Data verification method and device, computer equipment and computer readable storage medium

Abstract

This application proposes a data verification method and apparatus, computer equipment, and computer-readable storage medium, the method comprising: obtaining basic product information and product risk association information of the inspected subject; determining a target verification rule associated with the inspected subject based on the basic product information of the inspected subject and a preset verification rule database; determining whether the product risk association information of the inspected subject matches the target verification rule; if the product risk association information of the inspected subject matches the target verification rule, determining that the inspected subject is a compliant subject; otherwise, determining that the inspected subject is a non-compliant subject. The technical solution of this application improves the convenience and accuracy of compliance inspections for property insurance business.

Description

Technical field

[0001] The present application relates to the field of data verification technology, and in particular to a data verification method and apparatus, a computer device, and a computer-readable storage medium. [Background Technology]

[0002] With the development of society, individual and corporate users' awareness of wealth protection has gradually increased, and a large number of businesses providing users with property protection certificates have emerged. To ensure the legality of such businesses, relevant national regulatory authorities have established a variety of restrictive rules, including laws, regulations, and rules. Currently, the compliance verification of such businesses mainly relies on manual review. Faced with businesses with different protection content, manual review is obviously difficult, time-consuming, and has a high error rate. At the same time, due to the diverse number and types of such businesses, manual review is limited by the professional level of the reviewer, and it is impossible to standardize and uniformly handle businesses of different types and contents. These issues all lead to difficult and complex supervision of businesses providing users with property protection certificates. These difficulties and complexities directly affect the management difficulty and service effectiveness of the business issuers.

[0003] Therefore, how to balance the regulatory rationality and convenience of the business of providing users with property protection certificates has become a technical problem that needs to be solved urgently. [Summary of the invention]

[0004] The embodiments of the present application provide a data verification method and apparatus, a computer device, and a computer-readable storage medium, which aim to solve the technical problem in the related art that a single regulatory means for the business of providing property protection certificates to users is difficult to meet the regulatory convenience requirements and regulatory rationality requirements of the business.

[0005] In a first aspect, an embodiment of the present application provides a data verification method, comprising:

[0006] Obtaining basic product information and product risk-related information of the entity being tested, where the entity being tested is a business that provides property insurance certificates to users. The basic product information includes the business's product category, sales channel, development method, operating region, and insurance liability type. The product risk-related information is information that may affect the business's property insurance capabilities.

[0007] Determine the target verification rule associated with the detected subject based on the basic product information of the detected subject and a preset verification rule database;

[0008] Determining whether the product risk association information of the detected subject matches the target verification rule;

[0009] If the product risk association information of the detected subject matches the target verification rule, the detected subject is determined to be a compliant subject; otherwise, the detected subject is determined to be a non-compliant subject.

[0010] In one embodiment of the present application, optionally, before obtaining the basic product information and product risk association information of the detected subject, the method further includes:

[0011] Based on the single initial rule information and the verification rule identification model in the initial rule information sample set, the corresponding verification rule of the single initial rule information is determined, and the corresponding verification rule is added to the verification rule database, wherein the verification rule identification model is used to reflect the association relationship between the single initial rule information and the corresponding verification rule in the historical valid rule verification task.

[0012] In one embodiment of the present application, optionally, determining a target verification rule associated with the detected subject based on the basic product information of the detected subject and a preset verification rule database includes:

[0013] Obtaining an entity set of the preset verification rule database;

[0014] Based on the basic product information of the detected subject, determining a target entity associated with the detected subject in the entity set;

[0015] The verification rule having the target entity is screened in the preset verification rule database as the target verification rule associated with the detected subject.

[0016] In one embodiment of the present application, optionally, determining a target verification rule associated with the detected subject based on the basic product information of the detected subject and a preset verification rule database includes:

[0017] Determine search keywords based on the basic product information of the detected subject;

[0018] Determining valid rules for the search keyword hits in the preset verification rule database;

[0019] Determining entities and inter-entity relationships in the effective rule based on the effective rule and a preset large language model, wherein the preset large language model is used to reflect the association relationship between the description text of the effective rule and the entities and inter-entity relationships in the effective rule;

[0020] The target verification rules are generated based on the entities and the relationships between entities in the effective rules.

[0021] In one embodiment of the present application, optionally, the preset verification rule database includes completeness rules, compliance rules and early warning rules.

[0022] Before obtaining the basic product information and product risk association information of the subject to be tested, the method further includes:

[0023] Obtaining the verification requirements of the detected entity, wherein the verification requirements are used to reflect the type of verification that the detected entity needs to undergo, and the verification type includes one or more of completeness verification, compliance verification, and early warning verification;

[0024] The determining of the target verification rule associated with the detected subject based on the basic product information of the detected subject and a preset verification rule database includes:

[0025] A target verification rule that is associated with the detected subject and compatible with the verification requirement is selected from the preset verification rule database.

[0026] In one embodiment of the present application, optionally, determining whether the product risk association information of the detected subject matches the target verification rule includes at least one of the following detection methods:

[0027] Determining whether the value in the product risk association information matches the value range restriction information in the target verification rule;

[0028] Determining whether the segmented words in the product risk association information match all the keywords in the target verification rules;

[0029] Detecting whether the product risk association information contains designated signature information through OCR recognition;

[0030] Check whether the product risk association information includes a specified file.

