Adaptive risk assessment and adjustment method and system based on penetrating supervision

Abstract

The present invention belongs to the field of data processing technology. Provided is an adaptive risk assessment and adjustment method and system based on penetrating supervision, which pre-processes the acquired business data to be supervised and determines the evaluation index; determines multiple processing rules based on the original fields of the business data to be supervised and the evaluation index; processes the business data to be supervised through each processing rule, and automatically calculates the proportion of the data identified in each rule in the entire identification process after the processing of each rule is completed and the data is successfully identified; when the proportion of the data corresponding to any current rule is greater than the set threshold, generates warning information and adaptively adjusts the parameters of the current rule. The present invention has the ability to adaptively optimize rules, which improves the efficiency and effectiveness of risk assessment and reduces the risk of human error.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method and system for adaptive risk assessment and adjustment based on penetrating supervision. Background Art

[0002] The statements in this section merely provide background art related to the present invention and do not necessarily constitute prior art.

[0003] Risk management has always been a crucial component in many industries, including finance and insurance. With the continuous expansion of businesses and the increasing complexity of the market environment, traditional risk assessment and management methods are increasingly unable to meet demand. Financial markets encompass a rich and diverse range of business segments, such as credit and investment, each with distinct risk profiles, necessitating a tailored risk assessment system. At the same time, regulatory requirements for corporate risk management are becoming increasingly stringent, forcing companies to improve the accuracy and effectiveness of risk identification and management. Furthermore, the role of business data in risk management is becoming increasingly prominent. Companies are accumulating vast amounts of business data, and the in-depth processing and effective utilization of this data to achieve more accurate risk assessments has become a pressing issue.

[0004] Existing technologies mostly assess risk by building risk assessment models and analyzing business data. These models typically set fixed risk indicators and rules. For example, in credit transactions, credit risk is assessed based on basic financial indicators such as a customer's income and liabilities. Data processing techniques are then used to organize and perform preliminary analysis on the collected business data, extracting relevant information for risk assessment. For example, data is cleansed and filtered to remove outliers, followed by simple statistical analysis to calculate statistics such as the mean and standard deviation. Finally, based on the risk assessment results, risks are classified into different levels, such as low, medium, and high, so that companies can take appropriate risk management measures. Furthermore, risk early warning mechanisms are established to issue warning signals when risk indicators reach certain thresholds.

[0005] There are some problems with existing risk supervision methods: (1) Risk models lack flexibility. Most existing risk models are built based on fixed rules and indicators, which are difficult to adapt to the rapid changes in the business environment and the special needs of different business scenarios. When market conditions change or new business models emerge, the models cannot be adjusted, resulting in a decrease in the accuracy of risk assessment; (2) Risk identification is not accurate enough. Due to the limitations of risk models and insufficient data utilization, existing solutions are not accurate enough in risk identification. Misjudgments or omissions may occur, low risks may be misjudged as high risks, or potential high risks may not be discovered in time, causing unnecessary losses or risk hazards to the company; (3) Rules are not adjusted in a timely manner. When business data and market environment change, risk assessment rules need to be adjusted. However, the adjustment of rules in existing technologies requires manual judgment and manual operation, and lacks an automated and intelligent adjustment reference mechanism, resulting in untimely rule adjustments and inability to adapt to changing risk conditions in a timely manner. Summary of the Invention

[0006] In order to address the shortcomings of the existing technology, the present invention provides an adaptive risk assessment and adjustment method and system based on penetrating supervision, which automatically identifies and pushes risk data in business data without manual operation, improves the efficiency of risk assessment, and reduces the risk of human error; it has the ability to adaptively optimize rules, can automatically calculate the data ratio according to the rule execution results, and send an early warning message when the data ratio exceeds the threshold, adjust and optimize the rules based on actual conditions, and improve the effectiveness of risk assessment.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] In a first aspect, the present invention provides an adaptive risk assessment and adjustment method based on penetrating supervision.

[0009] An adaptive risk assessment and adjustment method based on penetrating supervision includes the following process:

[0010] Pre-process the acquired business data to be regulated and determine the evaluation indicators;

[0011] Determining multiple processing rules based on the original fields of the business data to be regulated and the evaluation indicators;

[0012] Through various processing rules, the business data to be regulated is processed. After the processing of each rule is completed and the data is successfully identified, the proportion of the data identified in each rule in the entire identification process is automatically calculated;

[0013] When the data proportion corresponding to any current rule is greater than a set threshold, an early warning message is generated and the parameters of the current rule are adaptively adjusted.

[0014] As a further limitation of the first aspect of the present invention, according to specific risk assessment requirements, multiple processing rules are determined based on the original fields of the business data to be supervised and the evaluation indicators.

