Insurance renewal prediction method and device, equipment and medium

Abstract

The invention relates to the technical field of artificial intelligence, in particular to an insurance renewal prediction method and device, equipment and a medium. For any target insurance policy, user information in the target insurance policy is extracted, a historical program operation behavior of a user corresponding to the target insurance policy is acquired from the insurance application program according to the user information, and an operation behavior of the user corresponding to the target insurance policy after the insurance application program is started next time is predicted according to the historical program operation behavior. And according to the historical program operation behavior and the behavior prediction result, predicting the insurance renewal behavior of the target insurance policy to obtain an insurance renewal prediction result. In combination with a plurality of program operation behaviors of a client, the operation behavior after the insurance application program is started next time is predicted, and the insurance renewal behavior is predicted according to the prediction result of the behavior and the corresponding operation behavior, so that the client with the high resurgence intention is screened out, and a specially-assigned person is arranged to follow up the client with the high resurgence intention. And the re-effect working efficiency is further improved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a renewal prediction method, device, equipment and medium. Background Art

[0002] In recent years, with the development of insurance business, the number of insurance policies sold through the Internet has also increased year by year. For insurance companies, the renewal operation of insurance policies will directly affect the scale of premiums. When the insurance products purchased by insured customers expire, they will be expected to renew the insurance. During this process, sales agents cannot know the customer's renewal intention, and therefore cannot conduct differentiated and efficient business development.

[0003] At present, the customer's renewal willingness is generally predicted based on the personal experience of the salesperson. Due to the differences in personal experience of different salespeople, the human judgment method is not accurate and unstable. Therefore, a customer renewal prediction solution is urgently needed to effectively and accurately predict the customer's renewal willingness. Summary of the invention

[0004] In view of this, the embodiments of the present invention provide a renewal prediction method, device, equipment and medium to solve the problem of low accuracy and instability when predicting customer renewal willingness.

[0005] In a first aspect, an embodiment of the present invention provides a renewal prediction method, the renewal prediction method comprising: Based on a preset search condition, obtaining a policy set from a database, wherein the policy set includes at least one target policy to be renewed; For any target insurance policy, extract the user information in the target insurance policy, and obtain the historical program operation behavior of the user corresponding to the target insurance policy from the insurance application according to the user information; According to the historical program operation behavior, predict the operation behavior of the user corresponding to the target insurance policy after starting the insurance application program next time to obtain a behavior prediction result; Based on the historical program operation behavior and the behavior prediction result, the renewal behavior of the target policy is predicted to obtain a renewal prediction result.

[0006] In a second aspect, an embodiment of the present invention provides a policy renewal prediction device, the policy renewal prediction device comprising: An acquisition module, configured to acquire a policy set from a database based on a preset search condition, wherein the policy set includes at least one target policy to be renewed; An extraction module, for extracting user information from any target policy, and obtaining historical program operation behaviors of the user corresponding to the target policy from the insurance application program according to the user information; A first prediction module is used to predict the operation behavior of the user corresponding to the target insurance policy after starting the insurance application program next time according to the historical program operation behavior, and obtain a behavior prediction result; The second prediction module is used to predict the renewal behavior of the target policy based on the historical program operation behavior and the behavior prediction result to obtain a renewal prediction result.

[0007] In a third aspect, an embodiment of the present invention provides a computer device, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer device is used to execute the renewal prediction method described in any one of the first aspects.

[0008] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to perform the renewal prediction method described in any one of the first aspects.

[0009] Compared with the prior art, the present invention has the following beneficial effects: Based on preset search conditions, a policy set is obtained from the database, the policy set includes at least one target policy to be renewed, for any target policy, the user information in the target policy is extracted, and based on the user information, the historical program operation behavior of the user corresponding to the target policy is obtained from the insurance application, and based on the historical program operation behavior, the operation behavior of the user corresponding to the target policy after the insurance application is started next time is predicted to obtain a behavior prediction result, and based on the historical program operation behavior and the behavior prediction result, the renewal behavior of the target policy is predicted to obtain a renewal prediction result. In this application, the operation behavior after the insurance application is started next time is predicted in combination with multiple program operation behaviors of the customer, and the renewal behavior is predicted based on the behavior prediction results and the corresponding operation behavior, so as to screen out customers with a high willingness to renew, thereby arranging for special personnel to follow up on customers with a high willingness to renew, and further improving the efficiency of the renewal work. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0011] Figure 1 This is a schematic diagram of an application environment of a renewal prediction method provided by an embodiment of the present invention; Figure 2It is a flowchart of a renewal prediction method provided by an embodiment of the present invention; Figure 3 This is a structural block diagram of a renewal prediction device provided by an embodiment of the present invention; Figure 4 It is a structural schematic diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0012] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0013] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0014] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0015] It should also be understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0016] As used in the present specification and the appended claims, the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" may be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]", depending on the context.

