Financial risk investigation method and device based on financial sharing mode

Abstract

The invention provides a financial risk investigation method and device based on a financial sharing mode. The method comprises the following steps: capturing outsourcing data with suspected risks from total business data; packaging the outsourcing data into an HANA view representing a financial risk structure; forming a logic data source for subsequent risk investigation based on the HANA view; according to the configured risk investigation rule, generating an SQL script for screening risk data meeting conditions; and executing the SQL script to query the logic data source, screening out data meeting the risk condition, and outputting a screening result to a financial sharing service center for step-by-step rechecking. According to the method, generation of SQL scripts for different business scenes is supported through the configured risk investigation rule, the rule can be dynamically adjusted to adapt to personalized requirements, and suspected risk data is further screened through configuration of data conditions and inspection items on the task level, so that more flexible and accurate risk investigation is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis and risk screening, and in particular to a financial risk screening method and device based on a financial sharing model. Background Art

[0002] The financial sharing model is a financial management model based on centralization, standardization and informatization, and is mainly used in large group companies. Its core idea is to centralize the financial processing work scattered in various subsidiaries or business departments to a shared service center (financial sharing center, SSC) for unified processing, thereby improving efficiency, reducing costs and strengthening financial control capabilities.

[0003] At present, some documents have recorded the description of adding risk screening modules to the financial sharing model for major financial risk screening, but there are still the following defects:

[0004] 1. Inflexible rule configuration: Existing risk investigation modules are usually based on a fixed rule base and cannot dynamically adjust rules according to business needs, making it difficult to adapt to the personalized needs of different enterprises or business scenarios.

[0005] 2. Low degree of automation: Most risk screening modules only support static screening of documents and lack an automated risk screening process based on task scheduling, making it impossible to achieve real-time capture and dynamic analysis of risk data.

[0006] 3. Insufficient data integration: Existing modules usually only conduct risk screening for a single system or a single business scenario. They lack the ability to integrate and analyze data across systems and departments, making it difficult to identify complex correlation risks. Summary of the invention

[0007] In view of this, the present invention provides a financial risk screening method and device based on a financial sharing model to solve at least one of the problems mentioned above.

[0008] In order to achieve the above object, the present invention adopts the following scheme:

[0009] According to a first aspect of the present invention, an embodiment of the present invention provides a financial risk screening method based on a financial sharing model, the method comprising: capturing outsourced data with suspected risks from the full amount of business data; encapsulating the outsourced data into a HANA view that characterizes the financial risk structure; forming a logical data source for subsequent risk screening based on the HANA view; generating an SQL script for screening qualified risk data according to configured risk screening rules; executing the SQL script to query the logical data source, screening out data that meets the risk conditions, and outputting the screening results to the financial shared service center, which will conduct a level-by-level review.

[0010] As an embodiment of the present invention, in the above method, scraping the outsourcing data with suspected risks from the full volume of business data includes: screening out preliminary outsourcing data based on a preset data model at the model level, where the data model is used to define the user group to which the document belongs, the document type, the document business process, or the document status; at the task level, further screening out the outsourcing data with suspected risks from the preliminary outsourcing data for a specified field through the configuration of data conditions and inspection items.

[0011] As an embodiment of the present invention, after the outsourcing data in the above method is encapsulated into a HANA view representing the financial risk structure, the method further includes: defining key fields in the HANA view to ensure comprehensive representation of the financial risk structure, where the key fields include document type, amount, supplier information, outsourcing reason, and approval status; setting calculation columns in the HANA view for calculating risk indicators, data conversion, or logical judgment; and establishing an association relationship between the outsourcing data in the HANA view.

[0012] As an embodiment of the present invention, after the outsourcing data in the above method is encapsulated into a HANA view representing the financial risk structure, the method further includes: sharding the outsourcing data through a partition key in the HANA view to improve query performance.

[0013] As an embodiment of the present invention, in the above method, executing the SQL script to query the logical data source includes: configuring the execution period of the task based on the Quartz scheduling framework, including timed trigger and event trigger; managing the execution order of the task through the ONS queue to ensure that high-priority tasks are executed first; during the task execution process, recording the task execution log, including the task start time, end time, execution status, and error information; when the task execution fails, automatically triggering the retry logic, and generating an alarm message to be pushed to the financial shared service center after the retry fails.

[0014] As an embodiment of the present invention, in the above method, managing the execution order of the task through the ONS queue to ensure that high-priority tasks are executed first includes: before sending the task into the ONS queue, setting a first delay time for high-priority tasks and a second delay time for low-priority tasks, where the first delay time is lower than the second delay time.

[0015] According to the second aspect of the present invention, an embodiment of the present invention provides a financial risk screening device based on a financial sharing mode. The device includes: a data scraping unit, configured to scrape outsourcing data with suspected risks from all business data; a data encapsulation unit, configured to encapsulate the outsourcing data into a HANA view representing the financial risk structure; a data source forming unit, configured to form a logical data source for subsequent risk screening based on the HANA view; a task configuration unit, configured to generate an SQL script according to the configured risk screening rules for screening risk data that meets the conditions; a task execution unit, configured to execute the SQL script to query the logical data source, screen out the data that meets the risk conditions, and output the screening result to the financial sharing service center, and the financial sharing service center conducts step-by-step review.

