risk handling policy selected by the user, such as who to report to, who is allowed to handle the risk, and when to handle the risk.

[0059] In an embodiment of the present disclosure, a smart risk control platform based on big data is constructed. The smart risk control platform can respond to a risk identification request received from a terminal device, determine a business data group that matches the terminal device, automatically match and call a risk identification model to perform risk identification on multiple business data in the assigned business data group, and generate a corresponding risk treatment strategy based on the user's risk treatment request to perform risk treatment. As a result, the embodiment of the present disclosure integrates multi-dimensional and multi-stage risk identification such as data allocation, model matching, and policy processing, and can perform multi-granular risk treatment based on processing parameters and processing permissions, thereby solving the technical problems of low risk identification accuracy, low risk identification efficiency, and imperfect risk treatment means, and achieving the technical effect of more accurate and faster risk identification and risk treatment.

[0060] Figure 3 FIG. 1 is a flow chart showing a method for determining a service data group matching a terminal device according to an embodiment of the present disclosure. Figure 3 As shown, the method 300 for determining a business data group matching a terminal device in this embodiment includes operations S311 to S314. The method 300 for determining a business data group matching a terminal device can be executed by the intelligent risk control system as a specific embodiment of operation S210.

[0061] In operation S311 , in response to receiving a risk identification request from a terminal device, a plurality of business data newly added on the day is determined from the data lake.

[0062] In operation S312, the business volume distribution model is called to distribute the multiple business data newly added on the day to each operating user in the daily schedule according to groups.

[0063] In operation S313, a target operating user matching the terminal device is determined based on the device information of the terminal device.

[0064] In operation S314, the service data group assigned to the target operating user is determined to be a service data group that matches the terminal device.

[0065] According to an embodiment of the present disclosure, the time when the risk identification request was initiated, such as the time of the day, can be determined from the risk identification request. Thereafter, multiple new business data with the same time of the day can be determined from the data lake of the head office based on the time of the day.

[0066] According to an embodiment of the present disclosure, a data lake may include data for a period of time, and this portion of data includes full data and incremental data. In this embodiment, the data lake's big data processing software can be used to calculate the incremental data added daily in advance. Thus, in response to receiving a risk identification request from a terminal device, multiple business data added that day, i.e., the incremental data for that day, can be directly determined from the data lake.

[0067] According to an embodiment of the present disclosure, the intelligent risk control system includes an intelligent performance module. The intelligent performance module includes five functions: a random allocation mechanism for business volume, a management workstation, regulatory matters, reconciliation performance, and reconciliation maintenance.

[0068] The random distribution mechanism of business volume includes a business volume distribution model, which automatically distributes the daily added business data to each operating user, such as the inspection manager, through the system's automated calculations.

[0069] According to an embodiment of the present disclosure, multiple business data items newly added that day can be sorted to obtain a task list, and then the order of the operating users on duty in the daily schedule can be shuffled. According to the order of the task list, the multiple business data items newly added that day are assigned to each operating user in the daily schedule one by one. After all the business data items in the task list are assigned, a business data group can be encoded for the multiple business data items assigned to each operating user, thereby providing each operator with a business data group for risk identification.

[0070] According to the embodiments of the present disclosure, the management personnel workstation covers functions such as indicator supervision, network work log, and automatic report collection and management. Normative matters are used to regulate the behavior of operating users. For businesses that have operational flaws but do not constitute the standards for assessing risk events, they can be recorded in matters to be regulated. Reconciliation duties are used to supervise the reconciliation business that is not performed daily, so that the reconciliation business data can be entered into the system in a timely manner. Reconciliation maintenance can provide a query function, and the reconciliation administrator can use this module to query the reconciliation progress and update the performance of duties.

[0071] According to embodiments of the present disclosure, device information refers to the binding information between the operator using a terminal device and the terminal device. Internal bank terminal devices can be mobile. During daily risk identification, the operator can log in their work ID information on the terminal device, thereby binding the terminal device and the operator to obtain device information.

[0072] Since operation S312 has assigned a business data group to each operating user, operation S313 can determine the target operating user matching the terminal device based on the device information of the terminal device, and determine the business data group assigned to the target operating user as the business data group matching the terminal device.

