Insurance policy processing method and device based on artificial intelligence, equipment and medium

Abstract

The invention relates to the technical field of information processing, is applied to financial or medical insurance scenes, and particularly relates to an insurance policy processing method, device, equipment and medium based on artificial intelligence. Insurance policy cancellation information of a target user for an insurance policy cancellation service is obtained, and the information comprises insurance policy payment type information and insurance policy appeal information; based on a pre-trained keyword recognition model and a preset keyword set, keyword recognition processing is carried out on the insurance policy payment type information, an insurance policy cancellation type is determined, the type comprises an amount-based insurance cancellation mode type or a time-based insurance cancellation mode type, and according to the insurance policy cancellation type and the insurance policy appeal information, an insurance policy cancellation amount is obtained through calculation; and based on a preset verification strategy, performing anti-loss verification on the insurance cancellation amount of the insurance policy to generate an insurance policy cancellation scheme for the target user. It can be seen that the problem that an existing insurance policy processing technology is difficult to adapt to complex and diversified special situations of customers is solved, so that the accuracy of insurance policy processing is improved, and user experience is enhanced.

Description

Technical Field

[0001] This invention relates to the field of information processing technology, and in particular to an insurance policy processing method, apparatus, equipment and medium based on artificial intelligence. Background Technology

[0002] With the rapid development of internet technology, various medical and financial insurance products have emerged. As people pay more attention to their health, especially the requirements for health insurance products are getting higher and higher, insurance companies are receiving all kinds of requests for cancellation of health insurance products. As a result, the importance of policy processing in the operation and management of insurance companies is becoming more and more prominent.

[0003] Existing policy processing technologies often rely on two fixed refund methods: full premium refund or a percentage refund based on the remaining coverage period. However, these methods lack flexibility and are ill-suited to handling complex and diverse customer situations (such as complaints, fraud, or potential erroneous refunds of used service fees). Furthermore, with a high volume of policy refunds and applications, the accuracy of policy processing decreases, negatively impacting user experience. Therefore, improving the accuracy of policy processing and enhancing user experience is a pressing technical challenge. Summary of the Invention

[0004] Therefore, it is necessary to address the above-mentioned technical problems by providing an artificial intelligence-based policy processing method, apparatus, device, and medium in this invention, in order to solve the problem that existing policy processing technologies are difficult to adapt to the complex and diverse special circumstances of customers, thereby leading to inaccuracies in policy processing and affecting user experience.

[0005] The first aspect of this application provides an artificial intelligence-based policy processing method, the artificial intelligence-based policy processing method comprising: Obtain policy surrender information of target users regarding policy surrender, wherein the policy surrender information includes policy payment type information and policy request information; Based on a pre-trained keyword recognition model and a preset keyword set, keyword recognition processing is performed on the policy payment type information to determine the policy surrender type, wherein the policy surrender type includes a surrender mode type based on amount or a surrender mode type based on period. Based on the policy surrender type and the policy request information, the policy surrender amount is calculated, wherein the policy surrender amount includes the surrender amount of the first policy or the surrender amount of the second policy; Based on a preset verification strategy, the policy surrender amount is verified to prevent financial loss, so as to generate a policy surrender plan for the target user.

[0006] A second aspect of this application provides an artificial intelligence-based policy processing device, the artificial intelligence-based policy processing device comprising: The acquisition module is used to acquire policy surrender information of the target user for policy surrender business, wherein the policy surrender information includes policy payment type information and policy request information; The determination module is used to perform keyword recognition processing on the policy payment type information based on a pre-trained keyword recognition model and a preset keyword set to determine the policy surrender type, wherein the policy surrender type includes a surrender mode type based on amount or a surrender mode type based on period. The calculation module is used to calculate the policy surrender amount based on the policy surrender type and the policy request information, wherein the policy surrender amount includes the first policy surrender amount or the second policy surrender amount; The generation module is used to perform loss prevention verification on the policy surrender amount based on a preset verification strategy, so as to generate a policy surrender plan for the target user.

[0007] Thirdly, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the artificial intelligence-based policy processing method as described in the first aspect.

[0008] Fourthly, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program, which, when executed by a processor, implements the artificial intelligence-based policy processing method as described in the first aspect.

[0009] In summary, this invention provides an artificial intelligence-based policy processing method, apparatus, device, and medium. It acquires policy cancellation information from a target user regarding policy cancellation, including policy payment type information and policy request information. Based on a pre-trained keyword recognition model and a preset keyword set, it performs keyword recognition processing on the policy payment type information to determine the policy cancellation type. The policy cancellation type includes either a cancellation model based on amount or a cancellation model based on policy period. Based on the policy cancellation type and policy request information, it calculates the policy cancellation amount, which includes either a first policy cancellation amount or a second policy cancellation amount. Finally, it performs loss prevention verification on the policy cancellation amount based on a preset verification strategy to generate a policy cancellation plan for the target user. As can be seen, this application calculates the policy surrender amount by supporting target users to surrender the policy by amount or by period, and then performs loss prevention verification on the policy surrender amount based on a preset verification strategy to generate a policy surrender plan for the target user. This solves the problem that existing policy processing technology is difficult to adapt to the complex and diverse special circumstances of customers, thereby improving the accuracy of policy processing and enhancing the user experience. Attached Figure Description

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

[0011] Figure 1 This is a schematic diagram of an application environment for an artificial intelligence-based policy processing method provided in an embodiment of the present invention; Figure 2 This is a flowchart illustrating an artificial intelligence-based policy processing method according to an embodiment of the present invention. Figure 3 This is a schematic diagram of the structure of an insurance policy processing device based on artificial intelligence according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

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

[0013] It should be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0014] It should also be understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0015] As used in this specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," or "in response to determination." Similarly, the phrase "if determined" or "if matched to [described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once matched to [described condition or event]," or "in response to matched to [described condition or event]."