[0015] As a further limitation of the first aspect of the present invention, the business data to be supervised is comprehensively retrieved in response to a manual click task or a timed task, and the retrieved business data to be supervised is processed according to various processing rules.

[0016] As a further limitation of the first aspect of the present invention, before automatically calculating the proportion of data identified in each rule in the entire identification process, the method further includes:

[0017] The identified data that meets the rules will be transmitted to the risk list table according to the pre-set push action, and an early warning will be issued based on the risk list table.

[0018] As a further limitation of the first aspect of the present invention, for rules based on threshold comparison, historical data is analyzed according to a deep learning model to determine the adjustment range of the threshold.

[0019] As a further limitation of the first aspect of the present invention, for complex rules containing multiple judgment conditions, the number of data occurrences of each condition in the rule is calculated one by one, the new weight of each condition is calculated based on the number of occurrences, and the weight of each condition of the complex rule is assigned based on the new weight.

[0020] As a further limitation of the first aspect of the present invention, when processing the service data to be supervised by various processing rules, rule priorities are introduced and the rules are executed according to the rule priorities:

[0021] ;

[0022] in, is the risk level, is the execution frequency, For the scope of influence, 、 and The sum of the three is 1, and all three are weight coefficients, priority values The higher the value, the higher the priority of the rule. For the Rules, 、 and All the values ​​were normalized.

[0023] In a second aspect, the present invention provides an adaptive risk assessment and adjustment system based on penetrating supervision.

[0024] An adaptive risk assessment and adjustment system based on penetrating supervision, comprising:

[0025] The data preprocessing unit is configured to: preprocess the acquired business data to be regulated and determine evaluation indicators;

[0026] a processing rule determination unit configured to: determine a plurality of processing rules based on the original fields of the business data to be regulated and the evaluation index;

[0027] The data identification unit is configured to: process the business data to be regulated according to various processing rules, and after the processing of each rule is completed and the data is successfully identified, automatically calculate the proportion of the data identified in each rule in the entire identification process;

[0028] The adaptive adjustment unit is configured to: when the data proportion corresponding to any current rule is greater than a set threshold, generate warning information and adaptively adjust the parameters of the current rule.

[0029] In a third aspect, the present invention provides a computer device comprising: a processor and a computer-readable storage medium;

[0030] a processor adapted to execute a computer program;

[0031] A computer-readable storage medium having a computer program stored therein, wherein the computer program, when executed by the processor, implements the adaptive risk assessment and adjustment method based on penetrating supervision as described in the first aspect of the present invention.

[0032] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, wherein the computer program is suitable for being loaded by a processor and executing the adaptive risk assessment and adjustment method based on penetration supervision as described in the first aspect of the present invention.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] 1. The present invention innovatively proposes an adaptive risk assessment and adjustment method based on penetrating supervision. It has the ability to adaptively optimize rules, automatically calculate the data proportion according to the rule execution results, and send an early warning message when the data proportion exceeds the threshold. Relevant personnel can adjust and optimize the rules based on actual conditions, thereby improving the effectiveness of risk assessment.

[0035] 2. The present invention innovatively proposes an adaptive risk assessment and adjustment method based on penetrating supervision. When faced with rules with multiple conditions, it can calculate the weight of each condition in the rule, provide a basis for rule adjustment, realize adaptive adjustment of the risk model, and solve the problem that the existing technology cannot adjust the rule parameters according to the actual situation as a reference.

[0036] 3. The present invention innovatively proposes an adaptive risk assessment and adjustment method based on penetrating supervision, which realizes the automatic identification and push of risk data in business data without manual operation, improves the efficiency of risk assessment, and reduces the risk of human error.

[0037] 4. The present invention innovatively proposes an adaptive risk assessment and adjustment method based on penetrating supervision, which supports in-depth processing of original data and generates new comprehensive indicators, which can reflect the risk status more comprehensively and accurately, avoiding the problem of bias in risk assessment results caused by existing technologies that only consider single-dimensional risk factors.

[0038] 5. The present invention innovatively proposes an adaptive risk assessment and adjustment method based on penetrating supervision, and provides a wealth of calculation or analysis rules, covering logical rules such as greater than, equal to or less than, included or not included, month-on-month or year-on-year based on indicators or original fields, which can adapt to complex risk identification needs in different business scenarios.

[0039] 6. The present invention innovatively proposes an adaptive risk assessment and adjustment method based on penetrating supervision. When processing the regulated business data through various processing rules, rule priorities are introduced and rules are executed according to the rule priorities, which can ensure that high-priority rules are processed first, thereby reducing the occupation of system resources by low-priority rules and improving the overall processing efficiency of the system; at the same time, for high-risk or emergency situations, the system can respond quickly and take corresponding measures, thereby improving the response speed of the system.

