Business query record quality inspection method and device, computer device, and storage medium

Abstract

The application relates to the technical field of data security, and discloses a service query record quality inspection method and device, computer equipment and a storage medium, which comprise the following steps: obtaining a record sampling ratio of a service query record; generating a query behavior risk score corresponding to the service query record according to a query scene corresponding to the service query record; extracting a to-be-inspected sample from the service query record based on the query behavior risk score corresponding to the service query record and the record sampling ratio; extracting service voucher information corresponding to the to-be-inspected sample; and verifying the query behavior compliance of the to-be-inspected sample based on the service voucher information corresponding to the to-be-inspected sample, and generating a record quality inspection result corresponding to the to-be-inspected sample. The application can be applied to the quality inspection scene of the service query record in the financial technology, realizes automatic sampling inspection of the service query record and automatic quality inspection of the to-be-inspected sample sampled out, and effectively improves the quality inspection efficiency of the service query record.

Description

Technical Field

[0001] This invention relates to the fields of data security technology and financial technology, and in particular to a method, apparatus, computer equipment and storage medium for quality inspection of business query records. Background Technology

[0002] Based on regulatory audit requirements, it is necessary to retain records of business inquiries involving sensitive business information and conduct regular quality checks for risk management. For example, in the credit reporting system within the fintech sector, the quality check of credit inquiry records is a crucial link in compliance and risk control within the system. Various financial institutions and credit reporting agencies need to conduct regular quality checks on credit inquiry records to ensure that inquiry behavior complies with relevant regulations such as industry management rules, avoid unauthorized or illegal inquiries, and thus prevent the leakage of sensitive business information.

[0003] Currently, the relevant technologies mainly rely on manual quality checks of business query records involving sensitive business information, which is inefficient. For example, for credit query records, each record is manually checked for compliance. However, when the number of credit query records is large, manual full-volume quality checks require a lot of manpower and time, making it difficult to meet the needs of efficient quality checks. Summary of the Invention

[0004] This invention provides a method, apparatus, computer equipment, and medium for quality inspection of business query records, in order to solve the technical problem that the current related technologies require manual quality inspection of each business query record involving sensitive business information, which results in low quality inspection efficiency.

[0005] Firstly, a quality inspection method for business query records is provided, including: Obtain the sampling rate of business query records; Based on the query scenario corresponding to the business query record, a query behavior risk score is generated for the corresponding business query record. Based on the query behavior risk score and record sampling ratio corresponding to the business query records, samples to be inspected are extracted from the business query records. Extract the business voucher information corresponding to the sample to be inspected; Based on the business voucher information corresponding to the sample to be inspected, verify the compliance of the query behavior of the sample to be inspected, and generate the record inspection results corresponding to the sample to be inspected.

[0006] Secondly, a quality inspection device for business query records is provided, including: The acquisition module is used to obtain the record sampling ratio of business query records; The generation module is used to generate a query behavior risk score for each business query record based on the query scenario corresponding to that business query record. The extraction module is used to extract samples to be inspected from business query records based on the query behavior risk score and record sampling ratio corresponding to the business query records. The extraction module is used to extract the business voucher information corresponding to the sample to be inspected. The verification module is used to verify the compliance of the query behavior of the sample to be inspected based on the business voucher information corresponding to the sample to be inspected, and generate the record inspection results corresponding to the sample to be inspected.

[0007] Thirdly, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the aforementioned quality inspection method for business query records.

[0008] Fourthly, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the steps of the aforementioned quality inspection method for querying business records.

[0009] In the aforementioned solution implemented by the quality inspection method, device, computer equipment, and storage medium for business query records, the server can obtain the sampling inspection ratio of the business query records; generate a query behavior risk score corresponding to the business query records based on the query scenario corresponding to the business query records; extract samples to be inspected from the business query records based on the query behavior risk score and the sampling inspection ratio; extract the business voucher information corresponding to the samples to be inspected; verify the compliance of the query behavior of the samples to be inspected based on the business voucher information corresponding to the samples to be inspected, and generate the record quality inspection result corresponding to the samples to be inspected. This invention can initially assess the query risk of each business query record based on the query scenario, and give a query behavior risk score. Then, based on the score and sampling ratio, it can extract high-risk samples from the business query records for quality inspection. It can then automatically extract the corresponding business voucher information of each sample for quality inspection, compare the information one by one, and verify whether the query behavior of the sample for quality inspection is compliant. Finally, it summarizes and generates record quality inspection results, realizing automatic sampling inspection of business query records and automatic quality inspection of the sampled samples for quality inspection. It eliminates the need for manual quality inspection, reduces the time spent on manual record extraction and quality inspection, and effectively improves the quality inspection efficiency of business query records. Attached Figure Description

[0010] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a schematic diagram of an application environment for a quality inspection method for business query records in one embodiment of the present invention; Figure 2 This is a flowchart illustrating a quality inspection method for business query records in one embodiment of the present invention; Figure 3 This is another flowchart illustrating the quality inspection method for business query records in one embodiment of the present invention; Figure 4 This is a schematic diagram of the automatic quality inspection process of the quality inspection method for business query records in one embodiment of the present invention; Figure 5 This is a schematic diagram of the system architecture of a quality inspection method for business query records in one embodiment of the present invention; Figure 6 This is a schematic diagram of the quality inspection data flow of a quality inspection method for business query records in one embodiment of the present invention; Figure 7 This is a schematic diagram of a quality inspection device for business query records in one embodiment of the present invention; Figure 8 This is a schematic diagram of the structure of a computer device according to an embodiment of the present invention; Figure 9 This is another structural schematic diagram of a computer device according to one embodiment of the present invention. Detailed Implementation

