Method and apparatus for determining abnormal policies

Abstract

The application discloses a kind of determination method and device of abnormal policy, it is related to computer technical field, it solves the problem that when the way of artificial auditing is used to audit huge number of high-risk policy, error is prone to occur, and the accuracy of the audit of abnormal policy is low.The method comprises the following steps: extracting a plurality of risk policy information from the database corresponding to each business execution system, converting the plurality of risk policy information into a plurality of policy information hash values using a hash algorithm, comparing using a comparison algorithm to obtain a comparison result, determining a plurality of abnormal policy information hash values indicated by the comparison result in the plurality of policy information hash values corresponding to the fixed main system, determining a plurality of target risk policy numbers corresponding to the plurality of abnormal policy information hash values, and regarding the plurality of target risk policies indicated by the plurality of target risk policy numbers as abnormal policies.

Description

Technical Field

[0001] This application belongs to the field of computer technology, and more specifically, relates to a method and apparatus for determining abnormal insurance policies. Background Technology

[0002] With the development of mobile internet, various business types can be operated and reviewed online based on pre-set processes. For example, in the insurance field, during the insurance application process, information entry, insurance plan selection, and plan approval can be performed according to pre-set processes, generating policy-related data. Since insurance companies typically set up multiple application channels online, such as online non-motor insurance, PICC E-Pass, and eLife policy issuance channels, each channel corresponds to a separate system. For example, online non-motor insurance corresponds to the online non-motor insurance system, PICC E-Pass corresponds to the PICC E-Pass system, and eLife policy issuance corresponds to the eLife policy issuance system. Here, the relationship between the eLife policy issuance system and the online non-motor insurance system and PICC E-Pass is similar to that of a head office and a branch office. For a single policy data point, the online non-motor insurance system and the PICC E-Pass system need to be synchronized. For instance, if an application is performed through the online non-motor insurance system and policy data is generated, this policy data needs to be synchronized to the PICC E-Pass system. Simultaneously, the policy data generated by the online non-motor insurance system and the PICC E-Pass system also needs to be uploaded to the eLife policy issuance system.

[0003] In practical applications, high-risk policies are generated during the insurance application process. These policies need to be reviewed to determine if they are abnormal. For abnormal policies, the subsequent issuance process needs to be terminated. Currently, the review of high-risk policies usually involves manually querying the databases of each system to obtain the corresponding high-risk policy data for each system, forming an Excel spreadsheet, and then manually comparing the high-risk policy data for each system to filter out abnormal policies. However, applicants realize that because the number of high-risk policy data to be reviewed is quite large, for example, more than 40,000 per day, manual review of such a large number of high-risk policy data is prone to errors, resulting in a low accuracy rate for reviewing abnormal policies. Summary of the Invention

[0004] In view of this, the present invention provides a method and apparatus for determining abnormal insurance policies, the main purpose of which is to solve the problem that errors are easily made and the accuracy of abnormal insurance policy review is low when a large number of high-risk insurance policies are reviewed manually.

[0005] According to the first aspect of this application, a method for determining abnormal insurance policies is provided, comprising:

[0006] In response to a policy review signal, identify the multiple business execution systems associated with the policy review signal;

[0007] Extract multiple risk policy information that do not meet the preset insurance conditions from the database corresponding to each business execution system, and use a hash algorithm to convert the multiple risk policy information corresponding to each business execution system into multiple policy information hash values, thereby obtaining multiple policy information hash values ​​corresponding to each business execution system. Each risk policy information corresponds to one policy information hash value, and each risk policy information is associated with one risk policy number, and each policy information hash value is associated with one risk policy number.

[0008] In the multiple business execution systems, a fixed master system and at least one comparison system are determined. The comparison algorithm is used to compare the hash values ​​of multiple policy information corresponding to the fixed master system with the hash values ​​of multiple policy information corresponding to each of the at least one comparison system to obtain the comparison results.

[0009] Among the multiple policy information hash values ​​corresponding to the fixed main system, determine the multiple abnormal policy information hash values ​​that indicate anomalies in the comparison results, determine the target risk policy number corresponding to each of the multiple abnormal policy information hash values, and regard the multiple target risk policies indicated by the multiple target risk policy numbers as abnormal policies.

[0010] Optionally, the step of extracting information on multiple risk policies that do not meet the preset conditions within the pending review period from the database corresponding to each business execution system includes:

[0011] Obtain the pending review period carried by the policy review signal;

[0012] For each business execution system, perform the following operations: determine the database corresponding to the business execution system, query multiple policy information whose policy generation time is within the pending review period from the database, obtain the preset insurance conditions, extract multiple pending insurance contents from each policy information, and when it is determined that any of the multiple pending insurance contents does not meet the preset insurance conditions, determine the policy information corresponding to the pending insurance contents as risk policy information, thereby obtaining multiple risk policy information, wherein each policy information includes the policy generation time and multiple pending insurance contents.

[0013] Optionally, the step of using a hash algorithm to convert multiple risk policy information corresponding to each business execution system into multiple policy information hash values, thereby obtaining multiple policy information hash values ​​corresponding to each business execution system, includes:

[0014] For each execution system, perform the following operations: using the hash algorithm, convert multiple target tags and target information corresponding to each target tag in each risk policy information corresponding to the business execution system into multiple tag hash values ​​and target information hash values ​​corresponding to each tag hash value; calculate the sum of the multiple tag hash values ​​to obtain a first hash value; calculate the sum of the multiple target information hash values ​​to obtain a second hash value; calculate the sum of the first hash value and the second hash value to obtain the policy information hash value corresponding to each risk policy information; based on the policy information hash value corresponding to each risk policy information, obtain multiple policy information hash values ​​corresponding to the business execution system, wherein the risk policy information includes the multiple target tags and target information corresponding to each target tag.

[0015] Optionally, the comparison algorithm is used to compare the hash values ​​of multiple policy information corresponding to the fixed master system with the hash values ​​of multiple policy information corresponding to each of the at least one comparison system to obtain the comparison results, including:

[0016] It is determined that the multiple business execution systems include a fixed master system, a first comparison system, and a second comparison system, and that the fixed master system corresponds to multiple first policy information hash values, the first comparison system corresponds to multiple second policy information hash values, and the second comparison system corresponds to multiple third policy hash values;

[0017] For each first policy information hash value in the fixed master system, perform the following operations: compare the first policy information hash value with each second policy information hash value in the first comparison system; if it is determined that the plurality of second policy information hash values ​​include a second policy information hash value that is consistent with the first policy information hash value, then determine that the comparison result corresponding to the first policy information hash value is normal; if it is determined that the plurality of second policy information hash values ​​do not include a second policy information hash value that is consistent with the first policy information hash value, then determine that the comparison result corresponding to the first policy information hash value is abnormal.

[0018] The hash value of the first policy information is compared with the hash value of each third policy information in the second comparison system. If it is determined that the multiple third policy information hash values ​​include a third policy information hash value that is consistent with the hash value of the first policy information, then the comparison result corresponding to the hash value of the first policy information is determined to be normal. If it is determined that the multiple third policy information hash values ​​do not include a third policy information hash value that is consistent with the hash value of the first policy information, then the comparison result corresponding to the hash value of the first policy information is determined to be abnormal.

[0019] Optionally, the method further includes:

[0020] The multiple business execution systems are determined to include a fixed master system, a first comparison system, and a second comparison system. The fixed master system corresponds to multiple first risk policy information, the first comparison system corresponds to multiple second risk policy information, and the second comparison system corresponds to multiple third risk policy information. Each first risk policy information is associated with a risk policy number, each second risk policy information is associated with a risk policy number, and each third risk policy information is associated with a risk policy number.

[0021] Determine multiple first tag hash values ​​corresponding to each first risk policy information and a first target information hash value corresponding to each first tag hash value; determine multiple second tag hash values ​​corresponding to each second risk policy information and a second target information hash value corresponding to each second tag hash value; and determine multiple first tag hash values ​​corresponding to each third policy information hash value and a third target information hash value corresponding to each third tag hash value.

[0022] For each first risk policy information in the fixed master system, the following operations are performed: Determine the risk policy number corresponding to the first risk policy information; determine the target second risk policy information indicated by the risk policy number among the plurality of second risk policy information; determine the plurality of second tag hash values ​​corresponding to the target second risk policy information and the second target information hash value corresponding to each second tag hash value; based on the plurality of first tag hash values ​​corresponding to the first risk policy information and the first target information hash value corresponding to each first tag hash value, and the plurality of second tag hash values ​​corresponding to the target second risk policy information and the second target information hash value corresponding to each second tag hash value, use a comparison algorithm to determine the target comparison result of the insurance content corresponding to each first tag hash value, wherein the first risk policy information includes multiple insurance contents, and each insurance content includes a target tag and corresponding target information, and...