[0040] Advantages of additional aspects of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0042] Figure 1 A schematic diagram of an adaptive risk assessment and adjustment method based on penetrating supervision provided by an exemplary embodiment of the present invention;

[0043] Figure 2 A schematic diagram of an adaptive risk assessment and adjustment system based on penetrating supervision provided by an exemplary embodiment of the present invention;

[0044] Figure 3 A schematic diagram of an adaptive risk assessment and adjustment system based on penetrating supervision provided by another exemplary embodiment of the present invention;

[0045] Figure 4 A schematic diagram of a computer device is provided for an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0046] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0047] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0048] As described in the background technology, existing risk management methods are highly subjective, easily affected by human factors, and cannot accurately identify and assess risks; quantitative analysis methods require a large amount of data support, and the model needs to be continuously optimized and updated, which increases the operating costs of corporate organizations; existing risk assessment methods usually use fixed rules and conditions, lack flexibility, and are difficult to adapt to changes in the business environment. When the business changes, the rules and conditions are difficult to adjust, resulting in a decrease in the accuracy of risk identification; existing risk assessment methods only consider risk factors in a single dimension, which cannot fully reflect the risk situation and easily cause deviations in risk assessment results. In view of the above problems existing in the existing solutions, the present invention proposes an adaptive risk assessment adjustment method based on penetrating supervision, such as Figure 1 As shown, the following process is included:

[0049] S101: Pre-process the acquired business data to be regulated and determine evaluation indicators;

[0050] S102: Determine multiple processing rules based on the original fields of the business data to be regulated and the evaluation indicators;

[0051] S103: Process the business data to be regulated through various processing rules. After each rule is processed and the data is successfully identified, the proportion of the data identified in each rule in the entire identification process is automatically calculated;

[0052] S104: When the data proportion corresponding to any current rule is greater than a set threshold, a warning message is generated and the parameters of the current rule are adaptively adjusted.

[0053] Before step S101 of the present invention, it is necessary to provide basic support for risk assessment through maintenance of the function menu, specifically including:

[0054] (1) Build specialized modules to create adaptive model scenarios for different business segments such as finance and supply chain, and clarify the core risk concerns and evaluation indicators of each scenario.

[0055] For example, for the financial business segment, the model scenario is the authenticity assessment scenario of corporate financial statements. In the financial field, companies may engage in behaviors such as embellishing financial statements to obtain financing and covering up losses. This scenario aims to identify such risks.

[0056] In this scenario, core risk concerns may include:

[0057] Revenue recognition: Pay attention to whether the company has engaged in behaviors such as premature revenue recognition and fictitious revenue. For example, some companies recognize revenue before goods are delivered or services are completed in order to embellish their performance.

[0058] Cost accounting: Checking whether costs are truly and accurately calculated, and whether there is any inflated or concealed cost; for example, a company may increase profits by understating costs;

[0059] Related-party transactions: Pay attention to whether the transactions between related companies are fair and whether there is any behavior of transferring profits or assets through related-party transactions, such as the sale of goods at unreasonable prices between the parent company and the subsidiary.

[0060] In this scenario, specific evaluation indicators may include:

[0061] Volatility of revenue growth rate: Assess the stability of revenue growth by calculating the fluctuation range of revenue growth rate in different periods; if the volatility is too large, there may be a risk of irregular revenue recognition.

[0062] Cost-to-income ratio: The ratio of cost-to-income to operating income, which reflects the company's cost control ability; if this indicator rises abnormally, it may indicate problems with cost accounting.

[0063] Proportion of related-party transactions: The proportion of the amount of related-party transactions to the total transaction amount of the enterprise. If the proportion is too high, there may be a risk of interest transfer.

[0064] For another example, for the supply chain business segment, the model scenario is the supplier performance capability assessment scenario. The performance of suppliers in the supply chain directly affects the production and operation of the enterprise. This scenario is used to evaluate whether the supplier can provide goods or services on time, in quality and in quantity.

[0065] In this scenario, core risk concerns may include:

[0066] Timeliness of supply: whether the supplier can deliver goods or provide services within the agreed time; for example, if the supplier's own production problems cause delivery delays, affecting the company's production schedule;

[0067] Product quality: whether the products or services provided by suppliers meet quality standards. If the product quality does not meet the standards, it may cause quality problems in the company's products and affect the company's reputation;

[0068] Supply stability: Is the supplier's business situation stable? Is there a risk of supply disruption due to the supplier's own faults? For example, a supplier may suspend production due to a broken capital chain.