[0073] In an embodiment of the present disclosure, by responding to a risk identification request received from a terminal device, multiple business data newly added on the day are determined from the data lake; the business volume allocation model is called to allocate the multiple business data newly added on the day to each operating user in the daily schedule according to groups, and based on the device information of the terminal device, the business data group of the target operating user matching the terminal device is used as the business data group matching the terminal device. This can automatically allocate business data to be risk identified to each terminal device, reduce the difficulty of resource allocation and management of risk business data, eliminate the need for repeated management of the same risk event, and save management resources.

[0074] Figure 4 FIG. 1 is a flow chart showing a method for determining risk identification results according to an embodiment of the present disclosure. Figure 4 As shown, the method 400 for determining the risk identification result of this embodiment includes operations S421 to S423. The method for determining the risk identification result can be executed by the intelligent risk control system as a specific embodiment of operation S220.

[0075] In operation S421 , at least one risk identification model matching the business data is determined according to the data type of the business data.

[0076] In operation S422, each risk identification model is called to perform risk identification on a plurality of business data, and a risk identification sub-result output by each risk identification model is obtained.

[0077] In operation S423 , a risk identification result is determined according to the risk identification sub-results output by each risk identification model.

[0078] According to an embodiment of the present disclosure, each type of business data may correspond to one or more risk identification models. For example, reconciliation type business data may correspond to multiple risk identification models.

[0079] According to an embodiment of the present disclosure, the intelligent risk control system may include a model mapping table that includes a correspondence between data types and risk identification models. For each piece of business data, at least one risk identification model that matches the business data may be determined based on the model mapping table.

[0080] According to an embodiment of the present disclosure, after determining the risk identification model, multiple business data in the business data group can be input as a whole, and each risk identification model can be called separately to perform an overall analysis on the multiple business data to obtain the risk identification sub-results output by each risk identification model.

[0081] According to embodiments of the present disclosure, after obtaining the risk identification sub-results output by each risk identification model, the risk identification result can be determined by combining the risk identification sub-results output by each risk identification model. For example, the risk identification sub-result can be a numerical output between 0 and 1, where a risk identification sub-result of 0 indicates no risk and a risk identification sub-result of 1 indicates risk. The risk identification sub-results output by each risk identification model can be summed and the average value calculated as the risk identification result.

[0082] In the embodiments of the present disclosure, by embedding multiple risk identification models in the intelligent risk control system, when performing risk identification on multiple business data, the risk identification sub-results output by at least one risk identification model that matches the business data are called, and the risk identification results are determined based on the risk identification sub-results output by each risk identification model. This will not only make the relevant management identification more accurate for each established data model, but also improve the internal risk identification rate.

[0083] According to an embodiment of the present disclosure, at least one risk identification model that matches the business data is determined based on the data type of the business data, including: when it is determined that the data type of the business data is a reconciliation type, multiple reconciliation risk identification models that match the business data are determined, wherein the reconciliation risk identification model includes at least one of the following: a first reconciliation risk identification model for risk identification in the reconciliation time dimension, a second reconciliation risk identification model for risk identification in the reconciliation method dimension, a third reconciliation risk identification model for risk identification in the reconciliation result dimension, and a fourth reconciliation risk identification model for risk identification in the reconciliation location dimension; when it is determined that the data type of the business data is an account opening type, an account opening risk identification model that matches the business data is determined.

[0084] According to an embodiment of the present disclosure, the first reconciliation risk identification model used for risk identification in the reconciliation time dimension can be a non-zero online banking limit model for long-term inactive accounts, which is used to screen accounts whose online banking limits of existing long-term (such as 7 days) inactive accounts have not been reduced to zero and issue early warnings to notify bank branches to complete the limit adjustment operation to fill the risk exposure.

[0085] According to an embodiment of the present disclosure, the second reconciliation risk identification model for risk identification in the reconciliation method dimension can be a model for early warning of account information rectification for unsuccessful mail-based reconciliations. This model aggregates accounts that failed mail-based reconciliations due to incorrect reconciliation information, distributes and pushes these accounts via the "Earning of Rectification of Account Information for Unsuccessful Mail-Based Reconciliation" model, automatically creates rectification tasks in the system, and provides feedback within the system. If reconciliation is still not completed by the end of the quarter, the account will be placed under control.

[0086] According to embodiments of the present disclosure, the third reconciliation risk identification model, used to identify risks in the reconciliation results dimension, can be a model for monitoring and controlling unreconciled accounts. Accounts that fail to cooperate with reconciliation within the specified timeframe and provide no reasonable justification are identified as unreconciled accounts, and control measures for receiving but not disbursing are implemented. Furthermore, the branch can proactively push this information to sub-branches, generating rectification tasks and urging sub-branches to promptly complete control measures.

