Data auditing method and computer equipment
Through computer equipment and methods, the target audit plan matching business data is selected, and multiple functional nodes of audit rules are called, which solves the problems of low efficiency and insufficient accuracy of manual audits, and achieves efficient and reliable data audits.
Patent Information
- Application Number
- CN202510217366.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-26
- Publication Date
- 2025-07-04
AI Technical Summary
In the prior art, the method of manually performing data review based on the audit documents is inefficient and easy to miss the audit dimension, resulting in inaccurate audit results.
Using computer equipment and methods, we receive risk audit requests and select a target audit plan that matches the business data to be reviewed. The plan includes multiple audit rules and functional nodes, and generates the first risk audit result by calling the functional nodes of these rules.
A comprehensive and systematic audit of business data is achieved, avoiding the omission of audit dimensions and improving the efficiency and reliability of audits.
Smart Images
Figure CN120258491A_ABST
Abstract
Description
Technical Field
[0001] This specification relates to the technical field of risk control, and particularly to a data review method and a computer device. Background Art
[0002] Data review refers to checking business data to determine its compliance. For example, the invoice data to be reimbursed can be checked to determine its compliance. In the related art, business data is reviewed manually according to a review document. The review dimensions to be reviewed are recorded in the review document. However, the method of manual review according to the review document has low review efficiency. Moreover, business data may need to be reviewed from multiple different dimensions. The method of manual review according to the review document may miss review dimensions, resulting in inaccurate review results. Summary of the Invention
[0003] Embodiments of this specification provide a data review method and a computer device, which are used to improve the efficiency and reliability of review.
[0004] Embodiments of this specification provide a data review method, including:
[0005] Receiving a risk review request, where the risk review request includes business data to be reviewed;
[0006] Selecting a target review plan that matches the business data to be reviewed, where the target review plan includes one or more review rules, each review rule corresponds to a review dimension, and includes multiple functional nodes;
[0007] According to the business data to be reviewed, calling the functional nodes under the review rule to obtain the first risk review result of the review rule;
[0008] Returning the first risk review result of the review rule under the target review plan.
[0009] Embodiments of this specification also provide a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the above data review method is implemented.
[0010] Embodiments of this specification also provide a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above data review method is implemented.
[0011] Embodiments of this specification also provide a computer program product, where the computer program product includes a computer program, and when the computer program is executed by a processor, the above data review method is implemented.
[0012] The technical solution of the embodiment of this specification can receive a risk review request, where the risk review request includes business data to be reviewed; it can select a target review plan that matches the business data to be reviewed, and the target review plan includes one or more review rules, each review rule corresponding to a review dimension and including multiple functional nodes; it can call the functional nodes under the review rule according to the business data to be reviewed to obtain the first risk review result of the review rule; and it can return the first risk review result of the review rule under the target review plan. Thus, the target review plan includes one or more review rules, each review rule corresponding to a review dimension and including multiple functional nodes. In this way, a comprehensive and systematic review of the business data to be reviewed can be achieved, avoiding omission of review dimensions, reducing human errors, and improving the efficiency and reliability of the review. Brief Description of the Drawings
[0013] To more clearly illustrate the technical solutions in the embodiments of this specification or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. The drawings in the following description are only some embodiments recorded in this specification. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.
[0014] Figure 1 It is a flowchart of the data review method in the embodiment of this specification;
[0015] Figure 2 It is a schematic diagram of the data review process in the embodiment of this specification;
[0016] Figure 3 It is a schematic diagram of the data review process in the embodiment of this specification;
[0017] Figure 4 It is a schematic diagram of the structure of the data review device in the embodiment of this specification. Detailed Description of the Embodiments
[0018] The following will clearly and completely describe the technical solutions in the embodiments of this specification in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only some embodiments of this specification, rather than all embodiments. The specific embodiments described here are only used to explain the present disclosure, rather than limiting the present disclosure. Based on the described embodiments of the present disclosure, all other embodiments obtained by those of ordinary skill in the art belong to the scope of protection of the present disclosure. In addition, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations.
