Enterprise risk data analysis method and device, electronic equipment and storage medium
By flexibly configuring analysis rules and prioritizing target scenario rules, the problem of low efficiency in enterprise risk data analysis is solved, and efficient and accurate risk data processing is achieved.
Patent Information
- Application Number
- CN202411972749.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-30
- Publication Date
- 2025-11-07
AI Technical Summary
In existing technologies, the analysis of enterprise risk data relies on manual verification, which is inefficient and prone to omissions or misjudgments due to human factors, making it impossible to efficiently process large amounts of risk data.
By flexibly configuring analysis rules, priority-ranked target scenario rules are generated based on the risk categories and business scenarios of enterprise risk data. The analysis is performed using business primary keys and auxiliary data, enabling rapid configuration and centralized management of rules.
It improves the efficiency and accuracy of enterprise risk data processing, reduces the amount and complexity of rule configuration, and saves analysis and calculation time.
Smart Images
Figure CN120911938A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of data analysis, in particular to an enterprise risk data analysis method and device, an electronic device and a storage medium. BACKGROUND
[0002] With the development of information technology, the amount of enterprise data is increasing, and enterprises are also facing potential risk challenges from business compliance, industry and financial balance, etc.
[0003] In related technologies, the risk control means often relies on manual checking and analysis. Since the amount of risk data is large, and the same scene is repeatedly scanned before the risk problem is rectified, manual checking and analysis not only has low efficiency, but also is prone to omissions or misjudgments due to human factors.
[0004] Therefore, how to efficiently analyze enterprise risk data is a problem to be solved. SUMMARY
[0005] To solve or partially solve the problems in related technologies, the present application provides an enterprise risk data analysis method, device, electronic device and storage medium, which can analyze risk data of different risk categories and different business scenarios by flexibly configuring analysis rules, realize rapid configuration and intensive management of analysis rules, and improve risk data processing efficiency and accuracy.
[0006] The first aspect of the present application provides an enterprise risk data analysis method, comprising the following steps:
[0007] Obtain enterprise risk data and its business primary key;
[0008] According to the risk category, business scenario and business primary key of the enterprise risk data, analyze the pre-configured analysis rule pool, and generate a plurality of target scene rules sorted by priority, wherein each target scene rule is configured with a main judgment condition and a scene label, and the main judgment conditions and scene labels of the plurality of target scene rules are different;
[0009] According to the priority of the plurality of target scene rules, in turn, judge whether the enterprise risk data meets the main judgment condition of the corresponding target scene rule, until the main judgment condition met by the enterprise risk data is determined;
[0010] According to the scene label of the target scene rule met by the enterprise risk data, determine the risk reason of the enterprise risk data, and mark the risk reason as a risk label for the enterprise risk data.
[0011] As an optional embodiment, the acquiring enterprise risk data and its business primary key comprises:
[0012] Acquiring enterprise risk data and its risk category to which it belongs;
[0013] According to the business scene of the enterprise risk data under the risk category to which it belongs, acquiring the business primary key of the enterprise risk data.
[0014] As an optional embodiment, before parsing the pre-configured analysis rule, the following steps are further included:
[0015] According to the business primary key of the enterprise risk data, determining whether the enterprise risk data and the historical risk data belong to the same type of risk data;
[0016] If the enterprise risk data and the historical risk data belong to the same type of risk data, acquiring the risk label of the historical risk data associated with the enterprise risk data, and labeling the enterprise risk data with the corresponding label;
[0017] Otherwise, parsing the pre-configured analysis rule.
[0018] As an optional embodiment, according to the business primary key of the enterprise risk data, determining whether the enterprise risk data and the historical risk data belong to the same type of risk data, comprises:
[0019] Traversing the business primary key of the historical risk data, determining whether the business primary key of the enterprise risk data is associated with the business primary key of the historical risk data;
[0020] If the business primary key of the enterprise risk data is associated with the business primary key of the historical risk data, it is determined that the enterprise risk data and the historical risk data belong to the same type of risk data;
[0021] If the business primary key of the enterprise risk data is not associated with the business primary key of the historical risk data, it is determined that the enterprise risk data and the historical risk data do not belong to the same type of risk data.
[0022] As an optional embodiment, according to the risk category to which the enterprise risk data belongs, the business scene and the business primary key of the enterprise risk data, parsing the pre-configured analysis rule pool, and generating a plurality of target scene rules sorted according to priority, comprises the following steps:
[0023] According to the risk category to which the enterprise risk data belongs, determining the corresponding rule category in the pre-configured analysis rule pool;
[0024] According to the business scene of the enterprise risk data under the risk category, a plurality of target scene rules corresponding to the rule category and a main judgment condition configured for each target scene rule are extracted;
[0025] According to the association degree of the business primary key of the enterprise risk data and the main judgment condition of the plurality of target scene rules, the plurality of target scene rules are sorted in descending order of the association degree.
[0026] As an optional embodiment, it further comprises:
[0027] Extracting auxiliary analysis data associated with the risk category to which the enterprise risk data belongs;
[0028] Associating the auxiliary analysis data with the business primary key of the enterprise risk data to form an auxiliary judgment condition;
[0029] Combining the auxiliary judgment condition with the main judgment condition of each target scene rule as a target judgment condition.
[0030] As an optional embodiment, it further comprises:
[0031] Splitting and assembling the main judgment condition configured for the target scene rule according to a preset template to generate an expression with a judgment condition.
