Financial data compliance intelligent quality inspection method and device

Abstract

The invention discloses a financial data compliance intelligent quality inspection method and device, and the method comprises the steps: describing different business scenes, defining a plurality of rule factors through business metadata in the different business scenes, and setting rule types; adding rule factors for the business metadata according to execution logic according to different business scenes, obtaining rule execution results according to rule types of the rule factors, generating result decision actions, obtaining intelligent strategies and storing the intelligent strategies; and triggering different service scenes according to a preset triggering condition to execute quality inspection by adopting a preset execution mode through an intelligent strategy to obtain a quality inspection report, and rectifying. Through the method provided by the invention, the massive financial data can be monitored and analyzed in real time, the potential compliance risk can be automatically identified, the compliance cost can be reduced, and the compliance examination efficiency and accuracy can be improved, so that the financial institution is helped to better cope with the supervision requirements, protect the rights and interests of customers and maintain the stability and safety of the financial market.

Description

Technical Field

[0001] The present invention relates to the field of intelligent quality inspection technology, and in particular to a method and device for intelligent quality inspection of financial data compliance. Background Art

[0002] In today's era of rapid digital development, the financial industry faces growing compliance challenges. With the continuous advancement of financial technology, a large number of transactions and data are generated, and regulators have increasingly stringent requirements for the transparency and compliance of financial data. Financial institutions must comply with various laws and regulations, such as Anti-Money Laundering (AML), Customer Due Diligence (CDD) and data protection regulations to ensure the legality and compliance of their operations.

[0003] However, traditional compliance inspection methods often rely too much on manual review, static rules and data structuring, which is not only inefficient but also susceptible to human errors and difficult to cope with the complex and changing financial environment. As the amount of data increases, the pressure of manual review is also increasing. Financial institutions need a more efficient, visual and intelligent solution to improve the accuracy and efficiency of data compliance inspection. Summary of the invention

[0004] To this end, the purpose of the present invention is to at least to some extent solve the deficiencies in the prior art, thereby proposing a method and device for intelligent quality inspection of financial data compliance.

[0005] In a first aspect, the present invention provides a method for intelligent quality inspection of financial data compliance, the method comprising:

[0006] Describing different business scenarios and defining a plurality of business metadata in the different business scenarios respectively, defining a plurality of rule factors respectively through the plurality of business metadata, and setting a rule type for each of the rule factors;

[0007] According to the different business scenarios, a plurality of rule factors are added to the business metadata according to the execution logic, a plurality of rule execution results are obtained according to the rule type of each rule factor, and corresponding result decision actions are generated according to the plurality of rule execution results, so that the corresponding intelligent strategies are obtained and saved for the plurality of different business scenarios;

[0008] According to the preset trigger conditions, multiple different business scenarios are triggered to perform quality inspection through the corresponding intelligent strategies using the preset execution method, obtain a quality inspection report, and make rectifications based on the quality inspection report.

[0009] In a second aspect, the present invention provides a device for intelligent quality inspection of financial data compliance, the device comprising:

[0010] Definition module: used to describe different business scenarios and respectively define a plurality of business metadata in the different business scenarios, respectively define a plurality of rule factors through the plurality of business metadata, and set the rule type of each rule factor;

[0011] Strategy module: used to add multiple rule factors to the business metadata according to the execution logic according to different business scenarios, obtain multiple rule execution results according to the rule type of each rule factor, and generate corresponding result decision actions according to the multiple rule execution results, so that the corresponding intelligent strategies are obtained and saved for the multiple different business scenarios;

[0012] Quality inspection module: used to trigger multiple different business scenarios according to preset trigger conditions, perform quality inspection through the corresponding intelligent strategies using preset execution methods, obtain quality inspection reports, and make rectifications based on the quality inspection reports.

[0013] In the third aspect, the present invention also provides a device for intelligent quality inspection of financial data compliance, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, each step in the method for intelligent quality inspection of financial data compliance as described in the first aspect is implemented.

[0014] In a fourth aspect, the present invention further provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the various steps in the method for intelligent quality inspection of financial data compliance as described in the first aspect are implemented.