[0031] In one embodiment of the present application, optionally, the target verification rule includes that if the comprehensive risk score of the product of the detected subject is less than or equal to a predetermined threshold, then

[0032] The determining whether the product risk association information of the detected subject matches the target verification rule includes:

[0033] Determining a first risk parameter corresponding to each item of product risk association information of the detected subject;

[0034] Determining, based on the first risk parameter, a second risk parameter corresponding to each plurality of product risk association information of the detected subject;

[0035] Constructing a risk matrix based on a risk parameter sequence obtained by arranging the first risk parameter and the second risk parameter from largest to smallest, wherein the risk matrix is used to reflect the risk level distribution of the inspected subject under the individual influence and interactive influence of each product risk association information;

[0036] Determining a comprehensive risk score of the product of the inspected subject based on the risk matrix and the risk parameter sequence;

[0037] If the comprehensive risk score of the product is less than or equal to the predetermined threshold, it is determined that the product risk association information of the detected subject matches the target verification rule; otherwise, it is determined that the product risk association information of the detected subject does not match the target verification rule.

[0038] In one embodiment of the present application, optionally, constructing a risk matrix based on a risk parameter sequence obtained by arranging the first risk parameter and the second risk parameter from large to small includes:

[0039] The element in the first designated row and the first designated column of the risk matrix is set to be the product of the risk parameter of the first designated order and the risk parameter of the second designated order in the risk parameter sequence.

[0040] In one embodiment of the present application, optionally, determining the comprehensive risk score of the product of the inspected subject based on the risk matrix and the risk parameter sequence includes:

[0041] Obtaining the rank of the risk matrix as a third risk parameter;

[0042] Obtaining the variance of the risk parameter sequence as a fourth risk parameter;

[0043] The third risk parameter and the fourth risk parameter are normalized, and the results of the normalization are averaged, and the average is used as the comprehensive risk score of the product of the detected subject.

[0044] In a second aspect, an embodiment of the present application provides a data verification device, characterized by comprising:

[0045] an information acquisition unit, configured to acquire basic product information and product risk-related information of a detected entity, wherein the detected entity is a business that provides property insurance certificates to users, the basic product information includes the product category, sales channel, development method, operating area, and insurance liability type of the business, and the product risk-related information is information that affects the property insurance capability of the business;

[0046] A verification rule determination unit, configured to determine a target verification rule associated with the detected subject based on the basic product information of the detected subject and a preset verification rule database;

[0047] A verification rule matching unit, configured to determine whether the product risk association information of the detected subject matches the target verification rule;

[0048] The compliance subject determination unit is used to determine that the detected subject is a compliant subject if the product risk association information of the detected subject matches the target verification rule; otherwise, determine that the detected subject is a non-compliant subject.

[0049] In one embodiment of the present application, optionally, the device further includes:

[0050] A verification rule database setting unit is used to determine the corresponding verification rule of the single initial rule information based on the single initial rule information and the verification rule identification model in the initial rule information sample set before obtaining the basic product information and product risk association information of the inspected subject, and add the corresponding verification rule to the verification rule database, wherein the verification rule identification model is used to reflect the association relationship between the single initial rule information and the corresponding verification rule in the historical valid rule verification task.

[0051] In one embodiment of the present application, optionally, the verification rule determination unit includes:

[0052] The first execution unit is used to obtain an entity set of the preset verification rule database; based on the basic product information of the detected subject, determine a target entity associated with the detected subject in the entity set; and filter the verification rules with the target entity in the preset verification rule database as the target verification rules associated with the detected subject.

[0053] In one embodiment of the present application, optionally, the verification rule determination unit includes:

[0054] The second execution unit is used to determine the search keywords based on the basic product information of the detected subject; determine the valid rules hit by the search keywords in the preset verification rule database; determine the entities and inter-entity relationships in the valid rules based on the valid rules and a preset large language model, wherein the preset large language model is used to reflect the association relationship between the description text of the valid rules and the entities and inter-entity relationships in the valid rules; generate the target verification rules based on the entities and inter-entity relationships in the valid rules.

[0055] In one embodiment of the present application, optionally, the preset verification rule database includes completeness rules, compliance rules, and early warning rules, and the device further includes:

[0056] a verification requirement acquisition unit, configured to acquire the verification requirement of the detected subject, wherein the verification requirement is used to reflect the type of verification required by the detected subject, and the verification type includes one or more of completeness verification, compliance verification, and early warning verification;

[0057] The verification rule determination unit is used to select a target verification rule from the preset verification rule database that is associated with the detected subject and is compatible with the verification requirement.

[0058] In one embodiment of the present application, optionally, the verification rule matching unit is configured to perform at least one of the following detection methods:

[0059] Determine whether the numerical value in the product risk association information matches the numerical range restriction information in the target verification rule; determine whether the segmented words in the product risk association information hit all the keywords in the target verification rule; detect whether the product risk association information contains specified signature information through OCR recognition; detect whether the product risk association information includes specified files.

[0060] In one embodiment of the present application, optionally, the target verification rule includes that the comprehensive risk score of the product of the detected subject is less than or equal to a predetermined threshold, and the verification rule matching unit includes:

[0061] A first risk parameter determination unit, configured to determine a first risk parameter corresponding to each item of product risk association information of the detected subject;

[0062] a second risk parameter determining unit, configured to determine, based on the first risk parameter, a second risk parameter corresponding to each plurality of product risk association information of the detected subject;

[0063] a matrix construction unit, configured to construct a risk matrix based on a risk parameter sequence obtained by arranging the first risk parameter and the second risk parameter from largest to smallest, wherein the risk matrix is configured to reflect the risk level distribution of the inspected subject under the individual influence and interactive influence of each product risk association information;

[0064] a risk score calculation unit, configured to determine a comprehensive risk score of the product of the inspected subject based on the risk matrix and the risk parameter sequence;

[0065] The third execution unit is used to determine that the product risk association information of the detected subject matches the target verification rule if the comprehensive risk score of the product is less than or equal to the predetermined threshold; otherwise, it is used to determine that the product risk association information of the detected subject does not match the target verification rule.