[0069] In this scenario, specific evaluation indicators may include:

[0070] On-time delivery rate, which is the ratio of orders delivered on time to the total number of orders, reflects the supplier's timely delivery;

[0071] Product qualification rate, the ratio of qualified products to the total number of products, measures the supplier's product quality;

[0072] Length of cooperation: The length of time that an enterprise cooperates with its supplier. A longer length of cooperation usually means a relatively stable supply relationship.

[0073] (2) Build a complete risk level system and flexibly set and adjust risk levels.

[0074] Risk levels are classified, which can include low risk, medium risk, high risk and extremely high risk.

[0075] Low risk: The possibility of the risk occurring is extremely low, and even if it occurs, the impact on the company is small. For example, in the company's daily procurement of office supplies, the supplier's delivery is delayed for half a day due to minor traffic congestion, which has basically no impact on the company's overall operations.

[0076] Medium risk: There is a certain possibility of the risk occurring, and if it occurs, it will cause a certain degree of loss or impact to the company. For example, an important raw material supplier of the company may delay delivery by one week due to equipment failure, which may affect the production progress of some of the company's products, but will not lead to a complete suspension of production.

[0077] High risk means the possibility of occurrence of the risk is high, and once it occurs, it will bring significant losses or serious impacts to the enterprise. For example, the core supplier of the enterprise may go bankrupt due to poor management, resulting in the interruption of the supply of key raw materials to the enterprise, which may cause the enterprise's production line to stop working and cause huge losses.

[0078] Extremely high risk, which is almost certain to occur and will cause a devastating blow to the company after it occurs; for example, the industry in which the company is located is facing major adjustments, and the company fails to adapt in time, which may lead to a sharp decline in the company's market share or even bankruptcy and liquidation.

[0079] Adjust according to changes in the market environment. When market competition intensifies, the operating risks faced by enterprises increase. At this time, the risk level of related businesses can be appropriately increased; for example, during a downturn in the real estate market, the project sales risk level of real estate companies can be adjusted from low risk to medium risk.

[0080] Adjustments should be made based on the company's own circumstances. If the company makes major adjustments, such as expanding into new business areas or entering new markets, the risk level of the related business may need to be increased due to unfamiliarity with the new areas. For example, if a traditional manufacturing company decides to enter the Internet e-commerce field, the risk level of its e-commerce business can be set to medium-high risk.

[0081] Adjustments should be made based on the occurrence of risk events. When a risk event occurs in a certain business area, even if the impact of the event is small, the risk level of the business should be reassessed. For example, a supplier of an enterprise may have had minor quality problems. Although it did not cause significant losses, the enterprise can adjust the business risk level related to the supplier from low risk to medium-low risk and strengthen subsequent monitoring.

[0082] (3) Set up the joint query configuration function and configure the routing and routing parameters of the jump page.

[0083] For example, in a joint query of financial business, when viewing the detailed information of a large account receivable of a company, the user hopes to quickly jump to the customer's credit evaluation page through the joint query function to understand the customer's credit status. A route is set from the "accounts receivable details page" to the "customer credit evaluation page", such as " / accountsReceivable / detail / [accounts receivable ID]" to jump to " / customerCredit / evaluation / [customer ID]". When jumping, the customer ID corresponding to the account receivable is passed as a routing parameter. In this way, the customer credit evaluation page can query and display the customer's detailed credit information, such as credit rating and overdue record, based on the received customer ID.

[0084] For another example, in supply chain business joint inquiry, when viewing the purchase order information for a batch of raw materials of an enterprise, the user wants to jointly query the basic information and historical cooperation records of the raw material supplier. Configure a route from the "Purchase Order Details Page" to the "Supplier Information Page", for example, " / purchaseOrder / detail / [Purchase Order ID]" jumps to " / supplier / info / [Supplier ID]", pass the supplier ID in the purchase order as a routing parameter, and on the supplier information page, display the supplier's basic information (such as name, address, contact information, etc.) and historical cooperation records with the supplier (such as cooperation projects, delivery status, quality feedback, etc.) based on the supplier ID.

[0085] (4) Establish a list management module to conveniently enter black and white lists for rule selection.

[0086] If a company discovers serious quality issues, delivery delays, or commercial fraud with certain suppliers during procurement, it can blacklist these suppliers to avoid future business dealings. Companies enter detailed information about the blacklisted supplier, including the supplier's name, unified social credit code, the reason for blacklisting (e.g., "repeatedly providing substandard products, causing production line shutdowns," "intentionally misrepresenting prices, engaging in commercial fraud"), and the date of blacklisting. Procurement rules can then be configured to automatically filter out blacklisted suppliers when selecting suppliers, prohibiting them from being selected for cooperation. Furthermore, in subsequent business processes, the system will provide risk warnings for any operations involving these blacklisted suppliers (e.g., contract signing, payments, etc.).