[0023] The target third-risk policy information indicated by the risk policy number is determined from the plurality of third-risk policy information. Multiple third-tag hash values ​​corresponding to the target third-risk policy information and a third target information hash value corresponding to each third-tag hash value are determined. Based on the multiple first-tag hash values ​​corresponding to the first risk policy information and the first target information hash value corresponding to each first-tag hash value, the multiple third-tag hash values ​​corresponding to the target third-risk policy information and the third target information hash value corresponding to each third-tag hash value, a comparison algorithm is used to determine the target comparison result of the insurance content corresponding to each first-tag hash value.

[0024] When an anomaly is detected in the target comparison result of any insured content in the first risk policy information, the risk policy number is determined to be an abnormal policy number, and the insured content is determined to be abnormal insured content. A processing template is obtained, and the abnormal insured number and the abnormal insured content are processed using the processing template to obtain an abnormal policy report. The abnormal policy report is sent to the terminal held by the maintenance personnel so that the maintenance personnel can download the abnormal policy report based on the terminal.

[0025] Optionally, the step of determining the target comparison result of the insurance content corresponding to each first tag hash value based on the multiple first tag hash values ​​corresponding to the first risk policy information and the first target information hash value corresponding to each first tag hash value, the multiple second tag hash values ​​corresponding to the target second risk policy information and the second target information hash value corresponding to each second tag hash value, using a comparison algorithm includes:

[0026] For each first tag hash value in the first risk policy information, perform the following operations: compare the first tag hash value with each of the plurality of second tag hash values ​​corresponding to the target second risk policy information; if it is determined that the plurality of second tag hash values ​​do not include a target second tag hash value that is consistent with the first tag hash value, determine that the target comparison result of the insurance content corresponding to the first tag hash value is abnormal, and...

[0027] When it is determined that the plurality of second tag hash values ​​include a target second tag hash value that is consistent with the first tag hash value, the first target information hash value corresponding to the first tag hash value is further compared with the second target information hash value corresponding to the target second tag hash value. If it is determined that the first target information hash value corresponding to the first tag hash value is consistent with the second target information hash value corresponding to the target second tag hash value, then the target comparison result of the insurance content corresponding to the first tag hash value is determined to be normal. If it is determined that the first target information hash value corresponding to the first tag hash value is inconsistent with the second target information hash value corresponding to the target second tag hash value, then the target comparison result of the insurance content corresponding to the first tag hash value is determined to be abnormal.

[0028] Optionally, the step of determining the target comparison result of the insurance content corresponding to each first tag hash value based on the multiple first tag hash values ​​corresponding to the first risk policy information and the first target information hash value corresponding to each first tag hash value, the multiple third tag hash values ​​corresponding to the target third risk policy information and the third target information hash value corresponding to each third tag hash value, using a comparison algorithm includes:

[0029] For each first tag hash value in the first risk policy information, perform the following operations: compare the first tag hash value with each of the plurality of third tag hash values ​​corresponding to the target third risk policy information; if it is determined that the plurality of third tag hash values ​​do not include a target third tag hash value that is consistent with the first tag hash value, determine that the target comparison result of the insurance content corresponding to the first tag hash value is abnormal, and...

[0030] When it is determined that the plurality of third tag hash values ​​include a target third tag hash value that is consistent with the first tag hash value, the first target information hash value corresponding to the first tag hash value is further compared with the third target information hash value corresponding to the target third tag hash value. If it is determined that the first target information hash value corresponding to the first tag hash value is consistent with the third target information hash value corresponding to the target third tag hash value, then the target comparison result of the insurance content corresponding to the first tag hash value is determined to be normal. If it is determined that the first target information hash value corresponding to the first tag hash value is inconsistent with the third target information hash value corresponding to the target third tag hash value, then the target comparison result of the insurance content corresponding to the first tag hash value is determined to be abnormal.

[0031] Optionally, after determining the target risk policy number corresponding to each of the plurality of abnormal policy information hash values, the method further includes:

[0032] Based on the multiple target risk policy numbers, an early warning message is generated and sent to the terminal held by the maintenance personnel. This allows the maintenance personnel to extract the multiple target risk policy numbers from the early warning message upon receiving it and terminate the multiple insurance application processes indicated by the multiple target risk policy numbers.

[0033] Optionally, after determining the target risk policy number corresponding to each of the plurality of abnormal policy information hash values, the method further includes:

[0034] The system queries multiple operation process video information corresponding to the multiple target risk policy numbers. The operation process video information is labeled with the target risk policy number to obtain multiple target operation process video information. The multiple target operation process video information is sent to the terminal held by the maintenance personnel. When the maintenance personnel receive the multiple target operation process video information, they can randomly extract the operation process video information corresponding to one target risk policy number from the multiple target operation process video information and perform abnormal process investigation based on the operation process video information.

[0035] According to a second aspect of this application, an apparatus for determining abnormal insurance policies is provided, comprising:

[0036] The first determining module is used to determine, in response to the policy review signal, multiple business execution systems associated with the policy review signal;

[0037] The conversion module is used to extract multiple risk policy information that do not meet the preset insurance conditions from the database corresponding to each business execution system, and to use a hash algorithm to convert the multiple risk policy information corresponding to each business execution system into multiple policy information hash values, thereby obtaining multiple policy information hash values ​​corresponding to each business execution system. Each risk policy information corresponds to one policy information hash value, and each risk policy information is associated with one risk policy number, and each policy information hash value is associated with one risk policy number.

[0038] The comparison module is used to determine a fixed master system and at least one comparison system in the multiple business execution systems, and to use a comparison algorithm to compare the hash values ​​of multiple policy information corresponding to the fixed master system with the hash values ​​of multiple policy information corresponding to each of the at least one comparison system to obtain the comparison results.

[0039] The second determining module is used to determine, from the multiple policy information hash values ​​corresponding to the fixed main system, multiple abnormal policy information hash values ​​whose comparison results indicate anomalies, determine the target risk policy number corresponding to each of the multiple abnormal policy information hash values, and treat the multiple target risk policies indicated by the multiple target risk policy numbers as abnormal policies.

[0040] By means of the above technical solution, this application provides a method and apparatus for determining abnormal insurance policies. First, in response to a policy review signal, this application identifies multiple business execution systems associated with the policy review signal. Then, it extracts information on multiple risky insurance policies that do not meet preset underwriting conditions from the database corresponding to each business execution system. Next, it uses a hash algorithm to convert the multiple risky insurance policy information corresponding to each business execution system into multiple policy information hash values, obtaining multiple policy information hash values ​​corresponding to each business execution system. Then, it identifies a fixed master system and at least one comparison system among the multiple business execution systems. Using a comparison algorithm, it compares the multiple policy information hash values ​​corresponding to the fixed master system with the multiple policy information hash values ​​corresponding to each of the at least one comparison system, obtaining comparison results. Finally, it identifies the multiple policy information hash values ​​corresponding to the fixed master system that indicate abnormality from the comparison results. The process involves identifying the target risk policy number corresponding to each of the multiple abnormal policy information hash values. These target risk policy numbers are then used to identify the multiple target risk policies as abnormal policies. The process involves converting any policy information into a fixed value (hash value) using a hash algorithm. By comparing these hash values, the system quickly identifies abnormal hash values ​​that are not found in other business execution systems within the same set of multiple policy information hash values. This ensures that the target risk policy number corresponding to the abnormal hash value is not abnormal, and the target risk policy indicated by this hash value is considered an abnormal policy. Since risk policy information includes many insurance details, converting it into hash values ​​significantly improves processing speed during subsequent comparisons. Furthermore, the entire process is automated, requiring no human intervention, and accurately identifies abnormal policies.

[0041] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0042] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0043] Figure 1 A flowchart illustrating a method for determining an abnormal insurance policy provided in an embodiment of this application is shown;

[0044] Figure 2A flowchart illustrating another method for determining abnormal insurance policies provided in an embodiment of this application is shown;

[0045] Figure 3A A schematic diagram of the structure of an abnormal insurance policy determination device provided in an embodiment of this application is shown;

[0046] Figure 3B A schematic diagram of the structure of an abnormal insurance policy determination device provided in an embodiment of this application is shown;

[0047] Figure 3C A schematic diagram of the structure of an abnormal insurance policy determination device provided in an embodiment of this application is shown. Detailed Implementation

[0048] Various embodiments and features of this application are described herein with reference to the accompanying drawings.