[0066] In one embodiment of the present application, optionally, the matrix construction unit is used to: set the elements of the first specified row and the first specified column in the risk matrix to be the product of the risk parameter of the first specified order and the risk parameter of the second specified order in the risk parameter sequence.

[0067] In one embodiment of the present application, optionally, the risk score calculation unit is used to: obtain the rank of the risk matrix as a third risk parameter; obtain the variance of the risk parameter sequence as a fourth risk parameter; normalize the third risk parameter and the fourth risk parameter, and calculate the average of the normalized results, and use the average as the comprehensive risk score of the product of the inspected subject.

[0068] In a third aspect, an embodiment of the present application provides a computer device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are configured to execute the method described in the first aspect above.

[0069] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute the method described in the first aspect above.

[0070] The above technical solution addresses the technical problem in related technologies that the single regulatory means for businesses that provide property insurance certificates to users cannot meet the regulatory convenience and rationality requirements of such businesses. For businesses that provide property insurance certificates to users, the applicable target verification rules can be automatically screened based on their basic product information, and then the product risk-related information can be automatically checked to see if it matches the target verification rules, thereby achieving automated judgment on whether the business is compliant. Through this technical solution, the labor cost and time cost of compliance inspections for property insurance business are reduced, and the convenience and accuracy of compliance inspections for property insurance business are improved. While reducing the management costs of business managers, it can also effectively enhance the effectiveness of compliance reviews for property insurance business, indirectly improving the experience of policyholders and more safely and effectively protecting their property rights and interests.

Brief Description of the Drawings

[0071] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0072] Figure 1 A flow chart of a data verification method according to an embodiment of the present application is shown;

[0073] Figure 2 A block diagram of a computer device according to an embodiment of the present application is shown;

[0074] Figure 3 A block diagram of a computer device according to another embodiment of the present application is shown. [Specific implementation method]

[0075] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0076] Figure 1 A flow chart of a data verification method according to an embodiment of the present application is shown.

[0077] like Figure 1 As shown, a data verification method according to an embodiment of the present application includes:

[0078] Step 102: Obtain basic product information and product risk association information of the subject being tested.

[0079] Among them, the detected entity is a business that provides property protection certificates to users, including but not limited to property insurance for various property guarantee needs such as property loss insurance, credit guarantee insurance, and liability insurance.

[0080] The basic product information describes the basic information of the business that provides property protection certificates to users, including the product category, sales channel, development method, operating area and insurance liability type of the business. For example, the product category of property insurance is transportation insurance under property damage insurance, the sales channel is online direct sales and online intermediaries, the development method is independent development by the insurance provider, the business scope is Shanghai, and the insurance liability types include transportation safety services and renewal liability.

[0081] Product risk-related information is information that impacts the property protection capabilities of the business, or in other words, information that may reduce the risk of the property protection capabilities provided by the business. Specifically, this product risk-related information includes, but is not limited to, the proportion of the principal service within the insurance to net premiums, the waiting period, the lowest comprehensive solvency ratio for the most recent four quarters, the lowest core solvency ratio for the most recent four quarters, the lowest liability reserve coverage ratio for the most recent four quarters, the lowest comprehensive risk rating for the most recent four quarters, and the insurance provider's corporate governance assessment rating.

[0082] Step 104 : determining a target verification rule associated with the detected subject based on the basic product information of the detected subject and a preset verification rule database.

[0083] The basic product information describes the basic information of the business that provides property protection certificates to users. These basic information can reflect the degree of standardization of the business and can therefore be used as the basic conditions for screening the standards that the business should comply with. Furthermore, the standards that the business should comply with are the target verification rules associated with the subject being tested. The preset verification rule database includes any form of normative documents such as laws, regulations, ordinances, etc. issued by administrative entities at all levels, or verification rules obtained through preliminary screening in any form of normative documents such as laws, regulations, ordinances, etc. issued by administrative entities at all levels, covering all rules that should be followed by businesses that provide property protection certificates to users. On this basis, the target verification rules that are associated with the basic product information of the subject being tested can be searched in the database as the standards that the subject being tested should comply with and need to be verified.

[0084] In one possible design, step 104 includes: obtaining an entity set of the preset verification rule database; determining a target entity associated with the detected subject in the entity set based on the basic product information of the detected subject; and screening the verification rules with the target entity in the preset verification rule database as the target verification rules associated with the detected subject.

[0085] An entity refers to a text body used to describe information of any dimension, such as a name, action, organization, expression, quantity, etc. The following is an example of extracting entities from the preset verification rule database.

[0086] For example, the preset verification rule database includes the "Regulations on the Administration of Insurance Terms and Premium Rates of Property Insurance Companies" promulgated by the China Banking and Insurance Regulatory Commission Order No. 10 of 2021. Article 16 of this document states that when property insurance companies submit insurance terms and premium rates for approval or filing, they shall submit the following materials: (2) Insurance terms and premium rate texts. Among them, the name entities that can be extracted are "insurance terms" and "insurance premium rate texts." Furthermore, if the verification of the inspected entity is because the property insurance company submits insurance terms and premium rates for approval or filing for the inspected entity, then the insurance terms and premium rate texts are the target entities associated with the inspected entity. At this point, the verification rule describing the insurance terms and premium rate texts, namely "When property insurance companies submit insurance terms and premium rates for approval or filing, they shall submit the following materials: (2) Insurance terms and premium rate texts" can be set as the target verification rule associated with the inspected entity.