[0087] After long-term cooperation, enterprises find that some suppliers have excellent performance in product quality, delivery time, service, etc., so they put these suppliers on the whitelist and give priority to cooperating with them in procurement business. The relevant information of the whitelist suppliers is entered, such as the supplier name, advantageous products or services, cooperation highlights (such as "100% product qualification rate for three consecutive years", "on-time delivery rate of up to 98%", etc.), the date of inclusion in the whitelist, etc., and is set in the procurement business rules. Under the same conditions, suppliers on the whitelist are recommended first; for example, when conducting procurement bidding, the bid documents of whitelist suppliers can get certain extra points in the evaluation process; at the same time, whitelist suppliers are specially marked in the business system to facilitate business personnel to quickly identify and select them.

[0088] Step S101 of the present invention specifically includes:

[0089] It supports users to deeply process raw data based on business needs. For example, in the credit business scenario, by innovatively integrating multiple key fields such as a customer's income, liabilities, and credit history, a new "credit risk comprehensive index" is generated. This new index can more comprehensively and accurately reflect the customer's credit risk status, providing a more scientific basis for risk assessment.

[0090] Step S102 of the present invention specifically includes:

[0091] A rich variety of rule-based calculation operations are provided, covering logic such as greater than, equal to, or less than, including or excluding, and month-on-month and year-on-year changes based on indicators or raw fields. Users can flexibly construct rule conditions based on specific risk assessment needs. For example, when the "credit risk comprehensive indicator" exceeds a specific threshold, the customer is identified as high-risk; or when the month-on-month change in a business indicator exceeds a preset ratio, a risk warning mechanism is triggered. This flexible rule setting can adapt to the complex risk identification needs of different business scenarios.

[0092] Optionally, the following example is given: Assume that the business data set to be regulated is ,in Indicates the Business data records, the original field set is , the evaluation index set is , define a set of rule conditions , each rule condition By operator set , data elements (original fields or evaluation indicators) and thresholds or reference values.

[0093] In order to ensure efficient execution under complex rule systems, a rule priority function is introduced:

[0094] (1);

[0095] in, is the risk level, is the execution frequency, For the scope of influence, 、 and The sum of the three is 1, and all three are weight coefficients. 、 and The priority values ​​are obtained through expert scoring or historical data analysis, and the value range is [0,1]. The higher the value, the higher the priority of the rule. When business data enters the system, it is executed according to the priority of the rule. Rule conditions are executed sequentially.

[0096] For rules that rely on threshold judgment (such as "credit risk comprehensive index" is greater than a specific threshold to determine high risk), a dynamic threshold calculation model is used, and the time series , using comprehensive credit risk indicators For example, its dynamic threshold The calculation formula is as follows:

[0097] (2);

[0098] in, is the average value of the comprehensive credit risk indicator during the historical period:

[0099] (3);

[0100] in, for A moment in is the standard deviation of the comprehensive credit risk indicator during the historical period, It is a risk adjustment coefficient and can be adjusted dynamically based on factors such as market environment and business direction.

[0101] Set business indicators In time The value of , month-on-month change rate The calculation formula is:

[0102] (4);

[0103] when When the risk warning is triggered, For business indicators In time The value of is the month-on-month change threshold.

[0104] Set business indicators In time The value of , the same period last year was , year-on-year change rate The calculation formula is:

[0105] (5);

[0106] when , triggering risk warning, is the year-on-year change threshold.

[0107] Basic rules are combined into compound rules through logical operators "AND" and "OR". For example, compound judgment rules for high-risk customers, such as:

[0108] and , that is, when the comprehensive credit risk index Above dynamic threshold , and the month-on-month change rate of transaction amount Exceeding the preset ratio When the customer is identified as a high-risk customer.

[0109] Step S103 of the present invention specifically includes:

[0110] Based on the calculation logic defined by the rules, the data set is fully searched through manual execution or scheduled tasks to quickly and accurately identify data that meets the corresponding rules. The identified data that meets the established rules is transmitted to the risk list table in strict accordance with the pre-set push action. At the same time, other auxiliary actions can be flexibly selected, such as selecting an early warning mechanism to remind relevant personnel in a striking manner; or sending detailed message reminders to convey risk-related information to the person in charge; or selecting risk levels and risk units, and making corresponding assignments while pushing data, so as to comprehensively present the risk status.

[0111] After each rule is executed and successfully identifies data, the system automatically calculates the percentage of data identified by each rule in the overall identification process. For example, if a risk assessment identified 1,000 pieces of data, and Rule A identified 200 of them, then Rule A accounted for 20% of the data identified. This percentage provides a direct reflection of the contribution and influence of each rule in the risk identification process.