[0049] It should be understood that various modifications can be made to the embodiments described herein. Therefore, the above description should not be considered as limiting, but merely as an example of embodiments. Other modifications within the scope and spirit of this application will be apparent to those skilled in the art.

[0050] The accompanying drawings, which are included in and form part of this specification, illustrate embodiments of the present application and, together with the general description of the present application given above and the detailed description of the embodiments given below, serve to explain the principles of the present application.

[0051] These and other features of this application will become apparent from the following description of preferred forms of embodiments given as non-limiting examples, with reference to the accompanying drawings.

[0052] It should also be understood that although this application has been described with reference to some specific examples, those skilled in the art can certainly implement many other equivalent forms of this application.

[0053] The above and other aspects, features and advantages of this application will become more apparent when taken in conjunction with the accompanying drawings and in view of the following detailed description.

[0054] Specific embodiments of this application are described thereafter with reference to the accompanying drawings; however, it should be understood that the claimed embodiments are merely examples of this application, which can be implemented in various ways. Well-known and / or repeated functions and structures are not described in detail to avoid unnecessary or redundant details that could obscure the application. Therefore, the specific structural and functional details claimed herein are not intended to be limiting, but merely serve as the basis and representative basis for the claims to teach those skilled in the art to use this application in a variety of substantially any suitable detailed structures.

[0055] This specification may use the phrases “in one embodiment,” “in another embodiment,” “in yet another embodiment,” or “in other embodiments,” all of which may refer to one or more of the same or different embodiments according to this application.

[0056] This application provides a method for determining abnormal insurance policies, such as... Figure 1 As shown, it includes:

[0057] 101. In response to the policy review signal, identify the multiple business execution systems associated with the policy review signal;

[0058] In the application embodiment, the executing entity is an automated operation and maintenance platform that can determine abnormal insurance policies. When the automated operation and maintenance platform receives the insurance policy review signal, it can determine the multiple business execution systems associated with the insurance policy review signal.

[0059] It should be noted that scheduled tasks can be set for the executing entity using XXL-Job (a scheduled task management system). For example, the task can specify when to retrieve policy data from which business system each day. This scheduled task also includes information on the time period to be reviewed. Since a large amount of policy data is generated daily in various systems, the policy generation time is used as the basis for selecting the review time period. For example, at 00:00 on November 2, 2023, policy data generated between 00:00 and 23:59 on November 1 can be retrieved. This time information is set when the scheduled task is written, and the executing entity can automatically execute the relevant tasks every day based on the scheduled task.

[0060] 102. Extract information on multiple risk policies that do not meet the preset insurance conditions from the database corresponding to each business execution system, and use a hash algorithm to convert the information on multiple risk policies corresponding to each business execution system into multiple policy information hash values, thereby obtaining multiple policy information hash values ​​corresponding to each business execution system.

[0061] In this embodiment of the application, after determining the multiple business execution systems associated with the policy review signal, multiple risk policy information that does not meet the preset insurance conditions is extracted from the database corresponding to each business execution system. Then, a hash algorithm is used to convert the multiple risk policy information corresponding to each business execution system into multiple policy information hash values, thus obtaining multiple policy information hash values ​​corresponding to each business execution system. It should be noted that each risk policy information corresponds to one policy information hash value, and each risk policy information is associated with one risk policy number, and each policy information hash value is associated with one risk policy number.

[0062] For example, to address potential unforeseen issues during the real-time review process of the eLife high-coverage system (such as the inability to match some data, resulting in high-coverage policies being issued normally), the automated operations and maintenance platform performs hourly data comparisons. Specifically, it queries and compares the three types of data mentioned above from midnight to the current hour every hour. If any abnormal data is subsequently identified, a problem report will be promptly created in the PICC internal work order system, and an abnormality list will be sent to the designated operations and maintenance personnel and the issuing agent via WeChat or email, providing timely notification of the risk policy list for relevant personnel to review.

[0063] It's important to further explain that hashing, also known as hashing, is the process of transforming inputs of various sizes (usually strings, and file content can also be considered a string) into a fixed-size value. The resulting hash value varies greatly depending on even small changes in the input. The purpose of a hash algorithm (or hash function) is to ensure that each unique input generates a unique result. This allows each file to be distinguished by a unique hash value. In essence, hashing technology accurately identifies the integrity and consistency of data, ensuring that data is not tampered with during transmission and storage. The principle is to input data into a hash function to generate a unique hash value. Any modification or corruption of the data will cause a change in the hash value, thus alerting us to a breach in the data's accuracy. For example, someone might replace some common files on our system with a Trojan file, or directly bundle them together (such as with browser files). When we open the browser, it still pops up normally, but the Trojan is also running secretly. In this case, a hash algorithm can be used to convert common files into hash values. By comparing them with preset hash values, if a file's hash value is different from the preset hash value, we can determine that the file has been modified. The above is one scenario of using a hash algorithm. In other scenarios, certain information can be converted into corresponding hash values. By mapping data to fixed-length hash values, a small amount of data can be stored using a small amount of storage space. Hash value comparison can effectively improve the comparison speed.

[0064] 103. In multiple business execution systems, determine a fixed master system and at least one comparison system. Use a comparison algorithm to compare the hash values ​​of multiple policy information corresponding to the fixed master system with the hash values ​​of multiple policy information corresponding to each of the at least one comparison system to obtain the comparison results.

[0065] In this embodiment, among the multiple business execution systems, there is a master system, referred to as the fixed master system, and the other systems are referred to as comparison systems. This means that policy data generated by the other business execution systems needs to be uploaded to the fixed master system. For example, for a policy with policy number 1000, the process involved in a policy, from the policyholder's initial application to the final completion, typically spans a long period due to the numerous nodes involved, such as a month. Synchronization can be used to synchronize the data corresponding to these nodes to other business execution systems, or it can be uploaded to the fixed master system. Then, a comparison algorithm is used to compare the hash values ​​of multiple policy information corresponding to the fixed master system with the hash values ​​of multiple policy information corresponding to each of at least one comparison system, obtaining the comparison results.

[0066] 104. Among the multiple policy information hash values ​​corresponding to the fixed main system, determine the multiple abnormal policy information hash values ​​that indicate abnormality in the comparison results, determine the target risk policy number corresponding to each of the multiple abnormal policy information hash values, and regard the multiple target risk policies indicated by the multiple target risk policy numbers as abnormal policies.

[0067] In this embodiment of the application, based on the comparison results, it can be determined which policy information hash value in the fixed master system is normal and which policy information hash value is abnormal. These abnormal policy information hash values ​​indicate that the corresponding policies are problematic, that is, abnormal policies. In other words, among the multiple policy information hash values ​​corresponding to the fixed master system, multiple abnormal policy information hash values ​​that indicate abnormality in the comparison results are identified, and the target risk policy number corresponding to each of the multiple abnormal policy information hash values ​​is determined. The multiple target risk policies indicated by the multiple target risk policy numbers are regarded as abnormal policies.

[0068] The method provided in this application first responds to a policy review signal and identifies multiple business execution systems associated with the policy review signal. Then, it extracts information on multiple risky policies that do not meet preset underwriting conditions from the database corresponding to each business execution system. A hash algorithm is then used to convert this information into multiple policy information hash values ​​for each business execution system. Next, a fixed master system and at least one comparison system are identified among the multiple business execution systems. A comparison algorithm is used to compare the hash values ​​of the multiple policy information corresponding to the fixed master system with the hash values ​​of the multiple policy information corresponding to each of the at least one comparison system, obtaining comparison results. Finally, multiple abnormal policy information hash values ​​indicating anomalies in the comparison results are identified from the hash values ​​of the multiple policy information corresponding to the fixed master system. The process involves identifying the target risk policy number corresponding to each of the multiple abnormal policy information hash values, and then designating the multiple target risk policies indicated by these target risk policy numbers as abnormal policies. A hash algorithm is used to convert any policy information into a fixed value, i.e., a hash value. By directly comparing these hash values, abnormal hash values ​​are quickly identified within the multiple policy information hash values ​​corresponding to the fixed main system, rather than within the multiple policy information hash values ​​corresponding to other business execution systems. This determines that the target risk policy number corresponding to the abnormal hash value is abnormal, and the target risk policy indicated by this target risk policy number is designated as an abnormal policy. Since risk policy information includes a lot of insurance details, converting the risk policy information into hash values ​​effectively improves the processing speed in subsequent comparisons. Furthermore, the above process is automated, requiring no human intervention, and can accurately identify abnormal policies.