[0087] In another possible design, step 104 includes: determining the search keyword based on the basic product information of the inspected subject; determining the valid rules hit by the search keyword in the preset verification rule database; determining the entities and inter-entity relationships in the valid rules based on the valid rules and a preset large language model, wherein the preset large language model is used to reflect the association between the description text of the valid rule and the entities and inter-entity relationships in the valid rule; generating the target verification rule based on the entities and inter-entity relationships in the valid rule.

[0088] Specifically, corresponding search keywords can be preset for different types of businesses. These search keywords include, but are not limited to, descriptions of the approval content of any approval process for the business. Using search keywords to find valid rules in the preset verification rule database is a preliminary screening of the rules. Then, the valid rules from the preliminary screening are directly input into the large language model, which automatically outputs the entities and inter-entity relationships in the valid rules as the basis for secondary screening of rules. The large language model includes any model that can understand and generate human language and can be trained through open source.

[0089] For example, after hitting the valid rule "Property insurance companies submitting insurance terms and insurance premiums for approval or filing shall submit the following materials: (2) Insurance terms and insurance premium texts" in the preset verification rule database, the valid rule and the automatically set prefix "Extract entities and inter-entity relationships within the rule" can be directly input into the large language model, so that the large language model outputs the name entities "Property Insurance Company", "Insurance Terms" and "Insurance Premium Text", as well as the behavior entity "Submitted", and the inter-entity relationship between them is that the property insurance company has submitted the insurance terms and insurance premium texts. At this point, based on these entities and inter-entity relationships, it can be determined that the target verification rule corresponding to the valid rule is "Insurance terms and insurance premium texts have been submitted."

[0090] In another possible design, before executing the steps of this technical solution, it is necessary to first construct a verification rule database. In this case, the specific method of constructing the verification rule database is: based on the individual initial rule information in the initial rule information sample set and the verification rule recognition model, determine the verification rule corresponding to the individual initial rule information, and add the corresponding verification rule to the verification rule database.

[0091] Specifically, among the historical tasks, historical valid rule verification tasks whose verification results are compliance, completeness, and controllable risks are selected as samples for training the verification rule identification model, wherein the input samples used for training the verification rule identification model are the single initial rule information involved in the historical valid rule verification tasks, and the output samples used for training the verification rule identification model are the corresponding verification rules associated with the single initial rule information involved in the historical valid rule verification tasks. In this way, the verification rule identification model actually reflects the association between the single initial rule information and the corresponding verification rules in the historical valid rule verification tasks. Therefore, for each single initial rule information in the initial rule information sample set, its corresponding verification rule can be obtained through the verification rule identification model, and then the corresponding verification rules of all the single initial rule information in the initial rule information sample set are added to the verification rule database, completing the preset of the verification rule database.

[0092] In this case, the pre-set verification rule database stores more precise verification rules derived from preliminary identification of any regulatory documents, such as laws, regulations, and ordinances, issued by administrative entities at all levels. This reduces redundant information within the verification rules during subsequent matching, helping to more quickly obtain more accurate verification results.

[0093] Step 106: Determine whether the product risk association information of the detected subject matches the target verification rule.

[0094] Optionally, when using target verification rules, the entities within the target verification rules can be encoded according to a preset coding method. For example, the insurance terms and insurance premium texts correspond to the preset codes a012 and a013 respectively, and the action of submitting corresponds to the preset code b01. Thus, all target verification rules can be converted into a coded form in combination with the preset coding table. In the subsequent rule verification process, it can be checked whether the coding obtained by converting the product risk association information of the inspected subject according to the preset coding table is consistent with the coding of the corresponding target verification rule. If they are consistent, it means that it has passed the rule verification; otherwise, it means that it has not passed the rule verification. Thus, the convenience of rule verification is increased.

[0095] In one possible design, step 106 includes at least one of the following detection methods.

[0096] First, it can determine whether the value in the product risk association information matches the value range restriction information in the target verification rule. If the value is within the value range specified by the value range restriction information in the target verification rule, it is determined that the product risk association information of the detected subject matches the target verification rule. Otherwise, it is determined that the product risk association information of the detected subject does not match the target verification rule.

[0097] Secondly, it can be determined whether the segmented words in the product risk association information match all the keywords in the target verification rule. If so, it means that the product risk association information includes the main content of the target verification rule. In this case, it can be determined that the product risk association information of the detected subject matches the target verification rule. Otherwise, it is determined that the product risk association information of the detected subject does not match the target verification rule.

[0098] Third, OCR recognition can be used to detect whether the product risk association information contains designated signature information. For businesses that provide property protection certificates to users, the product risk association information must be endorsed by a designated manager, and the designated signature information is the signature of the designated manager or a signature indicating approval. Therefore, whether the product risk association information is compliant can be determined by identifying whether the product risk association information contains designated signature information. At this point, if it is detected that the product risk association information contains designated signature information, it means that the product risk association information of the detected subject matches the target verification rules. Otherwise, it is determined that the product risk association information of the detected subject does not match the target verification rules.

[0099] Fourth, the product risk association information is tested to determine whether designated documents are included. This test method is intended to determine whether the product risk association information of the inspected entity is complete, that is, whether all the documents required for verification have been submitted. If the product risk association information includes designated documents, it indicates that the product risk association information of the inspected entity matches the target verification rules. Otherwise, it is determined that the product risk association information of the inspected entity does not match the target verification rules.