[0087] A third reconciliation risk identification model could also be a high online banking limit model for inefficient accounts. This model compares the maximum daily payment amount over the past three months against the group's daily cumulative limit for inefficient accounts with low transaction volume and low balances. This model screens for accounts whose group online banking payment limits exceed their actual payment limits by more than three times, issues an early warning, and requires branches to set reasonable limits based on the accounts' actual payment volumes. Another third reconciliation risk identification model could be a model for corporate accounts with failed automatic filings. This model notifies branches of accounts that have failed automatic filing, urging them to promptly log into the system and re-enter the information, thus reducing the likelihood of risk events.

[0088] According to an embodiment of the present disclosure, the fourth reconciliation risk identification model used to identify risks in the reconciliation location dimension can be an off-site account maintenance detection model. The system actively detects newly opened off-site accounts and pushes them to the branch. After receiving the task, the branch feeds back the reconciliation information to the system for maintenance.

[0089] According to an embodiment of the present disclosure, when the data type of business data is determined to be account opening, an account opening risk identification model matching the business data is determined. The account opening risk identification model builds a library of 13 suspicious features based on basic account opening information, contact information, business information, and address information within the business data. This model, in real time, uncovers subtle connections between different users and identifies suspicious features across different user groups. The model utilizes an advanced risk quantification scoring card mechanism to establish 23 risk indicators based on the risk characteristics of suspicious accounts, assigning differentiated risk scores to accounts based on their risk level. This provides a reference for determining account risk and allows comparison of anomalies between accounts, avoiding a rigid, qualitative assessment. This model is the first to implement real-time risk alerts for "off-list" accounts. Risk scoring results are provided before account opening approval is initiated. Branches can promptly revoke account opening applications and reject them if anomalies are identified. When a branch identifies a risky account and rejects the account, the assessment results are recorded in the scoring and warning model. When a user attempts to submit an account opening application at another branch, the warning model can retrieve the risk assessment results from the previous branch.

[0090] In an embodiment of the present disclosure, risk identification models of multiple dimensions such as reconciliation time, reconciliation method, reconciliation result and reconciliation location are constructed for the risk identification model of the reconciliation type, so that calling each risk identification model can output risk identification sub-results in the specific reconciliation time, reconciliation method, reconciliation result and reconciliation location dimensions, covering extensive and comprehensive risk identification and achieving higher risk identification accuracy.

[0091] According to an embodiment of the present disclosure, before obtaining business data, the user's consent or authorization may be obtained. For example, before obtaining business data, a request may be issued to the user to obtain business data related to the business to be performed. When the user agrees or authorizes that the business data can be obtained, the operation of obtaining the business data is performed. According to an embodiment of the present disclosure, before risk identification processing / decision-making is performed on business data, the user's consent or authorization may also be obtained. In an embodiment of the present disclosure, a corresponding operation entry may be provided for the user to choose to agree or refuse the automated decision result. That is, before risk identification processing / decision-making is performed on business data, an instruction to agree or refuse to process / decision-making may be obtained from the user input through the corresponding operation entry. If the user agrees to process / decision-making, risk identification processing / decision-making is subsequently performed on the business data. If the user refuses to process / decision-making, the expert decision-making process is entered.

[0092] According to an embodiment of the present disclosure, according to each risk identification model output risk identification sub-result, determining the risk identification result includes: determining the number of risk results according to the first risk identification sub-result, the second risk identification sub-result, the third risk identification sub-result and the fourth risk identification sub-result; and determining the risk identification result according to the number of risk results, the first risk identification sub-result, the second risk identification sub-result, the third risk identification sub-result and the fourth risk identification sub-result.

[0093] According to an embodiment of the present disclosure, multiple business data are input into the first reconciliation risk identification model, and the first risk identification sub-result of the reconciliation time dimension is output; multiple business data are input into the second reconciliation risk identification model, and the second risk identification sub-result of the reconciliation method dimension is output; multiple business data are input into the third reconciliation risk identification model, and the third risk identification sub-result of the reconciliation result dimension is output; multiple business data are input into the fourth reconciliation risk identification model, and the fourth risk identification sub-result of the reconciliation position dimension is output.