[0015] The present invention provides a method and device for intelligent quality inspection of financial data compliance, the method comprising: describing different business scenarios and defining business metadata in a plurality of different business scenarios respectively, defining a plurality of rule factors respectively through a plurality of business metadata, and setting the rule type of each rule factor; adding a plurality of rule factors to the business metadata according to the execution logic according to the different business scenarios respectively, obtaining a plurality of rule execution results according to the rule type of each rule factor, generating corresponding result decision actions according to the plurality of rule execution results, so that a plurality of different business scenarios obtain corresponding intelligent strategies and save them; triggering a plurality of different business scenarios according to preset trigger conditions to perform quality inspection through the corresponding intelligent strategies in a preset execution mode, obtaining a quality inspection report, and making rectification according to the quality inspection report. Through the method provided by the present invention, it is possible to monitor and analyze massive financial data in real time, automatically identify potential compliance risks, which can not only reduce compliance costs, but also improve the efficiency and accuracy of compliance review, thereby helping financial institutions to better respond to regulatory requirements, protect customer rights and interests, and maintain the stability and security of the financial market. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0017] Figure 1 A flowchart of the method for intelligent quality inspection of financial data compliance of the present invention;

[0018] Figure 2 It is a schematic diagram of the sub-flow of the method for intelligent quality inspection of financial data compliance of the present invention;

[0019] Figure 3 A schematic diagram of basic information of defining rule factors of the method for intelligent quality inspection of financial data compliance of the present invention;

[0020] Figure 4 A schematic diagram of input parameter variables of rule factors of the method for intelligent quality inspection of financial data compliance of the present invention;

[0021] Figure 5 A schematic diagram of defining rule types for rule factors of the method for intelligent quality inspection of financial data compliance of the present invention;

[0022] Figure 6A schematic diagram of the definition of some rule factors for personal account opening in the method for intelligent quality inspection of financial data compliance of the present invention;

[0023] Figure 7 A schematic diagram of an example of a decision action for a result of an ID card portrait quality inspection in the method for intelligent quality inspection of financial data compliance of the present invention;

[0024] Figure 8 It is another sub-flow diagram of the method for intelligent quality inspection of financial data compliance of the present invention;

[0025] Fig. 9 It is another sub-process diagram of the method for intelligent quality inspection of financial data compliance of the present invention;

[0026] Fig.10 A schematic diagram of a quality inspection report of a method for intelligent quality inspection of financial data compliance according to the present invention;

[0027] Fig.11 It is another sub-process diagram of the method for intelligent quality inspection of financial data compliance of the present invention;

[0028] Fig.12 It is an overall logic diagram of the method for intelligent quality inspection of financial data compliance of the present invention;

[0029] Fig.13 This is a schematic diagram of the program modules of the device for intelligent quality inspection of financial data compliance of the present invention. DETAILED DESCRIPTION

[0030] In order to make the purpose, features and advantages of the invention more obvious and easy to understand, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0031] Please refer to Figure 1 , Figure 1 : is a flow chart of a method for intelligent quality inspection of financial data compliance in an embodiment of the present application. In this embodiment, the method for intelligent quality inspection of financial data compliance includes:

[0032] Step S10: Describe different business scenarios and define a plurality of business metadata in the different business scenarios respectively, define a plurality of rule factors respectively through the plurality of business metadata, and set a rule type for each of the rule factors.

[0033] In this embodiment, the business scenario information may be preset and limited scenario information based on the financial business needs. For example, in the financial business scenario, it involves financial investment and research business scenarios, financial risk control and early warning business scenarios, financial marketing intelligence business scenarios, and financial gossip business scenarios, etc. It is also a scenario description of compliance quality inspection applications, including business types, data sources, associated metadata items, and quality inspection data scopes. For example, compliance quality inspection is performed on the existing account data of personal account opening in 2024. When the business scenario requires quality inspection later, the business scenario determines the data source accessed during the quality inspection and the data extraction scope.

[0034] Specifically, each business scenario has multiple business elements, namely, business metadata used to describe the business scenario. The definition of business metadata is the unified and standardized management of the attributes of various types of businesses such as securities personal account opening, personal business handling, institutional account opening, and institutional business handling, which is used to enhance the unification of business language, facilitate data management and sharing, and improve data model reuse. For example, the business elements in the off-site account opening business scenario include certificate type, certificate number, name, date of birth, certificate portrait, certificate back, and headshot. Each attribute item corresponds to a business metadata, including data type, data description, data name, data storage format, data length, and associated data dictionary items.