[0032] The second aspect of the present application provides an analysis device for enterprise risk data, comprising:
[0033] An acquisition module is configured to acquire enterprise risk data and a risk category to which the enterprise risk data belongs, and to acquire a business primary key of the enterprise risk data according to a corresponding business scene of the enterprise risk data under the risk category to which the enterprise risk data belongs;
[0034] An analysis module is configured to analyze a pre-configured analysis rule pool according to the risk category to which the enterprise risk data belongs and the business primary key of the enterprise risk data, and to generate a plurality of target scene rules sorted according to a priority, wherein each target scene rule is configured with a main judgment condition, and the main judgment conditions configured for the plurality of target scene rules are different from each other;
[0035] A judgment module is configured to sequentially judge whether the enterprise risk data meets the main judgment condition of the corresponding target scene rule according to the priority of the plurality of target scene rules, until the main judgment condition met by the enterprise risk data is determined;
[0036] A marking module is configured to determine a risk cause of the enterprise risk data according to the main judgment condition met by the enterprise risk data and the business primary key of the enterprise risk data, and to mark the risk cause as a tag for the enterprise risk data.
[0037] The third aspect of the present application provides an electronic device, comprising:
[0038] a processor; and
[0039] a memory having stored thereon executable code that, when executed by the processor, causes the processor to perform the method described above.
[0040] The fourth aspect of the present application provides a computer-readable storage medium having stored thereon executable code that, when executed by a processor of an electronic device, causes the processor to perform the method described above.
[0041] The technical solutions provided by the present application can include the following beneficial effects:
[0042] The present application divides the analysis rule pool into multiple rule categories based on configurable analysis rules, and makes each rule category correspond to a risk category; and the multiple analysis rules associated with the same business scenario in the rule category are summarized, so that the multiple analysis rules corresponding to a business scenario can be quickly found from a rule category, and when processing enterprise risk data, the corresponding rule category can be directly called from the analysis rule pool according to the risk category to which the enterprise risk data belongs, and the corresponding multiple target scene rules can be extracted from the rule category according to the business scenario corresponding to the enterprise risk data under the risk category, so as to realize the effect that one analysis rule pool is applicable to multiple risk categories and one rule category is applicable to multiple business scenarios, and the rule reuse can be realized, and the rule configuration amount and complexity are greatly reduced. The present application can also sort the multiple target scene rules according to the priority according to the association degree between the business primary key of the enterprise risk data and the primary judgment condition configured in the multiple target scene rules, so as to realize the targeted analysis of different enterprise risk data under the same risk category and the same business scenario, greatly save the analysis and calculation time, and improve the analysis efficiency.
[0043] It should be understood that the foregoing general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF DRAWINGS
[0044] The above and other objects, features and advantages of the present application will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings, in which like reference characters refer to like parts throughout the several views, and in which:
[0045] Figure 1 is a flow diagram of the analysis method for enterprise risk data shown in the embodiments of the present application;
[0046] Figure 2 is a flow diagram of the analysis method for enterprise risk data shown in the embodiments of the present application;
[0047] Figure 3 is a relationship framework diagram of data shown by an embodiment of the present application;
[0048] Figure 4 is another structural schematic diagram of an enterprise risk data analysis device shown by an embodiment of the present application;
[0049] Figure 5 is a structural schematic diagram of an electronic device shown by an embodiment of the present application. DETAILED DESCRIPTION
[0050] Embodiments of the present application will be described in more detail by referring to the attached drawings. Although embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present application is more thoroughly and completely conveyed to those skilled in the art, and the scope of the present application is fully conveyed to those skilled in the art.
[0051] The terminology used in the present application is merely for the purpose of describing specific embodiments and is not intended to limit the present application. The singular forms "a", "an", and "the" used in the present application and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.
[0052] It should be understood that although the terms "first", "second", "third", etc. can be used in the present application to describe various information, these information should not be limited by these terms. These terms are only used to distinguish the same type of information from each other. For example, the first information can also be referred to as the second information, and similarly, the second information can also be referred to as the first information without departing from the scope of the present application. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise specifically limited.
[0053] In the related art, the risk control means adopted often relies on manual checking and analysis. Since the amount of risk data is large, and the same scene is repeatedly scanned before the risk problem is completely rectified, manual checking and analysis not only has low efficiency, but also is prone to omissions or misjudgments due to human factors. Therefore, how to efficiently analyze enterprise risk data is a problem to be solved.
[0054] To solve the above problems, the embodiment of the present application provides an analysis method of enterprise risk data, which can analyze risk data of different risk categories and different business scenarios by flexibly configuring analysis rules, realize rapid configuration and intensive management of analysis rules, and improve risk data processing efficiency and accuracy.
[0055] Noun explanation:
[0056] Risk category: the enterprise divides the internal risks into various categories according to its own operation, such as business compliance, industry and finance balance, account adjustment compliance, channel commission, and fund safety.
[0057] Business scenario: the enterprise divides the risk categories into various business scenarios according to the actual business situation, such as income account balance, cost account balance, and tax account balance under the industry and finance balance.
[0058] Business primary key: information with unique identification function in the business scenario, such as order number, bill number, and account item, which are usually used to distinguish different business scenarios.
[0059] Risk list data: data that may have adverse effects or losses on the operation of the enterprise in the production and operation process of the enterprise, such as industry and finance balance difference list data, order negative gross profit list data, and account adjustment list data without basis.