[0017] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0018] References to "one embodiment" or "some embodiments" etc. described in the present specification mean that one or more embodiments of the present invention include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0019] The embodiments of the present invention can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0020] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0021] It should be understood that the order of execution of the steps in the following embodiments does not imply a precedence 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.

[0022] In order to illustrate the technical solution of the present invention, specific embodiments are provided below for illustration.

[0023] An embodiment of the present invention provides a renewal prediction method, which can be applied in the following cases: Figure 1In the application environment, the client communicates with the server. The client includes but is not limited to PDAs, desktop computers, laptops, ultra-mobile personal computers (UMPCs), netbooks, cloud computing devices, personal digital assistants (PDAs) and other computer devices. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0024] See also Figure 2 , is a flow chart of a renewal prediction method provided by an embodiment of the present invention. The renewal prediction method can be applied to Figure 1 The server in Figure 2 As shown, the renewal prediction method may include the following steps.

[0025] S201: Based on preset search conditions, obtain a policy set from a database, where the policy set includes at least one target policy to be renewed.

[0026] In step S201, based on preset search conditions, a policy set is obtained from the database, the policy set including at least one target policy to be renewed, wherein the preset search condition is whether the validity period of the policy has expired, and the expired policy is taken as the target policy.

[0027] In this embodiment, a target insurance policy with an expiration date is obtained from the insurance policy database. The expiration date of the insurance policy is the time when the user's insurance is due. For example, if the insurance policy is valid for one year, the same day of the next year is the time when the user's insurance is due. When obtaining an insurance policy set from the insurance policy database, it can be obtained by searching, and the search condition is that the time when the user's insurance is due is the time of the day, and the insurance policy set is obtained according to the search results.

[0028] In this embodiment, a policy set of the target policy is obtained so as to remind the user corresponding to the target policy so that the user can renew the policy in time and prevent the policy from being inactive due to the user forgetting to renew.

[0029] S202: For any target insurance policy, extract the user information in the target insurance policy, and obtain the historical program operation behavior of the user corresponding to the target insurance policy from the insurance application according to the user information.

[0030] In step S202, for any target policy, the user information in the target policy is extracted, and based on the user information, the historical program operation behavior of the user corresponding to the target policy is obtained from the insurance application, wherein the user information is the identification information of the user corresponding to the target policy, and the historical program operation behavior is the user's behavior during the process of insuring the target policy.

[0031] In this embodiment, for any target insurance policy, the user information in the target insurance policy is extracted, wherein the user information may be the user's identity information, or name information, or phone information, etc., wherein the user information is unique identification information, and the historical program operation behavior of the user corresponding to the target insurance policy is obtained from the insurance application according to the user information. The historical program operation behavior may be an operation behavior obtained from different channels, such as the historical program operation behavior obtained through the embedded data in the application for purchasing the insurance policy, or the historical program operation behavior obtained through the application for saving phone records, or the historical program operation behavior obtained through the business records handled by the offline counter application, etc. Among them, the historical program operation behaviors obtained through the buried data in the application for purchasing insurance policies may include the operation behavior of clicking on the application for purchasing insurance policies, or the operation behavior of purchasing on the application for purchasing insurance policies, or the operation behavior of transferring to manual on the application for purchasing insurance policies, such as the operation behavior of clicking, the operation behavior of purchasing, the operation behavior of transferring to manual, etc. The historical program operation behaviors obtained through the application for saving phone records may include the operation behavior of the time of connecting the phone, such as the operation behavior of connecting the phone on the same day, etc. The historical program operation behaviors obtained through the business records handled by the offline counter application may include the operation behavior of offline handling of business types, such as the operation behavior of handling business A.