[0035] That is, each business scenario contains multiple business metadata, and multiple business scenarios have different business metadata. Multiple rule factors are defined by different business metadata. That is, each business metadata can define multiple rule factors. The definition of rule factors is mainly based on the business metadata associated with different business scenarios, so that the association relationship between business metadata and rule factors can be established.

[0036] Furthermore, the defining of multiple rule factors through multiple business metadata respectively and setting the rule type of each rule factor includes:

[0037] Step 101: define the basic information and input parameter variables of the corresponding rule factors according to the business metadata in different business scenarios;

[0038] Step 102: Select the rule type of the corresponding rule factor according to the business metadata of different business scenarios, wherein the rule type includes an interface call type rule, an expression type rule and a dynamic groovy script type rule.

[0039] See also Figure 2-Figure 6 , Figure 2 is a schematic diagram of a sub-process in an embodiment of the present application, and Figure 3-Figure 5This is a schematic diagram of the basic information, input parameter variables and configured rule types defined by the rule factors in the embodiment of the present application after the business metadata is defined. In this embodiment, each rule factor defines its basic information and input parameter variables according to the business metadata in different business scenarios, so that an association relationship between the rule factor and the business metadata can be established. For example, the legality rule of the certificate, whether the gender is consistent with the 17th gender position of the identity card, the rule factor needs to be bound to two input parameter variables corresponding to the metadata gender and certificate number respectively.

[0040] After the basic information and input parameter variables of the rule factor are defined according to the business metadata in different business scenarios, the rule factor must also be bound to the rule type according to the business metadata of different scenarios. The rule type is also the judgment logic expression of the rule factor, which is a static rule base and AI algorithm binding respectively. It can be specifically divided into interface call type rules, expression type rules and dynamic groovy script type rules.

[0041] Specifically, business metadata is multi-type data, which may include text, images, and structured data, and the above three types of rules are defined specifically for business metadata. Among them, image quality inspection (certificate photo clarity, completeness, anti-counterfeiting, recognition, face comparison, etc.) uses interface call type rules, and the core of the interface rule is to bind open source or commercial AI models; structured data mainly uses expression type rules to support single object review, multi-object correlation review, and multi-object mutual exclusion review; text data is relatively complex because it needs to be intelligently extracted and converted, so it is necessary to use dynamic groovy script type rules to write data extraction and data secondary processing logic. And each business scenario will define a set of compliance rule factors, please refer to Figure 6 , Figure 6 Schematic diagram of the definition of some rule factors for opening an individual account.

[0042] Step S20: Add multiple rule factors to the business metadata according to the execution logic based on different business scenarios, obtain multiple rule execution results according to the rule type of each rule factor, and generate corresponding result decision actions based on the multiple rule execution results, so that the multiple different business scenarios can obtain corresponding intelligent strategies and save them.

[0043] In this embodiment, rule factors are assembled for different business scenarios, wherein each business scenario has different business metadata, and then multiple rule factors are assembled for the business metadata required in different business scenarios, and the execution logic of the multiple rule factors is arranged. The execution logic includes serial, parallel or cyclic execution of multiple rule factors, thereby simplifying the traditional workflow design, which is abstracted into only one table to store the predecessor and successor task relationships of the rule factors, and adopts the predecessor rule factor time-driven algorithm to implement the policy process execution plan, thereby improving high-performance execution and effectively reducing system complexity.

[0044] After assembling multiple rule factors for multiple business metadata according to the execution logic, they are executed according to the rule type of each rule factor to obtain multiple rule execution results. For example, a business metadata is an ID card, and one of the rule factors assembled is the quality detection of the ID card portrait. The rule type corresponding to the rule factor is the ID photo quality detection interface. The ID card is detected through the ID photo quality detection interface to obtain multiple rule execution results, including whether it is clear, original, reflective, or trimmed.