[0069] Furthermore, as a refinement and extension of the specific implementation methods of the above embodiments, and to fully illustrate the specific implementation process of this embodiment, this application provides another method for determining abnormal insurance policies, such as... Figure 2 As shown, it includes:

[0070] 201. In response to the policy review signal, identify the multiple business execution systems associated with the policy review signal.

[0071] 202. Extract information on multiple risk insurance policies that do not meet the preset insurance conditions from the database corresponding to each business execution system.

[0072] In this embodiment, the pending review time period carried by the policy review signal can be obtained first. Then, the following operations are performed on each business execution system: the database corresponding to the business execution system is determined, multiple policy information with policy generation time within the pending review time period is queried from the database, preset insurance conditions are obtained, multiple pending insurance contents are extracted from each policy information, and when it is determined that any pending insurance contents does not meet the preset insurance conditions, the policy information corresponding to the pending insurance contents is determined to be risk policy information, thus obtaining multiple risk policy information, wherein each policy information includes the policy generation time and multiple pending insurance contents.

[0073] For example, to avoid risks arising from individual insurance premiums exceeding the upper limit (5 million RMB) due to claims, the online non-motor vehicle insurance, PICC E-Pass, and eLife policy issuance channels need to conduct high-insurance-amount risk verification (or review). The verification process involves an automated operation and maintenance platform connecting to the corresponding databases of the PICC E-Pass, eLife policy issuance system, and the online non-motor vehicle insurance system via scheduled tasks.

[0074] For example, multiple business execution systems are identified, including the Internet Non-Motor Vehicle System, the PICC e-Pass System, and the eLife Policy Issuance System. The database corresponding to the Internet Non-Motor Vehicle System is used to query non-motor vehicle policy numbers, specifically those containing clauses 06 and 07 (policy numbers starting with PL, PE, or PW) within a certain time period, with policy types 01 or 99, and policy numbers whose ext1 (reserved flag) field in the prpcmain_common table contains (WAP, WAV, VAP, APP, MST, WAPXB, GWF). The database corresponding to the PICC e-Pass System is used to query PICC e-Pass policy numbers, specifically those PICC e-Pass policies generated within a certain time period with insurance types 06 and 07 (i.e., accident and health insurance), where the insurance type is not EAU, the policy was not issued through a shared online sales link, the policy type is empty or not 4, and the policy number has a single insured or beneficiary. The specific process is as follows: 1. Query the `mstnmain` and `mstnisured` tables to obtain the policy number information for a specific time period; 2. Query the policy terms information based on the policy number; 3. Compare the query results to see if they include the terms in the local `prpdcheckhighrisk` table. Specifically, query the database corresponding to the eLife policy issuance system to retrieve eLife high-coverage policies, that is, query the high-coverage push record table in the eLife policy issuance system's database to obtain the policy number information for each individual policy during a specific time period.

[0075] 203. Use a hash algorithm to convert the multiple risk policy information corresponding to each business execution system into multiple policy information hash values, thus obtaining multiple policy information hash values ​​corresponding to each business execution system.

[0076] In this embodiment, the following operations can be performed on each execution system: Using a hash algorithm, multiple target tags and target information corresponding to each target tag in each risk policy information corresponding to the business execution system are converted into multiple tag hash values ​​and target information hash values ​​corresponding to each tag hash value. The sum of the multiple tag hash values ​​is calculated to obtain a first hash value. The sum of the multiple target information hash values ​​is calculated to obtain a second hash value. The sum of the first and second hash values ​​is calculated to obtain the policy information hash value corresponding to each risk policy information. Based on the policy information hash value corresponding to each risk policy information, multiple policy information hash values ​​corresponding to the business execution system are obtained. The risk policy information includes multiple target tags and target information corresponding to each target tag. For example, if it can be determined that the data in one party's risk policy information is more than the push data in the risk policy information of the other party, then there is a situation where data has not been pushed.

[0077] Before converting risk insurance information, risk policy numbers can be compared. For each business execution system, the following operations can be performed: Multiple risk policy information entries corresponding to each business execution system are identified. Since each risk policy information entry is associated with a risk policy number, once the risk policy information is identified, the risk policy number is determined. After identifying the multiple risk policy numbers corresponding to each business execution system, a comparison algorithm can be used to compare each of the multiple risk policy numbers corresponding to the fixed main system with the multiple risk policy numbers corresponding to each of the other systems to be compared. This will allow you to see if the multiple risk policy numbers corresponding to the fixed main system are consistent with each other. If each risk policy number is found in multiple risk policy numbers in other systems to be compared, then the risk policy number is correct. For example, if the main system corresponds to 10 risk policy numbers (1, 2, ..., 10), the first system to be compared corresponds to 10 risk policy numbers (also 1, 2, ..., 10), and the second system to be compared corresponds to 10 risk policy numbers (also 1, 2, ..., 10), then by comparing them separately, it can be seen that the risk policy numbers can be completely matched, and subsequent comparisons of risk policy information (risk policy information can be understood as covering all insurance content) can be carried out. If any are missing, it indicates a problem with the risk policy number. For example, the main system corresponds to 10 risk policy numbers, 1, 2, ..., 10; the first system to be compared corresponds to 10 risk policy numbers, also 1, 2, ..., 9; and the second system to be compared corresponds to 10 risk policy numbers, also 1, 2, ..., 8. The abnormal risk policy numbers 9 and 10 can be filtered out, and the risk policy numbers 1, 2, ..., 8 corresponding to the main system can be carefully compared with subsequent risk insurance information.

[0078] 204. Using a comparison algorithm, the hash values ​​of multiple policy information corresponding to the fixed master system are compared with the hash values ​​of multiple policy information corresponding to each of at least one comparison system to obtain the comparison results.

[0079] In this embodiment, it is necessary to first determine a fixed master system and at least one comparison system among multiple business execution systems. Then, it is determined that the multiple business execution systems include a fixed master system, a first comparison system, and a second comparison system. It is also determined that the fixed master system corresponds to multiple first policy information hash values, the first comparison system corresponds to multiple second policy information hash values, and the second comparison system corresponds to multiple third policy hash values. For each first policy information hash value in the fixed master system, the following operations are performed: the first policy information hash value is compared with each second policy information hash value in the first comparison system. If it is determined that multiple second policy information hash values ​​include a second policy information hash value that is consistent with the first policy information hash value, then... If the comparison result corresponding to the hash value of the first policy information is determined to be normal, and if it is determined that multiple hash values ​​of the second policy information do not include any second policy information hash values ​​that are consistent with the hash value of the first policy information, then the comparison result corresponding to the hash value of the first policy information is determined to be abnormal. Furthermore, the hash value of the first policy information is compared with each hash value of the third policy information in the second comparison system. If it is determined that multiple hash values ​​of the third policy information include any third policy information hash values ​​that are consistent with the hash value of the first policy information, then the comparison result corresponding to the hash value of the first policy information is determined to be normal. If it is determined that multiple hash values ​​of the third policy information do not include any third policy information hash values ​​that are consistent with the hash value of the first policy information, then the comparison result corresponding to the hash value of the first policy information is determined to be abnormal.

[0080] It should be noted that in practical applications, due to the large amount of data to be compared, the data can be first split using the chunk_data function during the comparison process. Then, the data list can be traversed and the data sliced ​​into a new list according to the chunk size. Subsequently, multiple threads or thread locks can be used for comparison, which can effectively improve the comparison speed.

[0081] It should be noted that during the comparison process using the comparison algorithm, the process monitoring of the data comparison process can also be set up by adding try: the monitored program except exceptions try: to catch e for prediction. If e is the predicted value, different logic will be performed. For example, if there is a data error during program execution and the error message is e, it will be predicted and then the error message e will be sent to the WeChat group via WeChat to notify the developers to handle it in time.