[0060] Auxiliary data: in addition to risk data and rules, other data required in automatic analysis in the analysis process are auxiliary data, such as customer information number, product attribute, and financial account rule.
[0061] Tagging: recording the primary key and scenario tag of the risk list result according to the judgment result of the analysis.
[0062] Data backwrite: writing the analysis result and tag classification back to the risk list data for subsequent tracking and tracing.
[0063] The technical solutions of the embodiments of the present application are described in detail below with reference to the drawings.
[0064] Figure 1 is a flowchart of the analysis method of enterprise risk data shown in the embodiments of the present application.
[0065] Referring to Figure 1 , the embodiment of the present application provides an analysis method of enterprise risk data, which includes the following steps:
[0066] S1, obtaining enterprise risk data and its business primary key.
[0067] In the embodiments of the present application, the business primary key of the enterprise risk data can be obtained by the following method: obtaining enterprise risk data and its corresponding risk category; and obtaining the business primary key of the enterprise risk data according to the business scenario of the enterprise risk data in the corresponding risk category.
[0068] In the embodiments of the present application, the business primary key of the enterprise risk data is related to the business scenario of the risk category to which the enterprise risk data belongs, and the business primary key can be used as a unique identifier of the business scenario to distinguish the business scenarios. Different business scenarios have different business primary keys, such as order number, bill number, and account item.
[0069] S2, according to the risk category to which the enterprise risk data belongs and the business primary key of the enterprise risk data, analyzing the pre-configured analysis rule pool and generating a plurality of target scene rules sorted by priority, wherein each target scene rule is configured with a main judgment condition and a scene label, and the main judgment conditions and the scene labels of the plurality of target scene rules are different.
[0070] In the enterprise risk management system of the embodiments of the present application, the analysis rule pool is pre-configured according to the actual business situation of the enterprise. The analysis rule pool can be associated with and classified by the risk category, so that each risk category matches a rule category, and each rule category is configured with a plurality of analysis rules. The plurality of analysis rules in each rule category can be associated with and classified by the business scenario, so that the plurality of analysis rules can match a business scenario. In this way, the analysis rule pool can be divided into a plurality of rule categories, and each rule category can correspond to a risk category; and the plurality of analysis rules associated with the same business scenario in the rule category can be summarized, so that the plurality of analysis rules corresponding to a business scenario can be quickly found in a rule category. Therefore, when processing enterprise risk data, the corresponding rule category can be called from the analysis rule pool according to the risk category to which the enterprise risk data belongs, and the corresponding plurality of target scene rules can be extracted from the rule category according to the business scenario corresponding to the enterprise risk data in the risk category, so as to realize the effect that one analysis rule pool is applicable to a plurality of risk categories and one rule category is applicable to a plurality of business scenarios, and the rule reuse can be realized, thereby greatly reducing the rule configuration amount and complexity.
[0071] In the embodiments of the present application, the plurality of target scene rules can be sorted by priority according to the association degree between the business primary key of the enterprise risk data and the main judgment condition of the plurality of target scene rules, so as to realize the targeted analysis of different enterprise risk data under the same risk category and the same business scenario, thereby greatly saving the analysis and calculation time and improving the analysis efficiency.
[0072] In the embodiments of the present application, the target scene rule main judgment condition can be used to judge whether the enterprise risk data exists corresponding risk, and the scene label can be used to identify the risk reason corresponding to the target scene rule, that is, the reason causing the enterprise risk data to satisfy the main judgment condition.
[0073] S3, according to the priority of the plurality of target scene rules, in turn judge whether the enterprise risk data satisfies the main judgment condition of the corresponding target scene rule, until the enterprise risk data satisfying the main judgment condition is determined.
[0074] The embodiments of the present application prioritize the plurality of target scene rules according to the characteristics of different enterprise risk data, for example, if the main judgment condition configured by the target scene rule has high correlation with the business primary key of the enterprise risk data, the priority of the target scene rule is arranged in front; if the main judgment condition configured by the target scene rule has low correlation with the business primary key of the enterprise risk data, the priority of the target scene rule is arranged at the back.
[0075] In the analysis and judgment, first judge whether the enterprise risk data satisfies the main judgment condition of the target scene rule of the first priority, if satisfied, exit the judgment; if not satisfied, judge whether the enterprise risk data satisfies the main judgment condition of the target scene rule of the second priority, and so on, until the enterprise risk data satisfying the main judgment condition of the target scene rule of the i-th priority is determined. In this way, the calculation efficiency can be greatly improved, and the calculation time can be saved.
[0076] S4, according to the scene label of the target scene rule satisfied by the enterprise risk data, determine the risk reason of the enterprise risk data, and mark the risk reason as the risk label of the enterprise risk data.
[0077] Exemplarily: the main judgment conditions of the plurality of target scene rules associated with the income account balance business scene corresponding to the risk category of industry and finance balance include "accounting amount>accounting amount", "accounting subject=2202970000 and associated transaction flag=is" and the like, wherein the scene label of the target scene rule belonging to "accounting amount>accounting amount" can be one or more of "income penalty count not listed", "historical billing income reported in the same month" and the like, when the enterprise risk data satisfies the main judgment condition of "accounting amount>accounting amount", it is explained that the risk reason of the enterprise risk data is one or more of "income penalty count not listed", "historical billing income reported in the same month" and the like, and the risk reason is marked as the risk label of the enterprise risk data.