[0019] For example, business data may need to be audited from dimensions such as data accuracy, data integrity, data timeliness, and whether the data complies with specific regulations. In the related technologies described above, business data is audited manually according to an audit document. However, the method of manual auditing according to an audit document may miss audit dimensions, resulting in inaccurate audit results.
[0020] An embodiment of this specification provides a system.
[0021] The system may include an access device and a server. The access device may be a device for the service demander, including but not limited to smartphones, desktop computers, servers, laptops, etc. The server is a device for the service provider. The server may be a single server, or may also be a distributed server cluster including multiple servers.
[0022] In some embodiments, the access device may send a risk audit request to the server, and the risk audit request includes business data to be audited. The server may receive the risk audit request; may select a target audit plan that matches the business data to be audited. The target audit plan includes one or more audit rules, each audit rule corresponding to an audit dimension and including multiple functional nodes; may, according to the business data to be audited, call the functional nodes under the audit rule to obtain the first risk audit result of the audit rule; and may return the first risk audit result of the audit rule under the target audit plan to the access device. Optionally, an intelligent audit engine may run on the server. The audit engine may select a target audit plan that matches the business data to be audited. The target audit plan includes one or more audit rules, each audit rule corresponding to an audit dimension and including multiple functional nodes; may, according to the business data to be audited, call the functional nodes under the audit rule to obtain the first risk audit result of the audit rule.
[0023] In some embodiments, the access device may send an audit plan construction request to the server. The server may receive the audit plan construction request; may send multiple audit rules available for selection to the access device. The access device may receive and display the multiple audit rules; may obtain the audit rules selected by the tenant from the multiple audit rules; and may send the audit rules selected by the tenant to the server. The server may receive the audit rules selected by the tenant; may encapsulate the selected audit rules into an audit plan. The server may also establish a correspondence between the tenant identifier of the tenant and the audit plan.
[0024] A tenant can create multiple functional nodes on the access device; the invocation logic of the multiple functional nodes can be input on the access device. The access device can send the created multiple functional nodes and the invocation logic of the multiple functional nodes to the server. The server can encapsulate the received multiple functional nodes into audit rules according to the received invocation logic.
[0025] Please refer to Figure 1 , Figure 2 and Figure 3 . An embodiment of this specification provides a data audit method. The data audit method can be applied to a server. The data audit method can include the following steps.
[0026] Step 11: Receive a risk audit request, where the risk audit request includes business data to be audited.
[0027] In some embodiments, when a service requester needs to audit business data, the business data to be audited can be input on the access device; operations can be performed on the access device to cause the access device to send a risk audit request to the server. The server can receive the risk application request. The risk application request is used to request the server to audit the business data to be audited from one or more audit dimensions to determine whether the business data to be audited is compliant, so as to determine whether there is a risk.
[0028] The business data to be audited can be structured data. For example, it can be structured financial data, consumption data, transaction data, etc. The business data to be audited can include bill data and / or document data. Bill data is a written document with certain legal effect. Bill data can include invoices, drafts, checks, etc. Document data is data used to record events. Document data can include reimbursement forms, delivery notes, etc. Of course, the above business data can also be other data, such as report data, etc.
[0029] The business data to be audited includes multiple sub-business data to be audited. The multiple sub-business data to be audited are data of different types. For example, the multiple sub-business data to be audited include one or more bill sub-business data and one or more document sub-business data, etc.
[0030] In some embodiments, the risk application request may further include a target tenant identifier. The target tenant identifier is used to identify the tenant. The tenant can be a service requester. For example, the target tenant identifier can be the name, account number, etc. of the tenant.
[0031] Step 12: Select a target audit plan that matches the business data to be audited. The target audit plan includes one or more audit rules. Each audit rule corresponds to one audit dimension and includes multiple functional nodes.
