An Automated Approval Method for Electronic Guarantees Based on Big Data Screening

Abstract

This application provides an automated approval method for electronic guarantees based on big data screening. This application achieves fully automated online processing from project information entry, three-factor verification, blacklist screening, blackbox rule pre-review, automatic risk assessment, financial fee identification to final guarantee issuance, enabling second-level guarantee issuance without human intervention. It accesses multiple legally available and third-party data sources, such as industrial and commercial data, judicial data, tax data, invoice data, and industry data, supporting multi-source parallel verification, reducing risk omissions caused by delays in updating a single data source, and providing risk control personnel with comprehensive and real-time approval basis.

Description

Technical Field

[0001] This invention relates to the field of automatic approval technology for electronic guarantees, and more specifically, to an automatic approval method for electronic guarantees based on big data screening. Background Technology

[0002] Traditional guarantee business processes (such as electronic bid bonds and performance bonds) primarily rely on manual operation. Project managers need to manually enter company information, collect data from business registration, judicial, and tax authorities, and check the company's credit status in different systems. This process is cumbersome, inefficient, and prone to information omissions or untimely updates. Risk control personnel lack unified and visualized data support during the approval process, making it difficult to make quick and accurate judgments. Therefore, we propose an improved method for automated approval of electronic guarantees based on big data screening. Summary of the Invention

[0003] This invention provides an automatic approval method for electronic guarantees based on big data screening, comprising the following steps: Step A, Enterprise Access and Verification Steps: Obtain the applicant enterprise's unified social credit code or enterprise name, call the industrial and commercial interface to fill in the enterprise's basic information, and verify the legal representative's ID card and mobile phone number using the three elements; after verification, call the preset blacklist database and black box rule engine to screen the enterprise for access from the dimensions of industrial and commercial information, judicial data, and industry data. If the enterprise is on the blacklist or fails to meet the minimum access rules, the application will be blocked and the reason for the block will be recorded. Step B: Project Information Collection Steps, collecting project information: Method 1: Manually supplement the project details, guarantee elements, corporate financial information, business sharing, channel source, issuing institution and risk control requirements of the transaction flow information, and upload the scanned documents required by the issuing bank; Method 2: Automatically receive project information pushed by third-party business systems via API interface; Step C, Multi-source Big Data Risk Assessment Steps: Crawling enterprise business registration information, operating information, judicial data, tax data, and invoice data to generate a visualized electronic enterprise risk assessment report; Step D, Parallel Approval Processing by Multiple Positions: Based on the RBAC (Role-Based Access Control) model, the project and risk assessment report are pushed to the following positions for processing according to the preset approval process: Risk control role: Review risk assessment reports and supporting conclusions, activate preset big data risk automatic approval rules, automatically approve low-risk enterprises, and transfer high-risk enterprises to manual review; Legal position: Conduct compliance review and format classification of guarantee formats, and automatically match pricing and subsequent approval strategies based on the classification results; Finance position: Automatically calculate accounts receivable and security deposits, automatically identify and approve corporate bank transfer transactions, and perform basic account verification; Project Manager Role: Provide feedback and review information throughout the workflow, and monitor project progress. Each position makes decisions regarding approval, rejection, meeting arrangements, or approval for the use of official seals during the process; Step E, Automatic Letter Issuance and Electronic Signing: After approval, an electronic guarantee document is automatically generated, and the document is connected to the CFCA Secure Sign electronic signing platform to complete digital signing and electronic signing.

[0004] As a preferred technical solution of this application, in step A, the minimum admission rule is composed of a blacklist database and a blackbox rule engine; The blacklist database includes internal blacklists, external blacklists, and dynamic blacklists; companies that are on the blacklist will be directly blocked. The black box rule engine sets screening rules based on six dimensions: business information, judicial data, industry data, tax data, related risks, and custom rules. Each dimension's screening rules include two conclusions: interception and transfer to manual review. The black box rule engine calls multiple data sources in parallel and executes rules in order of priority. If an interception rule is hit, the judgment terminates and outputs three conclusions: "passed", "transferred to manual review", and "intercepted". Enterprises that hit the interception rule are automatically added to the internal blacklist.