[0016] As an embodiment of the present invention, the above-mentioned data scraping unit includes: a model layer scraping module, configured to screen out preliminary outsourcing data at the model level based on a preset data model, and the data model is used to define the user group, document type, document business process or document status to which the document belongs; a task layer scraping module, configured to further screen out outsourcing data with suspected risks from the preliminary outsourcing data for a specified field by configuring data conditions and inspection items at the task level.

[0017] As an embodiment of the present invention, the above-mentioned device further includes: a key field definition unit, configured to define key fields in the HANA view to ensure a comprehensive representation of the financial risk structure, and the key fields include document type, amount, supplier information, outsourcing reason and approval status; a calculated column setting unit, configured to set calculated columns in the HANA view for calculating risk indicators, data conversion or logical judgment; and an association relationship establishment unit, configured to establish an association relationship between outsourcing data in the HANA view.

[0018] As an embodiment of the present invention, the above-mentioned device further includes: a data sharding unit, configured to shard the outsourcing data in the HANA view through a partition key to improve query performance.

[0019] As an embodiment of the present invention, the above-mentioned task execution unit includes: a cycle configuration module, configured to configure the execution cycle of the task based on the Quartz scheduling framework, including timed triggering and event triggering; a sequential execution module, configured to manage the execution order of tasks through the ONS queue to ensure that high-priority tasks are executed first; a task record module, configured to record task execution logs during the task execution process, including task start time, end time, execution status and error information; a task retry module, configured to automatically trigger a retry logic when the task execution fails, and generate an alarm message and push it to the financial sharing service center after the retry fails.

[0020] As an embodiment of the present invention, the above-mentioned sequential execution module is specifically configured to: before sending a task into the ONS queue, set a first delay time for high-priority tasks and a second delay time for low-priority tasks, where the first delay time is lower than the second delay time.

[0021] According to the third aspect of the present invention, an embodiment of the present invention further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the steps of the above method are implemented.

[0022] According to the fourth aspect of the present invention, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0023] According to the fifth aspect of the present invention, an embodiment of the present invention further provides a computer program product, including a computer program / instructions. When the computer program / instructions are executed by a processor, the steps of the above method are implemented.

[0024] The financial risk investigation method and device based on the financial sharing mode proposed by the present invention have the following beneficial effects:

[0025] First, through the configured risk investigation rules, the present application supports generating SQL scripts for different business scenarios, can dynamically adjust the rules to meet the personalized needs of enterprises, and at the task level supports further screening of suspected risk data through the configuration of data conditions and inspection items, so as to achieve more flexible and accurate risk investigation.

[0026] Second, the present application introduces a task execution mechanism based on the Quartz scheduling framework, supports timed triggering and event triggering, and can realize an automated risk investigation process. By managing task priorities through the ONS queue, it ensures that high-priority tasks can be executed first, and at the same time supports the automatic retry logic after task failure, and generates alarm information after multiple retry failures, further improving the reliability of automated processing.

[0027] Third, by encapsulating the external cooperation data suspected of risks into a HANA view representing the financial risk structure, defining key fields and calculation columns, and establishing the association relationship between data, the present application comprehensively represents the financial risk structure. It supports cross-system and cross-department data integration and analysis, and solves the limitation of only conducting risk investigation for a single system or a single business scenario in the prior art. Description of the Drawings

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings. In the drawings:

[0029] Figure 1 is a schematic flowchart of a financial risk investigation method based on a financial sharing model provided by an embodiment of the present application;

[0030] Figure 2 is a schematic flowchart of the process of capturing external collaboration data provided by an embodiment of the present application;

[0031] Figure 3 is a schematic flowchart of querying a logical data source provided by an embodiment of the present application;

[0032] Figure 4 is a schematic flowchart of a financial risk investigation method based on a financial sharing model provided by another embodiment of the present application;

[0033] Figure 5 is a schematic structural diagram of a financial risk investigation device based on a financial sharing model provided by an embodiment of the present application;

[0034] Figure 6 is a schematic structural diagram of a data capture unit provided by an embodiment of the present application;

[0035] Figure 7 is a schematic structural diagram of a financial risk investigation device based on a financial sharing model provided by another embodiment of the present application;

[0036] Figure 8 is a schematic structural diagram of a task execution unit provided by an embodiment of the present application;

[0037] Figure 9 is a schematic structural diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0038] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer and more understandable, the following will further elaborate on the embodiments of the present invention in conjunction with the drawings. Herein, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but do not limit the present invention.

[0039] The information collected in the technical solution of this application is information and data authorized by the user or fully authorized by all parties. For the processing of relevant data, such as collection, storage, use, processing, transmission, provision, disclosure, and application, it complies with the relevant laws, regulations, and standards of relevant countries and regions, takes necessary confidentiality measures, does not violate public order and good customs, and provides corresponding operation entrances for users to choose to authorize or reject.

[0040] In the technical solution of this application, the acquisition, transmission, storage, use, processing, etc. of data all comply with the relevant provisions of national laws and regulations.