[0045] After obtaining multiple rule execution structures, corresponding result decision actions are generated according to the multiple rule execution results, so that it can be compatible with different business scenarios. The same rule execution results need to execute different result decision actions. For example, one of the rule execution results of the ID card portrait quality inspection is whether it is reflective. For reflection, the corresponding result decision action "forced interception" should be set, and the interception prompt is "ID photo reflective". Please refer to Figure 7 , Figure 7 This is a schematic diagram of an example of a decision-making action for the result of the ID card portrait quality inspection in an embodiment of the present application.

[0046] Ultimately, multiple rule factors are assembled to meet the needs of multiple business metadata in multiple different specific business scenarios, and multiple rule execution results are generated. The rule checking process that generates corresponding result decision actions based on the rule execution results is the intelligent strategy corresponding to the specific business scenario, and multiple intelligent strategies corresponding to different specific business scenarios are saved to achieve the purpose of calling intelligent strategies to check data compliance during or at some time in the future.

[0047] Furthermore, the multiple rule execution results are obtained according to the rule type of each rule factor, including:

[0048] Based on each of the rule factors, the corresponding business metadata is processed by the rule type to obtain a plurality of the rule execution results;

[0049] Among them, when the rule type of the rule factor is selected as the interface call type rule, the rule factor processes the business metadata by calling an interface, and the calling interface is configured with an SA interface scheduling module and an interface threshold module.

[0050] In this embodiment, each business metadata has corresponding multiple rule factors, wherein each rule factor is set with a corresponding rule type according to the business metadata, and the business metadata is executed and processed according to the corresponding rule type, thereby obtaining multiple rule execution results.

[0051] Specifically, when the rule type of the rule factor is an interface call type rule, the adopted call interface is configured with an SA interface scheduling module and an interface threshold module. Among them, the SA interface scheduling module includes the setting of interface access authorization and interface call current limiting rules, which is used to govern open interface services and meet the high availability requirements of the system; the interface threshold module is used to register the supplier AI capability interface and the interface result item. The interface result item is the data (business metadata) that needs to be processed by calling the interface. The function is to generate the rule execution result according to the interface result item by calling the interface, and return the generated rule execution result to the system for decision-making.

[0052] For further information, see Figure 8 , Figure 8 This is another sub-flow diagram of the method for intelligent quality inspection of financial data compliance in an embodiment of the present application. In this embodiment, the multiple rule execution results are obtained according to the rule type of each rule factor, and corresponding result decision actions are generated according to the multiple rule execution results, and further includes:

[0053] Step 201: assign values ​​to the plurality of rule execution results of each rule factor according to a preset judgment threshold and combine them to obtain a judgment value of each rule factor;

[0054] Step 202: Set the corresponding result decision action according to the determination value of each rule factor.

[0055] In this embodiment, when a business metadata obtains multiple rule execution results through the execution of a judgment expression logic formula (rule type) of a rule factor, each rule execution result is assigned a value, and the multiple assigned rule execution results are combined to obtain the judgment value of the rule factor, and then the corresponding result decision action is set according to the judgment value of the rule factor.

[0056] For example, after a ID photo is executed through the quality detection interface, multiple rule execution results are obtained, including whether it is clear, whether it is an original, whether it is reflective, whether it is cut, etc., where each rule execution result contains a range of judgment thresholds of yes and no. If a rule execution result is greater than the judgment threshold, it is assigned a value of 1. If it is less than the preset judgment threshold, it is assigned a value of 0. For example, whether it is clear, the judgment threshold of this result is 0.8 points. After the rule execution result is executed through the quality detection interface, 0.9 points are obtained, and 1 point is assigned. If the rule execution result is 0.3 points after being executed through the quality detection interface, it is assigned a value of 0. Then, the multiple assignments of the multiple rule execution results of the ID photo are combined to obtain the specific judgment value of a rule factor of the ID photo, and then the corresponding result decision action is set according to the judgment value.

[0057] For further information, see Fig. 9 , Fig. 9 This is another sub-flow diagram of the method for intelligent quality inspection of financial data compliance in an embodiment of the present application. In this embodiment, the corresponding result decision action is set according to the determination value of each rule factor, and further includes:

[0058] Step 2021: Compare the determination value of each rule factor with preset thresholds of multiple intervals to obtain a comparison result;

[0059] Step 2022: configure different result decision actions according to the comparison result.