[0094] According to embodiments of the present disclosure, the first, second, third, and fourth risk identification sub-results can each be represented by a numerical value between 0 and 1. For example, a value greater than 0.8 indicates a risk and fails risk identification; a value less than 0.8 indicates no risk and passes risk identification. Based on the corresponding results of the first, second, third, and fourth risk identification sub-results, the number of risk identification sub-results that pass can be determined.

[0095] It is understandable that the more risk results that are identified as passed, the lower the possibility that multiple business data are at risk; conversely, the fewer the risk results, the higher the possibility that there is a risk. Therefore, the weight of the weighted sum of the first risk identification sub-result, the second risk identification sub-result, the third risk identification sub-result, and the fourth risk identification sub-result can be determined based on the number of risk results. For example, a separate weight adjustment parameter can be determined based on the number of risk results, and the parameters of the sum of the above four risk identification sub-results can be adjusted to obtain the final risk identification result. Alternatively, a separate weight adjustment parameter can be determined based on the number of risk results, and the weight of each risk identification sub-result can be determined based on the ratio between each identification sub-result and the weight adjustment parameter.

[0096] Figure 5 FIG. 1 shows a schematic diagram of a scenario for determining risk identification results according to an embodiment of the present disclosure. Figure 5 As shown, in this embodiment 500, for business data group 501, first reconciliation risk identification model 502 outputs a first risk identification sub-result 506, second reconciliation risk identification model 503 outputs a second risk identification sub-result 507, third reconciliation risk identification model 504 outputs a third risk identification sub-result 508, and fourth reconciliation risk identification model 505 outputs a fourth risk identification sub-result 509. The number of risk results is determined by combining first risk identification sub-result 506, second risk identification sub-result 507, third risk identification sub-result 508, and fourth risk identification sub-result 509. A risk identification result 510 is determined based on the number of risk results and the four risk identification sub-models described above.

[0097] In an embodiment of the present disclosure, based on the risk identification sub-results in the dimensions of comprehensive reconciliation time, reconciliation method, reconciliation result and reconciliation position, the weight used for weighted summation to obtain the risk identification result is determined according to the number of risk results that pass the identification sub-results, so that a more reasonable risk identification result can be obtained and the risk identification accuracy is higher.

[0098] According to an embodiment of the present disclosure, each risk analysis model is called to perform risk identification on multiple business data, and a risk identification sub-result output by each risk identification model is obtained, including: for the second reconciliation risk identification model, obtaining the reconciliation track information of the business users related to the business data; and inputting the reconciliation track information, the generation time of the business data and the reconciliation duration into the second reconciliation risk identification model, and outputting the second risk identification sub-result.

[0099] According to an embodiment of the present disclosure, the second reconciliation risk identification model may also be a face-to-face reconciliation execution trajectory detection model.

[0100] In one embodiment, after determining a face-to-face reconciliation execution trajectory detection model that matches the business data, the intelligent risk control system generates a face-to-face reconciliation task using the face-to-face reconciliation execution trajectory detection model and obtains reconciliation trajectory information for the business users associated with the business data. The business users associated with the business data are the reconciliation personnel who operate on the business data. The reconciliation trajectory information is the electronic operation trajectory of the reconciliation personnel and the corresponding operation photos when completing the current business data. After inputting the reconciliation trajectory information, the business data generation time, and the reconciliation duration into the face-to-face reconciliation execution trajectory detection model, a second risk identification sub-result is output.

[0101] According to an embodiment of the present disclosure, the face-to-face reconciliation execution trajectory detection model can be a machine learning model, which abstracts the electronic operation trajectory in the reconciliation trajectory information into a trajectory operation sequence for operating the control, and uses the generation time of the business data and the reconciliation duration as other input parameters. Thus, the second risk identification sub-result is obtained by analyzing the above three types of parameters.

[0102] In an embodiment of the present disclosure, for face-to-face reconciliation in the reconciliation method, this special face-to-face reconciliation can be verified through reconciliation track information, generation time of business data and reconciliation duration to ensure the accuracy of risk identification under the face-to-face reconciliation method.

[0103] According to embodiments of the present disclosure, the intelligent analysis module within the smart risk control system can embed the aforementioned multiple risk identification models. In addition to these multiple risk identification models, a model for uncancelled teller numbers can also be included. This model identifies uncancelled teller numbers by comparing the teller numbers on the pending cancellation list with those currently in the system. If any uncancelled teller numbers are present, the fifth risk identification sub-result is considered a failure, and the branch is issued an early warning, urging prompt rectification.