[0005] As a preferred technical solution in this application, in step C, the enterprise risk electronic assessment report centrally displays business registration information, legal risks, operating status, financial data, and historical application comparison analysis on a single page, and highlights newly added judicial cases, administrative penalties, and dynamic risk information of abnormal operations.

[0006] As the preferred technical solution of this application, in step D, the parallel approval process for multiple positions adopts an overdue warning mechanism: a preset approval time limit is set for each position. When the approval time of a certain position reaches 80% of the time limit, a reminder notification is sent to the staff member of that position and the corresponding superior. If the approval is not completed within the preset time limit, the project is automatically transferred to the superior position for supervision, and the reason for the overdue time is recorded.

[0007] As the preferred technical solution of this application, in step D, the preset big data risk automatic approval rules activated by the risk control position adopt two review mechanisms: whitelist and blacklist.

[0008] As a preferred technical solution of this application, it also includes: Step F, Post-Guarantee Warning Step: According to the set monitoring cycle and post-guarantee data analysis rules, periodically re-crawl the company's operation and project construction data. If the post-guarantee warning rules are met, an automatic prompt will be made and the post-guarantee warning process will be initiated.

[0009] As a preferred technical solution in this application, the preset monitoring cycle is configured on a daily, weekly, monthly, or quarterly basis, or the monitoring frequency is dynamically adjusted according to the enterprise's risk rating. Post-insurance data analysis rules include: periodically re-crawling data on enterprise business registration changes, new judicial cases, administrative penalties, operational abnormalities, tax status, invoicing changes, and project construction progress; The post-guarantee early warning rules include: warnings when new records of persons subject to enforcement are added within the set monitoring period, warnings when new records of dishonesty are added, warnings when the business status changes to abnormal, warnings when the tax status changes to tax arrears, warnings when there are no invoice records for a set number of consecutive months, and warnings when the project construction progress is stagnant for more than a set number of days. The post-guarantee early warning process includes: automatically generating an early warning prompt and pushing it to risk control personnel after the early warning rule is triggered, marking the enterprise as a risk-concerned entity, initiating the post-guarantee verification task, and deciding whether to add additional guarantee measures, adjust the credit limit, or initiate the recovery process based on the verification results.

[0010] As a preferred technical solution of this application, it also includes: Step G, Recovery Management Steps: After a project encounters an accident, the recovery process is initiated, the project is added to the list of companies seeking recovery, recovery personnel enter the recovery measures and their effects, and the recovery progress is summarized and displayed for multi-department collaborative handling.

[0011] As the preferred technical solution in this application, the recovery process includes: recovery personnel entering recovery measures and the effects of each recovery, recovery measures including sending collection notices, telephone collection, on-site collection, entrusting third-party recovery, legal proceedings and property preservation; recovery effects including the amount recovered, the repayment agreement reached, the litigation process initiated and the lack of executable assets; automatic summary and display of recovered amount, unrecovered balance, recovery progress percentage and previous recovery records for multi-department collaborative handling; after recovery is completed, a decision is made based on the recovery results whether to remove the company from the recovery list or transfer it to the bad debt write-off process.

[0012] As a preferred technical solution of this application, it also includes: Step H, Integrated Business and Finance Processing Step: Through the interface, data is exchanged with the enterprise's financial software to automatically synchronize business data such as billing, invoicing, and guarantee, thereby achieving business and financial data integration. The business volume, billing amount, output function volume, and approval time are statistically analyzed by department, personnel, and time period, generating a visual performance evaluation report.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This application achieves fully automated online processing from project information entry, three-factor verification, blacklist screening, black box rule pre-review, automatic risk assessment, financial fee identification to final letter issuance, enabling letter issuance in seconds without human intervention; 2. It accesses various legal and publicly available data sources, including business registration, judicial, tax, invoice, and industry data, as well as third-party data, supporting multi-source parallel verification. This reduces risk omissions caused by delays in updating a single data source and provides risk control personnel with comprehensive and real-time approval data. 3. This application can automatically determine enterprise access, risk rating and generate approval conclusion according to preset rules, assisting or replacing manual risk control approval and reducing labor costs. Attached Figure Description