[0082] In another optional embodiment of this application, it can also be determined whether each insurance application in the risk policy information is abnormal, and the abnormal content can be directly located. For example, the risk policy information includes an insurance type label and the specific type information corresponding to the insurance type label. After being converted into hash values, they are the hash value of the insurance type label corresponding to the risk policy information and the hash value of the specific type information corresponding to the insurance type label hash value. If the hash value of the specific type information corresponding to the insurance type label hash value of the risk policy information corresponding to a risk policy number in the fixed main system is different from the hash value of the specific type information corresponding to the insurance type label hash value of the risk policy information corresponding to the same risk policy number in the system to be compared, it indicates that the insurance application is abnormal, and the abnormal insurance application can be directly located.Therefore, it is determined that multiple business execution systems include a fixed master system, a first comparison system, and a second comparison system. The fixed master system corresponds to multiple first-risk policy information entries, the first comparison system corresponds to multiple second-risk policy information entries, and the second comparison system corresponds to multiple third-risk policy information entries. Each first-risk policy information entry is associated with a risk policy number, each second-risk policy information entry is associated with a risk policy number, and each third-risk policy information entry is associated with a risk policy number. Multiple first-tag hash values ​​and a first target information hash value corresponding to each first-risk policy information entry are determined. Similarly, multiple second-tag hash values ​​and a first target information hash value corresponding to each second-risk policy information entry are determined. The second target information hash value corresponding to the second tag hash value, and the multiple first tag hash values ​​corresponding to each third policy information hash value and the third target information hash value corresponding to each third tag hash value; perform the following operations on each first risk policy information in the fixed main system: determine the risk policy number corresponding to the first risk policy information, determine the target second risk policy information indicated by the risk policy number in the multiple second risk policy information, determine the multiple second tag hash values ​​corresponding to the target second risk policy information and the second target information hash value corresponding to each second tag hash value, and based on the multiple first tag hash values ​​corresponding to the first risk policy information and the first target information hash value corresponding to each first tag hash value... The comparison algorithm is used to determine the target comparison result of the insurance content corresponding to each first tag hash value, based on the multiple second tag hash values ​​corresponding to the target second risk policy information and the second target information hash value corresponding to each second tag hash value. The first risk policy information includes multiple insurance contents, and each insurance content includes a target tag and corresponding target information. Furthermore, the target third risk policy information indicated by the risk policy number is determined from the multiple third risk policy information. The multiple third tag hash values ​​corresponding to the target third risk policy information and the third target information hash value corresponding to each third tag hash value are determined. Based on the multiple first tag hash values ​​corresponding to the first risk policy information and the first tag hash value... The system uses a comparison algorithm to determine the target comparison result of the insurance content corresponding to each first tag hash value, the hash values ​​of multiple third tags corresponding to the target third risk policy information, and the hash value of the third target information corresponding to each third tag hash value. Furthermore, when an abnormal target comparison result is detected for any insurance content in the first risk policy information, the risk policy number is determined as the abnormal policy number, and the insurance content is determined as the abnormal insurance content. A processing template is obtained, and the abnormal insurance number and abnormal insurance content are processed using the processing template to obtain an abnormal policy report. The abnormal policy report is then sent to the terminal held by the maintenance personnel so that the maintenance personnel can download the abnormal policy report based on the terminal.

[0083] Furthermore, based on the hash values ​​of multiple first tags corresponding to the first risk policy information and the hash value of the first target information corresponding to each first tag hash value, the hash values ​​of multiple second tags corresponding to the target second risk policy information and the hash value of the second target information corresponding to each second tag hash value, the specific method for determining the target comparison result of the insurance content corresponding to each first tag hash value using the comparison algorithm is as follows:

[0084] For each first tag hash value in the first risk policy information, perform the following operations: compare the first tag hash value with each of the multiple second tag hash values ​​corresponding to the target second risk policy information. If it is determined that the multiple second tag hash values ​​do not include a target second tag hash value that matches the first tag hash value, determine that the target comparison result of the insurance content corresponding to the first tag hash value is abnormal. If it is determined that the multiple second tag hash values ​​include a target second tag hash value that matches the first tag hash value, continue to compare the first target information hash value corresponding to the first tag hash value with the second target information hash value corresponding to the target second tag hash value. If it is determined that the first target information hash value corresponding to the first tag hash value matches the second target information hash value corresponding to the target second tag hash value, determine that the target comparison result of the insurance content corresponding to the first tag hash value is normal. If it is determined that the first target information hash value corresponding to the first tag hash value does not match the second target information hash value corresponding to the target second tag hash value, determine that the target comparison result of the insurance content corresponding to the first tag hash value is abnormal.

[0085] Furthermore, based on the hash values ​​of multiple first tags corresponding to the first risk policy information and the hash value of the first target information corresponding to each first tag hash value, the hash values ​​of multiple third tags corresponding to the target third risk policy information and the hash value of the third target information corresponding to each third tag hash value, the specific method for determining the target comparison result of the insurance content corresponding to each first tag hash value using the comparison algorithm is as follows:

[0086] For each first tag hash value in the first risk policy information, perform the following operations: compare the first tag hash value with each of the multiple third tag hash values ​​corresponding to the target third risk policy information; if it is determined that the multiple third tag hash values ​​do not include the target third tag hash value that is consistent with the first tag hash value, determine that the target comparison result of the insurance content corresponding to the first tag hash value is abnormal; and if it is determined that the multiple third tag hash values ​​include the target third tag hash value that is consistent with the first tag hash value, continue to compare the first target information hash value corresponding to the first tag hash value with the third target information hash value corresponding to the target third tag hash value; if it is determined that the first target information hash value corresponding to the first tag hash value is consistent with the third target information hash value corresponding to the target third tag hash value, determine that the target comparison result of the insurance content corresponding to the first tag hash value is normal; if it is determined that the first target information hash value corresponding to the first tag hash value is inconsistent with the third target information hash value corresponding to the target third tag hash value, determine that the target comparison result of the insurance content corresponding to the first tag hash value is abnormal.

[0087] 205. Among the multiple policy information hash values ​​corresponding to the fixed main system, identify the multiple abnormal policy information hash values ​​whose comparison results indicate anomalies.

[0088] In this embodiment of the application, multiple abnormal policy information hash values ​​indicating abnormality can be determined from multiple policy information hash values ​​corresponding to a fixed main system. The target risk policy number corresponding to each abnormal policy information hash value can be determined, and the multiple target risk policies indicated by the multiple target risk policy numbers can be regarded as abnormal policies.

[0089] 206. Generate early warning information and send it to the terminal held by the maintenance personnel.

[0090] Early warning information is generated based on multiple target risk policy numbers and sent to the terminal held by maintenance personnel. When the maintenance personnel receive the early warning information, they can extract the multiple target risk policy numbers from the early warning information and terminate the multiple insurance application processes indicated by the multiple target risk policy numbers.

[0091] 207. Query multiple operation process video information corresponding to multiple target risk policy numbers from the retrospective system.

[0092] The system queries multiple operation process video information corresponding to multiple target risk policy numbers. The operation process video information is labeled with the target risk policy number to obtain multiple target operation process video information. The multiple target operation process video information is sent to the terminal held by the maintenance personnel. When the maintenance personnel receive the multiple target operation process video information, they can extract the operation process video information corresponding to any one target risk policy number from the multiple target operation process video information and perform abnormal process investigation based on the operation process video information.

[0093] It should be noted that the execution entity in this application embodiment can interface with the insurance industry's retrospective system, i.e., a web application written based on the Flask framework in Python. This application extracts risk data through comparison and interfaces with the insurance industry's retrospective system, queries the operational processes corresponding to abnormal data, and then connects these operational processes to the alarm system via an HTTP interface. The alarm system, in turn, connects to the enterprise and email systems via HTTP and message queues, pushing the abnormal processes to relevant responsible parties. These responsible parties can then view the relevant abnormal processes, thereby preventing large-scale abnormal operations from causing system malfunctions or losses.

[0094] In another optional embodiment of this application, the executing entity can also interface with the enterprise WeChat-level work order system. This involves providing Python-written application scripts that are invoked through the XXL-Job scheduled task center. High-risk data from various business systems is periodically acquired and efficiently compared using a hash table data structure comparison algorithm. Abnormal data is generated into an Excel file and sent to the enterprise WeChat business group. Simultaneously, the work order system is integrated, relevant functions are created, and the abnormal data Excel file is attached and assigned to relevant responsible positions. The responsible personnel in these positions promptly handle the abnormal data to ensure the normal operation of the business system. In other words, the final target data is obtained and generated into an Excel file to promptly remind relevant personnel to terminate the order processing process and prevent related risks. The notification section can not only be linked to the PICC internal work order system and email alerts, but also add internet-based alert notifications and push high-risk data to the relevant responsible personnel's enterprise WeChat accounts, ensuring high-quality and efficient data detection results and significantly reducing the impact of human factors.