[0078] In the embodiments of the present application, marking the enterprise risk data can mean rewriting the risk reason label to the risk data list, or it can also be that the risk reason label is associated with the risk data list as additional data.
[0079] The embodiments of the present application divide the analysis rule pool into multiple rule categories based on configurable analysis rules, and make each rule category correspond to a risk category; and multiple analysis rules associated with the same business scenario in a rule category are summarized, so that multiple analysis rules corresponding to a business scenario can be quickly found from a rule category, and when processing enterprise risk data, the corresponding rule category can be directly called from the analysis rule pool according to the risk category to which the enterprise risk data belongs, and the corresponding multiple target scene rules are extracted from the rule category according to the business scenario corresponding to the enterprise risk data under the risk category, so that the effect of one analysis rule pool applicable to multiple risk categories and one rule category applicable to multiple business scenarios is realized, rule reuse is realized, and the rule configuration amount and complexity are greatly reduced. The embodiments of the present application can also sort multiple target scene rules according to priority based on the association degree between the business primary key of the enterprise risk data and the primary judgment condition configured for the multiple target scene rules, realize targeted analysis of different enterprise risk data under the same risk category and the same business scenario, greatly save analysis and calculation time, and improve analysis efficiency.
[0080] As an optional embodiment, the enterprise risk data and the business primary key thereof are acquired in step S1, including:
[0081] S101, acquiring enterprise risk data and a risk category to which the enterprise risk data belongs.
[0082] In the embodiments of the present application, an enterprise can divide its internal risks into various categories according to its own operation, such as business compliance, industry and finance balance, account adjustment compliance, channel commission, and fund safety.
[0083] The embodiments of the present application can classify and store enterprise risk data in risk categories, realize classification and arrangement of a large amount of enterprise risk data, and improve the efficiency of subsequent analysis of enterprise risk data.
[0084] S102, acquiring a business primary key of the enterprise risk data according to a business scenario corresponding to the enterprise risk data under the risk category to which the enterprise risk data belongs.
[0085] In the embodiments of the present application, an enterprise can also divide risk categories into various business scenarios according to actual business conditions, such as further dividing income account balance, cost account balance, and tax account balance into business scenarios under industry and finance balance. Similarly, the enterprise risk data can include industry and finance balance difference list data, order negative gross profit list data, and account adjustment list data without basis. Different business primary keys corresponding to different business scenarios are different, such as order number, bill number, and account item. The business primary key can be used as a unique identifier of the business scenario for distinguishing business scenarios. For example, the business primary key corresponding to the income account balance is the order number, the business primary key corresponding to the cost account balance is the bill number, and the business primary key corresponding to the tax account balance is the account item.
[0086] As an optional embodiment, before parsing the preconfigured analysis rule, the following steps are further included:
[0087] S201, judging whether the enterprise risk data and the historical risk data belong to the same type of risk data according to the business primary key of the enterprise risk data.
[0088] Step S201 can include: traversing the business primary key of the historical risk data, judging whether the business primary key of the enterprise risk data and the business primary key of the historical risk data are associated; if the business primary key of the enterprise risk data and the business primary key of the historical risk data are associated, it is determined that the enterprise risk data and the historical risk data belong to the same type of risk data; if the business primary key of the enterprise risk data and the business primary key of the historical risk data are not associated, it is determined that the enterprise risk data and the historical risk data do not belong to the same type of risk data.
[0089] Exemplarily: taking the association of the business primary key of the enterprise risk data and the business primary key of the historical risk data as an example, the association of the business primary key of the enterprise risk data and the business primary key of the historical risk data in the embodiment of the application can mean that the invoice number of the enterprise risk data and the invoice number of the historical risk data belong to the same period of accounts. Generally, the invoice is periodically accounted, and after the invoice number of the historical risk data corresponds to the account period, it is determined to be a risk and a label is completed. Then, the data of the same account period is the current enterprise risk data, which can be automatically judged as the same type of risk data according to the historical judgment result, and can be labeled with the same risk label.
[0090] S202, if the enterprise risk data and the historical risk data belong to the same type of risk data, obtaining the risk label of the historical risk data associated with the enterprise risk data, and labeling the enterprise risk data with the corresponding label.
[0091] In the embodiment of the application, the historical risk data can be risk data determined according to the analysis rule and labeled with a risk label. Therefore, when the enterprise risk data and the historical risk data belong to the same type of risk data, the enterprise risk data can be labeled with the same risk label as the historical risk data. This type of enterprise risk data does not need to be analyzed by the analysis rule, and the historical risk data can be directly labeled, saving calculation time and improving labeling efficiency.
[0092] S203, if the enterprise risk data and the historical risk data do not belong to the same type of risk data, the preconfigured analysis rule is parsed again.
[0093] When the enterprise risk data and the historical risk data do not belong to the same type of risk data, it means that the risk reason of the enterprise risk data does not appear in the historical risk data of the same period. Therefore, the enterprise risk data can be analyzed by the analysis rule.
[0094] As an optional embodiment, in step S2, the preconfigured analysis rule pool is parsed according to the risk category to which the enterprise risk data belongs and the business primary key of the enterprise risk data, and a plurality of target scene rules sorted by priority is generated, including the following steps:
[0095] S211. Determine the corresponding rule category in the preconfigured analysis rule pool according to the risk category to which the enterprise risk data belongs.