[0016] Furthermore, in the description of this invention and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0017] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of the invention include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0018] It should be understood that the sequence number of each step in the following embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0019] To illustrate the technical solution of the present invention, specific embodiments are described below.

[0020] An embodiment of the present invention provides an artificial intelligence-based policy processing method, which can be applied to, for example... Figure 1In this application environment, the terminal device communicates with the server. The terminal device includes, but is not limited to, PDAs, desktop computers, laptops, ultra-mobile personal computers (UMPCs), netbooks, and personal digital assistants (PDAs). The server can be implemented using a standalone server or a server cluster consisting of multiple servers.

[0021] See Figure 2 This is a flowchart illustrating an artificial intelligence-based policy processing method according to an embodiment of the present invention. Figure 2 As shown, this AI-based policy processing method can be implemented through the following steps.

[0022] S201: Obtain policy cancellation information from the target user regarding the policy cancellation process, wherein the policy cancellation information includes policy payment type information and policy request information.

[0023] In step S201, the target user can be any user, and can be any user with a policy surrender requirement. Policy surrender refers to the process whereby an insured applies to the insurance company to terminate the contract during its validity period, and the insurance company refunds the corresponding cash value (or premium) according to the contract. For example, if Mr. Wang submits a medical and health insurance surrender application through the insurance company, the system automatically retrieves the policy data for his "Million-Dollar Medical Insurance (Annual Payment)" and collects the policy surrender information. Upon policy surrender, the insurance contract terminates, and the policyholder no longer enjoys insurance coverage. Policy surrender can include one or more insurance categories, such as personal safety insurance, medical and health insurance, and financial property insurance. Policy surrender information refers to various data and materials related to the surrender process, used to record the surrender process and support surrender review. It serves as the information carrier for the surrender process, including policy payment type information and policy request information. Policy payment type information refers to the relevant information such as the premium payment method, cycle, and amount stipulated in the insurance contract, and is the basis for the policyholder to fulfill their payment obligations. Policy request information can be the user's demand information for one or more insurance categories, which may include one or more of the following: policy period, policy amount, etc.

[0024] A dedicated insurance policy cancellation processing platform can be developed. Users can request related services for policy cancellations through this platform. This platform can be a management platform based on policy cancellation process control, managing all individual policy cancellation transactions. It can include functions such as process change management, payment and collection process management, centralized policy cancellation management, and acceptance, approval, and processing of policy cancellation applications. Managing individual policy cancellation transactions through this platform can improve processing efficiency.

[0025] In practice, when an insurance user needs to cancel a policy, they can submit the cancellation request through the client. In response to the user's action, the client can generate a policy cancellation request for the refund of funds, such as a medical and health insurance policy cancellation request or a financial annuity insurance policy cancellation request. By sending the policy cancellation request to the insurance policy maintenance processing platform, the platform can parse the scheme generation instruction upon receiving the policy cancellation request and extract the policy cancellation information of the target user for the policy cancellation business.

[0026] In one embodiment of the invention, before obtaining the policy surrender information of the target user for policy surrender business, the process includes: Determine whether a policy cancellation request has been received from the client, wherein the policy cancellation request includes the target user's user information; When a policy cancellation request is received from the client, the policy cancellation time for the target user is obtained based on the user information. Calculate the number of working days between the current date and the policy surrender date; Determine whether the number of working days meets the preset number of days threshold; If the number of working days meets the preset number of days threshold, then obtain the policy cancellation information of the target user for the policy cancellation business.

[0027] Specifically, when a client (such as an insurance company's app, website, or offline counter system) sends a policy cancellation request to the insurance policy maintenance processing platform via API, HTTP / HTTPS protocol, or WebSocket, the platform captures the policy cancellation request through an interface listening mechanism (such as HTTP / HTTPS request listening or message queue subscription). The platform verifies the legality of the policy cancellation request (e.g., signature verification, anti-duplicate submission, anti-tampering). If the policy cancellation request is legal and contains complete and valid user information, the platform determines that it has received the policy cancellation request from the client; otherwise, it returns an error message. When a policy cancellation request is made, the system links the user's information (such as ID number and policy number) to the insurance company's policy database. The system then extracts the policy cancellation time from this database; this cancellation time is the time the user initiated the application on the client side. The system calculates the number of working days between the current date and the cancellation time. For example, if the cancellation time is July 1, 2025 (Tuesday), and the current date is July 5, 2025 (Saturday), the number of working days is 3 (July 2nd, 3rd, and 4th, excluding July 5th, Saturday). The preset threshold for the number of working days is determined by the insurance company according to regulatory requirements or business rules; this application does not impose any restrictions on this. The system then checks whether the number of working days meets the preset threshold. If it does, the system obtains the target user's policy cancellation information. These steps avoid overlooking cancellation requests during manual processing, shorten the cancellation processing cycle, and thus improve the efficiency of policy processing.