[0060] In this embodiment, the rule execution result is scored, and different decision actions are set for the rule execution results of different score intervals. The assignments of multiple rule execution results of each rule factor are combined to obtain the judgment value of the rule factor, and the judgment value of the rule factor is compared with the preset thresholds of multiple intervals to obtain the comparison result, and then different result decision actions are configured according to the comparison result, wherein each interval has a different result decision action. For example, if the judgment value of a certain ID photo is 3, which is between the preset threshold of 2-3 points, it means that the ID photo is the person himself, and the result decision action is set to "pass". If the judgment value is 0, which is between the preset threshold of 0-1, it means that the ID photo is not the person himself, and the result decision action is set to "reject". If the judgment value is 1, which is between the preset threshold of 1-2, it means that the ID photo is suspected to be the person himself, and the result decision action is set to "manual confirmation required".

[0061] Step S30: triggering a plurality of different business scenarios according to preset trigger conditions, performing quality inspections through corresponding intelligent strategies in a preset execution mode, obtaining quality inspection reports, and making corrections based on the quality inspection reports.

[0062] In this embodiment, when different business scenarios need to be inspected, each business scenario has a corresponding intelligent strategy, which can trigger quality inspection of different business scenarios according to preset trigger conditions. When a business scenario needs to be inspected, the business scenario that needs to be inspected will be inspected through its corresponding intelligent strategy in a preset execution method and a quality inspection report will be obtained, so that the detailed problem rectification items in the quality inspection report can be viewed and rectified. Fig.10 , Fig.10 Schematic diagram of the quality inspection report in the embodiment of the present application.

[0063] Furthermore, the preset trigger condition is set using a cron expression and includes a fixed time interval, a specified time point, and a specified time period; the preset execution method includes manual execution and an application system calling an API.

[0064] For further information, see Fig.11 , Fig.11 This is another sub-flow diagram of the method for intelligent quality inspection of financial data compliance in an embodiment of the present application. In this embodiment, obtaining a quality inspection report and making rectifications according to the quality inspection report include:

[0065] Step 301: Submit feedback according to the errors in the quality inspection report to obtain feedback results;

[0066] Step 302: Calculate the accuracy of the rule execution result according to the feedback result;

[0067] Step 303: If the accuracy rate is lower than a preset accuracy threshold, the preset determination threshold is automatically adjusted to improve the accuracy rate.

[0068] In this embodiment, in order to improve the accuracy of the quality inspection results, feedback is submitted for the quality inspection errors in the quality inspection report, thereby obtaining feedback results. The accuracy of the rule execution results can be calculated based on the feedback results. If the accuracy of the rule execution results is lower than the preset accuracy threshold, for example, the preset accuracy threshold is 95%. If the accuracy is lower than 95%, the preset threshold will be automatically adjusted. For example, if the face comparison similarity is greater than 0.8, it will be adjusted to greater than 0.75, thereby realizing autonomous learning to improve the accuracy of the rules.

[0069] The embodiment of the present application provides a method for intelligent quality inspection of financial data compliance, the method comprising: describing different business scenarios and defining business metadata in multiple different business scenarios respectively, defining multiple rule factors respectively through multiple business metadata, and setting the rule type of each rule factor; adding multiple rule factors to the business metadata according to the execution logic according to different business scenarios, obtaining multiple rule execution results according to the rule type of each rule factor, generating corresponding result decision actions according to the multiple rule execution results, so that multiple different business scenarios obtain corresponding intelligent strategies and save them; triggering multiple different business scenarios according to preset trigger conditions to perform quality inspection through the corresponding intelligent strategies using preset execution methods, obtain quality inspection reports, and make rectifications according to the quality inspection reports. Through the method provided by the present invention, massive financial data can be monitored and analyzed in real time, and potential compliance risks can be automatically identified, which can not only reduce compliance costs, but also improve the efficiency and accuracy of compliance review, thereby helping financial institutions to better respond to regulatory requirements, protect customer rights and interests, and maintain the stability and security of the financial market.