[0032] In this embodiment, the historical program operation behavior of the user corresponding to the target insurance policy is obtained from the insurance application according to the user information, so as to predict the operation behavior of the user after the insurance application is started next time according to the user's historical program operation behavior. Using the historical program operation behavior to predict the operation behavior of the user after the insurance application is started next time can improve the prediction accuracy.

[0033] S203: Based on the historical program operation behaviors, predict the operation behaviors of the user corresponding to the target insurance policy after starting the insurance application program next time to obtain the behavior prediction results.

[0034] In step S203, based on the historical program operation behavior, the operation behavior of the user corresponding to the target policy after the insurance application is started next time is predicted, that is, the channel for the user to operate the target policy again is predicted, where the next time the insurance application is started is the time when the corresponding user handles business next time.

[0035] In this embodiment, when predicting the operating behavior of the user corresponding to the target policy after the insurance application is started next time based on the historical program operating behavior, a preset behavior prediction model can be used for prediction, wherein the input of the preset behavior prediction model can be the historical program operating behavior, and the output is the probability of the behavior prediction result.

[0036] Optionally, based on the historical program operation behaviors, the operation behaviors of the user corresponding to the target insurance policy after the insurance application is started next time are predicted to obtain the behavior prediction results, including: Obtaining a preset behavior prediction model; Input the historical program operation behavior into the preset behavior prediction model and output the behavior prediction result.

[0037] In this embodiment, a preset behavior prediction model is obtained, wherein the preset behavior prediction model may be a MIMN (Multi-Channel User Interest Memory Network) model, and according to the corresponding model, the historical program operation behavior is input into the preset behavior prediction model, and the behavior prediction result is output. The behavior prediction result includes the predicted probabilities of operation behaviors of different channels. For example, the probability of clicking on the application for purchasing the insurance policy after the insurance application is started next time is predicted to be 0.7, the probability of purchasing operation is 0.2, the probability of transferring to manual operation is 0.56, etc., the probability of answering the phone on the same day is 0.8, and the probability of handling business A offline is 0.8, etc.

[0038] It should be noted that in order to remove low-probability events, a corresponding threshold is set. When the probability value of the behavior prediction result is less than the threshold, the operation behavior with a smaller corresponding probability value is removed. For example, when the probability of clicking on the application for purchasing the policy after the insurance application is started next time is 0.7, the probability of the purchase operation is 0.2, and the probability of the transfer to manual operation is 0.56, the purchase operation and the transfer to manual operation are removed.

[0039] S204: Predict the renewal behavior of the target policy based on the historical program operation behavior and the behavior prediction results to obtain a renewal prediction result.

[0040] In step S204, the renewal behavior of the target insurance policy is predicted based on the historical program operation behavior and the behavior prediction results, that is, the possibility of renewal of the corresponding user is determined based on the historical program operation behavior and the behavior prediction results.

[0041] In this embodiment, the renewal behavior of the target policy is predicted based on the historical program operation behavior and the behavior prediction result to obtain the renewal prediction result, wherein the renewal prediction result is the possibility of renewal. When predicting the renewal behavior of the target policy, the prediction can be made based on the number of behaviors in the historical program operation behavior and the behavior prediction result, and the weight value is determined based on the number of operation behaviors in the historical program operation behavior. The weight value corresponding to each operation behavior is multiplied by the predicted probability of the corresponding behavior in the behavior prediction result and then added to obtain the renewal behavior for prediction.

[0042] According to the behavior prediction results, determine the predicted behavior that meets the requirements in the behavior prediction results, that is, the behavior corresponding to the probability value in the behavior prediction results is greater than the preset threshold. According to the predicted behavior that meets the requirements, obtain the number of corresponding behaviors from the historical program operation behavior.

[0043] For example, the probability of clicking on the application for purchasing insurance policies after the next insurance application is predicted to be 0.7, and when the preset threshold is 0.6, the click operation behavior is considered to meet the requirements, and the number of clicks of the click operation behavior is obtained from the historical program operation behavior. If the number of clicks on the application for purchasing insurance policies in the historical program operation behavior is 10 times, the weight value of the click operation behavior is determined, such as the weight value of the number of clicks between 0-10 including 10 is 0.2, the weight value of the number of clicks between 10-50 including 50 is 0.5, the weight value of the number of clicks above 50 is 1, and the weight value of the number of clicks of the click operation behavior is 10 times. Then the weight value corresponding to the operation behavior is multiplied by the predicted probability of the corresponding behavior in the behavior prediction result, and the result is 0.14.