[0028] In this embodiment, by quickly and accurately obtaining policy cancellation information from target users regarding policy cancellation, insurance products and services can be optimized in a targeted manner, reducing user dissatisfaction and improving user satisfaction, thereby laying the foundation for improving the accuracy of policy processing in the future.

[0029] S102: Based on a pre-trained keyword recognition model and a preset keyword set, perform keyword recognition processing on the policy payment type information to determine the policy surrender type, wherein the policy surrender type includes a surrender mode type based on amount or a surrender mode type based on period.

[0030] In step S102, the policy payment type information is processed by a pre-trained keyword recognition model and a preset keyword set to determine the policy surrender type. If the policy payment type information contains amount-related keywords such as fixed amount, agreed amount, amount increment, amount decrement, or percentage of the sum assured, or scenario-based keywords such as lump sum payment (paying the entire amount at once, which is essentially paying the total amount at once), then the policy surrender type is determined to be the amount-based surrender type. If the policy payment type information contains period-related keywords such as periodic payment, installment payment, periodic payment, annual payment, or payment per transaction, then the policy surrender type is determined to be the period-based surrender type. The "based on premium amount" surrender model calculates the refundable amount based on the policy's "cumulative premiums paid" or "cash value," without considering the termination logic of "terms." For example, if a user purchases a whole life insurance policy with a coverage of 500,000 yuan and pays a one-time premium of 100,000 yuan, the refund upon surrender is based on the current policy cash value (e.g., 80,000 yuan in the third year). The "based on term" surrender model is for term life insurance policies with unpaid premiums. When surrendering, the user must explicitly terminate the payment obligation for the remaining terms. The refundable amount is calculated based on the "cash value corresponding to the paid terms." For example, if a user purchases a 10-year term annuity insurance policy with an annual premium of 10,000 yuan, and has paid for 3 terms (cumulative 30,000 yuan), and applies for surrender in the fourth year, the user must terminate the payment obligation for the remaining 7 terms and will be refunded based on the cash value after the third term (e.g., 25,000 yuan).

[0031] In one embodiment of the invention, based on a pre-trained keyword recognition model and a preset keyword set, keyword recognition processing is performed on policy payment type information to determine the policy surrender type, including: The policy payment type information is processed by a pre-trained keyword recognition model to obtain keyword recognition results. The keyword recognition results are matched with a preset keyword set to determine the distribution of each keyword in the policy payment type information. The preset keyword set includes amount keywords and period keywords. Based on the distribution of each keyword in the preset keyword set in the policy payment type information, the number of the amount keyword and the period keyword are determined respectively; The policy surrender type is determined based on the number of the monetary keywords and the term keywords.

[0032] Specifically, when obtaining policy payment type information, this information is input into a pre-trained keyword recognition model. The model utilizes a Bidirectional Long Short-Term Memory (BiLSTM) neural network architecture to perform keyword recognition processing, yielding the result. A pre-defined keyword set includes one or more keywords that are restricted from public disclosure, such as terms related to financial information or term information. Therefore, for one or more keywords in the keyword set, they can be labeled with different types during the setup process, and these keywords can be saved and recorded in different categories.

[0033] On the other hand, to ensure that the keywords recorded in the keyword set meet the requirements, the keyword set can be checked periodically and updated regularly according to the instructions or needs of the target users. During the update process, new keywords can be added, outdated keywords can be deleted, and different matching methods can be labeled for the same keywords in the keyword set. Furthermore, query and retrieval functions can be provided for the keyword set, allowing users to quickly locate the corresponding keywords when using the keyword set. By matching the keyword recognition results with the preset keyword set, the distribution of each keyword in the preset keyword set in the policy payment type information is determined. The preset keyword set includes amount keywords and period keywords. Amount keywords include lump sum payment, total premium, paid amount, cash value, accumulated payment, etc., while period keywords include installment payment, annual payment, monthly payment, quarterly payment, total number of periods, paid period, remaining period, installment, etc. Then, based on the distribution of each keyword in the preset keyword set in the policy payment type information, the amount keywords and period keywords are determined respectively. The number of keywords determines the policy's surrender type. If the number of monetary keywords exceeds the number of installment keywords, and the policy contains core monetary keywords such as "lump-sum payment" and "one-time payment," but lacks installment keywords such as "installment payment" or "remaining installments," the policy is judged as surrendering based on monetary amount. If the number of installment keywords exceeds the number of monetary keywords, and the policy contains core installment keywords such as "installment payment" and "remaining installments," the policy is judged as surrendering based on installment. If the number of monetary keywords and installment keywords are equal, the core keyword takes precedence. For example, if "installment payment" is included, surrendering based on installment is prioritized; if "lump-sum payment" is included, surrendering based on monetary amount is prioritized. This keyword recognition model, combined with a keyword set, can quickly and effectively identify and process a large amount of policy payment type information, thereby efficiently and accurately determining the policy surrender type and providing strong support for improving the accuracy of policy processing.