[0104] According to an embodiment of the present disclosure, for operation S240, a risk handling strategy for the business data group is determined based on the processing authority of the terminal device and the processing parameters in the risk handling request, including: when it is determined that the processing authority is the same as the institutional parameter, the risk handling strategy for the business data group is determined based on the policy parameters; when it is determined that the processing authority is different from the institutional parameter, the risk handling strategy for the business data group is determined based on the policy parameters and the institutional parameters.

[0105] According to an embodiment of the present disclosure, processing parameters include organizational parameters and policy parameters. The organizational parameter may be the source organization of the business data, and the policy parameter may be the policy information selected by the user to handle the risk, such as who to report to, who is allowed to handle the risk, and when to handle the risk.

[0106] According to embodiments of the present disclosure, during the risk processing process, the processing authority must be consistent with the source organization of the business data in order to promptly process risky business data. If they are inconsistent, repeated identification will lead to a waste of risk processing resources. Therefore, during the risk processing process, embodiments of the present disclosure first determine whether the processing authority and the organization parameters are consistent. If they are consistent, the risk processing strategy for the business data group is determined based on the policy parameters. For example, the risk processing strategy that matches the policy parameters is determined as the risk processing strategy for the business data group.

[0107] If the processing authority is determined to be inconsistent with the organization parameters, a risk treatment strategy for the business data group is determined based on the policy parameters and the organization parameters. For example, the generated risk treatment strategy is: a risk treatment strategy that matches the policy parameters is determined and pushed as a candidate treatment strategy to the business data source organization corresponding to the organization parameters.

[0108] In the embodiment of the present disclosure, by comparing the organizational parameters with the processing permissions, and based on the comparison results, determining the risk processing strategy for the business data group according to the policy parameters, or the policy parameters plus the organizational parameters, it is possible to ensure timely processing of risky business data and avoid repeated risk identification that leads to waste of risk processing resources.

[0109] Figure 6 FIG. 1 shows a scenario diagram of a method for processing business data according to a specific embodiment of the present disclosure. Figure 6 As shown, this embodiment includes S601 to S607.

[0110] Operation S601 can be performed through the model intelligent analysis module in the smart risk control system to add a new risk identification model, and operation S602 can be used to call a risk identification model warning. Operation S602 can call a new or existing risk identification model warning. Operation S603 can be performed through the risk event management module in the smart risk control system to query risks, and operation S604 can be used to determine whether there is a risk. Operation S605 can be performed if there is a risk, and operation S607 can be performed to record the operation if there is no risk. Operation S605 can be performed through the intelligent performance module in the smart risk control system to check risks, and operation S606 can be used to handle the risk according to the risk handling strategy.

[0111] The risk event management module of the intelligent risk control system can include a quasi-risk event management module, a risk event query module, an internal risk event query module, and a cross-regional push query module. The quasi-risk event management module is used to provide multiple screening conditions such as date, model identification, event status of risk identification results, etc., covering query purposes with different needs. The risk event query module can filter by institutional dimension, and can also query by different calibers and dimensions, and associate corrective measures to provide complete and effective information for indicator calculation and risk governance. The internal risk event query module covers the full amount of event information of internal risk events, and at the same time associates internal risk subjects and account numbers of risk events for secondary identity authentication and secondary risk verification. The cross-regional push query module summarizes and improves the relevant data of cross-regional push in risk identification, so that risk handling strategies can be pushed to the corresponding institutions across regions in a timely manner.

[0112] Figure 7 FIG1 shows a schematic diagram of the system framework of the smart wind control system according to a specific embodiment of the present disclosure. Figure 7 As shown in system architecture 700, the intelligent risk control system includes a model intelligent analysis module, an intelligent performance module, a risk event management module, and a graphical analysis module. The model intelligent analysis module embeds multiple risk identification models, enabling model maintenance and the invocation of risk identification models for model warnings. The intelligent performance module supports risk event query and risk event management. The risk event management module involves risk event allocation (such as determining the business data group matching each terminal device), risk event supervision, and risk event reporting. The graphical analysis module provides business volume statistics, branch operation risk indicators, and branch risk heat maps.