[0112] Step S104 of the present invention specifically includes:

[0113] Users can customize the data percentage threshold for each rule based on their business needs. When the data percentage identified in a rule exceeds the user-defined ratio, the system will automatically send a message to notify relevant personnel. For example, if the user sets the data percentage threshold for Rule A to 15%, when the data percentage of Rule A exceeds 15%, the system will immediately send a message to the risk management personnel to remind them to pay attention to whether the rule needs to be adjusted and optimized;

[0114] After receiving the warning information, relevant personnel can decide whether to adjust the rules based on the actual business situation (automatic visual adjustments are also possible). If adjustments are determined to be necessary, the rule parameters that need to be adjusted are intelligently determined. For example, for rules based on numerical comparisons, the system can accurately calculate the appropriate threshold adjustment range through in-depth analysis of historical data (which can be achieved using existing deep learning models). For complex rules involving multiple conditions, the system calculates the number of data occurrences for each condition in the rule one by one, calculates the weight of each condition, stores it, and displays it to business personnel for reference, allowing them to formulate weight adjustment plans for each condition. It can also be used as an influencing factor for targeted supervision and rectification of risks.

[0115] It is understandable that in some other implementations, when business data enters the system, it is prioritized according to the rules. Execute the rule conditions in sequence. If a certain data triggers a rule, a risk warning message is generated, which includes the details of the triggering rule and the risk level. Regularly optimize the rule system based on the actual risk occurrence and rule triggering situation. Use the error back propagation idea in machine learning to calculate the rule misjudgment rate and missed judgment rate, and adjust the rules according to the rule misjudgment rate and missed judgment rate. 、 、 、 、 and , and rule conditions to achieve dynamic optimization of the rule system.

[0116] Figure 2 A schematic diagram of an adaptive risk assessment and adjustment system based on penetrating supervision provided by an exemplary embodiment of the present invention is shown, including:

[0117] The data preprocessing unit 201 is configured to: preprocess the acquired business data to be regulated and determine the evaluation index;

[0118] The processing rule determination unit 202 is configured to: determine a plurality of processing rules based on the original fields of the business data to be regulated and the evaluation index;

[0119] The data identification unit 203 is configured to process the business data to be regulated according to various processing rules. After each rule is processed and the data is successfully identified, the data ratio of the data identified in each rule in the entire identification process is automatically calculated;

[0120] The adaptive adjustment unit 204 is configured to generate warning information and adaptively adjust the parameters of the current rule when the data proportion corresponding to any current rule is greater than a set threshold.

[0121] It is understandable that each of the above-mentioned units can be separately or completely combined into one or several other units to constitute, or one (or some) of the units can be further divided into multiple functionally smaller units to constitute, which can achieve the same operation without affecting the realization of the technical effects of the embodiments of the present application. The above-mentioned units are divided based on logical functions. In actual applications, the functions of one unit can also be implemented by multiple units, or the functions of multiple units can be implemented by one unit. In other embodiments of the present application, the system may also include other units. In actual applications, these functions can also be implemented with the assistance of other units, and can be implemented by the collaboration of multiple units.

[0122] According to another embodiment of the present application, the system described in this embodiment can be constructed and the method of Example 1 of the present application can be implemented by running a computer program (including program code) capable of executing the steps involved in the corresponding method described in Example 1 on a general-purpose computing device such as a computer, which includes processing elements and storage elements such as a central processing unit (CPU), random access memory (RAM), and read-only memory (ROM). The computer program can be recorded on, for example, a computer-readable recording medium, and loaded into the above-mentioned computing device through the computer-readable recording medium and run therein.

[0123] Figure 3 A schematic diagram of an adaptive risk assessment and adjustment system based on penetrating supervision provided by another exemplary embodiment of the present invention is shown, including:

[0124] The basic configuration module 301 specifically includes:

[0125] Build specialized basic configuration functions, create adaptive model scenarios for different business segments such as finance and supply chain, and clarify the core risk concerns and assessment indicators of each scenario; establish a complete risk rating system and flexibly set and adjust risk levels; set up joint query configuration functions, configure the routing and routing parameters of the jump page; establish a list management function to conveniently enter black and white lists for rule selection.

[0126] The rule definition module 302 specifically includes:

[0127] Data Processing: The rule definition module allows users to deeply process raw data based on business needs. For example, in a credit business scenario, a new "credit risk composite index" is generated by innovatively combining multiple key fields such as a customer's income, liabilities, and credit history. This new index can more comprehensively and accurately reflect a customer's credit risk status, providing a more scientific basis for risk assessment.