[0044] If the behavior prediction result also includes the probability of answering the phone on the same day, which is 0.8, in the historical program operation behavior, the operation behavior of answering the phone on the same day is considered to meet the requirements, and the number of operation behaviors of answering the phone on the same day is obtained from the historical program operation behavior. If the number of operation behaviors of answering the phone on the same day in the historical program operation behavior is 15 times, the weight value of the operation behavior of answering the phone on the same day is determined. For example, the weight value of the number of operation behaviors of answering the phone on the same day is between 0-10, including 10, and the weight value is 0.2. The number of operation behaviors of answering the phone on the same day is between 10-50, including 50, and the weight value is 1 if the number of operation behaviors of answering the phone on the same day is above 50. The number of operation behaviors of answering the phone on the same day in the historical program operation behavior is 15 times, and the corresponding weight value is 0.5. The weight value corresponding to the operation behavior is multiplied by the predicted probability of the corresponding behavior in the behavior prediction result to obtain 0.4.

[0045] If other behaviors in the behavior prediction result do not meet the corresponding requirements, 0.14 is added to 0.4 to get the renewal prediction result. The larger the renewal prediction result, the greater the possibility of the user renewing.

[0046] Optionally, obtaining a scorecard for scoring the target policy; Based on the historical program operation behavior and behavior prediction results, combined with the score card, the target policy is scored to obtain the scoring result, which is used as the renewal prediction result.

[0047] In this embodiment, a scoring card for scoring the target insurance policy is obtained, and the target insurance policy is scored based on the historical program operation behavior and the behavior prediction results, combined with the scoring card. Among them, the scoring card includes different scoring dimensions, such as the willingness to pay dimension, the satisfaction dimension, the activity dimension, etc. Different scoring dimensions can include a single criterion or multiple criterion. For example, the willingness to pay dimension can be a single criterion, and the criterion is the number of payments in the past two years. The satisfaction dimension can be multiple criteria, and the multiple criteria are the number of transfers to manual labor in the past three months and the probability of the next transfer to manual labor within 30 days. The activity dimension can be multiple criteria, and the multiple criteria are the number of clicks on the application for purchasing insurance policies in the past month and the number of business handling behaviors within 30 days.

[0048] The scoring criteria in each scoring dimension are scored, and based on the scoring results, the renewal behavior of the target policy is predicted to obtain the renewal prediction result. When scoring the scoring criteria in each scoring dimension, different scoring criteria are graded, and each grade corresponds to a certain score. For example, if the scoring criteria for the number of payments in the past two years are set to the first level, the second level, the third level, the fourth level, etc., the first level is the number of payments, the interval is (0,1], the second level is the number of payments, the interval is (1,5], the third level is the number of payments, the interval is (5,10], the fourth level is the number of payments, the interval is (10, 10 times or more], the first level is set to 10 points, the second level is set to 20 points, the third level is set to 30 points, and the fourth level is set to 40 points. For example, if the scoring criteria for the number of transfers to manual labor in the past three months are set to the first level, the second level, the third level, etc., the first level is set to the interval of the number of transfers to manual labor, the interval is (0,1], the second level is set to the interval of the number of transfers to manual labor, the interval is (1,2], the third level is set to the interval of the number of transfers to manual labor, the interval is (2, 2 times or more], the first level is set to 20 points, the second level is set to 10 points, and the third level is set to 0 points. For example, if the scoring criteria for the probability of the next transfer to manual labor within 30 days are set to the first level, the probability interval of the first level is (0,0.3] , the probability interval of the second level is (0.3,0.6], the probability interval of the third level is (0.6,1], the first level is set to 20 points, the second level is set to 10 points, and the third level is set to 0 points. For example, the first level, second level, third level, etc. are set in the scoring criteria for the number of clicks on the application for purchasing insurance policies in the past month. The first level click count interval is (0,1], the second level click count interval is (1,5], and the third level click count interval is (5,5 times or more], the first level is set to 0 points, the second level is set to 10 points, and the third level is set to 20 points. For example, the first level, second level, third level, etc. are set in the scoring criteria for the number of business handling behaviors within 30 days. The number interval of the first level business handling behaviors is (0,2], the number interval of the second level business handling behaviors is (2,5], and the number interval of the third level business handling behaviors is (5,10], the first level is set to 0 points, the second level is set to 10 points, and the third level is set to 20 points.