[0096] In the enterprise risk management system of the embodiments of the present application, the analysis rule pool is preconfigured according to the actual business situation of the enterprise. The analysis rule pool can be associated with and classified into risk categories, so that each risk category matches a rule category. In this way, the analysis rule pool can be divided into a plurality of rule categories, and each rule category corresponds to a risk category.
[0097] S212. Extract a plurality of target scene rules corresponding to the rule category and the primary judgment condition configured for each target scene rule according to the business scene of the enterprise risk data under the risk category.
[0098] In the embodiments of the present application, a plurality of analysis rules are configured under each rule category. The plurality of analysis rules under each rule category can be associated with and classified into business scenes, so that the plurality of analysis rules can match a business scene. In addition, the plurality of analysis rules associated with the same business scene in the rule category can be summarized, so that the plurality of analysis rules corresponding to a certain business scene can be quickly found in a certain rule category. The primary judgment condition configured for each target scene rule can be split and assembled according to a preset template to generate an expression with judgment conditions. For example, the primary judgment condition configured for the plurality of target scene rules corresponding to the income account balance business scene under the income and expense balance includes "accounting amount > debit amount", "accounting subject = 2202970000 and associated transaction flag = yes", etc.
[0099] When processing enterprise risk data, the corresponding rule category can be called from the analysis rule pool directly according to the risk category to which the enterprise risk data belongs, and the corresponding plurality of target scene rules can be extracted from the rule category according to the business scene of the enterprise risk data under the risk category. This achieves the effect that one analysis rule pool is applicable to a plurality of risk categories and one rule category is applicable to a plurality of business scenes, and realizes rule reuse, greatly reducing the rule configuration amount and complexity.
[0100] S213. According to the association degree between the business primary key of the enterprise risk data and the primary judgment condition of the plurality of target scene rules, the plurality of target scene rules are sorted from high to low according to the association degree.
[0101] In the embodiments of the present application, the business primary keys of the enterprise risk data and the association degrees of the primary judgment conditions configured by the plurality of target scene rules can be used to sort the plurality of target scene rules according to the priority, so that the targeted analysis of different enterprise risk data in the same risk category and the same business scene can be realized, the analysis and calculation time can be greatly saved, and the analysis efficiency can be improved. For example, the business primary key of the enterprise risk data belonging to the income account balance business scene is the bill number, the association degree of the bill number and "account balance amount>account amount" is greater than the association degree of "accounting subject=2202970000 and associated transaction flag=is", in the priority sorting, the target scene rule to which "account balance amount>account amount" belongs is arranged in front of the target scene rule to which "accounting subject=2202970000 and associated transaction flag=is" belongs, and in the analysis and processing, whether the enterprise risk data satisfies "account balance amount>account amount" is first judged.
[0102] As an optional embodiment, in the step S212, the plurality of target scene rules under the rule category and the primary judgment condition configured by each target scene rule are extracted, and further include:
[0103] S221, extracting auxiliary analysis data associated with the risk category to which the enterprise risk data belongs.
[0104] In the embodiments of the present application, the auxiliary analysis data is configured for each rule category, and is used to assist in analyzing the enterprise risk data. The auxiliary analysis data can be associated with the risk category, and each risk category can be configured with a plurality of auxiliary analysis data. For example, the auxiliary analysis data configured by the industry and finance balance risk category includes customer information number, product attribute, financial account rule, engineering progress attribute, etc.
[0105] S222, associating the auxiliary analysis data with the business primary key of the enterprise risk data to form an auxiliary judgment condition.
[0106] For example, the auxiliary analysis data under the rule category corresponding to the industry and finance balance risk category includes customer information number, product attribute, financial account rule, engineering progress attribute, etc. When judging the enterprise risk data under the industry and finance balance risk category, the corresponding auxiliary analysis data can be called under each target scene rule, or the corresponding auxiliary analysis data can be selectively called. Associating the auxiliary analysis data with the business primary key of the enterprise risk data can mean combining the auxiliary analysis data with the business primary key of the enterprise risk data. For example, the bill number of the enterprise risk data is associated with the customer information number in the auxiliary analysis data to form an auxiliary judgment condition such as "bill number=000000 and customer information number=111111", or the "financial account rule" in the auxiliary analysis data is directly used as an auxiliary judgment condition.
[0107] S223, combine the auxiliary judgment condition and the main judgment condition of each target scene rule as a target judgment condition.
[0108] Based on the above example: when the bill number of enterprise risk data is associated with the customer information number in auxiliary analysis data, forming an auxiliary judgment condition such as "bill number = 000000 and customer information number = 111111", the auxiliary judgment condition can be combined with the main judgment condition of each target scene rule, such as "column account amount > debit amount", "accounting subject = 2202970000 and related transaction flag = yes", etc. as a target judgment condition, that is, the target judgment condition can be "bill number = 000000 and customer information number = 111111" and "column account amount > debit amount", "accounting subject = 2202970000 and related transaction flag = yes", and "accounting subject = 2202970000 and related transaction flag = yes".
[0109] As an optional embodiment, the risk reason of the enterprise risk data is determined according to the scene label of the target scene rule satisfied by the enterprise risk data in step S4, comprising:
[0110] S401, determine the corresponding target scene rule and its scene label according to the main judgment condition satisfied by the enterprise risk data.