[0113] The embodiments of the present disclosure are based on data analysis, with risk models and management models as engines. By deeply exploring risk characteristics and management difficulties, and dynamically managing and utilizing various data, a highly integrated intelligent risk control system based on big data is created. By establishing an intelligent risk control system, the difficulty of performing risk identification duties is reduced, and monitoring resources are saved. Establishing a data model for risk identification in an intelligent risk control system can improve the accuracy of risk identification and increase the internal risk identification rate; by displaying dynamic charts of indicators, data, and risk events, a more flexible, prepared, and intuitive decision-making basis is provided to managers at all levels, risk display is more intelligent, management is more refined, and strong support is provided for operational management.

[0114] Figure 8 FIG. 1 shows a structural block diagram of a service data processing device according to an embodiment of the present disclosure. Figure 8 As shown, the business data processing device 800 of this embodiment includes a determination module 810 , a calling module 820 , a return module 830 and a processing module 840 .

[0115] Determination module 810 is configured to, in response to receiving a risk identification request from a terminal device, determine a business data group that matches the terminal device, where the business data group includes multiple business data of multiple data types. In one embodiment, determination module 810 may be configured to perform operation S210 described above, which will not be further described herein.

[0116] The calling module 820 is used to call the risk identification model that matches the business data, perform risk identification on the multiple business data, and obtain a risk identification result. In one embodiment, the calling module 820 can be used to perform the operation S220 described above, which will not be repeated here.

[0117] The return module 830 is configured to return the risk identification result and the model identifier of the risk identification model to the terminal device. In one embodiment, the return module 830 may be configured to execute the operation S230 described above, which will not be described in detail here.

[0118] Processing module 840 is configured to, in response to receiving a risk handling request from a terminal device, determine a risk handling strategy for the business data group based on the terminal device's handling permissions and the handling parameters in the risk handling request. In one embodiment, processing module 840 may be configured to perform operation S240 described above, which will not be further described here.

[0119] According to an embodiment of the present disclosure, the determination module 810 includes: a first determination submodule, which is used to determine multiple business data newly added on the day from the data lake in response to receiving a risk identification request from the terminal device; an allocation submodule, which is used to call the business volume allocation model and allocate the multiple business data newly added on the day to each operating user in the daily schedule according to groups; a second determination submodule, which is used to determine the target operating user that matches the terminal device based on the device information of the terminal device; and a third determination submodule, which is used to determine the business data group allocated to the target operating user as a business data group that matches the terminal device.

[0120] According to an embodiment of the present disclosure, the calling module 820 includes: a fourth determination submodule, used to determine at least one risk identification model that matches the business data based on the data type of the business data; a calling submodule, used to call each risk identification model, perform risk identification on multiple business data, and obtain the risk identification sub-result output by each risk identification model; and a fifth determination submodule, used to determine the risk identification result based on the risk identification sub-result output by each risk identification model.

[0121] According to an embodiment of the present disclosure, the fourth determination submodule includes: a first determination unit, which is used to determine multiple reconciliation risk identification models that match the business data when it is determined that the data type of the business data is a reconciliation type, wherein the reconciliation risk identification model includes at least one of the following: a first reconciliation risk identification model for risk identification in the reconciliation time dimension, a second reconciliation risk identification model for risk identification in the reconciliation method dimension, a third reconciliation risk identification model for risk identification in the reconciliation result dimension, and a fourth reconciliation risk identification model for risk identification in the reconciliation location dimension; a second determination unit, which is used to determine an account opening risk identification model that matches the business data when it is determined that the data type of the business data is an account opening type.

[0122] According to an embodiment of the present disclosure, the first reconciliation risk identification model outputs a first risk identification sub-result, the second reconciliation risk identification model outputs a second risk identification sub-result, the third reconciliation risk identification model outputs a third risk identification sub-result, and the fourth reconciliation risk identification model outputs a fourth risk identification sub-result.

[0123] The fifth determination submodule includes: a third determination unit, used to determine the number of risk results based on the first risk identification sub-result, the second risk identification sub-result, the third risk identification sub-result and the fourth risk identification sub-result; a fourth determination unit, used to determine the risk identification result based on the number of risk results, the first risk identification sub-result, the second risk identification sub-result, the third risk identification sub-result and the fourth risk identification sub-result.

[0124] According to an embodiment of the present disclosure, a calling submodule includes: for the second reconciliation risk identification model, an acquisition unit is used to obtain reconciliation track information of business users related to business data; an input unit is used to input the reconciliation track information, the generation time of the business data and the reconciliation duration into the second reconciliation risk identification model, and output a second risk identification sub-result.