[0032] In some embodiments, the server may provide a set of audit schemes. The set of audit schemes may include one or more audit schemes. Each audit scheme may include one or more audit rules, and each audit rule corresponds to an audit dimension. Thus, an audit scheme can audit the business data to be audited from one or more audit dimensions, so as to achieve a comprehensive and systematic audit of the business data to be audited, avoid omission of audit dimensions, reduce human errors, and improve the reliability of the audit.
[0033] The audit scheme may be pre-configured. Each audit scheme may correspond to an audit scenario. An audit scheme may be a set containing several audit rules configured for an audit scenario. For example, the set of audit schemes may include a bill audit scheme, a document audit scheme, etc. The bill audit scheme may be a set containing several audit rules configured for the bill audit scenario. The audit rules in the bill audit scheme are bill audit rules. The document audit scheme may be a set containing several audit rules configured for the document audit scenario. The audit rules in the document audit scheme are document audit rules. The audit rules may be pre-configured. Each audit rule may correspond to an audit dimension. An audit rule is used to represent the constraint conditions that the business data needs to meet under an audit dimension. For example, the document audit scheme includes audit rule 1 and audit rule 2. Audit rule 1 corresponds to the document remarks dimension and is used to constrain that the remarks column of the document cannot be empty. Audit rule 2 corresponds to the document amount and is used to constrain that the document amount cannot be 0. The functional node may be a pre-configured functional module. An audit rule may include multiple functional nodes. These multiple functional nodes cooperate with each other to jointly implement the audit under an audit dimension.
[0034] Optionally, different audit schemes may correspond to the same audit scenario, so that different audit schemes can be used to audit a single audit scenario. Of course, different audit schemes may also correspond to different audit scenarios.
[0035] Optionally, an audit rule may belong to multiple audit schemes, so that the same audit rule can be included in different audit schemes. Thus, the audit rule has high reusability. By reusing the audit rule, the development and configuration costs are reduced.
[0036] Optionally, a functional node may belong to multiple audit rules, so that the same functional node can be included in different audit rules. Thus, the functional node has high reusability. By reusing the functional node, the development and configuration costs are reduced.
[0037] In some embodiments, each audit scheme in the set of audit schemes may correspond to a data model. The data model may be a data structure including one or more fields required for auditing using the audit scheme.
[0038] In some embodiments, the audit plan set includes multiple sub - audit plan sets. Each sub - audit plan set corresponds to an audit scenario and may include one or more audit plans. Each audit plan corresponds to a credit rating. Thus, an appropriate audit plan can be selected for auditing according to the tenant's credit rating. The number of audit rules in an audit plan is positively correlated with the audit strictness of the audit plan. Also, the number of audit rules in an audit plan is inversely correlated with the credit rating corresponding to the audit plan. Thus, for tenants with a higher credit rating, an audit plan with fewer audit rules can be used for auditing, so as to adopt a simplified audit plan for auditing, improve the audit speed, and enhance the user experience. For tenants with a lower credit rating, an audit plan with more audit rules can be used for auditing, so as to conduct auditing in a more rigorous manner and improve the accuracy of the audit results. By adopting an adaptive approach, both the audit speed and the audit accuracy can be taken into account.
[0039] In some embodiments, the multiple functional nodes in the audit rules may include multiple of input nodes, data flow nodes, control flow nodes, external call nodes, local read nodes, and output nodes. The input node is used to extract second context data related to the audit rule where the input node is located from the first context data. The first context data is obtained by parsing the business data to be audited. The data flow node is used to process the second context data to obtain new second context data. The control flow node is used to make a judgment based on the second context data, and the judgment result points to the successor node to be called. The successor node can be a data flow node, a control flow node, an external call node, a local read node, or an output node, etc. The output node is used to output the first risk audit result based on the second context data. The external call node is used to obtain external data and incorporate the obtained external data into the second context data. The local read node is used to read local data and incorporate the read local data into the second context data. In an audit rule, the number of data flow nodes, control flow nodes, external call nodes, and local read nodes can each be one or more.