[0111] Exemplarily: the main judgment conditions of the multiple target scene rules associated with the business scene of income column account balance corresponding to the risk category of industry and finance balance include "column account amount > debit amount", "accounting subject = 2202970000 and related transaction flag = yes", etc. The scene label of the target scene rule to which "column account amount > debit amount" belongs can be one or more of "income penalty count not listed", "historical invoicing reported in the same month", etc. When the enterprise risk data satisfies the main judgment condition "column account amount > debit amount", it indicates that the risk reason of the enterprise risk data is one or more of "income penalty count not listed", "historical invoicing reported in the same month", etc. The risk reason is used as a risk label to mark the enterprise risk data.
[0112] S402, associate the scene label with the business primary key of the enterprise risk data to generate the risk reason.
[0113] Based on the above example: further associate one or more of the scene tags in "income penalty count not listed", "historical billing income reported this month" and the like with the business primary key of enterprise risk data, such as the invoice number, to generate risk reasons, give the enterprise risk data the content of the scene tag and record the corresponding business primary key. The enterprise risk data after being tagged is called historical risk data. When comparing the new round of enterprise risk data with the historical risk data again, the business primary key of the historical risk data can be quickly associated.
[0114] As an optional embodiment, the analysis method of the embodiment of the application further includes: splitting and assembling the main judgment condition of the target scene rule configuration according to a preset template to generate an expression with a judgment condition.
[0115] In the embodiment of the application, the preset template can be "field name + operator + field value + field relationship", wherein the field name can refer to risk data items such as column account amount, account amount, accounting subject, etc.; the operator can refer to judgment relationships such as ">", "=", "<", "is", "no", etc.; the field value can refer to "label", "ID", etc. for identifying the numerical value of the field name; and the field relationship can refer to "and", "or", "not", etc. for indicating the connection relationship between fields. Exemplarily, the expression with a judgment condition can be "column account amount > account amount", "accounting subject = 2202970000 and associated transaction flag = is", etc.
[0116] In order to further understand the application, the specific process of the application is specifically described below.
[0117] Referring to Figure 2 and Figure 3 , the analysis method of the enterprise risk data of the embodiment of the application specifically includes:
[0118] 1. Data loading: obtaining enterprise risk data for risk scanning (the risk list data in Figure 2 and Figure 3 ), and storing according to risk categories (such as business compliance, industry and finance balance, account adjustment compliance, channel commission, fund safety, etc. The categories correspond to the risk category definitions in Figure 3 ).
[0119] 2. Historical scanning data judgment: according to the business scene of the enterprise risk data under the risk category (such as the business scene under the industry and finance balance, which can be further divided into income column account balance, cost column account balance, tax column account balance, etc.), obtaining the business primary key of the enterprise risk data (such as order number, invoice number, account item, etc. which can be used as the primary key information for uniquely distinguishing the business scene), according to the business data primary key, scanning the historical risk data, and judging whether the enterprise risk data scanned this time belongs to the historical risk data Figure 2The data in the middle is similar to historical data.
[0120] 3. Historical Scenario Tagging: If the enterprise risk data and historical risk data belong to the same category, then according to the business primary key, obtain the risk tags of the historical risk data associated with the enterprise risk data, and assign the corresponding tags to the enterprise risk data (corresponding to...). Figure 2 (The middle section is labeled with historical scenes).
[0121] 4. Rule Loading: If the enterprise risk data and historical risk data do not belong to the same category, the preset analysis rule pool under the risk category to which the enterprise risk data belongs will be loaded synchronously as the rule basis for subsequent analysis.
[0122] 5. Rule Parsing: Based on the risk category to which the enterprise risk data belongs and the business primary key of the enterprise risk data, parse the pre-configured analysis rule pool (corresponding to...). Figure 2 Rule loading and parsing in Figure 3 The rule group definition determines the corresponding rule details, priorities, and expressions in the pre-configured analysis rule pool (corresponding to...). Figure 3 The detailed rule definition in the document generates multiple target scenario rules (corresponding to) sorted by priority. Figure 2 Scene rules and Figure 3 The scene category definition in the text, where each target scene rule is configured with a main judgment condition and a scene tag (corresponding to...). Figure 3 (The scenario definition in the text), and the main judgment conditions and scenario tags of the multiple target scenario rules are different from each other. See [link to main judgment conditions for multiple target scenario rules]. Figure 2 The rules are 1, 2, ..., N. The rule scenario conditions can be combined according to the dimensions of "field name + operator + field value + field relationship" to generate rule expressions for judgment (e.g., "listed amount > outgoing amount", "accounting subject = 2202970000 and related transaction flag = yes", etc.).
[0123] 6. Auxiliary Data Loading: Based on the auxiliary data source definition in the rules, load the required auxiliary data (such as customer information numbers, product attributes, financial accounting rules, and other data information that can provide assistance during the analysis process). Link the auxiliary data to the enterprise risk data according to the business primary key, generating a large wide table of data to be analyzed (corresponding to...). Figure 2 The data to be analyzed in the table includes information from the original risk list data and information from the loaded auxiliary data sources. The wide table format ensures that the information required for the analysis is complete and accurate.
[0124] 7. Rule judgment: rules are sorted according to rule priority, and the enterprise risk data (data that may have adverse effects or losses on the operation of the enterprise in the production and operation process, such as the data of the balance difference list, the order negative gross profit list, and the account adjustment list without basis, which are positioned by the risk scanning function, and the risk list data is the basic object of automatic difference analysis) is judged one by one according to the main judgment conditions corresponding to the scene rules in the rule order. If it meets, it is consistent, exits the judgment and records the scene tag of the corresponding scene rule. If it does not meet the rule, the next rule is obtained in sequence, and the enterprise risk data is judged again until the last rule judgment is completed.