[0128] Rule Calculation: A rich variety of rule calculation operations are provided, covering logic such as greater than or less than, inclusion or non-inclusion, and month-on-month and year-on-year comparisons based on indicators or raw fields. Users can flexibly construct rule conditions based on specific risk assessment needs. For example, when the "credit risk comprehensive indicator" exceeds a certain threshold, a customer can be identified as high-risk; or when the month-on-month change in a business indicator exceeds a preset ratio, a risk warning mechanism can be triggered. This flexible rule setting can adapt to the complex risk identification needs of different business scenarios.

[0129] Data identification: The system conducts a comprehensive search of the data set based on the calculation logic defined by the rules, either manually or through scheduled tasks, to quickly and accurately identify data that meets the corresponding rules.

[0130] Data push: The identified data that meets the established rules will be transmitted to the risk list table in strict accordance with the pre-set push actions; at the same time, other auxiliary actions can be flexibly selected, such as selecting an early warning mechanism to remind relevant personnel in a striking manner; or sending detailed message reminders to convey risk-related information to the person in charge; or selecting risk levels and risk units, and assigning corresponding values ​​while pushing data, so as to comprehensively present the risk situation.

[0131] The adaptive adjustment module 303, more specifically, includes:

[0132] Data percentage calculation: After each rule is executed and data is successfully identified, the system automatically calculates the percentage of data identified by each rule in the entire identification process. For example, in a risk assessment, a total of 1,000 data items were identified, of which Rule A identified 200 data items. The percentage of data identified by Rule A is 20%. This percentage data can intuitively reflect the contribution and influence of each rule in the risk identification process.

[0133] Threshold Judgment and Alerts: Users can customize the data percentage threshold for each rule based on their business needs. When the data percentage identified in a rule exceeds the user-defined ratio, the system will automatically send a message to notify relevant personnel. For example, if the user sets the data percentage threshold for Rule A to 15%, when the data percentage of Rule A exceeds 15%, an immediate message will be sent to risk management personnel, prompting them to check whether the rule needs to be adjusted and optimized.

[0134] Rule parameter adjustment: After receiving the early warning information, relevant personnel can decide whether to adjust the rules based on the actual business situation. If it is determined that adjustment is necessary, the rule parameters that need to be adjusted can be intelligently determined. For example, for rules based on numerical comparison, the system can accurately calculate the appropriate threshold adjustment range through in-depth analysis of historical data. For complex rules involving multiple conditions, the number of data occurrences of each condition in the rule is calculated one by one, and the weight of each condition is calculated. The data is stored and displayed to business personnel for reference, and weight adjustment plans for each condition are implemented. It can also be used as an influencing factor for targeted supervision and rectification of risks.

[0135] This embodiment also provides a risk management list 304, which specifically includes: risk list management, risk list disposal, risk list closure, risk list ledger, and smart supervision homepage functions.

[0136] Figure 4A computer device provided by an exemplary embodiment of the present invention is shown, which includes a processor 401, a communication interface 402, and a computer-readable storage medium 403. The processor 401, the communication interface 402, and the computer-readable storage medium 403 may be connected via a bus or other means.

[0137] Among them, the communication interface 402 is used to receive and send data, the computer-readable storage medium 403 can be stored in the memory of the electronic device, the computer-readable storage medium 403 is used to store computer programs, the computer programs include program instructions, and the processor 401 is used to execute the program instructions stored in the computer-readable storage medium 403.

[0138] The processor 401 is the computing core and control core of the electronic device, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement corresponding method processes or corresponding functions.

[0139] The processor 401 is configured to perform the following process:

[0140] Pre-process the acquired business data to be regulated and determine the evaluation indicators;

[0141] Determining multiple processing rules based on the original fields of the business data to be regulated and the evaluation indicators;

[0142] Through various processing rules, the business data to be regulated is processed. After the processing of each rule is completed and the data is successfully identified, the proportion of the data identified in each rule in the entire identification process is automatically calculated;

[0143] When the data proportion corresponding to any current rule is greater than a set threshold, an early warning message is generated and the parameters of the current rule are adaptively adjusted.

[0144] The present invention provides a computer-readable storage medium (Memory). This computer-readable storage medium is a memory device in an electronic device for storing programs and data. It should be understood that the computer-readable storage medium herein may include both built-in storage media in the electronic device and, of course, extended storage media supported by the electronic device. The computer-readable storage medium provides storage space that stores the processing system of the electronic device.

[0145] Furthermore, the storage space also stores one or more instructions suitable for being loaded and executed by the processor. These instructions may be one or more computer programs (including program code). It should be noted that the computer-readable storage medium herein may be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage device; alternatively, it may be at least one computer-readable storage medium located remotely from the processor.

