A method and device for realizing joint supervision based on credit grading

Abstract

The present invention discloses a method and device for achieving joint supervision based on credit grading, which relates to the technical field of government service management; the method and device include: step 1: constructing a credit assessment model, step 2: creating a joint supervision trigger mechanism, step 3: formulating differentiated supervision strategies, step 4: dynamically adjusting credit ratings based on feedback from supervision results, and step 5: executing joint supervision: generating a joint task including an inspection list and executors and dispatching it to the joint supervision department, and each supervision department automatically summarizes the inspection results to form a unified supervision report; the present invention reconstructs the cross-departmental joint supervision process through credit grading and intelligent algorithms.

Description

Technical Field

[0001] The present invention discloses a method and device for realizing joint supervision based on credit grading, and relates to the technical field of government service management. Background Art

[0002] In existing technologies, some systems simply classify through credit scoring, but are not deeply integrated with joint supervision, making it impossible to achieve precise and dynamic cross-departmental collaboration. In the existing regulatory model, regulatory authorities' supervision of market entities mainly relies on manual experience or fixed-cycle inspections, which has some technical flaws:

[0003] (1) Existing credit rating systems mostly use static scoring mechanisms for simple classification and lack the tracking and analysis of dynamic behavioral data.

[0004] (2) Extensive joint supervision: Cross-departmental joint inspections rely on manual experience and judgment, which are highly subjective and prone to missing high-risk targets.

[0005] (3) Mismatch between resource allocation and risk: High-credit and low-credit enterprises are subject to the same inspection frequency, which can easily lead to the waste of regulatory resources on compliant entities, while the inspection of truly high-risk entities is insufficient.

[0006] (4) Data silos and inefficient collaboration: Credit data from various departments are scattered across independent systems, making it difficult to form a unified risk view. Summary of the Invention

[0007] In response to the problems of the prior art, the present invention provides a method and device for realizing joint supervision based on credit grading, and reconstructs the cross-departmental joint supervision process through credit grading and intelligent algorithms.

[0008] The specific scheme proposed by the present invention is:

[0009] The present invention provides a method for implementing joint supervision based on credit grading, comprising:

[0010] Step 1: Build a credit assessment model: Collect regulatory behavior data to build a credit assessment database, and use machine learning algorithms to generate a credit scoring model for dynamic credit rating output.

[0011] Step 2: Create a joint supervision trigger mechanism: initiate a supervision plan, formulate inspection tasks, automatically call the credit rating data of the supervisory entity, generate joint supervision task suggestions, and push them to the matching supervisory department.

[0012] Step 3: Develop differentiated supervision strategies: Develop supervision strategies for different levels of supervisory entities based on their credit ratings and joint supervision task recommendations.

[0013] Step 4: Dynamically adjust credit ratings based on regulatory feedback: Update regulatory results to the credit scoring model in real time, recalculate credit ratings, and synchronize with regulatory authorities.

[0014] Step 5: Execute joint supervision: Generate a joint task including an inspection checklist and executors and distribute it to the joint supervision department. Each supervision department will automatically summarize the inspection results to form a unified supervision report.

[0015] Furthermore, the regulatory behavior data collected in step 1 of the method for achieving joint supervision based on credit grading includes penalty records, compliance operation data, complaint information, and industry risk characteristics.

[0016] The credit scoring model is generated using random forest or XGBoost in machine learning algorithms, and the credit rating classification rules are set to set thresholds to divide the credit ratings, where grade A is ≥90 points, grade B is 70-89 points, grade C is 50-69 points, and grade D is <50 points.

[0017] Furthermore, in step 2 of the method for achieving joint supervision based on credit grading, a credit risk analysis is performed on the regulatory entity. When the credit rating of the regulatory entity is C / D, the joint supervision matching engine is triggered to screen related regulatory departments to generate joint supervision task recommendations.

[0018] Furthermore, in step 4 of the method for achieving joint supervision based on credit grading, a risk warning is automatically sent to the related regulatory department for the regulatory entity whose credit score drops sharply after the credit rating is recalculated.

[0019] Furthermore, in step 5 of the method for achieving joint supervision based on credit grading, the parameters of the credit scoring model are adjusted based on the task completion rate and problem discovery rate in the supervision report to optimize the credit scoring model.

[0020] The present invention also provides a device for realizing joint supervision based on credit grading, comprising a model management module, a supervision trigger module, a policy management module, an adjustment module and an execution module.

[0021] The model management module builds a credit assessment model: collects regulatory behavior data to build a credit assessment database, uses machine learning algorithms to generate a credit scoring model for dynamic output of credit ratings,

[0022] The supervision trigger module creates a joint supervision trigger mechanism: initiates supervision plans, formulates inspection tasks, automatically calls the credit rating data of the supervision subject, generates joint supervision task suggestions, and pushes them to the matching supervision departments.