[0125] 8. Risk scene tagging: according to the rule analysis judgment result, the business primary key of the enterprise risk data and the scene tag are associated and corresponded.
[0126] 9. List data backwriting: the analysis result and the tag are classified and written back to the risk list data, which is used for subsequent tracking and tracing.
[0127] 10. Log recording: record the log of the scene analysis, including the analysis range, the analysis time, the analysis result, etc.
[0128] Corresponding to the foregoing application function implementation method embodiment, the present application also provides an enterprise risk data analysis device, an electronic device and corresponding embodiments.
[0129] Figure 4 is a structural schematic diagram of the enterprise risk data analysis device shown in the embodiments of the present application.
[0130] Referring to Figure 4 , the embodiments of the present application also provide an enterprise risk data analysis device 400, which comprises:
[0131] The acquisition module 410 is configured to acquire the enterprise risk data and the risk category to which the enterprise risk data belongs, and acquire the business primary key of the enterprise risk data according to the business scene corresponding to the enterprise risk data in the risk category.
[0132] The analysis module 420 is configured to parse the preconfigured analysis rule pool according to the risk category to which the enterprise risk data belongs and the business primary key of the enterprise risk data, and generate a plurality of target scene rules sorted according to priority, wherein each target scene rule is configured with a main judgment condition, and the main judgment conditions of the plurality of target scene rules are different from each other. The analysis module 420 is configured to determine a corresponding rule category in the preconfigured analysis rule pool according to the risk category to which the enterprise risk data belongs; extract a plurality of target scene rules and a main judgment condition configured for each target scene rule in the corresponding rule category according to the business scene of the enterprise risk data under the risk category; and sort the plurality of target scene rules according to the relevance from high to low according to the relevance between the business primary key of the enterprise risk data and the main judgment conditions of the plurality of target scene rules.
[0133] The judgment module 430 is configured to judge, according to the priority of the plurality of target scene rules, whether the enterprise risk data meets the main judgment condition of the corresponding target scene rule in sequence until the main judgment condition met by the enterprise risk data is determined.
[0134] The marking module 440 is configured to determine the risk cause of the enterprise risk data according to the main judgment condition met by the enterprise risk data and the business primary key of the enterprise risk data, and mark the risk cause as a label for the enterprise risk data. The marking module 440 includes a generation module configured to determine the corresponding target scene rule and a scene label thereof according to the main judgment condition met by the enterprise risk data; associate the scene label with the business primary key of the enterprise risk data, and generate the risk cause.
[0135] The analysis device 400 further includes a historical judgment module configured to judge, according to the business primary key of the enterprise risk data, whether the enterprise risk data and historical risk data belong to the same type of risk data. The acquisition module 410 is further configured to acquire the risk label of the historical risk data associated with the enterprise risk data and mark the enterprise risk data with the corresponding label when the enterprise risk data and the historical risk data belong to the same type of risk data.
[0136] The analysis device for enterprise risk data provided in the embodiments of the present application can be a configurable difference analysis rule model. By using the flexible and configurable rule model, various complex business scenes can be covered in the model, and the rule intensive management and rapid configuration can be realized.
[0137] As to the device in the above embodiments, the specific manners in which various modules perform operations have been described in details in the embodiments of the method, and will not be described in details here.
[0138] Figure 5 FIG. 1 is a structural schematic diagram of an electronic device according to an embodiment of the present application.
[0139] Referring to Figure 5 The electronic device 500 includes a memory 510 and a processor 520.
[0140] The processor 520 can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gates or transistor logic, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can be any conventional processor.
[0141] The memory 510 can include various types of storage units, such as a system memory, a read-only memory (ROM), and a permanent storage device. Among them, the ROM can store static data or instructions required by the processor 520 or other modules of the computer. The permanent storage device can be a read-write storage device. The permanent storage device can be a non-volatile storage device that does not lose stored instructions and data even after the computer is powered off. In some embodiments, the permanent storage device uses a mass storage device (such as a magnetic or optical disk, flash memory) as a permanent storage device. In some other embodiments, the permanent storage device can be a removable storage device (such as a floppy disk, an optical drive). The system memory can be a read-write storage device or a volatile read-write storage device, such as a dynamic random access memory. The system memory can store some or all of the instructions and data required by the processor during runtime. In addition, the memory 510 can include a combination of any computer readable storage media, including various types of semiconductor storage chips (such as DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), magnetic disks and / or optical disks. In some embodiments, the memory 510 can include a read and / or write removable storage device, such as a compact disc (CD), a read-only digital versatile disc (such as DVD-ROM, double-layer DVD-ROM), a read-only Blu-ray disc, an ultra-density optical disc, a flash memory card (such as an SD card, a min SD card, a Micro-SD card, etc.), a magnetic floppy disk, etc. The computer readable storage medium does not include a carrier wave and a transient electronic signal transmitted through a wireless or wired transmission.
[0142] The memory 510 stores executable code, which, when processed by the processor 520, can cause the processor 520 to perform part or all of the above-mentioned methods.
[0143] Furthermore, the method according to the present application can also be implemented as a computer program or a computer program product, which comprises computer program code instructions for executing some or all of the steps of the above-mentioned method according to the present application.