[0070] For further information, see Fig.12 , Fig.12 The overall logic diagram of the intelligent quality inspection of financial data compliance in the embodiment of the present application is as follows:

[0071] 1. Describe multiple different business scenarios and define business metadata in multiple different business scenarios;

[0072] 2. Define the rule factors according to the business metadata in different business scenarios, define the basic information of the rule factors, set the input parameter variables, and then bind the AI ​​interfaces, design static rule expressions, or write groovy scripts according to different scenarios, and set the rule execution results of the rule execution. At the same time, configure the SA interface scheduling module and interface threshold module for the called AI interface;

[0073] 3. Assemble multiple rule factors for different business scenarios and arrange the execution logic of the rule factors (serial, parallel or cyclic);

[0074] 4. For each rule factor, the corresponding business metadata is executed through the adapted rule type, thereby obtaining multiple rule execution results. The multiple rule execution results are assigned values ​​through the preset judgment threshold to obtain the judgment value of each rule factor;

[0075] 5. Compare the judgment value of each rule factor with the preset thresholds of multiple intervals, where each preset threshold of the interval has a different result decision action, so as to obtain the result decision action adapted to each rule factor, so as to be compatible with different business scenarios where the same rule execution results need to perform different result decision actions, thereby obtaining and saving the corresponding intelligent strategies for multiple different business scenarios;

[0076] 6. Trigger different business scenarios through preset trigger conditions, and use the corresponding intelligent strategies to adopt the preset execution method to perform quality inspection, and obtain quality inspection reports and rectification matters;

[0077] 7. Submit feedback on errors in the quality inspection report, calculate the accuracy of the rule execution results, and then make adjustments to improve the accuracy of the quality inspection results. In addition, the progress of rectification of matters can be tracked later.

[0078] Furthermore, the embodiment of the present application also provides a device 400 for intelligent quality inspection of financial data compliance. Fig.13 4 is a schematic diagram of a program module of a device for intelligent quality inspection of financial data compliance in an embodiment of the present application. In this embodiment, the device 400 for intelligent quality inspection of financial data compliance includes:

[0079] Definition module 401: used to describe different business scenarios and respectively define a plurality of business metadata in the different business scenarios, respectively define a plurality of rule factors through the plurality of business metadata, and set a rule type for each of the rule factors;

[0080] Strategy module 402: used to add multiple rule factors to the business metadata according to execution logic according to different business scenarios, obtain multiple rule execution results according to the rule type of each rule factor, and generate corresponding result decision actions according to the multiple rule execution results, so that corresponding intelligent strategies are obtained and saved for multiple different business scenarios;

[0081] Quality inspection module 403: used to trigger multiple different business scenarios according to preset trigger conditions, perform quality inspection through corresponding intelligent strategies using preset execution methods, obtain quality inspection reports, and make rectifications based on the quality inspection reports.

[0082] In the embodiment of the present application, a device for intelligent quality inspection of financial data compliance is provided, which can achieve: describing different business scenarios and defining business metadata in multiple different business scenarios respectively, defining multiple rule factors respectively through multiple business metadata, and setting the rule type of each rule factor; adding multiple rule factors to the business metadata according to the execution logic according to different business scenarios, obtaining multiple rule execution results according to the rule type of each rule factor, generating corresponding result decision actions according to the multiple rule execution results, so that multiple different business scenarios obtain corresponding intelligent strategies and save them; triggering multiple different business scenarios according to preset trigger conditions to perform quality inspection through the corresponding intelligent strategies using preset execution methods, obtain quality inspection reports, and make rectifications according to the quality inspection reports. Through the method provided by the present invention, massive financial data can be monitored and analyzed in real time, and potential compliance risks can be automatically identified, which can not only reduce compliance costs, but also improve the efficiency and accuracy of compliance review, thereby helping financial institutions to better respond to regulatory requirements, protect customer rights and interests, and maintain the stability and security of the financial market.

[0083] Furthermore, the present application also provides a device for intelligent quality inspection of financial data compliance, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the various steps in the method for intelligent quality inspection of financial data compliance as described above.

[0084] Furthermore, the present application also provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the various steps in the method for intelligent quality inspection of financial data compliance as described above are implemented.

[0085] Each functional module in each embodiment of the present invention may be integrated into a processing module, or each module may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may be stored in a computer-readable storage medium.