[0012] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0013] In related technologies, when relying on manual inspection of each business query record for quality control, manual sampling of some records can also be used. However, manual sampling is often random and cannot accurately target key objects such as high-risk query scenarios and high-frequency query subjects, easily overlooking key issues and resulting in incomplete quality control. At the same time, when manually comparing authorization information (such as authorization letters) with business query records, misjudgments and omissions are prone to occur, leading to insufficient accuracy of quality control. Furthermore, the quality control process is not standardized, lacking automated result generation and archiving mechanisms, making it difficult to trace quality control records and making subsequent review and rectification more difficult.

[0014] The quality inspection method for business query records provided in this embodiment of the invention can be applied to, for example... Figure 1In this application environment, the client can communicate with the server via the network. The server can obtain the sampling rate of business query records; generate a query behavior risk score for the corresponding business query record based on the query scenario; extract samples to be inspected from the business query records based on the query behavior risk score and the sampling rate; extract the business voucher information corresponding to the samples to be inspected; verify the compliance of the query behavior of the samples to be inspected based on the business voucher information, and generate the record quality inspection result for the samples to be inspected. This invention can initially assess the query risk of each business query record based on the query scenario, and give a query behavior risk score. Then, based on the score and sampling ratio, it can extract high-risk samples from the business query records for quality inspection. It can then automatically extract the corresponding business voucher information of each sample for quality inspection, compare the information one by one, and verify whether the query behavior of the sample for quality inspection is compliant. Finally, it summarizes and generates record quality inspection results, realizing automatic sampling inspection of business query records and automatic quality inspection of the sampled samples for quality inspection. It eliminates the need for manual quality inspection, reduces the time spent on manual record extraction and quality inspection, and effectively improves the quality inspection efficiency of business query records.

[0015] The client can be, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices. The server can be implemented using a standalone server or a server cluster consisting of multiple servers. The invention will now be described in detail through specific embodiments.

[0016] Please see Figure 2 As shown, Figure 2 A flowchart illustrating a quality inspection method for business query records provided in an embodiment of the present invention, the method comprising the following steps: Step 101: Obtain the sampling ratio of business query records.

[0017] In some embodiments, business query records may be records of query operations involving sensitive or private data, such as credit query records. Business query records may include information such as the query subject, query time, query purpose, the person being queried, their department, and the contracting party. Since these records typically involve a large volume of data, quality inspection can be conducted through sampling. A fixed sampling ratio can be preset to extract a portion of the business query records as a quality inspection sample. Alternatively, the sampling ratio can be configured by the user and dynamically changed to meet different user business needs.

[0018] Step 102: Generate a query behavior risk score for the business query record based on the query scenario corresponding to the business query record.

[0019] The query scenario can include the business reason, business type, and purpose of the query operation initiated by the business data query party. Based on the query scenario, it is possible to distinguish what kind of business needs the query behavior is initiated, which facilitates compliance verification and risk control of query behavior, thereby avoiding the leakage of sensitive business information.

[0020] For example, when a business inquiry record is a credit inquiry record, the inquiry scenarios involving credit reports may include: loan approval scenarios, such as when a borrower applies for a business loan or a consumer loan and inquires about the borrower's personal credit report through the lending institution; and self-service inquiry scenarios, such as when a user inquires about their personal credit report online or offline.

[0021] In some embodiments, the query scenario of each business query record can be determined based on the specific record information in each business query record. Then, a preliminary risk assessment can be conducted on each business query record according to the priority of different query scenarios, and a corresponding query behavior risk score can be generated to quantify the query behavior risk of each query operation.

[0022] Step 103: Based on the query behavior risk score and record sampling ratio corresponding to the business query records, extract samples to be inspected from the business query records.

[0023] In some embodiments, business query records can be arranged in descending order of their query behavior risk scores based on the corresponding query behavior risk scores. Then, records are extracted from the arranged business query records according to the record sampling ratio, starting with the business query record with the highest score, as samples to be inspected. This reduces the randomness of record sampling and allows for precise sampling of key control targets such as high-risk query scenarios and high-frequency query subjects. This reduces the chance of missing high-risk query behaviors during quality inspection, improves the efficiency of sampling samples to be inspected, and ensures the comprehensiveness of quality inspection.

[0024] Step 104: Extract the business voucher information corresponding to the sample to be inspected.

[0025] The business voucher information can be the operation voucher information corresponding to the query operation, such as the query subject authorization letter, which may include specific authorization information such as the authorized purpose and authorization period of the subject.

[0026] In some embodiments, based on the specific record information in the sample to be inspected, such as the query subject and the queried person, the entity voucher information corresponding to the query subject and the customer voucher information corresponding to the queried person can be retrieved from the business voucher database to conduct quality inspection of the query behavior of the sample to be inspected, thereby realizing the automated extraction of query voucher information without the need for manual extraction of corresponding information one by one for quality inspection, thus improving the efficiency of quality inspection.

