CN117114901A

CN117114901A - Method, device, equipment and medium for processing insurance data based on artificial intelligence

Info

Publication number: CN117114901A
Application number: CN202311137221.9A
Authority: CN
Inventors: 郁君俊
Original assignee: Ping An Health Insurance Company of China Ltd
Current assignee: Ping An Health Insurance Company of China Ltd
Priority date: 2023-09-04
Filing date: 2023-09-04
Publication date: 2023-11-24

Abstract

The application belongs to the field of big data and the field of digital medical treatment, and relates to an artificial intelligence-based insurance data processing method, which comprises the following steps: receiving an application request triggered by a user; generating and displaying a health nuclear questionnaire based on the personal information and the health database; receiving feedback data; checking the feedback data based on the check rule to generate a check result; if the check result is refusal, determining an insurance product to be recommended based on the historical application data of the applied person; analyzing health data of insurance products to be recommended and the insuring person based on the product recommendation model, and outputting target insurance products; and pushing the target insurance product to the user. The application also provides an artificial intelligence-based application data processing device, computer equipment and a storage medium. In addition, the application also relates to a blockchain technology, and the target insurance product can be stored in the blockchain. The application can be applied to insurance product recommendation scenes in the financial field, and the verification and protection efficiency and the insurance product recommendation accuracy are improved.

Description

Method, device, equipment and medium for processing insurance data based on artificial intelligence

Technical Field

The application relates to the technical field of artificial intelligence development and the field of digital medical treatment, in particular to an application data processing method, device, computer equipment and storage medium based on artificial intelligence.

Background

With the rapid development of computer technology, the insurance of insurance products becomes an important business in the insurance industry. At present, a check in an insurance company requires business personnel to communicate with clients, the business personnel confirms abnormal health information items according to own working experience and makes final check decision according to client confirmation information, and the processing mode is time-consuming and labor-consuming, so that the problem of low processing efficiency exists. In addition, if the check and guarantee decision is refusal, the customer needs to spend a great deal of time to screen the insurance products meeting the self requirements when browsing the insurance purchase page, but at present, the user requirements cannot be accurately analyzed, so that the wrong insurance products can be recommended to the user, the intelligent recommendation of the insurance products cannot be realized, and the recommendation accuracy of the insurance products is lower.

Disclosure of Invention

The embodiment of the application aims to provide an artificial intelligence-based insurance data processing method, an artificial intelligence-based insurance data processing device, computer equipment and a storage medium, so as to solve the technical problems that the existing nuclear insurance processing mode is time-consuming and labor-consuming, the processing efficiency is low, and intelligent recommendation of insurance products cannot be realized under the condition that the nuclear insurance decision is refused to be ensured, so that the recommendation accuracy of the insurance products is low.

In order to solve the technical problems, the embodiment of the application provides an artificial intelligence-based application data processing method, which adopts the following technical scheme:

receiving an application request triggered by a user; wherein, the application request carries personal information of the applied person;

generating a health nuclear questionnaire matched with the personal information based on the personal information and a preset health database, and displaying the health nuclear questionnaire;

receiving feedback data corresponding to the health nuclear questionnaire, which is input by the user;

verifying the feedback data based on a preset verification rule of the underwriting, and generating a underwriting result corresponding to the feedback data;

if the verification result is refusal, acquiring historical application data of the applied person, and determining an insurance product to be recommended corresponding to the applied person based on the historical application data; wherein the number of insurance products to be recommended includes a plurality of insurance products;

acquiring health data of the insured person, analyzing and processing the insurance product to be recommended and the health data based on a preset product recommendation model, and outputting a target insurance product matched with the health data;

Pushing the target insurance product to the user.

Further, the step of generating a health nuclear questionnaire matched with the personal information based on the personal information and a preset health database specifically includes:

acquiring a preset factor, and acquiring target information matched with the preset factor from the personal information;

invoking the health database;

acquiring a target health maintenance problem matched with the target information from the health database;

and constructing the health nuclear questionnaire based on the target health nuclear questionnaire.