[0034] In this embodiment, by using the policy payment type information, the policy cancellation type can be obtained more accurately and completely, avoiding errors in manual judgment. This achieves precise classification and processing of cancellation business, enabling more intelligent policy processing in the future and improving the efficiency of policy processing.

[0035] S203: Calculate the policy surrender amount based on the policy surrender type and the policy request information, wherein the policy surrender amount includes the surrender amount of the first policy or the surrender amount of the second policy.

[0036] In step S203, the surrender amounts for the first policy and the second policy are calculated sequentially based on whether the policy surrender type is the surrender mode by amount or the surrender mode by period, combined with the policy claim information.

[0037] In one embodiment of the invention, the policy claim information includes the policy claim surrender amount. The policy surrender amount is calculated based on the policy surrender type and the policy claim information, including: When the policy surrender type is the amount-based surrender mode, the target user's surrender type identifier, service insurance premium usage, and surrender time period are obtained for the policy surrender business. In the policy center, the policy information corresponding to the aforementioned cancellation type identifier has been found to have collected premiums and processed refunds for policy maintenance tasks. The total premium amount of the policy is calculated based on the premiums already collected for the policy and the collection and refund of the maintenance task. Determine whether the requested surrender amount is less than or equal to the total premium of the policy; If the requested surrender value of the policy is less than or equal to the total premium of the policy, the surrender value of the first policy is calculated based on the total premium of the policy, the usage of the premium for the service insurance, and the surrender period.

[0038] Specifically, when the policy surrender type is the amount-based surrender mode, the system obtains the surrender category identifier, service insurance premium usage status, and surrender time period for the target user's policy surrender business. The surrender category identifier refers to the category or code marked at the time of surrender to distinguish different types or reasons for surrender. Service insurance premium usage status refers to the status of premiums for different insurance types during use, such as paid, unpaid, partially used, or refunded. For example, by querying the usage status of medical service insurance, if it is unused, it is not included in the limit; if it has been used, the actual premium paid is deducted from the medical service insurance fee before calculating the refundable premium amount. The surrender time period refers to the time range within which the customer wants to surrender the policy, such as a specific year, quarter, or month. The system searches the policy center for the corresponding policy information and finds the policies with collected premiums and policy maintenance task refunds corresponding to the surrender category identifier. The premium refers to the total premium already collected from the user for this type of insurance (including the initial premium, renewal premium, etc.); the policy maintenance task collection and refund fee refers to the amount of additional or refunded fees generated due to policy maintenance operations (such as adjustment of the sum insured, change of policyholder, etc.) (for example, if the user applied to increase the sum insured and paid additional premiums, or reduced the sum insured and received a partial refund). Based on the premium already collected and the policy maintenance task collection and refund fee, the total premium of the policy amount is calculated, that is, the total premium of the policy amount = the premium already collected + the policy maintenance task collection and refund fee. Then, it is determined whether the requested refund amount is less than or equal to the total premium of the policy amount. Among them, the requested refund amount is the amount that the user expects to be refunded. If the requested refund amount is less than or equal to the total premium of the policy amount, the refund amount of the first policy is calculated based on the total premium of the policy amount, the usage of the service insurance premium, and the time period of the refund, that is, the refund amount of the first policy amount = (total premium of the policy amount - premium used) × the percentage of the time period of the refund. As can be seen, by deducting the incurred medical service fees from the total policy premium, loopholes in medical service refunds that could lead to insurance company losses are avoided. This automatically mitigates the risk of refunding used service fees and potential financial losses. The design incorporates service usage results for subsequent use in limiting the maximum refund threshold. This can be achieved by providing a medical service result identifier (0 - not used / 1 - in use / 2 - used). When the identifier is 1 or 2, the total policy premium needs to be deducted, and detailed medical service usage records are readily available for traceability. Through these steps, the refund type identifier accurately identifies premiums already collected and policy maintenance task refunds, ensuring no omissions in the total policy premium base, avoiding errors in calculating the first policy's refund amount, and improving the accuracy of the first policy's refund amount calculation.

[0039] In one embodiment of the invention, the policy appeal information includes the policy surrender period, and the policy surrender amount is calculated based on the policy surrender type and the policy appeal information, further comprising: If the policy surrender type is the periodic surrender mode, then obtain the target user's policy payment schedule, service insurance premium usage and surrender time period for the policy surrender business. The policy payment schedule includes the policy payment period, policy payment status and the actual premium received in the current period. Based on the policy payment schedule and the policy surrender period, the total premium for the corresponding policy period is calculated. The surrender amount for the second policy is calculated based on the total premium for the policy period, the usage of the premium for the service insurance, and the surrender period.