[0027] Step 105: Based on the business voucher information corresponding to the sample to be inspected, verify the compliance of the query behavior of the sample to be inspected, and generate the record inspection result corresponding to the sample to be inspected.

[0028] In some embodiments, the business credential information of the sample to be inspected can be compared with multiple verification information recorded in the sample itself. For example, the authorized subject in the business credential information can be compared with the query subject in the sample to be inspected to verify whether the query subject in the record is an authorized subject and whether it has specific query permissions. When the comparison determines that the business credential information of the sample to be inspected is consistent with the multiple verification information recorded in the sample itself, the query behavior of the sample to be inspected can be determined to be compliant, that is, the quality inspection passes. If inconsistent verification information is found after comparison, the query behavior of the sample to be inspected can be deemed to be in violation, and a quality inspection result of failing the quality inspection can be generated based on the inconsistent verification information. Multiple samples to be inspected can be verified one by one in this way to generate a summary record quality inspection result.

[0029] Compared with related technologies, this embodiment can preliminarily assess the query risk of each business query record based on the query scenario of each business query record, give a query behavior risk score, and then extract high-risk samples from the business query records based on the score and sampling ratio. Then, it can automatically extract the business voucher information corresponding to the samples to be inspected, compare the information one by one, verify whether the query behavior of the samples to be inspected is compliant, and finally summarize and generate record quality inspection results. This realizes automatic sampling inspection of business query records and automatic quality inspection of the sampled samples to be inspected, eliminating the need for manual quality inspection one by one, reducing the time for manual record extraction and quality inspection, and effectively improving the quality inspection efficiency of business query records.

[0030] Furthermore, to fully illustrate the implementation of this embodiment, this embodiment also provides another quality inspection method for business query records, such as... Figure 3 As shown, the method includes: Step 201: Obtain the sampling ratio of the records corresponding to the business query records through the sampling configuration interface.

[0031] The sampling configuration interface is used to configure the sampling ratio of records for different query subjects according to the type of query subject, and is also used to configure the preset priority rules corresponding to different record items in the business query records.

[0032] For example, a sampling configuration interface can be provided on the client side. Users can log in to the sampling configuration interface to configure the sampling period for business query records, such as supporting selection of daily, weekly, monthly or any period, and then set the sampling ratio, which can be adjusted from 0-100%. Users can set differentiated sampling ratios for different query subjects (such as financial institutions and individuals) according to actual quality inspection needs. For example, a higher sampling ratio can be set for financial institutions with high-frequency queries to improve the quality inspection coverage of key quality inspection targets. Users can also configure the quality inspection priority of business objects for each record item in the business query records according to business needs and build preset priority rules to adapt to different business needs.

[0033] This approach supports periodic quality inspections, allows for setting differentiated sampling ratios based on query subject type, and enables flexible adjustment of preset priority rules to adapt to the quality inspection needs of business query records in different industries and scenarios. It is highly versatile and can adaptively extract quality inspection samples during the quality inspection of a large number of records without the need for manual random sampling, thus improving sampling efficiency.

[0034] Step 202: Generate a query behavior risk score for the business query record based on the query scenario corresponding to the business query record.

[0035] Optionally, step 202 may specifically include: determining the priority of multiple record items in the business query record based on the preset priority rules corresponding to the business query record, wherein the preset priority rules are the query record quality inspection priorities preset according to different query behavior characteristics; determining the priority weighted score of multiple record items based on the priority of multiple record items; and generating a query behavior risk score corresponding to the business query record based on the priority weighted score of multiple record items.

[0036] The business query record can contain multiple record items, such as the query subject and the person being queried. Query behavior characteristics can include query scenario type (e.g., sensitive scenario, ordinary scenario), query subject type (e.g., account manager, asset manager, institutional system), query frequency, etc. Based on multiple record items in the business query record, the corresponding query behavior characteristics can be identified. Then, based on the identified query behavior characteristics and preset priority rules, the quality inspection weight corresponding to each query behavior characteristic is determined. Based on the quality inspection weight of each characteristic and each record item, a weighted calculation is used to determine the query behavior risk score of a single business query record.

[0037] In some embodiments, a weighted scoring mechanism can be constructed by configuring preset priority rules to ensure that high-risk query samples are extracted first. For example, the preset priority rules configured for the quality inspection of credit inquiry records are as follows: Query scenario priority: Sensitive scenarios (such as credit approval queries, third-party cooperation queries) > Ordinary scenarios (such as account manager queries, asset manager queries); Priority of query subjects: Account manager query > Asset manager query > Institutional system automatic query; Query frequency priority: high-frequency queries (e.g., ≥5 queries per day) > low-frequency queries; Correspondingly, quality inspection weights corresponding to query behavior features with different priorities can be configured. After identifying the priority of the query behavior features corresponding to each record item, a weighted score for each query record is calculated based on the quality inspection weights of different query behavior features, which serves as the query behavior risk score for the credit query record.