[0095] The method provided in this application converts any policy information into a fixed value, namely a hash value, using a hash algorithm. By directly comparing hash values, it quickly identifies abnormal hash values ​​that are not found in the hash values ​​of multiple policy information corresponding to the fixed main system, but are not found in the hash values ​​of multiple policy information corresponding to other business execution systems. It can be determined that the target risk policy number corresponding to the abnormal hash value is abnormal. The target risk policy number indicates the abnormal policy. Since the risk policy information includes a lot of insurance content, converting the risk policy information into a hash value can effectively improve the calculation speed in subsequent comparisons. Moreover, the above process is completed automatically without human intervention, and can accurately find abnormal policies.

[0096] Furthermore, as Figure 1 The specific implementation of the method is as follows: Figure 3A As shown, this embodiment of the invention provides a device for determining abnormal insurance policies, including: a first determining module 301, a conversion module 302, a comparison module 303, and a second determining module 304.

[0097] The first determining module 301 is used to determine, in response to the policy review signal, multiple business execution systems associated with the policy review signal;

[0098] The conversion module 302 is used to extract multiple risk policy information that does not meet the preset insurance conditions from the database corresponding to each business execution system, and to use a hash algorithm to convert the multiple risk policy information corresponding to each business execution system into multiple policy information hash values, thereby obtaining multiple policy information hash values ​​corresponding to each business execution system. Each risk policy information corresponds to one policy information hash value, and each risk policy information is associated with one risk policy number, and each policy information hash value is associated with one risk policy number.

[0099] The comparison module 303 is used to determine a fixed master system and at least one comparison system in the multiple business execution systems, and to use a comparison algorithm to compare the hash values ​​of multiple policy information corresponding to the fixed master system with the hash values ​​of multiple policy information corresponding to each of the at least one comparison system to obtain the comparison result.

[0100] The second determining module 304 is used to determine, from the multiple policy information hash values ​​corresponding to the fixed main system, multiple abnormal policy information hash values ​​whose comparison results indicate anomalies, determine the target risk policy number corresponding to each of the multiple abnormal policy information hash values, and treat the multiple target risk policies indicated by the multiple target risk policy numbers as abnormal policies.

[0101] In specific application scenarios, the conversion module 302 is also used to: obtain the pending review time period carried by the policy review signal; perform the following operations for each business execution system: determine the database corresponding to the business execution system, query multiple policy information with policy generation time within the pending review time period from the database, obtain the preset insurance conditions, extract multiple pending insurance contents from each policy information, and when it is determined that any pending insurance contents does not meet the preset insurance conditions, determine the policy information corresponding to the pending insurance contents as risk policy information, and obtain multiple risk policy information, wherein each policy information includes the policy generation time and multiple pending insurance contents.

[0102] In specific application scenarios, the conversion module 302 is further configured to perform the following operations on each execution system: using the hash algorithm to convert multiple target tags and target information corresponding to each target tag in each risk policy information corresponding to the business execution system into multiple tag hash values ​​and target information hash values ​​corresponding to each tag hash value; calculating the sum of the multiple tag hash values ​​to obtain a first hash value; calculating the sum of the multiple target information hash values ​​to obtain a second hash value; calculating the sum of the first hash value and the second hash value to obtain a policy information hash value corresponding to each risk policy information; and based on the policy information hash value corresponding to each risk policy information, obtaining multiple policy information hash values ​​corresponding to the business execution system, wherein the risk policy information includes the multiple target tags and target information corresponding to each target tag.

[0103] In specific application scenarios, the comparison module 303 is further configured to: determine that the plurality of business execution systems include a fixed master system and a first comparison system and a second comparison system, and determine that the fixed master system corresponds to a plurality of first policy information hash values, the first comparison system corresponds to a plurality of second policy information hash values, and the second comparison system corresponds to a plurality of third policy hash values; and perform the following operations on each first policy information hash value in the fixed master system: compare the first policy information hash value with each second policy information hash value in the first comparison system, and if it is determined that the plurality of second policy information hash values ​​include a second policy information hash value that is consistent with the first policy information hash value, then determine the first policy information hash value corresponding to the first policy information hash value. If the comparison result is normal, and if it is determined that the plurality of second policy information hash values ​​do not include a second policy information hash value that is consistent with the first policy information hash value, then the comparison result corresponding to the first policy information hash value is determined to be abnormal. Furthermore, the first policy information hash value is compared with each third policy information hash value in the second comparison system. If it is determined that the plurality of third policy information hash values ​​include a third policy information hash value that is consistent with the first policy information hash value, then the comparison result corresponding to the first policy information hash value is determined to be normal. If it is determined that the plurality of third policy information hash values ​​do not include a third policy information hash value that is consistent with the first policy information hash value, then the comparison result corresponding to the first policy information hash value is determined to be abnormal.

[0104] In specific application scenarios, the conversion module 302 is further configured to: determine that the multiple business execution systems include a fixed main system and a first comparison system and a second comparison system; determine that the fixed main system corresponds to multiple first risk policy information, the first comparison system corresponds to multiple second risk policy information, and the second comparison system corresponds to multiple third risk policy information, wherein each first risk policy information is associated with a risk policy number, each second risk policy information is associated with a risk policy number, and each third risk policy information is associated with a risk policy number; determine multiple first tag hash values ​​corresponding to each first risk policy information and a first target information hash value corresponding to each first tag hash value; determine multiple second tag hash values ​​corresponding to each second risk policy information and a second target information hash value corresponding to each second tag hash value; and determine multiple first tag hash values ​​corresponding to each third policy information hash value and a third target information hash value corresponding to each third tag hash value.For each first risk policy information in the fixed master system, the following operations are performed: Determine the risk policy number corresponding to the first risk policy information; determine the target second risk policy information indicated by the risk policy number among the plurality of second risk policy information; determine the plurality of second tag hash values ​​corresponding to the target second risk policy information and the second target information hash value corresponding to each second tag hash value; based on the plurality of first tag hash values ​​corresponding to the first risk policy information and the first target information hash value corresponding to each first tag hash value, the plurality of second tag hash values ​​corresponding to the target second risk policy information and the second target information hash value corresponding to each second tag hash value, use a comparison algorithm to determine the target comparison result of the insurance content corresponding to each first tag hash value, wherein the first risk policy information includes multiple insurance contents, and each insurance content includes a target tag and corresponding target information; and determine the target third risk policy information indicated by the risk policy number among the plurality of third risk policy information. The process involves determining multiple third-tag hash values ​​corresponding to the target third-risk policy information and the third target information hash value corresponding to each third-tag hash value. Based on the multiple first-tag hash values ​​corresponding to the first risk policy information and the first target information hash value corresponding to each first-tag hash value, the process also involves using a comparison algorithm to determine the target comparison result of the insurance content corresponding to each first-tag hash value. Furthermore, when an abnormal target comparison result is detected for any insurance content in the first risk policy information, the risk policy number is determined to be an abnormal policy number, and the insurance content is determined to be abnormal insurance content. A processing template is obtained, and the abnormal insurance number and the abnormal insurance content are processed using the processing template to obtain an abnormal policy report. The abnormal policy report is then sent to the terminal held by the maintenance personnel so that the maintenance personnel can download the abnormal policy report using the terminal.

[0105] In specific application scenarios, the conversion module 302 is further configured to: perform the following operations on each first tag hash value in the first risk policy information: compare the first tag hash value with each of the plurality of second tag hash values ​​corresponding to the target second risk policy information; when it is determined that the plurality of second tag hash values ​​do not include a target second tag hash value that is consistent with the first tag hash value, determine that the target comparison result of the insurance content corresponding to the first tag hash value is abnormal; and when it is determined that the plurality of second tag hash values ​​include a target second tag hash value that is consistent with the first tag hash value, continue to compare the first target information hash value corresponding to the first tag hash value with the second target information hash value corresponding to the target second tag hash value; if it is determined that the first target information hash value corresponding to the first tag hash value is consistent with the second target information hash value corresponding to the target second tag hash value, determine that the target comparison result of the insurance content corresponding to the first tag hash value is normal; if it is determined that the first target information hash value corresponding to the first tag hash value is inconsistent with the second target information hash value corresponding to the target second tag hash value, determine that the target comparison result of the insurance content corresponding to the first tag hash value is abnormal.

[0106] In specific application scenarios, the conversion module 302 is further configured to: perform the following operations on each first tag hash value in the first risk policy information: compare the first tag hash value with each of the plurality of third tag hash values ​​corresponding to the target third risk policy information; when it is determined that the plurality of third tag hash values ​​do not include a target third tag hash value that is consistent with the first tag hash value, determine that the target comparison result of the insurance content corresponding to the first tag hash value is abnormal; and when it is determined that the plurality of third tag hash values ​​include a target third tag hash value that is consistent with the first tag hash value, continue to compare the first target information hash value corresponding to the first tag hash value with the third target information hash value corresponding to the target third tag hash value; if it is determined that the first target information hash value corresponding to the first tag hash value is consistent with the third target information hash value corresponding to the target third tag hash value, determine that the target comparison result of the insurance content corresponding to the first tag hash value is normal; if it is determined that the first target information hash value corresponding to the first tag hash value is inconsistent with the third target information hash value corresponding to the target third tag hash value, determine that the target comparison result of the insurance content corresponding to the first tag hash value is abnormal.