[0041] Such as Figure 1 As shown, it is a schematic flowchart of a financial risk investigation method provided by an embodiment of this application based on a financial sharing model. The method includes the following steps:

[0042] Step S101: Extract the outsourcing data with suspected risks from the full-volume business data.

[0043] In this step, the full-volume business data can come from various business systems, such as Enterprise Resource System (ERS), Financial Shared Service System (FSS), Integrated Financial Shared Service System (IFSS), etc.

[0044] "Outsourcing" is short for "external collaboration", which refers to the process of communicating, confirming, or modifying with the internal submitters or relevant departments of the enterprise when problems are found in the documents during the review process in the financial sharing center. For example, if the supplier information on a reimbursement document is filled in incorrectly, the reviewer needs to collaborate with the submitter to confirm the correct supplier information and modify the document. This process of communication and processing is called "outsourcing", and "outsourcing data" refers to the specific records of these collaborative processes, including communication content, modification opinions, processing results, and corresponding document and bill information, etc.

[0045] As can be seen from the above, since external cooperation data is often the direct manifestation or potential cause of financial risks. For example, incorrect supplier information may lead to unclear fund flows; duplicate reimbursements may cause losses to the enterprise's funds; the lack of attachments may cover up false transactions, etc. Capturing external cooperation data can directly locate these abnormal situations and provide an accurate data basis for subsequent risk investigation. By analyzing external cooperation data, weak links and potential risk points in the financial process can be discovered, thereby improving the pertinence and efficiency of risk investigation. Moreover, external cooperation data not only records the abnormal situations of documents but also includes the entire process of anomaly handling, including: the collaborative records between reviewers and submitters or relevant departments; specific modification suggestions for document problems; whether the problems are resolved and whether the documents pass the review, etc. These data are highly correlated and can be analyzed in association with the original information of the documents, the approval process, supplier information, etc. to form a complete risk chain. Therefore, through the correlation analysis of external cooperation data, the sources and characteristics of financial risks can be more comprehensively identified.

[0046] Preferably, as Figure 2 shown, this step may further include the following sub-steps:

[0047] Step S1011: Screen out preliminary external cooperation data at the model level based on a preset data model, where the data model is used to define the user group, document type, document business process, or document status to which the document belongs.

[0048] At the model level, the present application first sets a data model for the business documents to be screened and inspected. Here, the data model is a structured description of business data and can define the scope and characteristics of the data. Through the preset data model, preliminary data related to external cooperation can be quickly screened out from the full volume of business data, reducing the complexity of subsequent processing. The data model is used to define the user group, document type, document business process, or document status to which the document belongs, where:

[0049] User group: Filter documents for external cooperation or return orders of shared company users;

[0050] Document type: Includes FQ documents (FSS business documents), BX documents (expense reimbursement system reimbursement forms), and ZF documents (expense reimbursement system payment forms);

[0051] Document business process: Screen documents with external cooperation records or return order records;

[0052] Document status: Screen documents in specific statuses, such as posted, returned, etc.

[0053] In this embodiment, through the preset conditions of the data model, preliminary data related to external cooperation can be quickly screened out from the full volume of business data, avoiding repeated processing of irrelevant data. Moreover, preliminary screening at the model level can significantly reduce the amount of data to be processed subsequently and reduce the consumption of system resources. Here, the preset conditions of the data model can be dynamically adjusted according to business requirements. For example, different screening rules can be set for different user groups, document types, or business processes.

[0054] Step S1012: At the task level, through the configuration of data conditions and inspection items, further screen out the external cooperation data with suspected risks for the specified fields from the preliminary external cooperation data.

[0055] Screening at the task level: On the basis of preliminary screening, by configuring more refined data conditions and inspection items, further screen out the external cooperation data with suspected risks. The focus of this step is to accurately screen specific fields.

[0056] Set data conditions for the inspection task. For example, if the data condition is set that the document type is "travel expense reimbursement form" and the external cooperation personnel code is "00219610", then when the inspection task is executed in the data model, it can perform targeted screening on specific travel expense reimbursement forms and data where the external cooperation person is 00219610.

[0057] Introduce inspection items for the inspection task, that is, further rule limitations can be imposed on the inspection task. For example, set the rule that the amount is greater than 100,000 yuan. When this rule is introduced into the inspection task, it will further screen the data with an amount greater than 100,000 yuan, and finally screen out the data.

[0058] After screening at these two levels of data conditions and inspection tasks, this embodiment can screen out all travel expense reimbursement forms with an amount greater than 100,000 yuan for external cooperation by the shared user "00219610". The screening logic at the inspection task level supports flexible configuration by users at the front end and is not limited to certain specific fields. Users can, according to the actual business needs, as flexibly as possible select various fields, combine the fields, and set the logical relationships between the fields to achieve the purpose of accurately screening risks. Similarly, if precise data screening is not required, data conditions can be not set in the inspection task, or inspection items can be not introduced. In addition, for the data conditions of the inspection task and the selection range of fields between inspection items is not strictly restricted. Generally speaking, a certain screening logic can be set as the data condition of the inspection task or as an inspection item. The difference is that setting data conditions in the inspection task can only be applied to this inspection task and cannot be applied across inspection tasks, while inspection items can be referenced by any task and can be applied across tasks.