[0038] By combining preset priority rules with a weighted scoring mechanism, the rules for random inspection of records can be standardized, the accuracy of quality inspection can be improved, and the leakage of sensitive business information can be avoided.

[0039] Step 203: Based on the query behavior risk score and record sampling ratio corresponding to the business query records, extract samples to be inspected from the business query records.

[0040] Optionally, step 203 may specifically include: sorting the business query records according to the query behavior risk score corresponding to the business query records to generate sorted business query records; and extracting the top-ranked samples to be inspected from the sorted business query records according to the record sampling ratio.

[0041] In some embodiments, original business query records can be sorted from highest to lowest according to the assessed query behavior risk score. Then, starting with the business query record with the highest score, samples are drawn for quality inspection according to the sampling ratio, thereby ensuring that high-risk samples are not missed. This embodiment can dynamically configure the sampling ratio and preset priority rules, sort business query records by risk score weight, and intelligently sort and sample business query records to reduce the randomness of sample sampling, improve the detection rate of high-risk samples, thereby reducing the omission of high-risk query behavior review, ensuring the accuracy of quality inspection of business query records, and thus ensuring the security of business data access.

[0042] Optionally, AI-powered intelligent sampling rules can be supported. The sampling mechanism is trained based on historical quality inspection results of business query records. Priority is given to selecting query records from historical quality inspection results that failed quality inspection, including those involving customers, account managers, authorization methods, and departments. When relevant historically failed quality inspection records are identified in the current quality inspection cycle's business query records, the quality inspection weight of these records can be increased to avoid missing high-risk records. Intelligent sampling engine technology: This includes building large-scale language models that integrate historical quality inspection customer profiles, query subject behavior dynamics, and historical violation data. It dynamically calculates the violation risk score for each query record and optimizes sampling priorities through reinforcement learning. An adaptive sampling strategy of sampling more high-risk samples and less low-risk samples further ensures the detection rate of high-risk samples.

[0043] For example, credit inquiry records can be automatically collected and samples can be extracted. Credit inquiry records include information such as the inquiring entity, inquiry time, inquiry purpose, inquired person, affiliated department, and contracting entity. During the extraction process, personal inquiries (approximately 5,000 records) and corporate inquiries (approximately 500 records) can be distinguished based on the inquiring entity. According to preset priority rules, the weighted score of each inquiry record is calculated and sorted from high to low scores. The top 275 records can be extracted as quality inspection samples at a sampling rate of 5%, and the sampling time (e.g., 1:00 AM on the same day), sample type, and other information can be recorded to ensure that the sampling process is traceable.

[0044] Step 204: Identify multiple record items in the sample to be inspected.

[0045] In some embodiments, high-risk samples to be inspected are screened out, and multiple records in each sample to be inspected can be automatically identified for verification one by one.

[0046] Step 205: Extract business voucher information corresponding to multiple record items from different business information databases corresponding to the business query records.

[0047] The business voucher information includes the authorization information of the inquiring entity, the qualification information of the inquiring entity, and supporting documents for the reason for the inquiry. Accordingly, the business voucher information can be directly obtained electronic data or data obtained by scanning paper materials.

[0048] For example, an automatic matching module can first identify each record item in each sample to be inspected, and then match the corresponding business voucher information for each record item. Business voucher information for different record items can be extracted from multiple databases with business associations. For instance, for the query subject record item and the queryee record item in a credit inquiry record, the authorization letter corresponding to the query subject can be extracted from the associated authorization letter database, and the identity verification information corresponding to the queryee can be extracted from the customer information database. In this way, the business voucher information corresponding to each record item can be automatically extracted to verify the record items, reducing the time spent manually extracting voucher information and thus improving the efficiency of record quality inspection.

[0049] Step 206: Based on the business voucher information corresponding to the sample to be inspected, verify the compliance of the query behavior of the sample to be inspected, and generate the record inspection result corresponding to the sample to be inspected.

[0050] Optionally, step 206 may specifically include: identifying the voucher items corresponding to multiple record items in the business voucher information; verifying the compliance of the query behavior of the sample to be inspected by comparing the information consistency between the multiple record items and the multiple voucher items, and generating information verification results for the multiple record items; and generating record quality inspection results corresponding to the sample to be inspected based on the information verification results for the multiple record items, wherein the record quality inspection results include sampling information, quality inspection result statistics, and quality inspection problem details.

[0051] In some embodiments, a key field comparison algorithm can be used to automatically verify the consistency of information between multiple record items and multiple voucher items in business voucher information, such as customer information, authorization information, certificate information, qualification information, etc., to achieve automatic information matching verification and automatic compliance determination. Specifically, for authorization letters and scanned copies of paper materials, optical character recognition (OCR) methods can be used to recognize the text of voucher items and extract fields, converting unstructured data into machine-verifiable data, and then comparing and verifying each record item one by one. The verification results of multiple record items are combined to determine the compliance of the query behavior of the sample to be inspected, generating information verification results for multiple record items. Finally, based on the information verification results of multiple record items in each sample to be inspected, the record quality inspection results can be summarized to generate the record quality inspection results.