[0146] In one embodiment, the computer-readable storage medium stores one or more instructions; the processor loads and executes the one or more instructions stored in the computer-readable storage medium to implement the following process:

[0147] Pre-process the acquired business data to be regulated and determine the evaluation indicators;

[0148] Determining multiple processing rules based on the original fields of the business data to be regulated and the evaluation indicators;

[0149] Through various processing rules, the business data to be regulated is processed. After the processing of each rule is completed and the data is successfully identified, the proportion of the data identified in each rule in the entire identification process is automatically calculated;

[0150] When the data proportion corresponding to any current rule is greater than a set threshold, an early warning message is generated and the parameters of the current rule are adaptively adjusted.

[0151] Those skilled in the art will appreciate that the units and algorithmic steps of each example described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional technical personnel may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0152] The above embodiments can be implemented in whole or in part through software, hardware, firmware, or any combination thereof. When implemented using software, they can be implemented in whole or in part in the form of a computer program product. A computer program product comprises one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions according to the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted via a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, optical fiber, digital line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data processing device such as a server or data center that integrates one or more available media. Available media can include magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives).

[0153] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. An adaptive risk assessment and adjustment method based on penetrating supervision, characterized in that: The following processes are included: Pre-process the acquired business data to be regulated and determine the evaluation indicators; Determining multiple processing rules based on the original fields of the business data to be regulated and the evaluation indicators; Through various processing rules, the business data to be regulated is processed. After the processing of each rule is completed and the data is successfully identified, the proportion of the data identified in each rule in the entire identification process is automatically calculated; When the data proportion corresponding to any current rule is greater than the set threshold, an early warning message is generated and the parameters of the current rule are adaptively adjusted; For rules based on threshold comparison, historical data is analyzed using a deep learning model to determine the threshold adjustment range; For complex rules containing multiple judgment conditions, the number of data occurrences of each condition in the rule is calculated one by one, the new weight of each condition is calculated based on the number of occurrences, and the weight of each condition of the complex rule is assigned based on the new weight.

2. The adaptive risk assessment and adjustment method based on penetrating supervision according to claim 1 is characterized in that: According to specific risk assessment requirements, multiple processing rules are determined based on the original fields of the business data to be regulated and the evaluation indicators.

3. The adaptive risk assessment and adjustment method based on penetrating supervision according to claim 1 is characterized in that: The business data to be supervised is comprehensively retrieved by responding to manual click tasks or scheduled tasks, and the retrieved business data to be supervised is processed through various processing rules.

4. The adaptive risk assessment and adjustment method based on penetrating supervision according to claim 1 is characterized in that: Before automatically calculating the proportion of data identified in each rule in the entire identification process, it also includes: The identified data that meets the rules will be transmitted to the risk list table according to the pre-set push action, and an early warning will be issued based on the risk list table.

5. The adaptive risk assessment and adjustment method based on penetrating supervision according to any one of claims 1 to 4, characterized in that: When processing the regulated business data through various processing rules, rule priorities are introduced and rules are executed according to the rule priorities: ； in, is the risk level, is the execution frequency, For the scope of influence, 、 and The sum of the three is 1, and all three are weight coefficients, priority values The higher the value, the higher the priority of the rule. Representative Rules, 、 and All the values ​​were normalized.

6. An adaptive risk assessment and adjustment system based on penetrating supervision, characterized in that: include: The data preprocessing unit is configured to: preprocess the acquired business data to be regulated and determine evaluation indicators; a processing rule determination unit configured to: determine a plurality of processing rules based on the original fields of the business data to be regulated and the evaluation index; The data identification unit is configured to: process the business data to be regulated according to various processing rules, and after the processing of each rule is completed and the data is successfully identified, automatically calculate the proportion of the data identified in each rule in the entire identification process; The adaptive adjustment unit is configured to: generate warning information and adaptively adjust the parameters of the current rule when the data proportion corresponding to any current rule is greater than a set threshold; For rules based on threshold comparison, historical data is analyzed using a deep learning model to determine the threshold adjustment range; For complex rules containing multiple judgment conditions, the number of data occurrences of each condition in the rule is calculated one by one, the new weight of each condition is calculated based on the number of occurrences, and the weight of each condition of the complex rule is assigned based on the new weight.

7. A computer device, characterized in that: include: a processor and a computer-readable storage medium; a processor adapted to execute a computer program; A computer-readable storage medium having a computer program stored therein, wherein the computer program, when executed by the processor, implements the adaptive risk assessment and adjustment method based on penetrating supervision as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which is suitable for being loaded by a processor and executing the adaptive risk assessment and adjustment method based on penetration supervision as described in any one of claims 1 to 5.

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