[0100] In one possible design, target verification rules can be converted into regular expressions. For example, "Insurance terms and premium rates have been submitted" can be converted into "`Product.term_element_table_path`match`\S`." This allows target verification rules to be described using regular expressions, a standardized statement, making rule verification more convenient.

[0101] Step 108 : If the product risk association information of the detected subject matches the target verification rule, the detected subject is determined to be a compliant subject; otherwise, the detected subject is determined to be a non-compliant subject.

[0102] At this point, if the two match, it means that the subject being tested meets the requirements of the target verification rules and is a compliant subject. Conversely, if the two do not match, it means that the subject being tested does not meet the requirements of the target verification rules and is a non-compliant subject. As shown in the example in Table 1 below, for the tested subjects xxxx Property and Casualty Insurance Co., Ltd. Beijing Middle-aged and Elderly Hospitalization Medical Insurance (Internet Exclusive) and xxxx Property and Casualty Insurance Co., Ltd. Beijing Middle-aged and Elderly Hospitalization Medical Insurance, the distribution of multiple product risk-related information and their corresponding verification results is as follows. In the end, xxxx Property and Casualty Insurance Co., Ltd. Beijing Middle-aged and Elderly Hospitalization Medical Insurance (Internet Exclusive) was judged as a compliant subject, and xxxx Property and Casualty Insurance Co., Ltd. Beijing Middle-aged and Elderly Hospitalization Medical Insurance was judged as a non-compliant subject.

[0103] Table 1

[0104]

[0105] The above technical solution can automatically screen applicable target verification rules based on the basic product information of businesses that provide property insurance certificates to users, and then automatically verify whether the product risk-related information matches the target verification rules, thereby realizing automated judgment on whether the business is compliant. Through this technical solution, the labor cost and time cost of compliance verification of property insurance business are reduced, and the convenience and accuracy of compliance verification of property insurance business are improved. While reducing the management costs of business managers, it can also effectively enhance the effectiveness of compliance review of property insurance business, indirectly improve the experience of insured users, and more safely and effectively protect their property rights and interests.

[0106] In one embodiment of the present application, the preset verification rule database includes completeness rules, compliance rules and early warning rules, then before step 102, it also includes: obtaining the verification requirements of the detected subject, wherein the verification requirements are used to reflect the type of verification that the detected subject needs to perform, and the verification type includes one or more of completeness verification, compliance verification and early warning verification; step 104 includes: selecting a target verification rule in the preset verification rule database that is associated with the detected subject and compatible with the verification requirements.

[0107] In other words, the pre-set verification rule database can be divided into different types according to the verification requirements of the business. When faced with different verification requirements, the target verification rules can be screened only under the rule types that meet the verification requirements. This reduces the base number of rules to be screened, making the rule screening easier and simplifying the overall compliance review process.

[0108] In addition, the target verification rule includes that the comprehensive product risk score of the inspected subject is less than or equal to a predetermined threshold, then step 106 includes: determining a first risk parameter corresponding to each item of product risk association information of the inspected subject; based on the first risk parameter, determining a second risk parameter corresponding to each item of product risk association information of the inspected subject; constructing a risk matrix based on a risk parameter sequence obtained by arranging the first risk parameter and the second risk parameter from large to small, wherein the risk matrix is used to reflect the risk level distribution of the inspected subject under the individual influence and interactive influence of each product risk association information; determining the comprehensive product risk score of the inspected subject based on the risk matrix and the risk parameter sequence; if the comprehensive product risk score is less than or equal to the predetermined threshold, determining that the product risk association information of the inspected subject matches the target verification rule; otherwise, determining that the product risk association information of the inspected subject does not match the target verification rule.

[0109] Specifically, each product risk association information of the detected subject is encoded to obtain a first risk parameter. Optionally, the first risk parameter can be normalized so that the values of each dimension are at the same level to facilitate subsequent calculation and processing.

[0110] Next, a second risk parameter corresponding to each of the multiple pieces of product risk association information for the inspected subject can be determined. Alternatively, the mean of the first risk parameters of the multiple pieces of product risk association information can be set as the second risk parameter corresponding to the multiple pieces of product risk association information. Alternatively, the square root of the product of the first risk parameters of the multiple pieces of product risk association information can be set as the second risk parameter corresponding to the multiple pieces of product risk association information. The second risk parameter reflects the comprehensive impact of the multiple pieces of product risk association information on the inspected subject.

[0111] At this point, a risk matrix is constructed based on a risk parameter sequence obtained by arranging the first risk parameter and the second risk parameter from large to small.

[0112] Specifically, the element in the first designated row and first designated column of the risk matrix is set to the product of the risk parameter of the first designated order and the risk parameter of the second designated order in the risk parameter sequence. This element then reflects the risk level of the subject under test under the combined influence of the product risk association information corresponding to the risk parameter of the first designated order and the multiple product risk association information corresponding to the risk parameter of the second designated order. The risk matrix reflects the distribution of the risk level of the subject under test under the individual influence and the interactive influence of each product risk association information.

[0113] Finally, based on the risk matrix and the risk parameter sequence, a comprehensive product risk score for the inspected entity is determined. If the comprehensive product risk score is less than or equal to the predetermined threshold, it is determined that the product risk association information of the inspected entity matches the target verification rule; otherwise, it is determined that the product risk association information of the inspected entity does not match the target verification rule.