[0052] Correspondingly, quality inspection results can be stored in the form of quality inspection reports, which can be exported to PDF, Excel, and other formats for easy viewing by users. Data such as recorded quality inspection results, sampling information, and quality inspection result statistics can be stored in the data storage module for archiving, providing data support for subsequent quality inspection reviews and compliance checks. Sampling information may include sampling cycle, sampling ratio, and total sample size; quality inspection result statistics may include the number of qualified quality inspection records, the number of unqualified quality inspection records, the number of records awaiting review, and the quality inspection pass rate; and quality inspection issue details may include the unqualified record number, issue type, relevant querying entity, and rectification suggestions.

[0053] Optionally, based on the information verification results of multiple record items, a record quality inspection result corresponding to the sample to be inspected is generated. Specifically, this may include: if the information verification result of the sample to be inspected shows that there are record items with inconsistent information, then the record quality inspection result of the sample to be inspected is determined to be a failed quality inspection, a quality inspection problem detail is generated based on the record items with inconsistent information, and a record review instruction is generated based on the quality inspection problem detail; if the information verification result of the sample to be inspected shows that there are no record items with inconsistent information, then the record quality inspection result of the sample to be inspected is determined to be a passed quality inspection.

[0054] For example, taking the quality inspection of credit inquiry records as an example, such as Figure 4 As shown, the system allows users to first set the sampling ratio and preset priority rules. It can directly collect credit inquiry records and extract samples based on the sampling ratio, or call an intelligent sampling model to intelligently sample. Then, it automatically matches the samples with corresponding credit authorization information, customer identification information, customer qualification information, and scanned copies of paper materials through different interfaces to generate preliminary quality inspection results. If the quality inspection is qualified or unqualified, the preliminary results can be fed back to the intelligent extraction model for training. For records with unclear or missing information, or unqualified records, a manual review module can be used, and the model can be trained based on the feedback from the manual review. Specifically, a visual review interface can be provided, displaying the complete query records of the sample to be inspected, scanned copies of the original authorization documents, preliminary quality inspection results, and details of mismatches. For example, for 8 unqualified records and 2 records to be reviewed, the reviewer can manually extract key information from the authorization documents and re-compare them. For unqualified samples, the reasons for the mismatch can be verified, it can be determined whether there is a misjudgment, and review opinions can be filled in, and the problem type can be marked, such as unauthorized query or incorrect authorization information.

[0055] For example, the automatic matching module can connect to the authorization management system and customer data system to obtain the authorization information and electronic and paper scanned copies of customer data corresponding to 275 samples to be inspected. Among them, 250 are electronic authorizations and 25 are scanned authorizations. The OCR recognition unit recognizes the 25 scanned authorizations and extracts key credential information such as the authorizing entity, the purpose of authorization, and the authorization period. The matching algorithm unit compares the key information of the authorization with each record item in the query records. 260 samples have complete information matching and no inconsistent records, and are judged as qualified. 8 samples have inconsistent records, such as the authorizing entity and the querying entity are different, the query purpose is outside the scope of authorization, and the query time is outside the authorization period, and are judged as unqualified. 2 samples have unclear or missing information, such as OCR recognition failure and key fields of authorization not being filled, and are judged as needing review. A record review instruction is generated to remind manual intervention.

[0056] Optionally, after generating a record review instruction based on the quality inspection issue details, the method further includes: receiving record correction information corresponding to the quality inspection issue details; generating corrected business voucher information based on the record correction information; re-verifying the sample to be inspected based on the corrected business voucher information; and regenerating the record quality inspection result corresponding to the sample to be inspected.

[0057] Correspondingly, after users correct the business voucher information according to the details of the quality inspection issues, the quality inspection results can be reviewed and corrected again. During the correction process, the correction record can be stored throughout the process. The correction record can include information such as the reviewer, review time, and reason for correction, ensuring that the review process is traceable, guaranteeing the compliance of credit inquiry, reducing risks, and promptly identifying unauthorized inquiries and other issues through a combination of automated quality inspection and manual review, reminding relevant personnel to handle them in a timely manner, reducing the risk of violations in credit inquiry, and complying with relevant industry management regulations and other relevant provisions.

[0058] As one possible implementation method, such as Figure 5 As shown, a quality inspection system capable of building business query records can include a user interface layer, a business logic layer, a data layer, and an interface layer. The user interface layer provides a sampling configuration interface, a sample list interface, an intelligent quality inspection interface, a manual review interface, and a report interface. The sampling configuration interface displays and configures items such as the sampling ratio; the sample list interface displays multiple business query records eligible for quality inspection, along with specific information within each record; the intelligent quality inspection interface displays the extracted samples to be inspected, the inspection progress of each sample, and quality inspection anomaly alerts; the manual review interface displays records requiring manual review and receives the manual inspection results; and the report interface displays the record inspection results and provides a download function for the record inspection results.

[0059] Correspondingly, the business logic layer may include configuration management logic, sample extraction logic, intelligent matching logic, manual review logic, report generation logic, and access control logic. Configuration management logic may include preset priority rules and dynamic change logic for configuration items related to the sampling ratio, such as adding, deleting, and updating configuration items. Sample extraction logic may include extraction logic based on sampling ratio and priority, and intelligent extraction logic that calls intelligent models. Intelligent matching logic may include automatic identification of record items and automatic matching of information from voucher items associated with the record items in different databases. Manual review logic may include judgment logic for identifying abnormal records such as quality inspection failure records and records with fuzzy or missing information as records for manual review. Report generation logic may include quality inspection results based on each record. The processing logic for automatically generating reports during the sampling inspection process; the access control logic may include configurable sampling ratios, preset priority rules, report downloads, and user access control and identification logic for accessing different databases.