[0107] In specific application scenarios, such as Figure 3B As shown, the device also includes: a warning module 305.

[0108] The early warning module 305 is used to generate early warning information based on the multiple target risk policy numbers, and send the early warning information to the terminal held by the maintenance personnel, so that when the maintenance personnel receive the early warning information on the terminal, they can extract the multiple target risk policy numbers from the early warning information and terminate the multiple insurance application processes indicated by the multiple target risk policy numbers.

[0109] In specific application scenarios, such as Figure 3B As shown, the device also includes a backtracking module 306.

[0110] The backtracking module 306 is used to query multiple operation process video information corresponding to the multiple target risk policy numbers from the backtracking system, annotate the corresponding operation process video information with the target risk policy number to obtain multiple target operation process video information, and send the multiple target operation process video information to the terminal held by the maintenance personnel, so that when the maintenance personnel receive the multiple target operation process video information on the terminal, they can arbitrarily extract the operation process video information corresponding to one target risk policy number from the multiple target operation process video information, and perform abnormal process investigation based on the operation process video information.

[0111] The apparatus provided in this application firstly determines multiple business execution systems associated with a policy review signal via a first determining module. Then, a conversion module extracts information on multiple risk policies that do not meet preset underwriting conditions from the database corresponding to each business execution system, and uses a hash algorithm to convert this information into multiple policy information hash values. Next, a comparison module determines a fixed master system and at least one comparison system among the multiple business execution systems. A comparison algorithm is then used to compare the multiple policy information hash values ​​corresponding to the fixed master system with the multiple policy information hash values ​​corresponding to each of the at least one comparison system, obtaining a comparison result. Finally, a second determining module determines a comparison result indication from the multiple policy information hash values ​​corresponding to the fixed master system. The process involves identifying multiple abnormal policy information hash values, determining the target risk policy number corresponding to each hash value, and then classifying the target risk policies indicated by these target risk policy numbers as abnormal policies. By using a hash algorithm to convert any policy information into a fixed value (hash value), and directly comparing these hash values, the system quickly identifies abnormal hash values ​​that are not found in the hash values ​​of multiple policy information systems corresponding to the main system. This ensures that the target risk policy number corresponding to the abnormal hash value is abnormal, and the target risk policy indicated by this target risk policy number is considered an abnormal policy. Since risk policy information includes many insurance details, converting the risk policy information into hash values ​​effectively improves the processing speed in subsequent comparisons. Furthermore, the entire process is automated, requiring no human intervention, and accurately identifies abnormal policies.

[0112] It should be noted that other corresponding descriptions of the functional units involved in the abnormal policy determination device provided in this embodiment of the invention can be found in the following references. Figure 1 and Figures 3A to 3C The corresponding description in [the document] will not be repeated here.

[0113] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing this application.

[0114] Those skilled in the art will understand that the modules in the apparatus of the implementation scenario can be distributed within the apparatus of the implementation scenario as described, or they can be located in one or more apparatuses different from this implementation scenario, with corresponding changes. The modules of the above-described implementation scenario can be combined into one module, or they can be further divided into multiple sub-modules.

[0115] The serial numbers in this application are for descriptive purposes only and do not represent the superiority or inferiority of the implementation scenario.

[0116] The above embodiments are merely exemplary embodiments of this application and are not intended to limit this application. The scope of protection of this application is defined by the claims. Those skilled in the art can make various modifications or equivalent substitutions to this application within its substance and scope of protection, and such modifications or equivalent substitutions should also be considered to fall within the scope of protection of this application.

Claims

1. A method for determining abnormal insurance policies, characterized in that, include: In response to a policy review signal, identify the multiple business execution systems associated with the policy review signal; Extract multiple risk policy information that do not meet the preset insurance conditions from the database corresponding to each business execution system, and use a hash algorithm to convert the multiple risk policy information corresponding to each business execution system into multiple policy information hash values, thereby obtaining multiple policy information hash values ​​corresponding to each business execution system. Each risk policy information corresponds to one policy information hash value, and each risk policy information is associated with one risk policy number, and each policy information hash value is associated with one risk policy number. In the multiple business execution systems, a fixed master system and at least one comparison system are determined. The comparison algorithm is used to compare the hash values ​​of multiple policy information corresponding to the fixed master system with the hash values ​​of multiple policy information corresponding to each of the at least one comparison system to obtain the comparison results. Among the multiple policy information hash values ​​corresponding to the fixed main system, determine the multiple abnormal policy information hash values ​​that indicate anomalies in the comparison results, determine the target risk policy number corresponding to each of the multiple abnormal policy information hash values, and regard the multiple target risk policies indicated by the multiple target risk policy numbers as abnormal policies; The step of using a hash algorithm to convert multiple risk policy information corresponding to each business execution system into multiple policy information hash values, thereby obtaining multiple policy information hash values ​​corresponding to each business execution system, includes: performing the following operations on each business execution system: using the hash algorithm to convert multiple target tags and target information corresponding to each target tag in each risk policy information corresponding to the business execution system into multiple tag hash values ​​and target information hash values ​​corresponding to each tag hash value; calculating the sum of the multiple tag hash values ​​to obtain a first hash value; calculating the sum of the multiple target information hash values ​​to obtain a second hash value; calculating the sum of the first hash value and the second hash value to obtain a policy information hash value corresponding to each risk policy information; and obtaining multiple policy information hash values ​​corresponding to the business execution system based on the policy information hash values ​​corresponding to each risk policy information, wherein the risk policy information includes the multiple target tags and target information corresponding to each target tag; The method further includes, after determining the target risk policy number corresponding to each of the multiple abnormal policy information hash values, the following steps: querying multiple operation process video information corresponding to the multiple target risk policy numbers from the backtracking system, labeling the corresponding operation process video information with the target risk policy number to obtain multiple target operation process video information, and sending the multiple target operation process video information to the terminal held by the maintenance personnel, so that when the maintenance personnel receive the multiple target operation process video information on the terminal, they can arbitrarily extract the operation process video information corresponding to a target risk policy number from the multiple target operation process video information and perform abnormal process investigation based on the operation process video information.

2. The method for determining abnormal insurance policies according to claim 1, characterized in that, The step of extracting information on multiple risk insurance policies that do not meet the preset underwriting conditions from the database corresponding to each business execution system includes: Obtain the pending review period carried by the policy review signal; For each business execution system, perform the following operations: determine the database corresponding to the business execution system, query multiple policy information whose policy generation time is within the pending review period from the database, obtain the preset insurance conditions, extract multiple pending insurance contents from each policy information, and when it is determined that any of the multiple pending insurance contents does not meet the preset insurance conditions, determine the policy information corresponding to the pending insurance contents as risk policy information, thereby obtaining multiple risk policy information, wherein each policy information includes the policy generation time and multiple pending insurance contents.

3. The method for determining abnormal insurance policies according to claim 1, characterized in that, The comparison algorithm is used to compare the hash values ​​of multiple policy information corresponding to the fixed master system with the hash values ​​of multiple policy information corresponding to each of the at least one comparison system to obtain the comparison results, including: It is determined that the multiple business execution systems include a fixed master system, a first comparison system, and a second comparison system, and that the fixed master system corresponds to multiple first policy information hash values, the first comparison system corresponds to multiple second policy information hash values, and the second comparison system corresponds to multiple third policy information hash values; For each first policy information hash value in the fixed master system, perform the following operations: compare the first policy information hash value with each second policy information hash value in the first comparison system; if it is determined that the plurality of second policy information hash values ​​include a second policy information hash value that is consistent with the first policy information hash value, then determine that the comparison result corresponding to the first policy information hash value is normal; if it is determined that the plurality of second policy information hash values ​​do not include a second policy information hash value that is consistent with the first policy information hash value, then determine that the comparison result corresponding to the first policy information hash value is abnormal. The hash value of the first policy information is compared with the hash value of each third policy information in the second comparison system. If it is determined that the multiple third policy information hash values ​​include a third policy information hash value that is consistent with the hash value of the first policy information, then the comparison result corresponding to the hash value of the first policy information is determined to be normal. If it is determined that the multiple third policy information hash values ​​do not include a third policy information hash value that is consistent with the hash value of the first policy information, then the comparison result corresponding to the hash value of the first policy information is determined to be abnormal.