[0114] The specific method for determining the comprehensive product risk score of the inspected entity is as follows: obtaining the rank of the risk matrix as the third risk parameter; obtaining the variance of the risk parameter sequence as the fourth risk parameter; normalizing the third and fourth risk parameters, averaging the normalized results, and using the average as the comprehensive product risk score of the inspected entity. Thus, the comprehensive product risk score reflects the risk assessment of the inspected entity, taking into account the risk levels represented by the risk matrix and the risk parameter sequence.

[0115] The above technical solution can analyze the risk level of the inspected entity in a multi-dimensional and multi-directional manner based on multiple product risk-related information of the inspected entity, thereby improving the accuracy of risk detection of the inspected entity, providing further guarantees for the compliance review of property insurance, and helping to protect the property safety of property insurance customers.

[0116] The present invention provides a data verification device, which includes:

[0117] an information acquisition unit, configured to acquire basic product information and product risk-related information of a detected entity, wherein the detected entity is a business that provides property insurance certificates to users, the basic product information includes the product category, sales channel, development method, operating area, and insurance liability type of the business, and the product risk-related information is information that affects the property insurance capability of the business;

[0118] A verification rule determination unit, configured to determine a target verification rule associated with the detected subject based on the basic product information of the detected subject and a preset verification rule database;

[0119] A verification rule matching unit, configured to determine whether the product risk association information of the detected subject matches the target verification rule;

[0120] The compliance subject determination unit is used to determine that the detected subject is a compliant subject if the product risk association information of the detected subject matches the target verification rule; otherwise, determine that the detected subject is a non-compliant subject.

[0121] In one embodiment of the present application, optionally, the device further includes:

[0122] A verification rule database setting unit is used to determine the corresponding verification rule of the single initial rule information based on the single initial rule information and the verification rule identification model in the initial rule information sample set before obtaining the basic product information and product risk association information of the inspected subject, and add the corresponding verification rule to the verification rule database, wherein the verification rule identification model is used to reflect the association relationship between the single initial rule information and the corresponding verification rule in the historical valid rule verification task.

[0123] In one embodiment of the present application, optionally, the verification rule determination unit includes:

[0124] The first execution unit is used to obtain an entity set of the preset verification rule database; based on the basic product information of the detected subject, determine a target entity associated with the detected subject in the entity set; and filter the verification rules with the target entity in the preset verification rule database as the target verification rules associated with the detected subject.

[0125] In one embodiment of the present application, optionally, the verification rule determination unit includes:

[0126] The second execution unit is used to determine the search keywords based on the basic product information of the detected subject; determine the valid rules hit by the search keywords in the preset verification rule database; determine the entities and inter-entity relationships in the valid rules based on the valid rules and a preset large language model, wherein the preset large language model is used to reflect the association relationship between the description text of the valid rules and the entities and inter-entity relationships in the valid rules; generate the target verification rules based on the entities and inter-entity relationships in the valid rules.

[0127] In one embodiment of the present application, optionally, the preset verification rule database includes completeness rules, compliance rules, and early warning rules, and the device further includes:

[0128] a verification requirement acquisition unit, configured to acquire the verification requirement of the detected subject, wherein the verification requirement is used to reflect the type of verification required by the detected subject, and the verification type includes one or more of completeness verification, compliance verification, and early warning verification;

[0129] The verification rule determination unit is used to select a target verification rule from the preset verification rule database that is associated with the detected subject and is compatible with the verification requirement.

[0130] In one embodiment of the present application, optionally, the verification rule matching unit is configured to perform at least one of the following detection methods:

[0131] Determine whether the numerical value in the product risk association information matches the numerical range restriction information in the target verification rule; determine whether the segmented words in the product risk association information hit all the keywords in the target verification rule; detect whether the product risk association information contains specified signature information through OCR recognition; detect whether the product risk association information includes specified files.

[0132] In one embodiment of the present application, optionally, the target verification rule includes that the comprehensive risk score of the product of the detected subject is less than or equal to a predetermined threshold, and the verification rule matching unit includes:

[0133] A first risk parameter determination unit, configured to determine a first risk parameter corresponding to each item of product risk association information of the detected subject;

[0134] a second risk parameter determining unit, configured to determine, based on the first risk parameter, a second risk parameter corresponding to each plurality of product risk association information of the detected subject;

[0135] a matrix construction unit, configured to construct a risk matrix based on a risk parameter sequence obtained by arranging the first risk parameter and the second risk parameter from largest to smallest, wherein the risk matrix is configured to reflect the risk level distribution of the inspected subject under the individual influence and interactive influence of each product risk association information;

[0136] a risk score calculation unit, configured to determine a comprehensive risk score of the product of the inspected subject based on the risk matrix and the risk parameter sequence;

[0137] The third execution unit is used to determine that the product risk association information of the detected subject matches the target verification rule if the comprehensive risk score of the product is less than or equal to the predetermined threshold; otherwise, it is used to determine that the product risk association information of the detected subject does not match the target verification rule.

[0138] In one embodiment of the present application, optionally, the matrix construction unit is used to: set the elements of the first specified row and the first specified column in the risk matrix to be the product of the risk parameter of the first specified order and the risk parameter of the second specified order in the risk parameter sequence.

[0139] In one embodiment of the present application, optionally, the risk score calculation unit is used to: obtain the rank of the risk matrix as a third risk parameter; obtain the variance of the risk parameter sequence as a fourth risk parameter; normalize the third risk parameter and the fourth risk parameter, and calculate the average of the normalized results, and use the average as the comprehensive risk score of the product of the inspected subject.

[0140] The device uses any one of the solutions in the above embodiments, and therefore has all the above technical effects, which will not be described in detail here.