[0060] Furthermore, the data layer can be used to store basic configuration data (such as recording sampling ratios and preset priority rules), business data (such as credit reports), result data (such as recording quality inspection data), and log data (such as operation logs corresponding to sampling and quality inspection operations). The interface layer may include connection interfaces with the credit inquiry database, authorization database, customer information database, customer paper document database, and credit management system. These databases can all adopt structured storage methods. The credit inquiry database can be used to store query operation logs for retrieving credit reports. The authorization database can be used to store a complete set of electronic compliance certificates for credit inquiry authorization, which may include the authorizing entity, authorization signing time, authorization validity period, limited query scenarios, and authorization scope. The customer information database can be used to store customer document information, such as the validity period of the document of the person being queried. The customer paper document database can be used to store scanned copies of paper business documents required to be retained by regulators, such as scanned copies of credit inquiry authorization letters. The credit management system can be used to store customer credit information files, such as credit reports.

[0061] In specific application scenarios, such as Figure 6As shown, the quality inspection administrator can first input information such as the quality inspection ratio, priority rules, and the corresponding weight configuration of the rules into the sampling configuration module. This module then constructs the sampling rules, which are input into the sample extraction module and stored in the data storage module. The sample extraction module can obtain sample sources based on credit query records and output samples to be inspected. The automatic matching module can output samples to be reviewed based on samples to be inspected, authorization records, credit information, qualification information, and paper materials using the OCR recognition unit and material comparison unit. The manual review module can output review opinions for the samples to be reviewed. Finally, the result output module outputs quality inspection reports, statistical reports, and visualized data, which are displayed to the quality inspection administrator. The data storage module can be used to store data output by each module, such as sampling rules, samples to be inspected, samples to be reviewed, quality inspection results, and operation records.

[0062] In this way, this embodiment can automatically complete the verification of authorization information through dynamic priority sampling, automatic matching of authorization letters, and human-machine collaborative quality inspection. The system operates stably and can complete the sampling and quality inspection of 5,500 query records per day. The quality inspection efficiency is improved by more than 90% compared with the traditional manual method, and the quality inspection accuracy reaches more than 99%, effectively solving the problems of low efficiency and poor accuracy in credit inquiry quality inspection for financial institutions. OCR image and text structure extraction technology is adopted, and AIGC technology is introduced to improve the sampling logic and output results, thereby improving the accuracy and effectiveness of quality inspection. Abnormal samples can be directed to manual review, balancing automation and audit reliability. The entire process is traceable, meeting the regulatory and compliance audit requirements of the credit reporting industry, effectively preventing the risk of illegal inquiries, and improving the standardization and security of credit reporting business management. From sample extraction, automatic matching, manual review to result archiving, a complete quality inspection process is formed. All operation records, quality inspection results, and review opinions are stored to ensure the traceability of the quality inspection process, facilitating subsequent compliance checks, problem review, and rectification.

[0063] Compared with related technologies, this embodiment can configure the priority of multiple record items in the business query record, determine the priority weighted score of multiple record items, generate a query behavior risk score for the business query record, sort the business query record according to the query behavior risk score, and extract the high-risk samples to be inspected from the sorted business query record. Then, it automatically extracts the business voucher information corresponding to multiple record items from different business information databases corresponding to the record items of the samples to be inspected. By comparing the consistency of information between multiple record items and multiple voucher items, it verifies the compliance of the query behavior of the samples to be inspected, generates information verification results for multiple record items, and generates record review instructions based on the quality inspection problem details for records with inconsistent information. The content can be corrected and the quality inspection can be carried out again. By automatically extracting samples and automatically matching business voucher information, it replaces manual sampling and manual comparison, realizes quality inspection automation, greatly improves quality inspection efficiency, effectively reduces manpower input, and shortens the quality inspection cycle. It is especially suitable for quality inspection scenarios with a large amount of query record data and can realize efficient batch quality inspection.

[0064] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0065] Based on the above Figure 2 The specific implementation of the method shown in this embodiment provides a quality inspection device for business query records, such as... Figure 7 As shown, the device includes: an acquisition module 31, a generation module 32, an extraction module 33, a decryption module 34, and a verification module 35. Detailed descriptions of each functional module are as follows: Module 31 is used to obtain the record sampling ratio of business query records; The generation module 32 is used to generate a query behavior risk score corresponding to the business query record based on the query scenario corresponding to the business query record. The extraction module 33 is used to extract samples to be inspected from the business query records based on the query behavior risk score and record sampling ratio corresponding to the business query records. Extraction module 34 is used to extract the business voucher information corresponding to the sample to be inspected; The verification module 35 is used to verify the compliance of the query behavior of the sample to be inspected based on the business voucher information corresponding to the sample to be inspected, and generate the record inspection result corresponding to the sample to be inspected.