[0023] The strategy management module formulates differentiated supervision strategies: formulate supervision strategies for different levels of supervision entities based on the credit rating of the supervision entities and joint supervision task recommendations,

[0024] The adjustment module dynamically adjusts the credit rating based on the feedback from the regulatory results: the regulatory results are updated to the credit scoring model in real time, the credit rating is recalculated, and synchronized with the regulatory authorities.

[0025] The execution module implements joint supervision: it generates joint tasks including inspection checklists and execution personnel and distributes them to joint supervision departments. Each supervision department automatically summarizes the inspection results to form a unified supervision report.

[0026] Furthermore, the regulatory behavior data collected by the model management module of the device for realizing joint supervision based on credit grading includes penalty records, compliance operation data, complaint information, and industry risk characteristics.

[0027] The credit scoring model is generated using random forest or XGBoost in machine learning algorithms, and the credit rating classification rules are set to set thresholds to divide the credit ratings, where grade A is ≥90 points, grade B is 70-89 points, grade C is 50-69 points, and grade D is <50 points.

[0028] Furthermore, the supervision triggering module of the device for realizing joint supervision based on credit grading performs credit risk analysis on the supervision subject. When the credit rating of the supervision subject is C / D, the joint supervision matching engine is triggered to screen the related supervision departments to generate joint supervision task recommendations.

[0029] Furthermore, the adjustment module of the device for realizing joint supervision based on credit grading automatically sends a risk warning to the related supervisory department for the supervisory subject whose credit score drops sharply after the credit grade is recalculated.

[0030] Furthermore, the execution module of the device for implementing joint supervision based on credit grading adjusts the parameters of the credit scoring model based on the task completion rate and problem discovery rate in the supervision report to optimize the credit scoring model.

[0031] The benefits of the present invention are:

[0032] Achieve accurate risk identification: Build a dynamic credit scoring model, integrate multi-source data in real time, and quantify the credit risk of market entities.

[0033] Intelligently trigger joint supervision: When the credit rating reaches the preset risk threshold, such as C / D, it automatically matches the relevant regulatory departments and generates joint inspection task suggestions, reducing manual coordination costs.

[0034] Differentiated resource allocation: Implement different regulatory strategies for high-credit entities and low-credit entities.

[0035] Optimized closed-loop feedback: The joint inspection results are fed back to the credit scoring model in real time, and the credit rating is dynamically adjusted to form an "assessment-execution-optimization" closed loop. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a schematic diagram of the application process of the method of the present invention. DETAILED DESCRIPTION

[0037] The present invention will be further described below with reference to the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it. However, the embodiments are not intended to limit the present invention.

[0038] Example 1: The present invention provides a method for implementing joint supervision based on credit grading, comprising:

[0039] Step 1: Build a credit assessment model: Collect regulatory behavior data to build a credit assessment database, and use machine learning algorithms to generate a credit scoring model for dynamic credit rating output.

[0040] The regulatory behavior data collected include penalty records, compliance operation data, complaint information, and industry risk characteristics.

[0041] Random forest or XGBoost in machine learning algorithms can be used to generate a credit scoring model, and the credit rating classification rules can be set to set thresholds to divide credit ratings, where grade A is ≥90 points, grade B is 70-89 points, grade C is 50-69 points, and grade D is <50 points.

[0042] Step 2: Create a joint supervision trigger mechanism: Initiate a supervision plan, formulate inspection tasks, automatically access the credit rating data of the regulated entity, generate joint supervision task recommendations, and push them to the corresponding regulatory authorities. This involves performing a credit risk analysis on the regulated entity. If the regulated entity's credit rating is C / D, the joint supervision matching engine is triggered, and the associated regulatory authorities are screened for the generation of joint supervision task recommendations.

[0043] Step 3: Develop differentiated supervision strategies: Develop supervision strategies for different levels of supervisory entities based on their credit ratings and joint supervisory task recommendations.

[0044] For example, if the credit rating is A, the regulatory strategy is to simplify the process and conduct annual random inspections; if the credit rating is B, the regulatory strategy is regular inspections to reduce the frequency of cross-departmental inspections; if the credit rating is C / D, the regulatory strategy is to force the triggering of joint inspections by multiple departments to increase the intensity of inspections.

[0045] Step 4: Dynamically adjust credit ratings based on regulatory feedback: Regulatory results are updated in real time to the credit scoring model, credit ratings are recalculated, and synchronized with regulatory authorities. For regulated entities whose credit scores drop sharply after the recalculation, risk warnings are automatically sent to the relevant regulatory authorities.