[0040] The external call node obtains external data through a pre - configured call interface.
[0041] The local read node can read the table data in the local library according to the pre - configuration.
[0042] The data flow nodes may include grouping nodes, association nodes, filtering nodes, merging nodes, etc. Among them, the grouping node is used to group - process the second context data. The association node is used to associate - process the second context data. The filtering node is used to filter - process the second context data. The merging node is used to merge - process the second context data.
[0043] The control flow node may include a conditional branch node. The conditional branch node judges the second context data through a conditional branch formula and points to different successor nodes according to the true or false result of the judgment.
[0044] The output node may determine the first review result according to the second context data through a set formula.
[0045] Thus, the second context data can be transmitted between the various functional nodes of the review rule. During the transmission process, the second context data is continuously enriched and increased, and finally the output node outputs the first review result according to the second context data.
[0046] In some embodiments, the server may obtain a target review plan that matches the business data to be reviewed through plan mapping. Specifically, the server may identify a review scenario according to the business data to be reviewed; and may select a corresponding review plan in the review plan set as the target review plan according to the review scenario. The server may determine the review scenario according to the data type of the business data to be reviewed. For example, when the business data to be reviewed is bill data, the review scenario may be determined as a bill review scenario. Another example is that when the business data to be reviewed is document data, the review scenario may be determined as a document review scenario.
[0047] Optionally, the business data to be reviewed may include multiple sub-business data to be reviewed. The multiple sub-business data to be reviewed are data of different types. The server may identify multiple review scenarios according to the multiple sub-business data to be reviewed; and may select corresponding multiple review plans in the review plan set as the target review plans according to the multiple review scenarios.
[0048] In some embodiments, the server may obtain the historical risk audit results of the target tenant identifier; and may calculate the target credit rating corresponding to the target tenant identifier according to the historical risk audit results. For example, the historical risk audit results may include the historical second audit risk audit results. The server may obtain one or more second risk audit results of the target tenant identifier within a set time interval. The set time interval may be, for example, the most recent 1 month, 2 months, 6 months, etc. Each historical risk audit result may be selected from types such as passed, basically passed, partially passed, not passed, etc. The server may count the proportion of each type of historical risk audit result; and may calculate the target credit rating corresponding to the target tenant identifier according to the proportions of the historical risk audit results of each type. For example, the server may calculate the target credit rating using a set formula or rule. The target credit rating may be selected from excellent credit, medium credit, poor credit, etc. Of course, the server may also use other methods to determine the target credit rating corresponding to the target tenant identifier. For example, the server may input the obtained historical risk audit results into a credit rating model to obtain the target credit rating output by the credit rating model. The credit rating model includes a machine learning model, such as a neural network model, etc.
[0049] The server may identify the audit scenario according to the business data to be audited; may select the corresponding sub-audit plan set from the audit plan set according to the audit scenario; and may select the target audit plan from the selected sub-audit plan set according to the target credit rating. In this way, the audit plan can be adaptively selected according to the tenant's credit rating. For tenants with a higher credit rating, an audit plan with fewer audit rules may be used for auditing to improve the audit speed. For tenants with a lower credit rating, an audit plan with more audit rules may be used for auditing to improve the accuracy of the audit results. Thus, both the efficiency of the audit and the accuracy of the audit are ensured.
[0050] Optionally, the business data to be audited may include multiple sub-business data to be audited. The multiple sub-business data to be audited are data of different types. For each sub-business data to be audited, the server may identify the audit scenario according to the sub-business data to be audited; may select the corresponding sub-audit plan set from the audit plan set according to the audit scenario; and may select the corresponding target audit plan from the selected sub-audit plan set according to the target credit rating.
[0051] Step 13: According to the business data to be audited, call the function node under the audit rule to obtain the first risk audit result of the audit rule.