[0040] Specifically, when the policy surrender type is the installment surrender mode, the system obtains the target user's policy payment schedule, service insurance premium usage, and surrender time period for the policy surrender business. The policy payment schedule records detailed information for each payment period, including the policy payment period, policy payment status, and the actual premium received for that period. The policy payment period is categorized as "Period 1," "Period 2," ..., "Period N" (corresponding to the installment payment cycle, such as monthly, quarterly, or annually). The policy payment status marks the payment status of each period's premium (e.g., "Paid," "Unpaid," "Supplementary Payment," "Reduction," etc.). The actual premium received for that period is the actual premium collected for each period (which may vary depending on discounts, adjustments, etc.). (Standard premiums differ); the usage status of service insurance premiums refers to the usage status of premiums corresponding to different insurance types during the process, such as paid, unpaid, partially used, or refunded. For example, by querying the usage status of medical service insurance, if it is not used, it will not be included in the limit; if it has been used, the actual premium paid will be deducted from the cost of medical service insurance before calculating the amount of premium to be refunded; the cancellation period refers to the time range within which the customer wants to cancel the policy, such as a certain year, quarter, or month. The period that matches the policy cancellation request period and whose payment status is "paid" is selected from the policy payment schedule. Among them, the policy cancellation request period is the period in which the user clearly applies for cancellation (such as "cancel the 3rd period", "cancel the 2nd-4th period", etc.). By summing the actual premiums received for the selected valid periods, the total premium for each policy period is obtained, i.e., Total premium for each policy period = ∑ (Actual premiums received for the current period in which the payment status is "paid" during the requested surrender period). Then, based on the total premium for each policy period, the usage of the serviced insurance premiums, and the surrender period, the surrender amount for the second policy is calculated, i.e., Surrender amount for the second policy = (Total premium for each policy period - Premiums used in the period) × Percentage of surrender period within the period. It is evident that by deducting the medical service expenses incurred within the period from the total premium for each policy period, loopholes in medical service surrenders leading to insurance company losses are avoided. This automatically mitigates the risk of refunding used service fees and potential financial losses. The design incorporates service usage results for subsequent use in limiting the maximum surrender amount threshold. This can be achieved by providing a medical service result identifier (0 - not used / 1 - in use / 2 - used). When the identifier is 1 or 2, the total premium needs to be deducted, and detailed medical service insurance usage records are provided for easy traceability. By using the above steps, the policy payment schedule and the policy surrender period can be used to ensure that the total premium base for each period is not omitted, avoid errors in the calculation of the surrender amount of the second policy, and improve the accuracy of the calculation of the surrender amount of the second policy.

[0041] In this embodiment, by using either the amount-based or period-based surrender mode in the policy surrender type, and combining it with the policy request information, a more accurate policy surrender amount can be calculated. This solves the problem of the fixed refund mode in the existing surrender mechanism, so as to provide sales personnel with accurate surrender decision support, avoid one-size-fits-all or vague compensation methods, improve customer satisfaction, and help insurance companies better manage compensation risks and avoid financial risks or losses caused by inaccurate estimation.

[0042] S204: Based on a preset verification strategy, perform a loss prevention verification on the policy surrender amount to generate a policy surrender plan for the target user.

[0043] In step S204, after calculating the surrender amount of the first policy by the surrender amount mode type or the surrender amount of the second policy by the surrender period mode type, the surrender amount of the first policy or the surrender amount of the second policy needs to be verified against financial losses through a preset verification strategy (such as verification of the reasonableness of the amount, verification of business rules, and verification of data consistency). This is to prevent the insurance company from suffering financial losses due to calculation errors, rule loopholes, etc. When the surrender amount of the policy passes the verification against financial losses, a compliant policy surrender plan is finally generated.

[0044] In one embodiment of the invention, a loss prevention verification is performed on the policy surrender amount based on a preset verification strategy to generate a policy surrender plan for the target user, including: Determine whether the surrender amount of the policy is within the preset surrender amount range; If the policy surrender amount is within the preset surrender amount range, then the policy surrender amount is determined to pass the loss prevention verification, and the target user's target historical data is obtained; Based on the target historical data, the policy surrender amount, and the policy surrender information, a policy surrender plan is generated for the target user.

[0045] Specifically, based on business needs and system characteristics, a pre-configured reasonable surrender amount range is defined, but this application does not impose any restrictions on this range. The surrender amount is determined by whether it falls within the preset surrender amount range. If it does, the surrender amount passes the loss prevention verification. For example, if the cash value of a medical health insurance policy is 8000 yuan, and the preset surrender amount range is [6400, 9600], and the calculated surrender amount is 7500 yuan, then the surrender amount passes the loss prevention verification. This allows the acquisition of target user historical data, including: policy history data: historical surrender records (such as whether a surrender has been applied for, the number of times, and the amount), and policy claims. The system collects data on various aspects, including: records of claims (whether claims have occurred, claim amounts, and claim dates), policy change records (such as adjustments to the sum insured or payment methods); user behavior data (interaction records with the insurance company, such as complaints, inquiries, and service evaluations), product holdings (whether other valid policies are held simultaneously, policy types, and values); and risk rating data (user risk preferences, such as conservative or moderate risk tolerance), and historical credit records (such as timely payments and outstanding debts). By combining this historical data, policy surrender amounts, and policy surrender information, a policy surrender plan tailored to the target user can be generated. These steps ensure the efficient and accurate generation of policy surrender plans for the target user, guaranteeing the precision of policy processing and improving user satisfaction.