[0086] Based on this understanding, the technical solution of the specification of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., various media that can store program codes.

[0087] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present invention is not limited by the described order of actions, because according to the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention. In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0088] For those skilled in the art, according to the concept of the embodiments of the present application, there may be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for intelligent quality inspection of financial data compliance, characterized in that: The method comprises: Describing different business scenarios and defining a plurality of business metadata in the different business scenarios respectively, defining a plurality of rule factors respectively through the plurality of business metadata, and setting a rule type for each of the rule factors; According to the different business scenarios, a plurality of rule factors are added to the business metadata according to the execution logic, a plurality of rule execution results are obtained according to the rule type of each rule factor, and corresponding result decision actions are generated according to the plurality of rule execution results, so that the corresponding intelligent strategies are obtained and saved for the plurality of different business scenarios; According to the preset trigger conditions, multiple different business scenarios are triggered to perform quality inspection through the corresponding intelligent strategies using the preset execution method, obtain a quality inspection report, and make rectifications based on the quality inspection report.

2. The method according to claim 1, characterized in that: The defining of the plurality of rule factors by using the plurality of business metadata respectively and setting the rule type of each rule factor includes: Defining the basic information and input parameter variables of the corresponding rule factors according to the business metadata in different business scenarios respectively; The rule type of the corresponding rule factor is selected according to the business metadata of different business scenarios, and the rule type includes interface call type rule, expression type rule and dynamic groovy script type rule.

3. The method according to claim 2, characterized in that The obtaining of a plurality of rule execution results according to the rule type of each rule factor includes: Based on each of the rule factors, the corresponding business metadata is processed by the rule type to obtain a plurality of the rule execution results; Among them, when the rule type of the rule factor is selected as the interface call type rule, the rule factor processes the business metadata by calling an interface, and the calling interface is configured with an SA interface scheduling module and an interface threshold module.

4. The method according to claim 1, characterized in that: The step of obtaining a plurality of rule execution results according to the rule type of each rule factor, and generating corresponding result decision actions according to the plurality of rule execution results, further includes: Assigning and combining the plurality of rule execution results of each rule factor according to a preset judgment threshold value to obtain a judgment value of each rule factor; The corresponding result decision action is set according to the determination value of each rule factor.

5. The method according to claim 3, characterized in that: The step of setting the corresponding result decision action according to the determination value of each rule factor further includes: Comparing the determination value of each of the rule factors with preset thresholds of multiple intervals to obtain a comparison result; Different result decision actions are configured according to the comparison result.

6. The method according to claim 4, characterized in that Obtain the quality inspection report and make rectifications according to the quality inspection report, including: Submit feedback based on the errors in the quality inspection report and obtain feedback results; Calculating the accuracy of the rule execution result according to the feedback result; If the accuracy rate is lower than a preset accuracy threshold, the preset determination threshold is automatically adjusted to improve the accuracy rate.

7. The method according to claim 1, characterized in that The preset trigger condition is set using a cron expression and includes a fixed time interval, a specified time point, and a specified time period; the preset execution method includes manual execution and an application system calling an API.

8. A device for intelligent quality inspection of financial data compliance, characterized in that: The device comprises: Definition module: used to describe different business scenarios and respectively define a plurality of business metadata in the different business scenarios, respectively define a plurality of rule factors through the plurality of business metadata, and set the rule type of each rule factor; Strategy module: used to add multiple rule factors to the business metadata according to the execution logic according to different business scenarios, obtain multiple rule execution results according to the rule type of each rule factor, and generate corresponding result decision actions according to the multiple rule execution results, so that the corresponding intelligent strategies are obtained and saved for the multiple different business scenarios; Quality inspection module: used to trigger multiple different business scenarios according to preset trigger conditions, perform quality inspection through the corresponding intelligent strategies using preset execution methods, obtain quality inspection reports, and make rectifications based on the quality inspection reports.

9. A device for intelligent quality inspection of financial data compliance, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, it implements the various steps in the method for intelligent quality inspection of financial data compliance as described in any one of claims 1-7.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, each step of the method for intelligent quality inspection of financial data compliance as described in any one of claims 1 to 7 is implemented.

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