[0014] Figure 1 A schematic diagram of the automatic approval method for electronic guarantees based on big data screening provided in this application; Figure 2 A schematic diagram of step F provided in this application; Figure 3 A diagram illustrating the recovery management steps provided for this application. Detailed Implementation

[0015] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0016] It should be noted that, unless otherwise specified, the embodiments and features and technical solutions in the present invention can be combined with each other.

[0017] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0018] For an example, please refer to... Figures 1-3 An automated approval method for electronic guarantees based on big data screening includes the following steps: Step A, Enterprise Access and Verification Steps: Obtain the applicant enterprise's unified social credit code or enterprise name, call the industrial and commercial interface to fill in the enterprise's basic information, and verify the legal representative's ID card and mobile phone number using the three elements; after verification, call the preset blacklist database and black box rule engine to screen the enterprise for access from the dimensions of industrial and commercial information, judicial data, and industry data. If the enterprise is on the blacklist or fails to meet the minimum access rules, the application will be blocked and the reason for the block will be recorded. Step B, Project Information Collection Steps: Collect project information using one of the following two methods: Method 1: Manually supplement the project details, guarantee elements, corporate financial information, business sharing, channel source, issuing institution and risk control requirements of the transaction flow information, and upload the scanned documents required by the issuing bank; Method 2: Automatically receive project information pushed by third-party business systems via API interface; Step C, Multi-source Big Data Risk Assessment Steps: Crawling enterprise business registration information, operating information, judicial data, tax data, and invoice data to generate a visualized electronic enterprise risk assessment report; Step D, Parallel Approval Processing by Multiple Positions: Based on the RBAC (Role-Based Access Control) model, the project and risk assessment report are pushed to the following positions for processing according to the preset approval process: Risk control role: Review risk assessment reports and supporting conclusions, activate preset big data risk automatic approval rules, automatically approve low-risk enterprises, and transfer high-risk enterprises to manual review; Legal position: Conduct compliance review and format classification of guarantee formats, and automatically match pricing and subsequent approval strategies based on the classification results; Finance position: Automatically calculate accounts receivable and security deposits, automatically identify and approve corporate bank transfer transactions, and perform basic account verification; Project Manager Role: Provide feedback and review information throughout the workflow, and monitor project progress. Each position makes decisions regarding approval, rejection, meeting arrangements, or approval for the use of official seals during the process; Step E, Automatic Issuance and Electronic Signing: After approval, an electronic guarantee document is automatically generated, and the system connects to the CFCA Secure Signature electronic signing platform to complete digital signing and electronic signing; one of the following three modes is used to connect with the public resource trading center: Model 1: The guarantee company connects with the transaction center, and the bank issues a letter which is then sent to the transaction center through the guarantee company; Mode 2: The bank directly connects with the transaction center and synchronizes business data to the guarantee company after issuing the letter; Mode 3: The guarantee company directly connects with the transaction center and issues a corporate guarantee; The generated electronic guarantee will be automatically pushed to the applicant company and the financial service platform of the transaction center.