[0141] In addition, in one embodiment, the present application provides a computer device, which may be a server, and its internal structure diagram may be as follows: Figure 2As shown. The computer device includes a processor, memory, network interface and database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external client via a network connection. When the computer program is executed by the processor, it can implement the method described in any of the above embodiments.

[0142] In one embodiment, the present application further provides a computer device, which may be a client, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, memory, a network interface, a display screen, and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external server via a network connection. When executed by the processor, the computer program can implement the method described in any of the above embodiments.

[0143] Any of the aforementioned computer devices in the embodiments of the present application may exist in various forms, including but not limited to:

[0144] (1) Mobile communication devices: These devices are characterized by their mobile communication capabilities and are primarily designed to provide voice and data communications. These terminals include smartphones (e.g., iPhones), multimedia phones, feature phones, and low-end phones.

[0145] (2) Ultra-mobile personal computer devices: These devices fall under the category of personal computers, have computing and processing capabilities, and generally also have mobile Internet access. These terminals include PDAs, MIDs, and UMPCs, such as the iPad.

[0146] (3) Portable entertainment devices: These devices can display and play multimedia content. These devices include audio and video players (such as iPods), handheld game consoles, e-books, as well as smart toys, wearable devices, and portable car navigation devices.

[0147] (4) Server: A device that provides computing services. The server consists of a processor, hard disk, memory, system bus, etc. The server is similar to a general computer architecture, but because it needs to provide highly reliable services, it has higher requirements in terms of processing power, stability, reliability, security, scalability, and manageability.

[0148] (5) Other electronic devices with data interaction functions.

[0149] In addition, an embodiment of the present application provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to perform the following steps:

[0150] Obtaining basic product information and product risk-related information of the entity being tested, where the entity being tested is a business that provides property insurance certificates to users. The basic product information includes the business's product category, sales channel, development method, operating region, and insurance liability type. The product risk-related information is information that may affect the business's property insurance capabilities.

[0151] Determine the target verification rule associated with the detected subject based on the basic product information of the detected subject and a preset verification rule database;

[0152] Determining whether the product risk association information of the detected subject matches the target verification rule;

[0153] If the product risk association information of the detected subject matches the target verification rule, the detected subject is determined to be a compliant subject; otherwise, the detected subject is determined to be a non-compliant subject.

[0154] It should be noted that the above functions or steps that can be implemented by the computer-readable storage medium or computer device can refer to the relevant description in the aforementioned method embodiment. To avoid repetition, they will not be described one by one here.

[0155] The above describes the technical solution of the present application in detail in conjunction with the accompanying drawings. Through the technical solution of the present application, for businesses that provide property insurance certificates to users, the applicable target verification rules can be automatically screened based on their basic product information, and then their product risk-related information can be automatically checked to see if it matches the target verification rules, thereby achieving automated judgment on whether the business is compliant. Through this technical solution, the labor cost and time cost of compliance inspections for property insurance business are reduced, and the convenience and accuracy of compliance inspections for property insurance business are improved. While reducing the management costs of business managers, it can also effectively enhance the effectiveness of compliance reviews of property insurance business, indirectly improving the experience of policyholders and more safely and effectively protecting their property rights and interests.

[0156] It should be understood that although the terms "first," "second," etc. may be used to describe risk parameters in the embodiments of this application, these risk parameters should not be limited to these terms. These terms are merely used to distinguish one risk parameter from another. For example, without departing from the scope of the embodiments of this application, a first risk parameter may also be referred to as a second risk parameter, and similarly, a second risk parameter may also be referred to as a first risk parameter.

[0157] The word "if," as used herein, may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to the determination" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)," depending on the context.

[0158] The terms used in the embodiments of the present application are for the purpose of describing specific embodiments only and are not intended to limit the present application. The singular forms "a", "an", "the" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise.

[0159] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed may be through some interface, indirect coupling or communication connection of the device or unit, which may be electrical, mechanical or other forms.

[0160] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0161] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can 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), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0162] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A data verification method, characterized in that: include: Obtaining basic product information and product risk-related information of the entity being tested, where the entity being tested is a business that provides property insurance certificates to users. The basic product information includes the business's product category, sales channel, development method, operating region, and insurance liability type. The product risk-related information is information that may affect the business's property insurance capabilities. Determine the target verification rule associated with the detected subject based on the basic product information of the detected subject and a preset verification rule database; Determining whether the product risk association information of the detected subject matches the target verification rule; If the product risk association information of the detected subject matches the target verification rule, the detected subject is determined to be a compliant subject; otherwise, the detected subject is determined to be a non-compliant subject; The target verification rule includes that the comprehensive risk score of the product of the detected subject is less than or equal to a predetermined threshold, then The determining whether the product risk association information of the detected subject matches the target verification rule includes: Determining a first risk parameter corresponding to each item of product risk association information of the detected subject; Determining, based on the first risk parameter, a second risk parameter corresponding to each plurality of product risk association information of the detected subject; Constructing a risk matrix based on a risk parameter sequence obtained by arranging the first risk parameter and the second risk parameter from largest to smallest, wherein the risk matrix is used to reflect the risk level distribution of the inspected subject under the individual influence and interactive influence of each product risk association information; Determining a comprehensive risk score of the product of the inspected subject based on the risk matrix and the risk parameter sequence; If the comprehensive product risk score is less than or equal to the predetermined threshold, it is determined that the product risk association information of the detected subject matches the target verification rule; otherwise, it is determined that the product risk association information of the detected subject does not match the target verification rule; The risk parameter sequence obtained by arranging the first risk parameter and the second risk parameter from large to small is constructed to form a risk matrix, including: Setting the element in the first designated row and the first designated column in the risk matrix to be the product of the risk parameter of the first designated order and the risk parameter of the second designated order in the risk parameter sequence; Determining the comprehensive risk score of the product of the inspected subject based on the risk matrix and the risk parameter sequence includes: Obtaining the rank of the risk matrix as a third risk parameter; Obtaining the variance of the risk parameter sequence as a fourth risk parameter; The third risk parameter and the fourth risk parameter are normalized, and the results of the normalization are averaged, and the average is used as the comprehensive risk score of the product of the detected subject.