[0066] In one embodiment, the extraction module 34 is specifically used for: Identify multiple records in the sample to be inspected; From the different business information databases corresponding to the business query records, extract the business voucher information corresponding to multiple record items. The business voucher information includes the authorization information of the querying entity, the qualification information of the querying entity, and the supporting materials for the reason for the query.

[0067] In one embodiment, the verification module 35 is specifically used for: Identify the voucher items corresponding to multiple record items in the business voucher information; By comparing the consistency of information between multiple record items and multiple voucher items, the compliance of the query behavior of the sample to be inspected is verified, and the information verification results of multiple record items are generated. Based on the verification results of information from multiple record items, the quality inspection results corresponding to the sample to be inspected are generated. The quality inspection results include sampling information, quality inspection result statistics, and quality inspection problem details.

[0068] In one embodiment, the verification module 35 is specifically used for: If the information verification result of the sample to be inspected is that there are inconsistent records, then the record quality inspection result of the sample to be inspected is determined to be that the quality inspection has failed. A quality inspection problem detail is generated based on the inconsistent records, and a record review instruction is generated based on the quality inspection problem detail. If the information verification result of the sample to be inspected is that there are no inconsistent records, then the record quality inspection result of the sample to be inspected is determined to be passed. After generating record review instructions based on the quality inspection issue details, the method also includes: Receive and correct the records corresponding to the quality inspection issue details; Based on the record correction information, the corrected business voucher information is generated. Based on the corrected business voucher information, the sample to be inspected is re-verified, and the record inspection result corresponding to the sample to be inspected is regenerated.

[0069] In one embodiment, the generation module 32 is specifically used for: Based on the preset priority rules corresponding to the business query records, the priority of multiple record items in the business query records is determined. The preset priority rules are the query record quality inspection priorities preset according to different query behavior characteristics. Based on the priority of multiple record items, determine the priority-weighted score of the multiple record items; Based on the priority-weighted scores of multiple record items, a query behavior risk score is generated for each business query record.

[0070] In one embodiment, the acquisition module 31 is specifically used for: The sampling configuration interface corresponding to the business query record can be used to obtain the sampling ratio of the record corresponding to the business query record. The sampling configuration interface is used to configure the sampling ratio of different query subjects according to the type of query subject, and is also used to configure the preset priority rules corresponding to different record items in the business query record.

[0071] In one embodiment, the extraction module 33 is specifically used for: Based on the query behavior risk score corresponding to the business query record, sort the business query records to generate sorted business query records; Based on the sampling ratio of the records, the top-ranked samples to be inspected are drawn from the sorted business query records.

[0072] This invention provides a quality inspection device for business query records. It can configure the priorities of multiple record items within a business query record, determine the weighted scores of these priorities, generate a query behavior risk score for each record, sort the business query records according to the risk score, and extract high-risk samples from the sorted records. Then, it automatically extracts business voucher information corresponding to multiple record items from different business information databases corresponding to the record items of the samples to be inspected. By comparing the consistency of information between the multiple record items and the multiple voucher items, it verifies the compliance of the query behavior of the samples to be inspected, generates information verification results for multiple record items, and generates record review instructions based on quality inspection problem details for records with inconsistent information. Content corrections allow for re-inspection. By automatically extracting samples and automatically matching business voucher information, it replaces manual sampling and comparison, achieving automated quality inspection, significantly improving efficiency, effectively reducing manpower, and shortening the inspection cycle. It is particularly suitable for quality inspection scenarios with large amounts of query record data, enabling efficient batch quality inspection.

[0073] Specific limitations regarding the quality inspection device for business query records can be found in the above description of the quality inspection methods for business query records, and will not be repeated here. Each module in the aforementioned quality inspection device for business query records can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can reference and execute the operations corresponding to each module.

[0074] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 8As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with external clients via a network connection. When the computer program is executed by the processor, it implements the server-side functions or steps of a business query record quality inspection method.

[0075] In one embodiment, a computer device is provided, which may be a client, and its internal structure diagram may be as follows: Figure 9 As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with an external server via a network connection. When the computer program is executed by the processor, it implements the client-side functions or steps of a quality inspection method for querying business records.

[0076] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps: Obtain the sampling rate of business query records; Based on the query scenario corresponding to the business query record, a query behavior risk score is generated for the corresponding business query record. Based on the query behavior risk score and record sampling ratio corresponding to the business query records, samples to be inspected are extracted from the business query records. Extract the business voucher information corresponding to the sample to be inspected; Based on the business voucher information corresponding to the sample to be inspected, verify the compliance of the query behavior of the sample to be inspected, and generate the record inspection results corresponding to the sample to be inspected.

[0077] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor: Obtain the sampling rate of business query records; Based on the query scenario corresponding to the business query record, a query behavior risk score is generated for the corresponding business query record. Based on the query behavior risk score and record sampling ratio corresponding to the business query records, samples to be inspected are extracted from the business query records. Extract the business voucher information corresponding to the sample to be inspected; Based on the business voucher information corresponding to the sample to be inspected, verify the compliance of the query behavior of the sample to be inspected, and generate the record inspection results corresponding to the sample to be inspected.