[0125] According to an embodiment of the present disclosure, the processing parameters include organizational parameters and policy parameters. Processing module 840 includes: a sixth determination submodule for determining, if the processing authority is determined to be the same as the organizational parameter, a risk processing strategy for the business data group based on the policy parameters; and a seventh determination submodule for determining, if the processing authority is determined to be different from the organizational parameter, a risk processing strategy for the business data group based on the policy parameters and the organizational parameter.

[0126] According to embodiments of the present disclosure, any multiple modules among the determination module 810, the call module 820, the return module 830, and the processing module 840 may be combined into a single module, or any one of these modules may be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of other modules and implemented in a single module. According to embodiments of the present disclosure, at least one of the determination module 810, the call module 820, the return module 830, and the processing module 840 may be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or may be implemented in hardware or firmware through any other reasonable means of circuit integration or packaging, or may be implemented in any one of the three implementation methods of software, hardware, and firmware, or any appropriate combination of any of these. Alternatively, at least one of the determination module 810, the call module 820, the return module 830, and the processing module 840 may be at least partially implemented as a computer program module that, when executed, performs the corresponding functionality.

[0127] Figure 9 A block diagram of an electronic device suitable for implementing a business data processing method according to an embodiment of the present disclosure is shown.

[0128] like Figure 9As shown, the electronic device 900 according to an embodiment of the present disclosure includes a processor 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 902 or a program loaded from a storage unit 908 into a random access memory (RAM) 903. The processor 901 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 901 may also include onboard memory for caching purposes. The processor 901 may include a single processing unit or multiple processing units for performing different actions of the method flow according to the embodiment of the present disclosure.

[0129] Various programs and data required for the operation of the electronic device 900 are stored in the RAM 903. The processor 901, the ROM 902, and the RAM 903 are connected to each other via a bus 904. The processor 901 executes the various operations of the method flow according to the embodiment of the present disclosure by executing the programs in the ROM 902 and / or the RAM 903. It should be noted that the programs may also be stored in one or more memories other than the ROM 902 and the RAM 903. The processor 901 may also execute the various operations of the method flow according to the embodiment of the present disclosure by executing the programs stored in the one or more memories.

[0130] According to an embodiment of the present disclosure, electronic device 900 may further include an input / output (I / O) interface 905, which is also connected to bus 904. Electronic device 900 may also include one or more of the following components connected to I / O interface 905: an input section 906 including a keyboard, mouse, etc.; an output section 907 including devices such as a cathode ray tube (CRT), liquid crystal display (LCD), and speakers; a storage section 908 including a hard disk; and a communication section 909 including a network interface card such as a LAN card or modem. Communication section 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to I / O interface 905 as needed. Removable media 911, such as a magnetic disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed in drive 910 as needed, so that computer programs read from the removable media can be installed into storage section 908 as needed.

[0131] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments, or may exist independently and not be incorporated into the device / apparatus / system. The computer-readable storage medium carries one or more programs, and when executed, implements the method according to the embodiments of the present disclosure. According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, such as, but not limited to, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of the present disclosure, the computer-readable storage medium may include the ROM 902 and / or RAM 903 described above, and / or one or more memories other than ROM 902 and RAM 903.

[0132] The embodiments of the present disclosure also include a computer program product, which includes a computer program containing program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the business data processing method provided by the embodiments of the present disclosure.

[0133] The computer program executes the above functions defined in the system / device of the embodiment of the present disclosure when the processor 901 executes the computer program. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0134] In one embodiment, the computer program may be stored on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may be transmitted and distributed in the form of a signal on a network medium, downloaded and installed via the communication portion 909, and / or installed from a removable medium 911. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0135] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 909, and / or installed from a removable medium 911. When the computer program is executed by the processor 901, the above-described functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the systems, devices, means, modules, units, etc. described above can be implemented by computer program modules.

[0136] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computer programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C" or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect via the Internet).

[0137] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0138] Those skilled in the art will appreciate that the features described in the various embodiments of the present disclosure may be combined and / or coupled in a variety of ways, even if such combinations or couplings are not explicitly described in the present disclosure. In particular, without departing from the spirit and teachings of the present disclosure, the features described in the various embodiments of the present disclosure may be combined and / or coupled in a variety of ways. All of these combinations and / or couplings fall within the scope of the present disclosure. The embodiments of the present disclosure have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the various embodiments have been described above separately, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. Without departing from the scope of the present disclosure, those skilled in the art may make various substitutions and modifications, and these substitutions and modifications should all fall within the scope of the present disclosure.