[0049] It should be noted that the scoring is performed based on the historical program operation behavior and behavior prediction results combined with the score card, and the scoring results are used as the renewal prediction results.

[0050] For example, when scoring user A corresponding to the target insurance policy, if user A has paid twice in the past two years, has transferred to manual service 0 times in the past three months, and has a probability of transferring to manual service within 30 days of 0.3, and has clicked 7 times on the application for purchasing the insurance policy in the past month, then the target insurance policy is scored and the score is 80 points, then the renewal prediction result is 80 points. In this embodiment, the target insurance policy is scored according to different scoring dimensions, the behavior prediction results are combined with the historical program operation behavior, the historical program operation behavior is fully utilized, and the accuracy of predicting the renewal behavior of the target insurance policy is improved.

[0051] Optionally, the target policy is scored based on the historical program operation behavior and the behavior prediction results in combination with the score card to obtain the scoring results, including: Set weight values ​​for each scoring dimension; According to the historical program operation behavior and behavior prediction results, combined with the score card, the dimension score results of each scoring dimension are calculated; According to the weight value set for each scoring dimension and the dimension scoring result of each scoring dimension, the target insurance policy is scored to obtain the scoring result.

[0052] In this embodiment, a weight value is set for each scoring dimension. When setting the weight value, it can be set according to the number of evaluation criteria in each scoring dimension. For example, the willingness to pay dimension includes one evaluation criteria, the satisfaction dimension includes 2 evaluation criteria, and the activity dimension includes 2 evaluation criteria. A weight value of 0.2 can be set for the willingness to pay dimension, a weight value of 0.4 can be set for the satisfaction dimension, and a weight value of 0.4 can be set for the activity dimension.

[0053] In this embodiment, the dimension scoring results of each scoring dimension are calculated based on the historical program operation behavior and the behavior prediction results, combined with the score card. For example, when scoring the target insurance policy corresponding to user A, if user A has paid twice in the past two years, has transferred to manual service 0 times in the past three months, and has a probability of transferring to manual service within 30 days of 0.3, has clicked 7 times on the application for purchasing the insurance policy in the past month, and has handled business 3 times within 30 days, then the score of the payment willingness dimension is 20 points, the score of the satisfaction dimension is 40 points, and the score of the activity dimension is 30 points. According to the weight value set for each scoring dimension and the dimension scoring result of each scoring dimension, the target insurance policy is scored to obtain the scoring result, that is, the scoring result is 32.

[0054] Optionally, based on the historical program operation behavior and the behavior prediction results, the renewal behavior of the target policy is predicted. After the renewal prediction result is obtained, the following is further included: According to the renewal forecast results, each target policy is sorted according to the size of the renewal forecast results to obtain a sorting result; Based on the sorting results, the renewal reminder method for each target policy is determined, and based on the reminder method for each target policy, the user corresponding to each target policy is reminded.

[0055] In this embodiment, according to the renewal prediction result, each target policy is sorted according to the size of the renewal prediction result to obtain a sorting result, that is, the score result of each target policy is sorted from large to small to obtain a sorting result. According to the sorting result, the reminder method for renewal reminder for each target policy is determined, that is, the reminder method for renewal reminder for different score results may be different, and according to the reminder method of each target policy, the user corresponding to each target policy is reminded.

[0056] In this embodiment, the renewal reminder method for each target policy is determined based on the sorting results, so that the target policies can be screened according to the sorting results, and the policies that can be reminded for renewal by manual customer service are screened out. Manual customer service reminders are given to the screened out policies that can be reminded for renewal by manual customer service, and no manual customer service reminders are given to the remaining policies, thereby improving the work efficiency of manual customer service.

[0057] Optionally, according to the sorting result, a reminder method for renewal of each target policy is determined, including: When the prediction result is greater than a preset threshold, determining the first reminder method as a reminder method for the insurance policy whose prediction result is greater than the preset threshold; When the prediction result is not greater than the preset threshold, the second reminder method is determined to be the reminder method for the insurance policy whose prediction result is not greater than the preset threshold.