[0078] It should be noted that the functions or steps that can be implemented by the computer-readable storage medium or computer device described above can be referred to the relevant descriptions on the server side and client side in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0079] Those skilled in the art will understand that implementing all or part of the processes in the above embodiments can be accomplished by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0080] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0081] It should be noted that if any AI models, software tools, or components not belonging to this company appear in the embodiments of this invention, they are merely illustrative examples and do not represent actual use. All user personal information involved in the embodiments of this invention is authorized (with the knowledge and consent) by the relevant parties or fully authorized by all parties, and the executing entity may obtain it through various legal and compliant means. The collection, storage, use, processing, transmission, provision, and disclosure of the information, data, and signals involved all comply with relevant laws and regulations and do not violate public order and good morals.

[0082] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A quality inspection method for business query records, characterized in that, include: Obtain the sampling rate of business query records; Based on the query scenario corresponding to the business query record, a query behavior risk score is generated for the business query record. Based on the query behavior risk score corresponding to the business query record and the sampling ratio of the record, samples to be inspected are extracted from the business query record; Extract the business voucher information corresponding to the sample to be inspected; Based on the business voucher information corresponding to the sample to be inspected, the compliance of the query behavior of the sample to be inspected is verified, and the record inspection result corresponding to the sample to be inspected is generated.

2. The quality inspection method for business query records as described in claim 1, characterized in that, The step of extracting the business voucher information corresponding to the sample to be inspected includes: Identify multiple records in the sample to be inspected; From the different business information databases corresponding to the business query records, extract the business voucher information corresponding to the multiple record items. The business voucher information includes the query subject's authorization information, the query subject's qualification information, and the supporting materials for the query reason.

3. The quality inspection method for business query records as described in claim 2, characterized in that, The process of verifying the compliance of the query behavior of the sample to be inspected based on the business voucher information corresponding to the sample to be inspected, and generating the record inspection result corresponding to the sample to be inspected, includes: Identify the voucher items corresponding to the multiple record items in the business voucher information; By comparing the consistency of information between the multiple record items and multiple voucher items, the compliance of the query behavior of the sample to be inspected is verified, and the information verification results of the multiple record items are generated. Based on the verification results of the information of the multiple record items, the record quality inspection results corresponding to the sample to be inspected are generated. The record quality inspection results include sampling information, quality inspection result statistics, and quality inspection problem details.

4. The quality inspection method for business query records as described in claim 3, characterized in that, The step of generating the record quality inspection result corresponding to the sample to be inspected based on the information verification results of the multiple record items includes: If the information verification result of the sample to be inspected is that there are inconsistent records, then the record quality inspection result of the sample to be inspected is determined to be that the quality inspection has failed. The quality inspection problem details are generated based on the inconsistent records, and a record review instruction is generated based on the quality inspection problem details. If the information verification result of the sample to be inspected is that there are no inconsistent records, then the record quality inspection result of the sample to be inspected is determined to be a pass. After generating the record review instruction based on the quality inspection issue details, the method further includes: Receive the record correction information corresponding to the quality inspection problem details; Based on the record correction information, the corrected business voucher information is generated. Based on the corrected business voucher information, the sample to be inspected is re-verified, and the record inspection result corresponding to the sample to be inspected is regenerated.

5. The quality inspection method for business query records as described in claim 1, characterized in that, The step of generating a query behavior risk score corresponding to the business query record based on the query scenario corresponding to the business query record includes: Based on the preset priority rules corresponding to the business query records, the priority of multiple record items in the business query records is determined. The preset priority rules are the query record quality inspection priorities preset according to different query behavior characteristics. Based on the priorities corresponding to the multiple record items, determine the priority-weighted score corresponding to the multiple record items; Based on the priority-weighted scores corresponding to the multiple record items, a query behavior risk score is generated for the business query record.

6. The quality inspection method for business query records as described in claim 5, characterized in that, The sampling ratio for obtaining the records corresponding to the business query records includes: The sampling configuration interface corresponding to the business query record can be used to obtain the sampling ratio of the record corresponding to the business query record. The sampling configuration interface is used to configure the sampling ratio of the record of different query subjects according to the different query subject types, and is also used to configure the preset priority rules corresponding to different record items in the business query record.

7. The quality inspection method for business query records as described in claim 1, characterized in that, The step of extracting samples for quality inspection from the business query records based on the query behavior risk score corresponding to the business query records and the record sampling ratio includes: Based on the query behavior risk score corresponding to the business query record, sort the business query record to generate sorted business query records; According to the sampling ratio of the records, the top-ranked samples to be inspected are extracted from the sorted business query records.

8. A quality inspection device for business query records, characterized in that, include: The acquisition module is used to obtain the record sampling ratio of business query records; The generation module is used to generate a query behavior risk score corresponding to the business query record based on the query scenario corresponding to the business query record. The extraction module is used to extract samples to be inspected from the business query records based on the query behavior risk score corresponding to the business query records and the record sampling ratio. The extraction module is used to extract the business voucher information corresponding to the sample to be inspected; The verification module is used to verify the compliance of the query behavior of the sample to be inspected based on the business voucher information corresponding to the sample to be inspected, and generate the record inspection result corresponding to the sample to be inspected.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the quality inspection method for business query records as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the quality inspection method for business query records as described in any one of claims 1 to 7.

Images