[0144] Alternatively, the present application can also be implemented as a computer readable storage medium (or a non-transitory machine readable storage medium or a machine readable storage medium) having stored thereon executable codes (or computer programs or computer instruction codes) which, when executed by a processor of an electronic device (or a server, etc.), cause the processor to perform some or all of the steps of the above-mentioned method according to the present application.
[0145] The above has described the embodiments of the present application, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes are obvious to those skilled in the art without departing from the scope and spirit of the described embodiments. The selection of the terms used herein is intended to best explain the principles of the embodiments, practical application, or improvement to the technology in the market, or to enable other ordinary skilled in the art to understand the embodiments disclosed herein.
Claims
1. A method of analyzing enterprise risk data, characterized by, The method comprises the following steps: obtaining enterprise risk data and its business primary key; analyzing a pre-configured analysis rule pool according to a risk category to which the enterprise risk data belongs, a business scenario and the business primary key of the enterprise risk data, and generating a plurality of target scenario rules in priority order, wherein each target scenario rule is configured with a main judgment condition and a scenario label, and the main judgment conditions and scenario labels of the plurality of target scenario rules are different from each other; judging whether the enterprise risk data meets the main judgment condition of the corresponding target scenario rule in priority order according to the plurality of target scenario rules until the main judgment condition met by the enterprise risk data is determined; determining the risk cause of the enterprise risk data according to the scenario label of the target scenario rule met by the enterprise risk data, and labeling the risk cause as a risk label for the enterprise risk data.
2. The method of claim 1, wherein, The step of obtaining enterprise risk data and its business primary key comprises: obtaining enterprise risk data and a risk category to which the enterprise risk data belongs; obtaining the business primary key of the enterprise risk data according to the business scenario corresponding to the enterprise risk data in the risk category to which the enterprise risk data belongs.
3. The method of claim 1, wherein, Before analyzing the pre-configured analysis rule, the following steps are further included: judging whether the enterprise risk data and historical risk data belong to the same type of risk data according to the business primary key of the enterprise risk data; if the enterprise risk data and the historical risk data belong to the same type of risk data, obtaining the risk label of the historical risk data associated with the enterprise risk data, and labeling the corresponding label for the enterprise risk data; otherwise, analyzing the pre-configured analysis rule.
4. The method of claim 3, wherein, The step of judging whether the enterprise risk data and historical risk data belong to the same type of risk data according to the business primary key of the enterprise risk data comprises: traversing the business primary key of the historical risk data to judge whether the business primary key of the enterprise risk data is associated with the business primary key of the historical risk data; if the business primary key of the enterprise risk data is associated with the business primary key of the historical risk data, it is determined that the enterprise risk data and the historical risk data belong to the same type of risk data; if the business primary key of the enterprise risk data is not associated with the business primary key of the historical risk data, it is determined that the enterprise risk data and the historical risk data do not belong to the same type of risk data.
5. The method of claim 1, wherein, The step of analyzing the pre-configured analysis rule pool according to the risk category to which the enterprise risk data belongs, the business scenario and the business primary key of the enterprise risk data, and generating a plurality of target scenario rules in priority order comprises the following steps: determining the corresponding rule category in the pre-configured analysis rule pool according to the risk category to which the enterprise risk data belongs; extracting a plurality of target scenario rules and the main judgment condition of each target scenario rule in the rule category according to the business scenario of the enterprise risk data under the risk category; sorting the plurality of target scenario rules in descending order of the correlation degree according to the correlation degree between the business primary key of the enterprise risk data and the main judgment condition of the plurality of target scenario rules.
6. The method of claim 5, wherein, Further comprising: extracting auxiliary analysis data associated with the risk category to which the enterprise risk data belongs; The auxiliary analysis data is associated with the business primary key of the enterprise risk data to form an auxiliary judgment condition; The auxiliary judgment condition is combined with the main judgment condition of each target scene rule as a target judgment condition.
7. The method of claim 1, wherein, Further comprising: The main judgment condition of the target scene rule configuration is split and assembled according to a preset template to generate an expression with a judgment condition.
8. An analysis device of enterprise risk data, characterized by, Comprise: An acquisition module is configured to acquire enterprise risk data and its corresponding risk category, and acquire the business primary key of the enterprise risk data according to the business scene corresponding to the enterprise risk data in the risk category; An analysis module is configured to analyze a preconfigured analysis rule pool according to the risk category of the enterprise risk data and the business primary key of the enterprise risk data, and generate a plurality of target scene rules sorted by priority, wherein each target scene rule is configured with a main judgment condition, and the main judgment conditions of the plurality of target scene rules are different from each other; A judgment module is configured to determine whether the enterprise risk data meets the main judgment condition of the corresponding target scene rule according to the priority of the plurality of target scene rules in sequence until the enterprise risk data meets the main judgment condition is determined; A marking module is configured to determine the risk cause of the enterprise risk data according to the main judgment condition met by the enterprise risk data and the business primary key of the enterprise risk data, and mark the risk cause as a label for the enterprise risk data.
9. An electronic device, comprising: Comprise: A processor; And A memory having executable code stored thereon, when the executable code is executed by the processor, the processor executes the method of any one of claims 1-7.
10. A computer-readable storage medium having executable code stored thereon, when the executable code is executed by a processor of an electronic device, the processor executes the method of any one of claims 1-7.