[0019] Furthermore, in step A, the minimum admission rules are composed of a blacklist database and a blackbox rule engine. The blacklist database includes internal blacklists, external blacklists, and dynamic blacklists. Companies on the blacklists are directly blocked. The internal blacklist is derived from historically risky companies and their affiliates. The external blacklist is accessed from third-party data sources, including dishonest judgment debtors and entities prohibited from entering certain industries. The dynamic blacklist is automatically added based on post-guarantee warning results. The black box rule engine sets screening rules based on six dimensions: business registration information, judicial data, industry data, tax data, related risks, and custom rules. Each dimension's screening rules include two conclusions: interception and referral to manual review. The black box rule engine calls multiple data sources in parallel, executing rules in priority order. Upon hitting an interception rule, the judgment terminates, outputting three conclusions: "Pass," "Refer to Manual Review," or "Intercept." Companies hitting interception rules are automatically added to the internal blacklist. Specifically, the business registration information dimension includes interception when the company's establishment time is less than the set limit, its registered capital is below the entry standard, or its operating status is abnormal; the judicial data dimension includes interception when there are records of being an enforcement target or a record of dishonesty, and referral to manual review when there are pending lawsuits; the industry data dimension includes interception when the company belongs to a prohibited industry, and referral to manual review when the risk rating is higher than the threshold; the tax data dimension includes interception when there are tax arrears records, and referral to manual review when there are no invoice records in the recent set months; the related risk dimension includes interception when related companies are on the blacklist; and the custom rule dimension uses a weighted scoring mechanism, intercepting companies with a total score lower than the threshold.

[0020] Furthermore, in step C, the enterprise risk electronic assessment report centrally displays business registration information, legal risks, operating status, financial data, and historical application comparison analysis on a single page, and highlights newly added judicial cases, administrative penalties, and dynamic risk information related to abnormal operations.

[0021] Furthermore, in Step D, the parallel approval process for multiple positions adopts an overdue warning mechanism: preset approval time limits are set for each position (e.g., risk control position ≤ 2 working days, legal position ≤ 1 working day, finance position ≤ 1 working day, project manager position ≤ 1 working day). When the approval time for a certain position reaches 80% of the time limit, a reminder notification is sent to the staff member in that position and the corresponding supervisor; if the approval is not completed within the preset time limit, the project is automatically transferred to the superior position for supervision, and the reason for the overdue time is recorded.

[0022] Furthermore, in step D, the pre-defined automatic approval rules for big data risks activated by the risk control position employ both whitelist and blacklist review mechanisms: The whitelist review mechanism includes: including companies with a good historical performance record, an establishment period exceeding a set threshold, clean judicial data, and a low industry risk rating into the whitelist database. Companies that are on the whitelist will automatically pass the review process more quickly without manual intervention. The blacklist review mechanism includes: including companies with records of dishonesty, being subject to enforcement, tax arrears, belonging to prohibited industries, having associated companies with risks, or having a history of compensation, recovery, or extension in the blacklist database; and forcing companies on the blacklist to undergo manual review.

[0023] Furthermore, it also includes: Step F, Post-warranty Warning Step: According to the set monitoring cycle and post-warranty data analysis rules, periodically re-crawl the company's operation and project construction data. If the post-warranty warning rules are met, an automatic prompt will be made and the post-warranty warning process will be initiated. The preset monitoring cycle can be configured on a daily, weekly, monthly, or quarterly basis, or the monitoring frequency can be dynamically adjusted according to the enterprise's risk rating. Post-insurance data analysis rules include: periodically re-crawling data on enterprise business registration changes, new judicial cases, administrative penalties, operational abnormalities, tax status, invoicing changes, and project construction progress; The post-guarantee early warning rules include: warnings when new records of persons subject to enforcement are added within the set monitoring period, warnings when new records of dishonesty are added, warnings when the business status changes to abnormal, warnings when the tax status changes to tax arrears, warnings when there are no invoice records for a set number of consecutive months, and warnings when the project construction progress is stagnant for more than a set number of days. The post-guarantee early warning process includes: automatically generating an early warning prompt and pushing it to risk control personnel after the early warning rule is triggered, marking the enterprise as a risk-concerned entity, initiating the post-guarantee verification task, and deciding whether to add additional guarantee measures, adjust the credit limit, or initiate the recovery process based on the verification results.

[0024] Furthermore, it also includes: Step G, Recovery Management Steps: After a project encounters an accident, the recovery process is initiated, the project is added to the list of companies seeking recovery, recovery personnel enter the recovery measures and their effects, and the recovery progress is summarized and displayed for multi-department collaborative handling; The recovery process includes: recovery personnel entering recovery measures and the effects of each recovery, which include sending collection notices, telephone collection, on-site collection, entrusting third-party recovery, legal proceedings and property preservation; The effects of recovery include the amount already recovered, the repayment agreement reached, the litigation process initiated, and the absence of executable assets; Automatically summarizes and displays recovered amounts, outstanding balances, recovery progress percentages, and all recovery records for collaborative processing by multiple departments; After the recovery is completed, a decision will be made on whether to remove the company from the recovery list or transfer it to the bad debt write-off process, based on the recovery results.