2. The data verification method according to claim 1, characterized in that: Before obtaining the basic product information and product risk association information of the subject to be tested, the method further includes: Based on the single initial rule information and the verification rule identification model in the initial rule information sample set, the corresponding verification rule of the single initial rule information is determined, and the corresponding verification rule is added to the verification rule database, wherein the verification rule identification model is used to reflect the association relationship between the single initial rule information and the corresponding verification rule in the historical valid rule verification task.

3. The data verification method according to claim 1, characterized in that: The determining of the target verification rule associated with the detected subject based on the basic product information of the detected subject and a preset verification rule database includes: Obtaining an entity set of the preset verification rule database; Based on the basic product information of the detected subject, determining a target entity associated with the detected subject in the entity set; The verification rule having the target entity is screened in the preset verification rule database as the target verification rule associated with the detected subject.

4. The data verification method according to claim 1, characterized in that: The determining of the target verification rule associated with the detected subject based on the basic product information of the detected subject and a preset verification rule database includes: Determine search keywords based on the basic product information of the detected subject; Determining valid rules for the search keyword hits in the preset verification rule database; Determining entities and inter-entity relationships in the effective rule based on the effective rule and a preset large language model, wherein the preset large language model is used to reflect the association relationship between the description text of the effective rule and the entities and inter-entity relationships in the effective rule; The target verification rules are generated based on the entities and the relationships between entities in the effective rules.

5. The data verification method according to any one of claims 1 to 4, characterized in that: The preset verification rule database includes completeness rules, compliance rules and early warning rules. Before obtaining the basic product information and product risk association information of the subject to be tested, the method further includes: Obtaining the verification requirements of the detected entity, wherein the verification requirements are used to reflect the type of verification that the detected entity needs to undergo, and the verification type includes one or more of completeness verification, compliance verification, and early warning verification; The determining of the target verification rule associated with the detected subject based on the basic product information of the detected subject and a preset verification rule database includes: A target verification rule that is associated with the detected subject and compatible with the verification requirement is selected from the preset verification rule database.

6. The data verification method according to claim 1, characterized in that: The determining whether the product risk association information of the detected subject matches the target verification rule includes at least one of the following detection methods: Determining whether the value in the product risk association information matches the value range restriction information in the target verification rule; Determining whether the segmented words in the product risk association information match all the keywords in the target verification rules; Detecting whether the product risk association information contains designated signature information through OCR recognition; Check whether the product risk association information includes a specified file.

7. A data verification device, characterized in that: include: an information acquisition unit, configured to acquire basic product information and product risk-related information of a detected entity, wherein the detected entity is a business that provides property insurance certificates to users, the basic product information includes the product category, sales channel, development method, operating area, and insurance liability type of the business, and the product risk-related information is information that affects the property insurance capability of the business; A verification rule determination unit, configured to determine a target verification rule associated with the detected subject based on the basic product information of the detected subject and a preset verification rule database; A verification rule matching unit, configured to determine whether the product risk association information of the detected subject matches the target verification rule; a compliance subject determination unit, configured to determine that the detected subject is a compliant subject if the product risk association information of the detected subject matches the target verification rule; otherwise, determine that the detected subject is a non-compliant subject; The target verification rule includes that the comprehensive risk score of the product of the detected subject is less than or equal to a predetermined threshold, and the verification rule matching unit includes: A first risk parameter determination unit, configured to determine a first risk parameter corresponding to each item of product risk association information of the detected subject; a second risk parameter determining unit, configured to determine, based on the first risk parameter, a second risk parameter corresponding to each plurality of product risk association information of the detected subject; a matrix construction unit, configured to construct a risk matrix based on a risk parameter sequence obtained by arranging the first risk parameter and the second risk parameter from largest to smallest, wherein the risk matrix is configured to reflect the risk level distribution of the inspected subject under the individual influence and interactive influence of each product risk association information; a risk score calculation unit, configured to determine a comprehensive risk score of the product of the inspected subject based on the risk matrix and the risk parameter sequence; a third execution unit, configured to determine that the product risk association information of the detected subject matches the target verification rule if the comprehensive product risk score is less than or equal to the predetermined threshold; otherwise, determine that the product risk association information of the detected subject does not match the target verification rule; The matrix construction unit is used to: set the element of the first specified row and the first specified column in the risk matrix to be the product of the risk parameter of the first specified order and the risk parameter of the second specified order in the risk parameter sequence; The risk score calculation unit is used to: obtain the rank of the risk matrix as a third risk parameter; obtain the variance of the risk parameter sequence as a fourth risk parameter; normalize the third risk parameter and the fourth risk parameter, and calculate the average of the normalized results, and use the average as the comprehensive risk score of the product of the inspected subject.

8. A computer device, characterized in that: include: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are configured to execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that Computer-executable instructions are stored, and the computer-executable instructions are used to execute the method according to any one of claims 1 to 6.

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