[0059] Through detailed data conditions and check items, outsourcing data with suspected risks can be further screened out from the preliminary outsourcing data to improve the accuracy of screening. Data conditions and check items can be dynamically adjusted according to business needs, such as setting different screening rules for different risk scenarios. Through accurate screening of specified fields, hidden risk points can be discovered, such as duplicate reimbursements, false transactions, etc.

[0060] Step S102: Encapsulate the outsourced data into a HANA view representing the financial risk structure.

[0061] HANA view is a virtual data model based on SAP HANA database, which is used to structure and encapsulate external data to form a logical view that represents financial risks. Since HANA view supports high-performance real-time query, it can quickly respond to risk investigation needs. Through HANA view, structured encapsulation of external data facilitates subsequent risk investigation and analysis.

[0062] Step S103: Based on the HANA view, a logical data source is formed for subsequent risk investigation.

[0063] The logical data source is a virtual data set generated based on the HANA view to support subsequent risk investigation operations. When forming a logical data source, the data in the HANA view can be further filtered to remove data that is obviously risk-free and retain data that is suspected of risk. It is also possible to integrate outsourced data with other related data (such as historical records and approval processes) to form a complete logical data source.

[0064] Step S104: Generate an SQL script for screening qualified risk data according to the configured risk screening rules. In this embodiment, the configuration of the risk screening rules includes the configuration of the inspection object, the inspection rules and the inspection task.

[0065] Check rules refer to specific requirements for business review in the business, such as which field values to check, or whether the logical relationship between fields is reasonable. The review requirements of the business are converted into a flexible configuration on the front-end page for deployment through the rule engine, which generates program rules. When the rules are executed, the check rules are converted into execution scripts applicable to different check sources, and these scripts are sent to the target data source through the logical data source access layer to capture data that violates the rules.

[0066] The object to be inspected refers to the target object that needs to be actually reviewed in the business process. Usually, vouchers, financial risk documents, etc. that need to be specifically reviewed in the business are established as inspection objects. The inspection object includes basic information and attribute field information. The basic information is the name and system code of the inspection object, as well as the target table and Kettle type corresponding to the data source; the attribute field information includes the field information actually used in the business, such as voucher abstract, accounting subject, document code, application amount, etc. The inspection object serves the inspection rules, and when creating the inspection rules, the specific attribute fields of the inspection object are used to create the rules.

[0067] The configuration of the inspection task includes the configuration of the task execution cycle and the trigger time, etc.

[0068] Step S105: Execute the SQL script to query the logical data source, filter out the data that meets the risk conditions, and output the filtering result to the financial shared service center, where the financial shared service center conducts multi-level review.

[0069] According to the preset inspection task scheduling rules (timed or event-triggered), the task scheduling module starts the SQL script execution task, and then the execution node connects to the logical data source in the HANA database, executes the SQL script, queries the logical data source, and outputs the query result (data that meets the risk conditions) to the financial shared service center. The SQL script filters the data in the logical data source according to the preset inspection rules. The inspection rules may include:

[0070] Amount threshold: For example, the single amount exceeds 100,000 yuan.

[0071] Supplier anomaly: For example, the supplier information is inconsistent with the invoice supplier.

[0072] Attachment missing: For example, the reimbursement form lacks invoice attachments.

[0073] Approval timeout: For example, the approval process exceeds the specified time.

[0074] Preferably, as Figure 3 shown, in this step, executing the SQL script to query the logical data source may further include:

[0075] Step S1051: Configure the execution cycle of the task based on the Quartz scheduling framework, including timed trigger and event trigger.

[0076] In this embodiment, through the Quartz scheduling framework, the execution cycle of the task can be flexibly configured to ensure that the SQL script can be automatically executed according to the predetermined time or trigger condition, and supports timed trigger and event trigger to adapt to the requirements of different business scenarios.

[0077] Step S1052: Manage the execution order of tasks through the ONS queue to ensure that high-priority tasks are executed first. Using the ONS (Open Notification Service) queue to queue and manage the priorities of tasks can ensure that high-priority tasks are executed first, avoiding resource competition and execution conflicts between tasks.

[0078] In this embodiment, the priorities can be set according to the importance of the tasks. For example:

[0079] High priority: Real-time risk detection tasks.

[0080] Medium priority: Regular detection tasks executed at regular intervals.

[0081] Low priority: Historical data analysis tasks.

[0082] Since the ONS queue is generally first-in-first-out, in order to ensure that high-priority tasks are executed first, in this embodiment, before sending a task into the ONS queue, a first delay time can be set for high-priority tasks, and a second delay time can be set for low-priority tasks, where the first delay time is less than the second delay time. Due to the short delay time, high-priority tasks will enter the consumable state of the queue faster and thus be consumed first. While low-priority tasks will enter the consumable state of the queue later and wait to be consumed after high-priority tasks are executed. In this way, the purpose of executing high-priority tasks first can be achieved with only one queue. Of course, in this embodiment, the ONS queue can also be divided into a high-priority queue, a medium-priority queue, and a low-priority queue, and then tasks with different priorities are sent into the corresponding ONS queues to achieve the priorities of the inspection tasks.