[0058] In this embodiment, the reminder method includes a first reminder method and a second reminder method, wherein the first reminder method is a manual customer service reminder, and the second reminder method is a text message reminder. When the prediction result is greater than a preset threshold, the first reminder method is determined to be a reminder method for insurance policies whose prediction results are greater than the preset threshold, that is, a reminder is made using manual customer service. When the prediction result is not greater than the preset threshold, the second reminder method is determined to be a reminder method for insurance policies whose prediction results are not greater than the preset threshold, that is, a reminder is made using text messages.

[0059] Optionally, according to the reminder method of each target insurance policy, remind the user corresponding to each target insurance policy, including: Generate reminder words that match the reminder method according to the reminder method; Remind each target policy user based on the reminder words.

[0060] In this embodiment, when reminding the user to renew the policy, a reminder script matching the reminder method is generated. For example, for the first reminder method, i.e., the reminder method of manual customer service reminder, the manual customer service outbound call script is generated, and for the second reminder method, i.e., the reminder method of SMS reminder, the SMS content is generated, wherein the manual customer service outbound call script and the SMS content can be generated according to the corresponding model, such as generated according to the chatGLM model.

[0061] For example, when generating outbound call scripts, the chatGLM model input can be, "I am an insurance customer service staff, and I am going to make an outbound call to remind customer A that his insurance policy is about to expire. The model predicts that he may answer the phone between 8 and 10 o'clock every day, and the business issue he is concerned about recently is how to collect the survival benefit. Please generate an outbound call script for me to guide the customer to pay the premium as soon as possible to avoid the risks caused by the policy being terminated. It is recommended to start with: Hello, Mr. / Ms. A! I am XXX. Then ask if the issue he is concerned about has been resolved. Finally, remind him to pay the premium as soon as possible." The output outbound call script can be "Hello! I am XXX, thank you very much for answering our call. Has the XXX issue you consulted last time been resolved? This call is mainly to remind you that your insurance policy is about to expire on X month X day. Please renew the premium as soon as possible to avoid other risks caused by the policy being terminated. Thank you again for your patient listening, and I wish you a happy life!".

[0062] When generating SMS content, the chatGLM model input can be, "I am an insurance customer service staff, preparing a SMS to remind customer A that his insurance policy is about to expire. Please help me generate the SMS content. If he needs my assistance, please reply 1." The output SMS content is, "Dear Mr. / Ms. A, hello! Thank you for choosing our insurance service. This SMS reminds you that your policy will expire on X month X day. In order to ensure that your protection rights are not affected, please renew the premium in time. If you need assistance from a customer service specialist, please reply 1. I wish you a happy life!".

[0063] It should be noted that customer service personnel can modify the outbound call script / text message content generated by chatGLM and upload it to the work platform for recording. The system will record each customer service personnel's input, output, and modified content each time, and will give priority to displaying the modified outbound call script / text message content when outputting next time.

[0064] Based on preset search conditions, a policy set is obtained from the database, the policy set includes at least one target policy to be renewed, for any target policy, the user information in the target policy is extracted, and based on the user information, the historical program operation behavior of the user corresponding to the target policy is obtained from the insurance application, and based on the historical program operation behavior, the operation behavior of the user corresponding to the target policy after the insurance application is started next time is predicted to obtain a behavior prediction result, and based on the historical program operation behavior and the behavior prediction result, the renewal behavior of the target policy is predicted to obtain a renewal prediction result. In this application, the operation behavior after the insurance application is started next time is predicted in combination with multiple program operation behaviors of the customer, and the renewal behavior is predicted based on the behavior prediction results and the corresponding operation behavior, so as to screen out customers with a high willingness to renew, thereby arranging for special personnel to follow up on customers with a high willingness to renew, and further improving the efficiency of the renewal work.

[0065] See also Figure 3 , Figure 3 1 is a structural block diagram of a renewal prediction device provided by an embodiment of the present invention, and the renewal prediction device is applied to the above-mentioned server. For the convenience of explanation, only the part related to the embodiment of the present application is shown. Figure 3 The renewal prediction device 30 includes an acquisition module 31 , an extraction module 32 , a first prediction module 33 , and a second prediction module 34 .