[0046] In one embodiment of the invention, a policy surrender plan is generated for a target user based on target historical data, policy surrender amount, and policy surrender information, including: The target historical data is preprocessed to obtain preprocessed target historical data; The preprocessed target historical data is input into the policy surrender prediction model to obtain the policy surrender prediction amount; Determine whether the surrender amount of the policy is consistent with the predicted surrender amount of the policy; If the surrender amount of the policy is consistent with the predicted surrender amount of the policy, then the policy surrender information is matched using a preset policy surrender scheme model to generate a policy surrender scheme for the target user. The preset policy surrender scheme model is used to characterize the relationship between the policy surrender information and the policy surrender scheme.

[0047] Specifically, data quality is ensured by cleaning, feature engineering, and standardizing the target historical data. Data cleaning includes: handling missing values ​​(e.g., filling missing items in "premium payment records" with the mean), removing outliers (e.g., filtering extreme values ​​in "claims amount"), and deduplicating duplicate data (e.g., merging multiple identical consultation records into one). Feature engineering includes: extracting key features (e.g., "policy holding years", "historical surrender count", "average premium"), constructing combined features (e.g., "premium / sum insured ratio", "claims frequency"), and encoding categorical features (e.g., converting "insurance type" into a one-hot vector). Standardization includes: normalizing numerical features (e.g., scaling "age" to the [0,1] range) to ensure consistency of model input. The preprocessed target historical data is then input into the policy surrender prediction model to obtain the predicted policy surrender amount. The model checks if the actual surrender amount matches the predicted amount. If they do, a pre-defined policy surrender plan model is used to match the policy surrender information and generate a policy surrender plan tailored to the target user. This pre-defined plan model represents the relationship between policy surrender information and the surrender plan. It's worth noting that this model contains one or more plan template libraries, each labeled with different attributes or tags. These tags help the model identify which templates are most suitable for the current business information. The pre-defined plan templates include the insurance name, insurance details, insurance date, and insurance amount. By combining data preprocessing, prediction model validation, and plan generation from the pre-defined model, intelligent and personalized policy surrender plan generation is achieved, thereby improving the accuracy of policy processing, increasing user satisfaction, and avoiding increased risks of refund costs for the company.

[0048] In this embodiment, the policy surrender amount is verified based on a preset verification strategy to prevent financial loss, thereby generating a more accurate policy surrender plan for the target user. This can effectively prevent customers from declaring high amounts or false information when surrendering the policy, avoid financial losses or risks for the insurance company, improve the efficiency and accuracy of policy processing, and promote business development.

[0049] In summary, this invention provides an artificial intelligence-based policy processing method, apparatus, device, and medium. It acquires policy cancellation information from a target user regarding policy cancellation, including policy payment type information and policy request information. Based on a pre-trained keyword recognition model and a preset keyword set, it performs keyword recognition processing on the policy payment type information to determine the policy cancellation type. The policy cancellation type includes either a cancellation model based on amount or a cancellation model based on policy period. Based on the policy cancellation type and policy request information, it calculates the policy cancellation amount, which includes either a first policy cancellation amount or a second policy cancellation amount. Finally, it performs loss prevention verification on the policy cancellation amount based on a preset verification strategy to generate a policy cancellation plan for the target user. As can be seen, this application calculates the policy surrender amount by supporting target users to surrender the policy by amount or by period, and then performs loss prevention verification on the policy surrender amount based on a preset verification strategy to generate a policy surrender plan for the target user. This solves the problem that existing policy processing technology is difficult to adapt to the complex and diverse special circumstances of customers, thereby improving the accuracy of policy processing and enhancing the user experience.

[0050] Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of the AI-based policy processing device provided in an embodiment of the present invention. This AI-based policy processing device corresponds one-to-one with the AI-based policy processing method in the above embodiments. Please refer to the following for details. Figure 2 as well as Figure 2 The relevant descriptions in the corresponding embodiments are shown below. For ease of explanation, only the parts relevant to this embodiment are shown. See also... Figure 3 The AI-based policy processing device 30 includes: an acquisition module 31, a determination module 32, a calculation module 33, and a generation module 34.

[0051] The acquisition module 31 is used to acquire policy cancellation information of the target user for policy cancellation business, wherein the policy cancellation information includes policy payment type information and policy request information; The determination module 32 is used to perform keyword recognition processing on the policy payment type information based on a pre-trained keyword recognition model and a preset keyword set to determine the policy surrender type, wherein the policy surrender type includes a surrender mode type based on amount or a surrender mode type based on period. Calculation module 33 is used to calculate the policy surrender amount based on the policy surrender type and the policy request information, wherein the policy surrender amount includes the first policy surrender amount or the second policy surrender amount; The generation module 34 is used to perform a loss prevention verification on the policy surrender amount based on a preset verification strategy, so as to generate a policy surrender plan for the target user.

[0052] Optionally, the aforementioned acquisition module 31 is specifically used for: Determine whether a policy cancellation request has been received from the client, wherein the policy cancellation request includes the target user's user information; When a policy cancellation request is received from the client, the policy cancellation time for the target user is obtained based on the user information. Calculate the number of working days between the current date and the policy surrender date; Determine whether the number of working days meets the preset number of days threshold; If the number of working days meets the preset number of days threshold, then obtain the policy cancellation information of the target user for the policy cancellation business.