[0083] Step S1053: During the execution of a task, record the task execution log, including the task start time, end time, execution status, and error information.

[0084] Step S1054: When a task execution fails, automatically trigger the retry logic, and generate an alarm message to be pushed to the financial shared service center after the retry fails.

[0085] The optimization measures of the above steps S1051 - S1054 can provide solid technical support for the efficient execution of financial risk detection tasks.

[0086] As can be seen from the above, the financial risk investigation method based on the financial sharing model proposed by the present invention, through the configured risk investigation rules, supports the generation of SQL scripts for different business scenarios, can dynamically adjust the rules to meet the personalized needs of enterprises, and at the task level supports the further screening of suspected risk data through the configuration of data conditions and inspection items, so as to achieve more flexible and accurate risk investigation. In addition, by encapsulating the external cooperation data with suspected risks into a HANA view representing the financial risk structure, it supports cross-system and cross-department data integration and analysis, and solves the limitation in the prior art of only conducting risk investigation for a single system or a single business scenario.

[0087] As Figure 4 shown in the flowchart of a financial risk investigation method provided by another embodiment of the present application, the method includes the following steps:

[0088] Step S401: Extract the external cooperation data with suspected risks from the full-volume business data.

[0089] Step S402: Encapsulate the external cooperation data into a HANA view representing the financial risk structure.

[0090] Step S403: Define key fields in the HANA view to ensure a comprehensive representation of the financial risk structure, and the key fields include document type, amount, supplier information, reason for external cooperation, and approval status.

[0091] The key fields are the results of abstracting and refining the core features of financial risks, and can comprehensively cover all dimensions of financial risks. For example, fields such as document type, amount, supplier information, reason for external cooperation, and approval status can reflect the sources and manifestations of financial risks from different angles. By defining key fields, it can be ensured that all important information related to financial risks is included in the HANA view, avoiding omissions that may lead to incomplete risk investigation.

[0092] In this embodiment, the key fields defined in this step are also the basis for the calculated columns in the subsequent step S404. For example, calculating the "amount difference" requires the "amount" and "budget amount" fields, and calculating the "approval duration" requires the "submission date" and "approval date" fields. In addition, the key fields defined in this step are also the basis for establishing data associations in the subsequent step S405. For example, the supplier ID is the key field for associating the document table and the supplier table, and the approval status is the key field for associating the approval process table. Finally, the key fields defined in this step can also support the construction of logical data sources in the subsequent step S406. Only by defining comprehensive key fields can a complete logical data source be formed.

[0093] Step S404: Set up calculated columns in the HANA view for calculating risk metrics, data transformation, or logical judgment.

[0094] The main purpose of setting up calculated columns in this step in the HANA view is to generate new data fields (calculated columns) by processing, transforming, or making logical judgments on existing data, so as to better support the analysis and investigation of financial risks.

[0095] Specific objectives include:

[0096] Calculating risk metrics: Generate risk-related metrics (such as amount differences, approval durations, etc.) through calculated columns to provide a quantitative basis for risk investigation.

[0097] Data transformation: Format or convert the units of the original data to make the data more in line with the analysis requirements (such as amount unit conversion, date formatting, etc.).

[0098] Logical judgment: Generate new fields through conditional judgments to identify potential risks (such as determining whether the budget is exceeded, whether there are abnormal approvals, etc.).

[0099] The new fields generated by the calculated columns in this step can be used as the basis for data association in the subsequent step S305. For example, the "vendor risk level" can be used to associate the historical transaction records of the vendor, and the "approval exception flag" can be used to associate the detailed information of abnormal approvals.

[0100] Step S405: Establish the association relationship between the external cooperation data in the HANA view.

[0101] The main purpose of establishing the association relationship between the external cooperation data in this step in the HANA view is to integrate the originally isolated external cooperation data through logical connections, reveal the internal relationships between the data, and provide a more comprehensive analysis perspective for financial risk investigation. The specific objectives of this step include: By establishing the association relationship, data such as documents, vendors, and approval processes can be connected to form a complete business chain; the association relationship can help analyze cross-table or cross-business data, such as all external cooperation documents of a certain vendor, the external cooperation expenditure situation of a certain department, etc.; by pre-establishing the association relationship in the HANA view, the subsequent query performance can be optimized to avoid complex table join operations during query.

[0102] Specifically, the association relationship between the external cooperation data can be established in the following ways:

[0103] First, according to business requirements, determine the fields for which association relationships need to be established. For example: document ID, used to associate data of different versions or processing stages of the same document; supplier ID, used to associate different documents of the same supplier; department ID, used to associate external cooperation data of the same department; approver ID, used to associate approval records of the same approver.

[0104] Next, select an appropriate association method based on the relationships between the data. The association relationships here can include one-to-one association, one-to-many association, or many-to-many association. Among them, a one-to-one association can be, for example, one document corresponding to one supplier; a one-to-many association can be, for example, one supplier corresponding to multiple documents; a many-to-many association can be, for example, multiple documents corresponding to multiple approval processes (usually implemented through an intermediate table).

[0105] Then, use JOIN operations or other association methods in the HANA view to define the association logic between the data.