[0066] The acquisition module 31 is used to acquire a policy set from the database based on a preset search condition, wherein the policy set includes at least one target policy to be renewed.

[0067] The extraction module 32 is used to extract the user information in any target insurance policy, and obtain the historical program operation behavior of the user corresponding to the target insurance policy from the insurance application according to the user information.

[0068] The first prediction module 33 is used to predict the operation behavior of the user corresponding to the target insurance policy after starting the insurance application program next time based on the historical program operation behavior, and obtain the behavior prediction result.

[0069] The second prediction module 34 is used to predict the renewal behavior of the target policy based on the historical program operation behavior and the behavior prediction result to obtain the renewal prediction result.

[0070] Optionally, the first prediction module 33 includes: The first acquisition unit is used to acquire a preset behavior prediction model.

[0071] The output unit is used to input the historical program operation behavior into the preset behavior prediction model and output the behavior prediction result.

[0072] Optionally, the second prediction module 34 includes: The second acquisition unit is used to acquire a score card for scoring the target insurance policy.

[0073] The scoring unit is used to score the target policy based on the historical program operation behavior and behavior prediction results in combination with the scoring card, obtain the scoring result, and use the scoring result as the renewal prediction result.

[0074] Optionally, the scoring unit includes: The setting subunit is used to set the weight value for each scoring dimension.

[0075] The calculation subunit is used to calculate the dimension scoring result of each scoring dimension based on the historical program operation behavior and behavior prediction results combined with the score card.

[0076] The scoring subunit is used to set the weight value of each scoring dimension and the dimension scoring result of each scoring dimension, score the target insurance policy, and obtain the scoring result.

[0077] Optionally, the insurance renewal prediction device 30 further includes: The sorting module is used to sort each target policy according to the renewal prediction result to obtain the sorting result.

[0078] The determination module is used to determine the reminder method for renewal of each target policy according to the sorting result, and to remind the user corresponding to each target policy according to the reminder method of each target policy.

[0079] Optionally, the above-mentioned determination module includes: The first determination unit is used to determine, when the prediction result is greater than a preset threshold, that the first reminder method is a reminder method for the insurance policy whose prediction result is greater than the preset threshold.

[0080] The second determination unit is used to determine, when the prediction result is not greater than the preset threshold, that the second reminder method is a reminder method for the insurance policy whose prediction result is not greater than the preset threshold.

[0081] Optionally, the above-mentioned determination module further includes: The generating unit is used to generate a reminder speech matching the reminder mode according to the reminder mode.

[0082] The reminder unit is used to remind the user of each target insurance policy according to the reminder words.

[0083] It should be noted that the information interaction, execution process and other contents between the above-mentioned modules are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0084] Figure 4 FIG. 1 is a schematic diagram of the structure of a computer device provided by an embodiment of the present invention. Figure 4 As shown, the computer device of this embodiment includes: at least one processor ( Figure 4 Only one is shown in the figure), a memory, and a computer program stored in the memory and executable on at least one processor, wherein when the processor executes the computer program, the steps in any of the above-mentioned renewal prediction method embodiments are implemented.

[0085] The computer device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that Figure 4 These are merely examples of computer devices and do not constitute limitations on the computer devices. The computer devices may include more or fewer components than those shown in the figure, or a combination of certain components, or different components. For example, they may also include a network interface, a display screen, and an input device.

[0086] The processor may be a CPU, or other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0087] The memory includes a readable storage medium, an internal memory, etc., wherein the internal memory may be the memory of a computer device, and the internal memory provides an environment for the operation of the operating system and computer-readable instructions in the readable storage medium. The readable storage medium may be a hard disk of a computer device, and in other embodiments, it may also be an external storage device of the computer device, for example, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the computer device. Further, the memory may also include both an internal storage unit of the computer device and an external storage device. The memory is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of a computer program, etc. The memory may also be used to temporarily store data that has been output or is to be output.

[0088] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned device can refer to the corresponding process in the above-mentioned method embodiment, which will not be repeated here. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned method embodiment can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device capable of carrying computer program code, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0089] The present application implements all or part of the processes in the above-mentioned embodiment method, and may also be completed through a computer program product. When the computer program product runs on a computer device, the computer device can implement the steps in the above-mentioned method embodiment when executing the computer program product.