[0052] In some embodiments, the number of target audit schemes may be one or more. For each target audit scheme, the server may obtain the target data model corresponding to the target audit scheme; may parse the business data to be audited according to the target data model to obtain the first context data of the target audit scheme; and may, according to the first context data, invoke the function nodes under each audit rule in the target audit scheme to obtain the first risk audit results of each audit rule.
[0053] The target data model contains the fields involved in the execution of the target audit scheme. The server may extract the corresponding data from the business data to be audited according to the fields in the target data model, so as to obtain the first context data.
[0054] The first context data includes the data required for the execution of the target audit scheme. The structure of the first context data may be a data table structure. The first context data may include one or more fields and their corresponding values. For example, the target audit scheme may include a document audit scheme, and the first context data includes the number of characters in the document remarks field and the document amount in the document amount field. The second context data includes the data required for the execution of the audit rule. The structure of the second context data may be a data table structure. The second context data includes part or all of the first context data.
[0055] In some embodiments, for each audit rule in the target audit scheme, the server may pass the first context data into the input node to extract, by using the input node, the second context data required for the audit rule from the first context data; may pass the second context data into the data flow node to process, by using the data flow node, the second context data to obtain new second context data; may pass the second context data into the control flow node to make a judgment, by using the control flow node, according to the second context data, and the judgment result points to the successor node to be invoked; and may pass the second context data into the output node to obtain the first risk audit result output by the output node.
[0056] The first risk audit result may include passed, not passed, etc. Optionally, when the first risk audit result is not passed, the first risk audit result may further include the reason for not passing. The reason may be related to the audit dimension corresponding to the audit rule. For example, a certain audit rule corresponds to the document remarks dimension and is used to constrain that the remarks column of the document cannot be empty. Then the reason may include that the remarks column is empty. Another example is that a certain audit rule corresponds to the document amount and is used to constrain that the document amount cannot be 0. Then the reason may include that the document amount is 0.
[0057] Optionally, the server may also transmit the second context data to an external call node. The external call node is used to obtain external data and incorporate the obtained external data into the second context data.
[0058] Optionally, the server may also transmit the second context data to a local reading node; the local reading node is used to read local data (also referred to as internal data) and incorporate the read local data into the second context data.
[0059] For example, for each audit rule in the target audit plan, the server may create a corresponding scheduling thread. The scheduling thread may send the first context data to an input node. The input node may extract the second context data required by the audit rule from the first context data. After monitoring that the input node has completed execution, the scheduling thread may send the second context data output by the input node to an external call node. The external call node may obtain external data and incorporate the obtained external data into the second context data. After monitoring that the external call node has completed execution, the scheduling thread may send the second context data output by the external call node to a local reading node. The local reading node may read local data and incorporate the read local data into the second context data. After monitoring that the local reading node has completed execution, the scheduling thread may send the second context data output by the local reading node to a data flow node. The data flow node may process the second context data to obtain new second context data. After monitoring that the data flow node has completed execution, the scheduling thread may send the second context data output by the data flow node to a control flow node. The control flow node may make a judgment based on the second context data, and the judgment result points to the successor node to be called. After monitoring that the control flow node has completed execution, the scheduling thread may send the second context data to the successor node. The successor node may be another data flow node, another control flow node, another external call node, another local reading node, or an output node. The scheduling thread may send the second context data to the output node. The output node may output a first audit result based on the second context data.
[0060] Step 14: Return the first risk audit result of the audit rules under the target audit plan.
[0061] In some embodiments, the server may send the first risk audit results of the respective audit rules under the target audit plan to the access party device. The access party device may receive the first risk audit results of the respective audit rules under the target audit plan.