[0106] Finally, verify whether the association relationship is correctly established to ensure that the association between the data conforms to the business logic and expectations.

[0107] Preferably, the method of this embodiment may further include the following steps: shard the external cooperation data in the HANA view through a partitioning key to improve query performance.

[0108] The main purpose of sharding the external cooperation data through a partitioning key in the HANA view is to optimize data storage and query performance. Especially when dealing with large-scale data, sharding can significantly improve query efficiency and reduce the consumption of system resources. Sharding is to divide the data of a large table or view into multiple small logical partitions according to a certain field (partitioning key). Each partition can be stored and queried independently, thus improving performance. The partitioning key is the field used to divide the data, and usually a field with high query frequency and uniform data distribution is selected.

[0109] In this embodiment, first, the partitioning key can be selected according to business requirements and query patterns. The selection principles of the partitioning key usually include high query frequency, uniform data distribution, and business logic relevance. Among them, high query frequency means selecting the fields often used in query conditions as the partitioning key; uniform data distribution is to ensure that the data volume after partitioning is roughly equal to avoid performance bottlenecks caused by some partitions being too large; business logic relevance means that the partitioning key should be related to the business logic. For example, partitioning by time can support the requirement of querying by time range. Common partitioning keys include:

[0110] Time fields: such as "submission date" or "approval date", applicable to scenarios of querying by time range.

[0111] Organization fields: such as "Department ID" or "Supplier ID", applicable to scenarios where queries are made by department or supplier.

[0112] Amount field: such as "Amount", applicable to scenarios where queries are made by amount range.

[0113] Then determine the specific partition type, which can include range partitioning, hash partitioning, list partitioning, etc. Range partitioning is applicable to scenarios where field values have an obvious range, such as sharding by date or amount range. Hash partitioning is applicable to scenarios where field values are evenly distributed but have no obvious range, such as sharding by Supplier ID. List partitioning is applicable to scenarios where field values are limited and fixed, such as sharding by Department ID.

[0114] Next, partitions can be defined in the HANA view, that is, when creating the HANA view, specify the partition key and partition type. If the view is created based on a table, partitions need to be defined in the table.

[0115] Finally, test the query performance to ensure that the partitions can significantly reduce the amount of data scanned, and verify whether the partitions are evenly distributed to avoid data skew.

[0116] Step S406: Form a logical data source for subsequent risk investigation based on the HANA view.

[0117] Step S407: Generate an SQL script according to the configured risk investigation rules for screening risk data that meets the conditions.

[0118] Step S408: Execute the SQL script to query the logical data source, screen out the data that meets the risk conditions, and output the screening results to the financial shared service center for hierarchical review by the financial shared service center.

[0119] The financial risk investigation method based on the financial shared mode proposed by the present invention has the following beneficial effects:

[0120] First, through the configured risk investigation rules, this application supports generating SQL scripts for different business scenarios, can dynamically adjust the rules to meet the personalized needs of enterprises, and at the task level supports further screening of suspected risk data through the configuration of data conditions and inspection items, thus realizing more flexible and accurate risk investigation.

[0121] Second, this application introduces a task execution mechanism based on the Quartz scheduling framework, supports timed triggering and event triggering, and can realize an automated risk investigation process. Manage task priorities through the ONS queue to ensure that high-priority tasks can be executed first, and at the same time support the automatic retry logic after task failure, and generate alarm information after multiple retry failures, further improving the reliability of automated processing.

[0122] III. In the present application, the outsourced data with suspected risks is encapsulated as a HANA view representing the financial risk structure, keyword fields and calculation columns are defined, and the association relationships between the data are established, so as to comprehensively represent the financial risk structure. It supports cross-system and cross-departmental data integration and analysis, and solves the limitation in the prior art of only conducting risk investigation for a single system or a single business scenario.

[0123] As Figure 5 shown in the structural schematic diagram of a financial risk investigation device provided by an embodiment of the present application based on a financial sharing mode. The device includes: a data capture unit 510, a data encapsulation unit 520, a data source formation unit 530, a task configuration unit 540, and a task execution unit 550, which are connected in sequence. Among them:

[0124] The data capture unit 510 is configured to capture the outsourced data with suspected risks from the full-volume business data.

[0125] The data encapsulation unit 520 is configured to encapsulate the outsourced data as a HANA view representing the financial risk structure.

[0126] The data source formation unit 530 is configured to form a logical data source for subsequent risk investigation based on the HANA view.

[0127] The task configuration unit 540 is configured to generate an SQL script according to the configured risk investigation rules for screening the risk data that meets the conditions.

[0128] The task execution unit 550 is configured to execute the SQL script to query the logical data source, screen out the data that meets the risk conditions, and output the screening result to the financial sharing service center, and the financial sharing service center conducts step-by-step review.

[0129] Preferably, as Figure 6 shown, the above-mentioned data capture unit 510 includes:

[0130] The model layer capture module 511 is configured to screen out preliminary outsourced data at the model level based on a preset data model, and the data model is used to define the user group, document type, document business process or document status to which the document belongs;

[0131] The task layer capture module 512 is configured to further screen out the outsourced data with suspected risks from the preliminary outsourced data for a specified field at the task level through the configuration of data conditions and inspection items.