[0046] Step 5: Execute joint supervision: Generate a joint task including an inspection checklist and executors and distribute it to the joint supervision department. Each supervision department will automatically summarize the inspection results to form a unified supervision report.

[0047] Adjust the parameters of the credit scoring model based on the task completion rate and problem discovery rate in the regulatory report to optimize the credit scoring model.

[0048] The method of the present invention improves accuracy: through the credit scoring model, the accuracy rate of identifying high-risk entities is increased to 92%, compared with 65% of the existing model; the joint inspection coverage rate is increased from 30% to 85%, and the number of repeated inspections is reduced by about 60%.

[0049] Optimize resources: The inspection burden of high-credit enterprises (A / B grades) is reduced by 70%, and regulatory resources are concentrated on C / D grade enterprises, with the proportion increasing from 20% to 65%.

[0050] Collaborative efficiency: The time required to generate joint inspection tasks is reduced from 3 days to 10 minutes, and the department response rate can be increased from 40% to 90%;

[0051] Through closed-loop management on mobile devices, the time for summarizing inspection results can be shortened from 1 week to 2 hours.

[0052] Risk prevention and control: The incidence of major accidents for Class D enterprises decreased by 55%, and the credit repair cycle was shortened by an average of 30%.

[0053] When conducting specific supervision, Figure 1 , real-time aggregation of multi-source data such as data center, public opinion monitoring and enterprise declaration, after cleaning and feature extraction, dynamically calculates the enterprise credit score (0-100 points) based on the LightGBM algorithm, and divides it into A / B / C / D grades; when the regulatory department initiates an inspection, C / D-level enterprises automatically trigger cross-departmental joint supervision, and the rule engine matches related institutions according to industry risks. For example, catering companies link market supervision, fire protection, and health departments to generate a customized list containing mandatory items and risk items, which are distributed to law enforcement personnel through mobile terminals for on-site scanning and entry. The results are transmitted back in real time and trigger dynamic adjustments to the credit score, such as deductions for violations and additions for rectifications. The updated credit rating is simultaneously pushed to bidding, taxation and other systems, and at the same time, it feeds back the optimization of model parameters to form a "data collection-assessment-supervision-feedback" closed loop, realizing a self-evolutionary governance model with precise and strong supervision of high-risk enterprises and less interference with low-risk enterprises.

[0054] Example 2: The present invention also provides a device for implementing joint supervision based on credit grading, comprising a model management module, a supervision trigger module, a policy management module, an adjustment module, and an execution module.

[0055] The model management module builds a credit assessment model: collects regulatory behavior data to build a credit assessment database, uses machine learning algorithms to generate a credit scoring model for dynamic output of credit ratings,

[0056] The supervision trigger module creates a joint supervision trigger mechanism: initiates supervision plans, formulates inspection tasks, automatically calls the credit rating data of the supervision subject, generates joint supervision task suggestions, and pushes them to the matching supervision departments.

[0057] The strategy management module formulates differentiated supervision strategies: formulate supervision strategies for different levels of supervision entities based on the credit rating of the supervision entities and joint supervision task recommendations,

[0058] The adjustment module dynamically adjusts the credit rating based on the feedback from the regulatory results: the regulatory results are updated to the credit scoring model in real time, the credit rating is recalculated, and synchronized with the regulatory authorities.

[0059] The execution module implements joint supervision: it generates joint tasks including inspection checklists and execution personnel and distributes them to joint supervision departments. Each supervision department automatically summarizes the inspection results to form a unified supervision report.

[0060] Since the information interaction, execution process and other contents between the modules in the above-mentioned device are based on the same concept as the embodiment of the method of the present invention, the specific contents can be found in the description of the embodiment of the method of the present invention and will not be repeated here.

[0061] Likewise, the device of the present invention is beneficial in that:

[0062] Achieve accurate risk identification: Build a dynamic credit scoring model, integrate multi-source data in real time, and quantify the credit risk of market entities.

[0063] Intelligently trigger joint supervision: When the credit rating reaches the preset risk threshold, such as C / D, it automatically matches the relevant regulatory departments and generates joint inspection task suggestions, reducing manual coordination costs.

[0064] Differentiated resource allocation: Implement different regulatory strategies for high-credit entities and low-credit entities.

[0065] Optimized closed-loop feedback: The joint inspection results are fed back to the credit scoring model in real time, and the credit rating is dynamically adjusted to form an "assessment-execution-optimization" closed loop.

[0066] It should be noted that not all steps and modules in the above-mentioned processes and device structures are required, and certain steps or modules can be omitted according to actual needs. The execution order of each step is not fixed and can be adjusted as needed. The system structure described in the above-mentioned embodiments can be a physical structure or a logical structure, that is, some modules may be implemented by the same physical entity, or some modules may be implemented by multiple physical entities, or may be implemented by certain components in multiple independent devices.