[0053] Optionally, the determining module 32 is specifically used for: The policy payment type information is processed by a pre-trained keyword recognition model to obtain keyword recognition results. The keyword recognition results are matched with a preset keyword set to determine the distribution of each keyword in the policy payment type information. The preset keyword set includes amount keywords and period keywords. Based on the distribution of each keyword in the preset keyword set in the policy payment type information, the number of the amount keyword and the period keyword are determined respectively; The policy surrender type is determined based on the number of the monetary keywords and the term keywords.

[0054] Optionally, the above-mentioned calculation module 33 is specifically used for: When the policy surrender type is the amount-based surrender mode, the target user's surrender type identifier, service insurance premium usage, and surrender time period are obtained for the policy surrender business. In the policy center, the policy information corresponding to the aforementioned cancellation type identifier has been found to have collected premiums and processed refunds for policy maintenance tasks. The total premium amount of the policy is calculated based on the premiums already collected for the policy and the collection and refund of the maintenance task. Determine whether the requested surrender amount is less than or equal to the total premium of the policy; If the requested surrender value of the policy is less than or equal to the total premium of the policy, the surrender value of the first policy is calculated based on the total premium of the policy, the usage of the premium for the service insurance, and the surrender period.

[0055] Optionally, the above-mentioned calculation module 33 is specifically used for: If the policy surrender type is the periodic surrender mode, then obtain the target user's policy payment schedule, service insurance premium usage and surrender time period for the policy surrender business. The policy payment schedule includes the policy payment period, policy payment status and the actual premium received in the current period. Based on the policy payment schedule and the policy surrender period, the total premium for the corresponding policy period is calculated. The surrender amount for the second policy is calculated based on the total premium for the policy period, the usage of the premium for the service insurance, and the surrender period.

[0056] Optionally, the above-mentioned generation module 34 is specifically used for: Determine whether the surrender amount of the policy is within the preset surrender amount range; If the policy surrender amount is within the preset surrender amount range, then the policy surrender amount is determined to pass the loss prevention verification, and the target user's target historical data is obtained; Based on the target historical data, the policy surrender amount, and the policy surrender information, a policy surrender plan is generated for the target user.

[0057] Optionally, the above-mentioned generation module 34 is further used for: The target historical data is preprocessed to obtain preprocessed target historical data; The preprocessed target historical data is input into the policy surrender prediction model to obtain the policy surrender prediction amount; Determine whether the surrender amount of the policy is consistent with the predicted surrender amount of the policy; If the surrender amount of the policy is consistent with the predicted surrender amount of the policy, then the policy surrender information is matched using a preset policy surrender scheme model to generate a policy surrender scheme for the target user. The preset policy surrender scheme model is used to characterize the relationship between the policy surrender information and the policy surrender scheme.

[0058] It should be noted that the information interaction and execution process between the above-mentioned units are based on the same concept as the method embodiments of the present invention. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0059] Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. Figure 4 As shown, the computer device of this embodiment includes: at least one processor ( Figure 4Only one is shown in the diagram), a memory, and a computer program stored in the memory and capable of running on at least one processor, which, when executing the computer program, implements the steps in any of the above-described embodiments of the AI-based policy processing method.

[0060] This computer device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that... Figure 4 The examples of computer devices are merely examples and do not constitute a limitation on computer devices. Computer devices may include more or fewer components than shown in the illustration, or combinations of certain components, or different components, such as network interfaces, displays, and input systems.

[0061] In one embodiment, a computer-readable storage medium is provided that, when the instructions in the computer-readable storage medium are executed by a processor in a computer device, enables the computer device to perform the steps of any embodiment of the artificial intelligence-based policy processing method disclosed in this invention, which will not be repeated here. The computer-readable storage medium may be non-volatile or volatile.

[0062] The processor referred to can be a CPU, but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0063] Memory includes readable storage media, internal memory, etc., wherein internal memory can be the RAM of a computer device, providing an environment for the operation of the operating system and computer-readable instructions stored in the readable storage media. The readable storage media can be the hard drive of the computer device, or in other embodiments, it can be an external storage device of the computer device, such as a plug-in hard drive, SmartMediaCard (SMC), SecureDigital (SD) card, or FlashCard. Furthermore, memory can include both internal storage units and external storage devices of the computer device. Memory is used to store the operating system, cooperative applications, bootloader, data, and other programs, such as program code of computer programs. Memory can also be used to temporarily store data that has been output or will be output.

[0064] Those skilled in the art will understand that implementing all or part of the processes in the above embodiments can be accomplished by a computer program instructing related hardware. This computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0065] Those familiar with the technical field will understand that, for ease of description and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this invention. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here. If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0066] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A policy processing method based on artificial intelligence, characterized in that, include: Obtain policy surrender information of target users regarding policy surrender, wherein the policy surrender information includes policy payment type information and policy request information; Based on a pre-trained keyword recognition model and a preset keyword set, keyword recognition processing is performed on the policy payment type information to determine the policy surrender type, wherein the policy surrender type includes a surrender mode type based on amount or a surrender mode type based on period. Based on the policy surrender type and the policy request information, the policy surrender amount is calculated, wherein the policy surrender amount includes the surrender amount of the first policy or the surrender amount of the second policy; Based on a preset verification strategy, the policy surrender amount is verified to prevent financial loss, so as to generate a policy surrender plan for the target user.