[0062] In some embodiments, the server may determine the second risk audit result of the target audit plan based on the first risk audit results of each audit rule under the target audit plan, and may send the second risk audit result of the target audit plan to the access device. The access device may receive the second risk audit result of the target audit plan. The second risk audit result may be the audit result of the entire target audit plan. For example, the server may incorporate the first risk audit results of each audit rule under the target audit plan into the second risk audit result. Then, the second risk audit result of the target audit plan may include the first risk audit results of each audit rule under the target audit plan. As another example, the server may count the proportion of each type of first risk audit result in the target audit plan and may determine the second risk audit result of the target audit plan based on the proportion. The types of first risk audit results may include passed, not passed, etc. Optionally, the importance levels of the various audit rules in the target audit plan are different. Weights may be set for the various audit rules in the target audit plan, and the weights are used to indicate the importance levels of the audit rules. The server may count the proportion of each type of first risk audit result in the target audit plan and may determine the second risk audit result of the target audit plan based on the proportion and the weights. For example, the server may use a set formula or rule to determine the second risk audit result of the target audit plan based on the proportion and the weights. Thus, the server may determine the second risk audit result as a whole based on the first risk audit results of the various audit rules.
[0063] Optionally, the server may also store the tenant identifier and the second risk audit result in a corresponding manner. Thus, the server may obtain the historical second risk audit results of the tenant identifier, and may determine the credit rating based on the historical second risk audit results.
[0064] In some embodiments, the access device may send an audit plan configuration request to the server. The server may receive the audit plan configuration request and may send multiple audit rules available for selection to the access device. The multiple audit rules may be pre-configured. The access device may receive and display the multiple audit rules. The tenant may select from the audit rules displayed by the access device. For example, the access device may display the multiple audit rules in the form of a drop-down box. The tenant may select an audit rule through the drop-down box. The access device may send one or more audit rules selected by the tenant to the server. The server may receive the audit rules selected by the tenant and may encapsulate the received audit rules into an audit plan. Optionally, the audit plan configuration request may include the tenant identifier. Then, the server may also establish a corresponding relationship between the audit plan and the tenant identifier. Thus, the configuration of the audit plan is realized, which facilitates the auditing of business data through the audit plan.
[0065] The technical solution of the embodiments of this specification can receive a risk review request, where the risk review request includes business data to be reviewed; it can select a target review plan that matches the business data to be reviewed. The target review plan includes one or more review rules, each review rule corresponds to a review dimension, and includes multiple functional nodes; it can call the functional nodes under the review rule according to the business data to be reviewed to obtain the first risk review result of the review rule; it can return the first risk review result of the review rule under the target review plan. Thus, the target review plan includes one or more review rules, each review rule corresponds to a review dimension, and includes multiple functional nodes. In this way, a comprehensive and systematic review of the business data to be reviewed can be achieved, avoiding omission of review dimensions, reducing human errors, and improving the efficiency and reliability of the review.
[0066] Please refer to Figure 4 This specification also provides a data review device, which includes the following units.
[0067] A receiving unit 41, which receives a risk review request, where the risk review request includes business data to be reviewed;
[0068] A selection unit 42, which is used to select a target review plan that matches the business data to be reviewed. The target review plan includes one or more review rules, each review rule corresponds to a review dimension, and includes multiple functional nodes;
[0069] A calling unit 43, which is used to call the functional nodes under the review rule according to the business data to be reviewed to obtain the first risk review result of the review rule;
[0070] A return unit 44, which is used to return the first risk review result of the review rule under the target review plan.
[0071] This specification also provides a computer device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the above data review method is implemented.
[0072] This specification also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above data review method is implemented.
[0073] This specification also provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the above data review method is implemented.
[0074] Those skilled in the art can understand that this specification can be provided as a method, a system, or a computer program product. Therefore, this specification can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, this specification can 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.) that contain computer-usable program code.
[0075] This specification is described with reference to the flowcharts and / or block diagrams of the methods, apparatuses (systems), and computer program products of the embodiments of this specification. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. The computer can be a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.
[0076] Each functional unit in the embodiments of this specification can be integrated into one processing unit, or each functional unit can exist physically alone, or two or more functional units can be integrated into one processing unit.