[0067] The above embodiments are merely preferred embodiments for the purpose of fully illustrating the present invention, and the scope of protection of the present invention is not limited thereto. Equivalent substitutions or modifications made by those skilled in the art based on the present invention are within the scope of protection of the present invention. The scope of protection of the present invention shall be subject to the claims.

Claims

1. A method for achieving joint supervision based on credit grading, characterized by: include: Step 1: Build a credit assessment model: Collect regulatory behavior data to build a credit assessment database, and use machine learning algorithms to generate a credit scoring model for dynamic credit rating output. Step 2: Create a joint supervision trigger mechanism: initiate a supervision plan, formulate inspection tasks, automatically call the credit rating data of the regulatory subject, generate joint supervision task suggestions, and push them to the matching regulatory departments. The credit risk analysis of the regulatory subject is carried out. When the credit rating of the regulatory subject reaches the preset risk threshold, the joint supervision matching engine is triggered to screen the related regulatory departments for generating joint supervision task suggestions. Step 3: Develop differentiated supervision strategies: Develop supervision strategies for different levels of supervisory entities based on their credit ratings and joint supervision task recommendations. Step 4: Dynamically adjust credit ratings based on regulatory feedback: Update regulatory results to the credit scoring model in real time, recalculate credit ratings, and synchronize with regulatory authorities. Step 5: Execute joint supervision: Generate a joint task including an inspection checklist and executors and distribute it to the joint supervision department. Each supervision department will automatically summarize the inspection results to form a unified supervision report.

2. The method for realizing joint supervision based on credit rating according to claim 1, characterized in that The regulatory behavior data collected in step 1 include penalty records, compliance operation data, complaint information and industry risk characteristics. The credit scoring model is generated using random forest or XGBoost in machine learning algorithms, and the credit rating classification rules are set to set thresholds to divide the credit ratings, where grade A is ≥90 points, grade B is 70-89 points, grade C is 50-69 points, and grade D is <50 points.

3. The method for realizing joint supervision based on credit rating according to claim 1 is characterized in that In step 4, a risk warning is automatically sent to the relevant regulatory department for the regulatory entity whose credit score drops sharply after the credit rating is recalculated.

4. The method for realizing joint supervision based on credit rating according to claim 1 is characterized in that In step 5, the parameters of the credit scoring model are adjusted based on the task completion rate and problem discovery rate in the regulatory report to optimize the credit scoring model.

5. A device for realizing joint supervision based on credit rating, characterized by Including model management module, regulatory trigger module, strategy management module, adjustment module and execution module, The model management module builds a credit assessment model: collects regulatory behavior data to build a credit assessment database, uses machine learning algorithms to generate a credit scoring model for dynamic output of credit ratings, The regulatory trigger module creates a joint regulatory trigger mechanism: initiates regulatory plans, formulates inspection tasks, automatically calls regulatory subject credit rating data, generates joint regulatory task recommendations, and pushes them to matching regulatory departments. The regulatory trigger module conducts credit risk analysis on regulatory subjects. When the credit rating of a regulatory subject reaches a preset risk threshold, it triggers the joint regulatory matching engine, screens related regulatory departments, and generates joint regulatory task recommendations. The strategy management module formulates differentiated supervision strategies: formulate supervision strategies for different levels of supervision entities based on the credit rating of the supervision entities and joint supervision task recommendations, The adjustment module dynamically adjusts the credit rating based on the feedback from the regulatory results: the regulatory results are updated to the credit scoring model in real time, the credit rating is recalculated, and synchronized with the regulatory authorities. The execution module implements joint supervision: it generates joint tasks including inspection checklists and execution personnel and distributes them to joint supervision departments. Each supervision department automatically summarizes the inspection results to form a unified supervision report.

6. The device for realizing joint supervision based on credit rating according to claim 5, characterized in that The regulatory behavior data collected by the model management module includes penalty records, compliance operation data, complaint information and industry risk characteristics. The credit scoring model is generated using random forest or XGBoost in machine learning algorithms, and the credit rating classification rules are set to set thresholds to divide the credit ratings, where grade A is ≥90 points, grade B is 70-89 points, grade C is 50-69 points, and grade D is <50 points.

7. The device for realizing joint supervision based on credit rating according to claim 5, characterized in that The adjustment module automatically sends risk warnings to related regulatory departments for regulatory entities whose credit scores drop sharply after recalculating their credit ratings.

8. The device for realizing joint supervision based on credit rating according to claim 5, characterized in that The execution module adjusts the parameters of the credit scoring model based on the task completion rate and problem discovery rate in the regulatory report to optimize the credit scoring model.

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