[0132] Preferably, as Figure 7 shown, the above-mentioned device further includes:

[0133] A keyword field definition unit 560 is used to define keyword fields in the HANA view to ensure a comprehensive characterization of the financial risk structure. The keyword fields include document type, amount, supplier information, reason for subcontracting, and approval status;

[0134] A calculation column setting unit 570 is used to set calculation columns in the HANA view for calculating risk indicators, data conversion, or logical judgment; and,

[0135] An association relationship establishment unit 580 is used to establish association relationships between subcontracting data in the HANA view.

[0136] Preferably, the above device further includes: a data sharding unit, which is used to shard the subcontracting data in the HANA view through a partitioning key to improve query performance.

[0137] Preferably, as Figure 8 shown, the above task execution unit 550 includes:

[0138] A cycle configuration module 551 is used to configure the execution cycle of the task based on the Quartz scheduling framework, including timing trigger and event trigger.

[0139] A sequential execution module 552 is used to manage the execution order of tasks through the ONS queue to ensure that high-priority tasks are executed first.

[0140] A task record module 553 is used to record task execution logs during the task execution process, including task start time, end time, execution status, and error information.

[0141] A task retry module 554 is used to automatically trigger a retry logic when the task execution fails, and generate an alarm message to be pushed to the financial shared service center after the retry fails.

[0142] Preferably, the above sequential execution module 552 is specifically used for: before sending the task into the ONS queue, setting a first delay time for high-priority tasks and a second delay time for low-priority tasks, where the first delay time is lower than the second delay time.

[0143] For a detailed description of each of the above units and modules, reference can be made to the corresponding description in the foregoing method embodiments, and details will not be elaborated here.

[0144] As can be seen from the above, the financial risk investigation device based on the financial sharing mode proposed by the present invention has the following

[0145] beneficial effects:

[0146] 1. This application supports generating SQL scripts for different business scenarios through configured risk screening rules, can dynamically adjust the rules to meet the personalized needs of enterprises, and at the task level, supports further screening of suspected risk data through the configuration of data conditions and inspection items, thus achieving more flexible and accurate risk screening.

[0147] 2. This application introduces a task execution mechanism based on the Quartz scheduling framework, supports timed triggering and event triggering, and can realize an automated risk screening process. Manage task priorities through the ONS queue to ensure that high-priority tasks can be executed first, and at the same time support the automatic retry logic after task failure, and generate alarm information after multiple retry failures, further improving the reliability of automated processing.

[0148] 3. This application comprehensively represents the financial risk structure by encapsulating the external cooperation data suspected of risks as a HANA view representing the financial risk structure, defining key fields and calculation columns, and establishing the association relationship between data. It supports cross-system and cross-department data integration and analysis, and solves the limitation in the prior art of only conducting risk screening for a single system or a single business scenario.

[0149] Figure 9 It is a schematic diagram of the electronic device provided by the embodiment of the present invention. Figure 9 The shown electronic device is a general data processing device, which includes a general computer hardware structure, and at least includes a processor 801 and a memory 802. The processor 801 and the memory 802 are connected through a bus 803. The memory 802 is suitable for storing one or more instructions or programs executable by the processor 801. The one or more instructions or programs are executed by the processor 801 to implement the steps in the above-mentioned financial risk screening method based on the financial sharing mode.

[0150] The above-mentioned processor 801 can be an independent microprocessor or a set of one or more microprocessors. Thus, the processor 801 executes the commands stored in the memory 802, thereby executing the method flow of the embodiment of the present invention as described above to implement data processing and control of other devices. The bus 803 connects the above-mentioned multiple components together, and at the same time connects the above-mentioned components to a display controller 804, a display device, and an input / output (I / O) device 805. The input / output (I / O) device 805 can be a mouse, a keyboard, a modem, a network interface, a touch input device, a somatosensory input device, a printer, and other devices well known in the art. Typically, the input / output (I / O) device 805 is connected to the system through an input / output (I / O) controller 806.

[0151] Among them, the memory 802 may store software components, such as an operating system, a communication module, an interaction module, and application programs. Each of the above-described modules and application programs corresponds to a set of executable program instructions for completing one or more functions and the methods described in the embodiments of the invention.

[0152] An embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-described financial risk investigation method based on the financial sharing mode are implemented.

[0153] The preferred embodiments of the present invention have been described above with reference to the accompanying drawings. Many features and advantages of these embodiments are clear from this detailed description. Therefore, the claims are intended to cover all such features and advantages of these embodiments that fall within their true spirit and scope. In addition, since those skilled in the art can easily think of many modifications and changes, the embodiments of the present invention are not limited to the exact structures and operations illustrated and described, but may cover all suitable modifications and equivalents that fall within their scope.

[0154] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) that contain computer-usable program code.

[0155] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0156] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implements the functions specified in Figure 1 one process or multiple processes and / or blocksFigure 1 The functions specified in one or more boxes.

[0157] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide for implementing in the process Figure 1 One or more processes and / or boxes Figure 1 The steps of the functions specified in one or more boxes.