[0090] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0091] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0092] In the embodiments provided in the present application, it should be understood that the disclosed devices / computer equipment and methods can be implemented in other ways. For example, the device / computer equipment embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0093] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0094] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A renewal prediction method, characterized in that: The renewal prediction method comprises: Based on a preset search condition, obtaining a policy set from a database, wherein the policy set includes at least one target policy to be renewed; For any target insurance policy, extract the user information in the target insurance policy, and obtain the historical program operation behavior of the user corresponding to the target insurance policy from the insurance application according to the user information; According to the historical program operation behavior, predict the operation behavior of the user corresponding to the target insurance policy after starting the insurance application program next time to obtain a behavior prediction result; Based on the historical program operation behavior and the behavior prediction result, the renewal behavior of the target policy is predicted to obtain a renewal prediction result.

2. The renewal prediction method according to claim 1, characterized in that: The step of predicting the operation behavior of the user corresponding to the target insurance policy after starting the insurance application program next time based on the historical program operation behavior to obtain the behavior prediction result includes: Obtaining a preset behavior prediction model; The historical program operation behavior is input into the preset behavior prediction model, and the behavior prediction result is output.

3. The renewal prediction method according to claim 1, characterized in that: The step of predicting the renewal behavior of the target policy based on the historical program operation behavior and the behavior prediction result to obtain the renewal prediction result includes: Obtaining a scorecard for scoring the target insurance policy; According to the historical program operation behavior and the behavior prediction result, combined with the scoring card, the target policy is scored to obtain a scoring result, and the scoring result is used as a renewal prediction result.

4. The renewal prediction method according to claim 3, characterized in that: The scorecard includes N scoring dimensions, where N is an integer greater than zero; The target insurance policy is scored based on the historical program operation behavior and the behavior prediction result in combination with the scoring card to obtain a scoring result, including: Set weight values ​​for each scoring dimension; Calculate the dimension scoring result of each scoring dimension according to the historical program operation behavior and the behavior prediction result in combination with the scoring card; According to the weight value set for each scoring dimension and the dimension scoring result of each scoring dimension, the target insurance policy is scored to obtain a scoring result.

5. The renewal prediction method according to claim 1, characterized in that: After predicting the renewal behavior of the target policy based on the historical program operation behavior and the behavior prediction result and obtaining the renewal prediction result, the method further includes: According to the renewal prediction result, each target policy is sorted according to the size of the renewal prediction result to obtain a sorting result; According to the ranking results, a reminder method for renewal of each target policy is determined, and according to the reminder method of each target policy, a reminder is given to the user corresponding to each target policy.

6. The renewal prediction method according to claim 5, characterized in that: The reminder mode includes a first reminder mode and a second reminder mode; Determining a reminder method for renewal of each target insurance policy according to the ranking result includes: When the prediction result is greater than a preset threshold, determining the first reminder method as a reminder method for the insurance policy whose prediction result is greater than the preset threshold; When the prediction result is not greater than the preset threshold, the second reminder method is determined to be the reminder method for the insurance policy whose prediction result is not greater than the preset threshold.

7. The renewal prediction method according to claim 6, characterized in that: The reminding of each target policy to the user corresponding to each target policy according to the reminder method of each target policy includes: According to the reminder method, generating a reminder speech matching the reminder method; According to the reminder words, remind the user of each target insurance policy.

8. A renewal prediction device, characterized in that: The insurance renewal prediction device comprises: An acquisition module, configured to acquire a policy set from a database based on a preset search condition, wherein the policy set includes at least one target policy to be renewed; An extraction module, for extracting user information from any target policy, and obtaining historical program operation behaviors of the user corresponding to the target policy from the insurance application program according to the user information; A first prediction module is used to predict the operation behavior of the user corresponding to the target insurance policy after starting the insurance application program next time according to the historical program operation behavior, and obtain a behavior prediction result; The second prediction module is used to predict the renewal behavior of the target policy based on the historical program operation behavior and the behavior prediction result to obtain a renewal prediction result.

9. A computer device, characterized in that: The computer device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, and the computer device is used to execute the renewal prediction method according to 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 a processor, the renewal prediction method according to any one of claims 1 to 7 is implemented.

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