[0025] Furthermore, it also includes: Step H, Integrated Business and Finance Processing Step: Through the interface, data is exchanged with the enterprise's financial software to automatically synchronize business data such as billing, invoicing, and guarantee, thereby achieving business and financial data integration. The business volume, billing amount, output function volume, and approval time are statistically analyzed by department, personnel, and time period, generating a visual performance evaluation report.

[0026] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection, an electrical connection, or a connection that allows communication between them; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components, unless otherwise explicitly limited. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0027] Obviously, the embodiments described above are merely some embodiments of the present invention, not all embodiments. The accompanying drawings show preferred embodiments of the present invention, but do not limit the patent scope of the present invention. The present invention can be implemented in many different forms; rather, these embodiments are provided to provide a more thorough and complete understanding of the disclosure of the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the patent protection scope of this invention.

Claims

1. A method for automatic approval of electronic guarantees based on big data screening, characterized in that, Includes the following steps: Step A, Enterprise Access and Verification Steps: Obtain the applicant enterprise's unified social credit code or enterprise name, call the industrial and commercial interface to fill in the enterprise's basic information, and verify the legal representative's ID card and mobile phone number using the three elements; after verification, call the preset blacklist database and black box rule engine to screen the enterprise for access from the dimensions of industrial and commercial information, judicial data, and industry data. If the enterprise is on the blacklist or fails to meet the minimum access rules, the application will be blocked and the reason for the block will be recorded. Step B: Project Information Collection Steps, collecting project information: Method 1: Manually supplement the project details, guarantee elements, corporate financial information, business sharing, channel source, issuing institution and risk control requirements of the transaction flow information, and upload the scanned documents required by the issuing bank; Method 2: Automatically receive project information pushed by third-party business systems via API interface; Step C, Multi-source Big Data Risk Assessment Steps: Crawling enterprise business registration information, operating information, judicial data, tax data, and invoice data to generate a visualized electronic enterprise risk assessment report; Step D, Parallel Approval Processing by Multiple Positions: Based on the RBAC (Role-Based Access Control) model, the project and risk assessment report are pushed to the following positions for processing according to the preset approval process: Risk control role: Review risk assessment reports and supporting conclusions, activate preset big data risk automatic approval rules, automatically approve low-risk enterprises, and transfer high-risk enterprises to manual review; Legal position: Conduct compliance review and format classification of guarantee formats, and automatically match pricing and subsequent approval strategies based on the classification results; Finance position: Automatically calculate accounts receivable and security deposits, automatically identify and approve corporate bank transfer transactions, and perform basic account verification; Project Manager Role: Provide feedback and review information throughout the workflow, and monitor project progress. Each position makes decisions regarding approval, rejection, meeting arrangements, or approval for the use of official seals during the process; Step E, Automatic Letter Issuance and Electronic Signing: After approval, an electronic guarantee document is automatically generated, and the document is connected to the CFCA Secure Sign electronic signing platform to complete digital signing and electronic signing.

2. The method for automatic approval of electronic guarantees based on big data screening according to claim 1, characterized in that, In step A, the minimum admission rules are composed of a blacklist database and a blackbox rule engine; The blacklist database includes internal blacklists, external blacklists, and dynamic blacklists; companies that are on the blacklist will be directly blocked. The black box rule engine sets screening rules based on six dimensions: business information, judicial data, industry data, tax data, related risks, and custom rules. Each dimension's screening rules include two conclusions: interception and transfer to manual review. The black box rule engine calls multiple data sources in parallel and executes rules in priority order. If an interception rule is hit, the judgment terminates and outputs three conclusions: "passed", "transfer to manual review", and "intercepted". Enterprises that hit the interception rule are automatically added to the internal blacklist.