Claims

1. A business data processing method, characterized in that: The method comprises: In response to receiving a risk identification request from a terminal device, determining a business data group that matches the terminal device, wherein the business data group includes a plurality of business data of a plurality of data types; Invoking a risk identification model that matches the business data, performing risk identification on the plurality of business data, and obtaining a risk identification result; Returning the risk identification result and the model identifier of the risk identification model to the terminal device; and In response to receiving a risk handling request from the terminal device, determining a risk handling strategy for the business data group according to a handling authority of the terminal device and a handling parameter in the risk handling request; The step of determining, in response to receiving a risk identification request from a terminal device, a business data group matching the terminal device includes: In response to receiving a risk identification request from a terminal device, determining multiple business data newly added on the day from the data lake; Call the business volume allocation model to allocate multiple newly added business data to each operating user in the daily schedule according to groups; determining a target operating user matching the terminal device according to the device information of the terminal device; and determining the service data group assigned to the target operating user as a service data group matching the terminal device; The risk identification model matching the business data is called to perform risk identification on the plurality of business data, and the risk identification results obtained include: Determining, according to a data type of the business data, at least one risk identification model that matches the business data; Calling each of the risk identification models to perform risk identification on the plurality of business data, and obtaining a risk identification sub-result output by each of the risk identification models; and Determining the risk identification result according to the risk identification sub-results output by each of the risk identification models; The processing parameters include an organization parameter and a policy parameter; and determining the risk processing policy for the business data group based on the processing authority of the terminal device and the processing parameters in the risk processing request includes: In a case where it is determined that the processing authority is the same as the organization parameter, determining a risk processing strategy for the business data group according to the strategy parameter; In the case where it is determined that the processing authority is different from the organizational parameter, a risk processing strategy for the business data group is determined according to the strategy parameter and the organizational parameter.

2. The method according to claim 1, characterized in that The determining, based on the data type of the business data, at least one risk identification model that matches the business data includes: If it is determined that the data type of the business data is a reconciliation type, multiple reconciliation risk identification models matching the business data are determined, wherein the reconciliation risk identification models include at least one of the following: a first reconciliation risk identification model for identifying risks in a reconciliation time dimension, a second reconciliation risk identification model for identifying risks in a reconciliation method dimension, a third reconciliation risk identification model for identifying risks in a reconciliation result dimension, and a fourth reconciliation risk identification model for identifying risks in a reconciliation location dimension; When it is determined that the data type of the business data is an account opening type, an account opening risk identification model matching the business data is determined.

3. The method according to claim 2, characterized in that The first reconciliation risk identification model outputs a first risk identification sub-result, the second reconciliation risk identification model outputs a second risk identification sub-result, the third reconciliation risk identification model outputs a third risk identification sub-result, and the fourth reconciliation risk identification model outputs a fourth risk identification sub-result; Determining the risk identification result according to the risk identification sub-result output by each risk identification model includes: determining a number of risk results based on the first risk identification sub-result, the second risk identification sub-result, the third risk identification sub-result, and the fourth risk identification sub-result; as well as The risk identification result is determined according to the number of risk results, the first risk identification sub-result, the second risk identification sub-result, the third risk identification sub-result, and the fourth risk identification sub-result.

4. The method according to claim 2, characterized in that The calling of each of the risk identification models to perform risk identification on the plurality of business data, and obtaining a risk identification sub-result output by each of the risk identification models, includes: For the second reconciliation risk identification model, obtaining reconciliation track information of business users related to the business data; and The reconciliation track information, the generation time of the business data, and the reconciliation duration are input into the second reconciliation risk identification model, and a second risk identification sub-result is output.

5. A business data processing device, configured to execute the method according to any one of claims 1 to 4, characterized in that: The device comprises: a determination module, configured to, in response to receiving a risk identification request from a terminal device, determine a business data group that matches the terminal device, wherein the business data group includes a plurality of business data of a plurality of data types; A calling module is used to call a risk identification model that matches the business data, perform risk identification on a plurality of the business data, and obtain a risk identification result; a returning module, configured to return the risk identification result and the model identifier of the risk identification model to the terminal device; and The processing module is configured to, in response to receiving a risk processing request from the terminal device, determine a risk processing strategy for the business data group according to a processing authority of the terminal device and processing parameters in the risk processing request.

6. An electronic device comprising: one or more processors; a memory for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

8. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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