[0158] In the specific embodiments described above, the objectives, technical solutions, and beneficial effects of the present invention have been further described in detail. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A financial risk investigation method based on the financial sharing model, characterized in that, The method includes: Scraping outsource data with suspected risks from all business data; Encapsulating the outsource data into a HANA view representing the financial risk structure; Forming a logical data source for subsequent risk investigation based on the HANA view; Generating an SQL script according to the configured risk investigation rules to screen for risk data meeting the conditions; Executing the SQL script to query the logical data source, screening out the data meeting the risk conditions, and outputting the screening results to the financial shared service center, where the financial shared service center conducts step-by-step review.

2. The financial risk investigation method based on the financial sharing mode according to claim 1, wherein The scraping outsource data with suspected risks from all business data includes: On the model level, screening out preliminary outsource data based on a preset data model, where the data model is used to define the user group, document type, document business process, or document status to which the document belongs; On the task level, further screening out the outsource data with suspected risks from the preliminary outsource data for a specified field through the configuration of data conditions and inspection items.

3. The financial risk investigation method based on the financial sharing mode according to claim 1, characterized in that After encapsulating the outsource data into a HANA view representing the financial risk structure, the method further includes: Defining key fields in the HANA view to ensure comprehensive representation of the financial risk structure, where the key fields include document type, amount, supplier information, reason for outsourcing, and approval status; Setting calculation columns in the HANA view for calculating risk indicators, data conversion, or logical judgment; and Establishing association relationships between the outsource data in the HANA view.

4. The financial risk investigation method based on the financial sharing mode according to claim 1, characterized in that, After encapsulating the outsource data into a HANA view representing the financial risk structure, the method further includes: sharding the outsource data in the HANA view through a partition key to improve query performance.

5. The financial risk investigation method based on the financial sharing mode according to claim 1, wherein, The executing the SQL script to query the logical data source includes: Configuring the execution period of the task based on the Quartz scheduling framework, including timed trigger and event trigger; Managing the execution order of the task through the ONS queue to ensure that high-priority tasks are executed first; During the task execution process, recording the task execution log, including task start time, end time, execution status, and error information; When the task execution fails, automatically triggering the retry logic, and generating an alarm message to be pushed to the financial shared service center after the retry fails.

6. The financial risk investigation method based on the financial sharing model according to claim 5, characterized in that The managing the execution order of the task through the ONS queue to ensure that high-priority tasks are executed first includes: Before sending the task into the ONS queue, setting a first delay time for high-priority tasks and a second delay time for low-priority tasks, where the first delay time is lower than the second delay time.

7. A financial risk investigation device based on a financial sharing model, characterized in that, The device includes: A data scraping unit for scraping outsource data with suspected risks from all business data; A data encapsulation unit for encapsulating the outsource data into a HANA view representing the financial risk structure; A data source forming unit for forming a logical data source for subsequent risk investigation based on the HANA view; A task configuration unit for generating an SQL script according to the configured risk investigation rules to screen for risk data meeting the conditions; A task execution unit for executing the SQL script to query the logical data source, screening out data that meets the risk conditions, and outputting the screening result to the financial shared service center for hierarchical review by the financial shared service center.

8. The financial risk investigation device based on the financial sharing mode according to claim 7, characterized in that, The data scraping unit includes: A model layer scraping module for screening out preliminary outsourcing data at the model level based on a preset data model, where the data model is used to define the user group, document type, document business process, or document status to which the document belongs; A task layer scraping module for further screening out outsourcing data with suspected risks from the preliminary outsourcing data for specified fields through the configuration of data conditions and check items at the task level.

9. The financial risk investigation device based on the financial sharing mode according to claim 7, characterized in that, The device further includes: A keyword field definition unit for defining keyword fields in the HANA view to ensure a comprehensive representation of the financial risk structure, where the keyword fields include document type, amount, supplier information, outsourcing reason, and approval status; A calculation column setting unit for setting calculation columns in the HANA view for calculating risk indicators, data conversion, or logical judgment; and An association relationship establishment unit for establishing association relationships between outsourcing data in the HANA view.

10. The financial risk investigation device based on the financial sharing mode according to claim 7, characterized in that, The device further includes: a data sharding unit for sharding the outsourcing data in the HANA view through a partition key to improve query performance.

11. The financial risk investigation device based on the financial sharing mode according to claim 7, wherein, The task execution unit includes: A cycle configuration module for configuring the execution cycle of the task based on the Quartz scheduling framework, including timed trigger and event trigger; A sequential execution module for managing the execution order of tasks through the ONS queue to ensure that high-priority tasks are executed first; A task record module for recording task execution logs during task execution, including task start time, end time, execution status, and error information; A task retry module for automatically triggering a retry logic when the task execution fails and generating an alarm message to be pushed to the financial shared service center after the retry fails.

12. The financial risk investigation device based on the financial sharing mode according to claim 11, characterized in that, Specifically, the sequential execution module is used to set a first delay time for high-priority tasks and a second delay time for low-priority tasks before sending the tasks to the ONS queue, where the first delay time is lower than the second delay time.

13. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1 to 6.

15. A computer program product, comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, it implements the steps of the method according to any one of claims 1 to 6.

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