3. The method for automatic approval of electronic guarantees based on big data screening according to claim 1, characterized in that, In step C, the enterprise risk electronic assessment report displays business registration information, legal risks, operating status, financial data, and historical application comparison analysis on a single page, and highlights newly added judicial cases, administrative penalties, and dynamic risk information related to abnormal operations.

4. The method for automatic approval of electronic guarantees based on big data screening according to claim 1, characterized in that, In Step D, the parallel approval process for multiple positions adopts an overdue warning mechanism: a preset approval time limit is set for each position. When the approval time for a certain position reaches 80% of the time limit, a reminder notification is sent to the staff member of that position and the corresponding supervisor. If the approval is not completed within the preset time limit, the project is automatically transferred to the superior position for supervision, and the reason for the overdue time is recorded.

5. The automatic approval method for electronic guarantees based on big data screening according to claim 1, characterized in that, In step D, the preset big data risk automatic approval rules activated by the risk control position adopt two review mechanisms: whitelist and blacklist.

6. The method for automatic approval of electronic guarantees based on big data screening according to claim 1, characterized in that, Also includes: Step F, Post-Guarantee Warning Step: According to the set monitoring cycle and post-guarantee data analysis rules, periodically re-crawl the company's operation and project construction data. If the post-guarantee warning rules are met, an automatic prompt will be made and the post-guarantee warning process will be initiated.

7. The automatic approval method for electronic guarantees based on big data screening according to claim 6, characterized in that, The preset monitoring cycle can be configured on a daily, weekly, monthly, or quarterly basis, or the monitoring frequency can be dynamically adjusted according to the enterprise's risk rating. Post-insurance data analysis rules include: periodically re-crawling data on enterprise business registration changes, new judicial cases, administrative penalties, operational abnormalities, tax status, invoicing changes, and project construction progress; The post-guarantee early warning rules include: warnings when new records of persons subject to enforcement are added within the set monitoring period, warnings when new records of dishonesty are added, warnings when the business status changes to abnormal, warnings when the tax status changes to tax arrears, warnings when there are no invoice records for a set number of consecutive months, and warnings when the project construction progress is stagnant for more than a set number of days. The post-guarantee early warning process includes: automatically generating an early warning prompt and pushing it to risk control personnel after the early warning rule is triggered, marking the enterprise as a risk-concerned entity, initiating the post-guarantee verification task, and deciding whether to add additional guarantee measures, adjust the credit limit, or initiate the recovery process based on the verification results.

8. The automatic approval method for electronic guarantees based on big data screening according to claim 1, characterized in that, Also includes: Step G, Recovery Management Steps: After a project encounters an accident, the recovery process is initiated, the project is added to the list of companies seeking recovery, recovery personnel enter the recovery measures and their effects, and the recovery progress is summarized and displayed for multi-department collaborative handling.

9. The automatic approval method for electronic guarantees based on big data screening according to claim 8, characterized in that, The recovery process includes: recovery personnel entering recovery measures and the effects of each recovery, including sending collection notices, telephone collection, on-site collection, entrusting third-party recovery, legal proceedings and property preservation; recovery effects include the amount recovered, the repayment agreement reached, the litigation process initiated, and the absence of executable assets; the system automatically summarizes and displays the recovered amount, the outstanding balance, the recovery progress percentage, and all recovery records for multi-departmental collaborative handling; after recovery is completed, the system decides whether to remove the company from the recovery list or transfer it to the bad debt write-off process based on the recovery results.

10. The method for automatic approval of electronic guarantees based on big data screening according to claim 1, characterized in that, Also includes: Step H, Integrated Business and Finance Processing Step: Through the interface, data is exchanged with the enterprise's financial software to automatically synchronize business data such as billing, invoicing, and guarantee, thereby achieving business and financial data integration. The business volume, billing amount, output function volume, and approval time are statistically analyzed by department, personnel, and time period, generating a visual performance evaluation report.

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