2. The policy processing method based on artificial intelligence as described in claim 1, characterized in that, The pre-trained keyword recognition model and preset keyword set are used to perform keyword recognition processing on the policy payment type information to determine the policy surrender type, including: The policy payment type information is processed by a pre-trained keyword recognition model to obtain keyword recognition results. The keyword recognition results are matched with a preset keyword set to determine the distribution of each keyword in the policy payment type information. The preset keyword set includes amount keywords and period keywords. Based on the distribution of each keyword in the preset keyword set in the policy payment type information, the number of the amount keyword and the period keyword are determined respectively; The policy surrender type is determined based on the number of the monetary keywords and the term keywords.

3. The policy processing method based on artificial intelligence as described in claim 1, characterized in that, The policy request information includes the requested surrender amount. The calculation of the surrender amount based on the policy surrender type and the policy request information includes: When the policy surrender type is the amount-based surrender mode, the target user's surrender type identifier, service insurance premium usage, and surrender time period are obtained for the policy surrender business. In the policy center, the policy information corresponding to the aforementioned cancellation type identifier has been found to have collected premiums and processed refunds for policy maintenance tasks. The total premium amount of the policy is calculated based on the premiums already collected for the policy and the collection and refund of the maintenance task. Determine whether the requested surrender amount is less than or equal to the total premium of the policy; If the requested surrender value of the policy is less than or equal to the total premium of the policy, the surrender value of the first policy is calculated based on the total premium of the policy, the usage of the premium for the service insurance, and the surrender period.

4. The policy processing method based on artificial intelligence as described in claim 1, characterized in that, The policy appeal information includes the policy appeal surrender period. The calculation of the policy surrender amount based on the surrender amount type or the surrender period type also includes: If the policy surrender type is the periodic surrender mode, then obtain the target user's policy payment schedule, service insurance premium usage and surrender time period for the policy surrender business. The policy payment schedule includes the policy payment period, policy payment status and the actual premium received in the current period. Based on the policy payment schedule and the policy surrender period, the total premium for the corresponding policy period is calculated. The surrender amount for the second policy is calculated based on the total premium for the policy period, the usage of the premium for the service insurance, and the surrender period.

5. The policy processing method based on artificial intelligence as described in claim 1, characterized in that, The method of performing loss prevention verification on the policy surrender amount based on a preset verification strategy to generate a policy surrender plan for the target user includes: Determine whether the surrender amount of the policy is within the preset surrender amount range; If the policy surrender amount is within the preset surrender amount range, then the policy surrender amount is determined to pass the loss prevention verification, and the target user's target historical data is obtained; Based on the target historical data, the policy surrender amount, and the policy surrender information, a policy surrender plan is generated for the target user.

6. The policy processing method based on artificial intelligence as described in claim 5, characterized in that, The step of generating a policy surrender plan for the target user based on the target historical data, the policy surrender amount, and the policy surrender information includes: The target historical data is preprocessed to obtain preprocessed target historical data; The preprocessed target historical data is input into the policy surrender prediction model to obtain the policy surrender prediction amount; Determine whether the surrender amount of the policy is consistent with the predicted surrender amount of the policy; If the surrender amount of the policy is consistent with the predicted surrender amount of the policy, then the policy surrender information is matched using a preset policy surrender scheme model to generate a policy surrender scheme for the target user. The preset policy surrender scheme model is used to characterize the relationship between the policy surrender information and the policy surrender scheme.

7. The policy processing method based on artificial intelligence as described in claim 1, characterized in that, Before obtaining the policy payment type information and policy request information of the target user regarding the policy surrender business, the following steps are included: Determine whether a policy cancellation request has been received from the client, wherein the policy cancellation request includes the target user's user information; When a policy cancellation request is received from the client, the policy cancellation time for the target user is obtained based on the user information. Calculate the number of working days between the current date and the policy surrender date; Determine whether the number of working days meets the preset number of days threshold; If the number of working days meets the preset number of days threshold, then obtain the policy cancellation information of the target user for the policy cancellation business.

8. An insurance policy processing device based on artificial intelligence, characterized in that, include: The acquisition module is used to acquire policy surrender information of the target user for policy surrender business, wherein the policy surrender information includes policy payment type information and policy request information; The determination module is used to perform keyword recognition processing on the policy payment type information based on a pre-trained keyword recognition model and a preset keyword set to determine the policy surrender type, wherein the policy surrender type includes a surrender mode type based on amount or a surrender mode type based on period. The calculation module is used to calculate the policy surrender amount based on the policy surrender type and the policy request information, wherein the policy surrender amount includes the first policy surrender amount or the second policy surrender amount; The generation module is used to perform loss prevention verification on the policy surrender amount based on a preset verification strategy, so as to generate a policy surrender plan for the target user.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the artificial intelligence-based policy processing method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the artificial intelligence-based policy processing method as described in any one of claims 1 to 7.

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