[0077] Those skilled in the art can understand that the descriptions of the embodiments in this specification each have their own focuses. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments. Additionally, it can be understood that after reading this specification document, those skilled in the art can, without creative effort, think of combining some or all of the embodiments listed in this specification arbitrarily, and these combinations are also within the scope of disclosure and protection of this specification.
[0078] Although this specification is depicted through embodiments, those of ordinary skill in the art know that the above embodiments are only used to help understand the core idea of this specification. Those skilled in the art can understand that this specification has many variations and changes. It is hoped that the appended claims will cover these variations and changes without departing from the spirit of this specification.
Claims
1. A data review method, characterized in that, including: Receiving a risk audit request, where the risk audit request includes business data to be audited; Selecting a target audit plan that matches the business data to be audited, where the target audit plan includes one or more audit rules, each audit rule corresponds to an audit dimension, and includes multiple functional nodes; Invoking the functional nodes under the audit rule according to the business data to be audited to obtain a first risk audit result of the audit rule; Returning the first risk audit result of the audit rule under the target audit plan.
2. The method according to claim 1, characterized in that The selection of the target audit plan that matches the business data to be audited includes: Identifying an audit scenario according to the business data to be audited; Selecting a corresponding audit plan from the audit plan set as the target audit plan according to the audit scenario; The audit plan set includes one or more audit plans, and each audit plan corresponds to an audit scenario.
3. The method according to claim 2, wherein The audit plan set includes multiple sub-audit plan sets, each sub-audit plan set corresponds to an audit scenario, and includes one or more audit plans, and each audit plan corresponds to a credit rating; The risk audit request further includes a target tenant identifier; The selection of the corresponding audit plan from the audit plan set as the target audit plan according to the audit scenario includes: Obtaining the historical risk audit result of the target tenant identifier; Calculating the target credit rating corresponding to the target tenant identifier according to the historical risk audit result; Selecting a corresponding sub-audit plan set from the audit plan set according to the audit scenario; Selecting the target audit plan from the selected sub-audit plan set according to the target credit rating.
4. The method according to claim 1, wherein The invoking of the functional nodes under the audit rule according to the business data to be audited to obtain a first risk audit result of the audit rule includes: Obtaining a target data model corresponding to the target audit plan; Parsing the business data to be audited according to the target data model to obtain first context data of the target audit plan; Invoking the functional nodes under the audit rule according to the first context data to obtain a first risk audit result of the audit rule.
5. The method according to claim 4, wherein The multiple functional nodes include an input node, a data flow node, a control flow node, and an output node; the invoking of the functional nodes under the audit rule according to the first context data to obtain a first risk audit result of the audit rule includes: Passing the first context data into the input node, where the input node is used to extract second context data required by the audit rule from the first context data; Passing the second context data into the data flow node, where the data flow node is used to process the second context data; Passing the second context data into the control flow node, where the control flow node is used to make a judgment according to the second context data, and the judgment result points to the successor node to be invoked; Passing the second context data into the output node to obtain the first risk audit result output by the output node.
6. The method according to claim 5, wherein The multiple functional nodes further include an external call node; the method further includes: Passing the second context data into the external call node; the external call node is used to obtain external data and include the obtained external data in the second context data.
7. The method according to claim 5, characterized in that, The multiple functional nodes further include a local reading node; The method further includes: Transmitting the second context data to the local reading node; the local reading node is configured to read local data and incorporate the read local data into the second context data.
8. The method according to claim 1, wherein The method further includes: Determining a second risk audit result of the target audit plan based on the first risk audit results of each audit rule under the target audit plan; and feeding back the second risk audit result of the target audit plan.
9. The method according to claim 1, characterized in that, The method further includes: Receiving an audit plan configuration request sent by an access device; Sending a plurality of selectable audit rules to the access device; Receiving an audit rule selected from the plurality of audit rules; Encapsulating the selected audit rule into an audit plan.
10. A computer device, characterized in that, It includes: A memory, a processor, and a computer program stored on the memory, where the processor executes the computer program to implement the steps of the method according to any one of claims 1-9.