4. The method for determining abnormal insurance policies according to claim 1, characterized in that, The method further includes: The multiple business execution systems are determined to include a fixed master system, a first comparison system, and a second comparison system. The fixed master system corresponds to multiple first risk policy information, the first comparison system corresponds to multiple second risk policy information, and the second comparison system corresponds to multiple third risk policy information. Each first risk policy information is associated with a risk policy number, each second risk policy information is associated with a risk policy number, and each third risk policy information is associated with a risk policy number. Determine multiple first tag hash values ​​corresponding to each first risk policy information and a first target information hash value corresponding to each first tag hash value; determine multiple second tag hash values ​​corresponding to each second risk policy information and a second target information hash value corresponding to each second tag hash value; and determine multiple third tag hash values ​​corresponding to each third policy information hash value and a third target information hash value corresponding to each third tag hash value. For each first risk policy information in the fixed master system, the following operations are performed: Determine the risk policy number corresponding to the first risk policy information; determine the target second risk policy information indicated by the risk policy number among the plurality of second risk policy information; determine the plurality of second tag hash values ​​corresponding to the target second risk policy information and the second target information hash value corresponding to each second tag hash value; based on the plurality of first tag hash values ​​corresponding to the first risk policy information and the first target information hash value corresponding to each first tag hash value, and the plurality of second tag hash values ​​corresponding to the target second risk policy information and the second target information hash value corresponding to each second tag hash value, use a comparison algorithm to determine the target comparison result of the insurance content corresponding to each first tag hash value, wherein the first risk policy information includes multiple insurance contents, and each insurance content includes a target tag and corresponding target information, and... The target third-risk policy information indicated by the risk policy number is determined from the plurality of third-risk policy information. Multiple third-tag hash values ​​corresponding to the target third-risk policy information and a third target information hash value corresponding to each third-tag hash value are determined. Based on the multiple first-tag hash values ​​corresponding to the first risk policy information and the first target information hash value corresponding to each first-tag hash value, the multiple third-tag hash values ​​corresponding to the target third-risk policy information and the third target information hash value corresponding to each third-tag hash value, a comparison algorithm is used to determine the target comparison result of the insurance content corresponding to each first-tag hash value. When an anomaly is detected in the target comparison result of any insured content in the first risk policy information, the risk policy number is determined to be an abnormal policy number, and the insured content is determined to be abnormal insured content. A processing template is obtained, and the abnormal policy number and the abnormal insured content are processed using the processing template to obtain an abnormal policy report. The abnormal policy report is sent to the terminal held by the maintenance personnel so that the maintenance personnel can download the abnormal policy report based on the terminal.

5. The method for determining abnormal insurance policies according to claim 4, characterized in that, The step of determining the target comparison result of the insurance content corresponding to each first tag hash value based on multiple first tag hash values ​​corresponding to the first risk policy information and the first target information hash value corresponding to each first tag hash value, multiple second tag hash values ​​corresponding to the target second risk policy information and the second target information hash value corresponding to each second tag hash value, using a comparison algorithm includes: For each first tag hash value in the first risk policy information, perform the following operations: compare the first tag hash value with each of the plurality of second tag hash values ​​corresponding to the target second risk policy information; if it is determined that the plurality of second tag hash values ​​do not include a target second tag hash value that is consistent with the first tag hash value, determine that the target comparison result of the insurance content corresponding to the first tag hash value is abnormal, and... When it is determined that the plurality of second tag hash values ​​include a target second tag hash value that is consistent with the first tag hash value, the first target information hash value corresponding to the first tag hash value is further compared with the second target information hash value corresponding to the target second tag hash value. If it is determined that the first target information hash value corresponding to the first tag hash value is consistent with the second target information hash value corresponding to the target second tag hash value, then the target comparison result of the insurance content corresponding to the first tag hash value is determined to be normal. If it is determined that the first target information hash value corresponding to the first tag hash value is inconsistent with the second target information hash value corresponding to the target second tag hash value, then the target comparison result of the insurance content corresponding to the first tag hash value is determined to be abnormal.

6. The method for determining abnormal insurance policies according to claim 4, characterized in that, The step of determining the target comparison result of the insurance content corresponding to each first tag hash value based on multiple first tag hash values ​​corresponding to the first risk policy information and the first target information hash value corresponding to each first tag hash value, multiple third tag hash values ​​corresponding to the target third risk policy information and the third target information hash value corresponding to each third tag hash value, using a comparison algorithm includes: For each first tag hash value in the first risk policy information, perform the following operations: compare the first tag hash value with each of the plurality of third tag hash values ​​corresponding to the target third risk policy information; if it is determined that the plurality of third tag hash values ​​do not include a target third tag hash value that is consistent with the first tag hash value, determine that the target comparison result of the insurance content corresponding to the first tag hash value is abnormal, and... When it is determined that the plurality of third tag hash values ​​include a target third tag hash value that is consistent with the first tag hash value, the first target information hash value corresponding to the first tag hash value is further compared with the third target information hash value corresponding to the target third tag hash value. If it is determined that the first target information hash value corresponding to the first tag hash value is consistent with the third target information hash value corresponding to the target third tag hash value, then the target comparison result of the insurance content corresponding to the first tag hash value is determined to be normal. If it is determined that the first target information hash value corresponding to the first tag hash value is inconsistent with the third target information hash value corresponding to the target third tag hash value, then the target comparison result of the insurance content corresponding to the first tag hash value is determined to be abnormal.

7. The method for determining abnormal insurance policies according to claim 1, characterized in that, After determining the target risk policy number corresponding to each of the plurality of abnormal policy information hash values, the method further includes: Based on the multiple target risk policy numbers, an early warning message is generated and sent to the terminal held by the maintenance personnel. This allows the maintenance personnel to extract the multiple target risk policy numbers from the early warning message upon receiving it, and to terminate the multiple insurance application processes indicated by the multiple target risk policy numbers.

8. A device for determining abnormal insurance policies, characterized in that, The device for determining abnormal policies is used to implement the steps of the method according to any one of claims 1 to 7, including: The first determining module is used to determine, in response to the policy review signal, multiple business execution systems associated with the policy review signal; The conversion module is used to extract multiple risk policy information that do not meet the preset insurance conditions from the database corresponding to each business execution system, and to use a hash algorithm to convert the multiple risk policy information corresponding to each business execution system into multiple policy information hash values, thereby obtaining multiple policy information hash values ​​corresponding to each business execution system. Each risk policy information corresponds to one policy information hash value, and each risk policy information is associated with one risk policy number, and each policy information hash value is associated with one risk policy number. The comparison module is used to determine a fixed master system and at least one comparison system in the multiple business execution systems, and to use a comparison algorithm to compare the hash values ​​of multiple policy information corresponding to the fixed master system with the hash values ​​of multiple policy information corresponding to each of the at least one comparison system to obtain the comparison results. The second determining module is used to determine, from the multiple policy information hash values ​​corresponding to the fixed main system, multiple abnormal policy information hash values ​​whose comparison results indicate anomalies, determine the target risk policy number corresponding to each of the multiple abnormal policy information hash values, and regard the multiple target risk policies indicated by the multiple target risk policy numbers as abnormal policies. The conversion module is configured to perform the following operations on each business execution system: using the hash algorithm, converting multiple target tags and target information corresponding to each target tag in each risk policy information corresponding to the business execution system into multiple tag hash values ​​and target information hash values ​​corresponding to each tag hash value; calculating the sum of the multiple tag hash values ​​to obtain a first hash value; calculating the sum of the multiple target information hash values ​​to obtain a second hash value; calculating the sum of the first hash value and the second hash value to obtain a policy information hash value corresponding to each risk policy information; and based on the policy information hash value corresponding to each risk policy information, obtaining multiple policy information hash values ​​corresponding to the business execution system, wherein the risk policy information includes the multiple target tags and target information corresponding to each target tag; The device further includes: The backtracking module is used to query multiple operation process video information corresponding to the multiple target risk policy numbers in the system. The operation process video information is labeled with the target risk policy number to obtain multiple target operation process video information. The multiple target operation process video information is sent to the terminal held by the maintenance personnel, so that when the maintenance personnel receive the multiple target operation process video information on the terminal, they can randomly extract the operation process video information corresponding to one target risk policy number from the multiple target operation process video information and perform abnormal process investigation based on the operation process video information.

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