Further, the step of obtaining the health data of the insured person, analyzing and processing the insurance product to be recommended and the health data based on a preset product recommendation model, and outputting a target insurance product matched with the health data specifically includes:

based on the personal information, acquiring health data of the insured person from a preset database;

calling a preset constructed product recommendation model;

inputting the insurance products to be recommended and the health data into the product recommendation model, and generating matching values between the insurance products to be recommended and the health data through the product recommendation model;

Screening out the appointed insurance products with the largest matching values from all the insurance products to be recommended;

and taking the appointed insurance product as the target insurance product.

Further, after the step of verifying the feedback data based on the preset verification rule to generate the verification result corresponding to the feedback data, the method further includes:

if the nuclear protection result is refusal, establishing communication connection with a preset human nuclear platform;

generating a health kernel questionnaire based on the feedback data and the health kernel questionnaire, wherein the kernel task corresponds to the user;

determining a target auditor, and acquiring communication information of the target auditor based on the human-core platform;

and sending the underwriting task to a target auditor based on the communication information so as to audit the underwriting task through the target auditor and generate a designated underwriting result corresponding to the underwriting task.

Further, after the step of sending the underwriting task to a target auditor based on the communication information to audit the underwriting task by the target auditor to generate a specified underwriting result corresponding to the underwriting task, the method further includes:

Receiving the appointed underwriting result returned by the human kernel platform;

if the specified underwriting results are all refused, refusing to respond to the application request;

generating corresponding reminding information based on the verification result;

and sending the reminding information to a user terminal corresponding to the user.

Further, after the step of receiving the feedback data corresponding to the health kernel questionnaire input by the user, the method further comprises:

acquiring a preset disease code;

performing code matching processing on each topic contained in the health nuclear questionnaire fed back by the user based on the disease codes to obtain corresponding code matching data;

and storing the code pair data.

if the verification result is verification passing, generating the policy confirmation information of the target policy corresponding to the application request, and displaying the policy confirmation information;

judging whether a policy confirmation operation triggered by the user is received or not;

if the policy confirmation operation is received, displaying a preset premium payment page;

If the user is detected to finish the payment operation on the premium payment page, judging whether the target policy meets a preset auditing condition or not;

and if the auditing conditions are not met, generating the target policy, and carrying out underwriting treatment on the target policy.

In order to solve the technical problems, the embodiment of the application also provides an artificial intelligence-based insuring data processing device, which adopts the following technical scheme:

the first receiving module is used for receiving an application request triggered by a user; wherein, the application request carries personal information of the applied person;

the first generation module is used for generating a health nuclear questionnaire matched with the personal information based on the personal information and a preset health database and displaying the health nuclear questionnaire;

the second receiving module is used for receiving feedback data corresponding to the health kernel questionnaire, which is input by the user;

the second generation module is used for checking the feedback data based on a preset check rule and generating a check result corresponding to the feedback data;

the first determining module is used for acquiring historical application data of the applied person if the verification result is refusal, and determining an insurance product to be recommended corresponding to the applied person based on the historical application data; wherein the number of insurance products to be recommended includes a plurality of insurance products;

The second determining module is used for acquiring the health data of the insuring person, analyzing and processing the insurance product to be recommended and the health data based on a preset product recommendation model, and outputting a target insurance product matched with the health data;

and the pushing module is used for pushing the target insurance product to the user.

In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical schemes:

pushing the target insurance product to the user.

In order to solve the above technical problems, an embodiment of the present application further provides a computer readable storage medium, which adopts the following technical schemes:

pushing the target insurance product to the user.

Compared with the prior art, the embodiment of the application has the following main beneficial effects:

after receiving an application request triggered by a user, the embodiment of the application firstly generates a health nuclear questionnaire matched with personal information based on the personal information in the application request and a preset health database, and displays the health nuclear questionnaire; then receiving feedback data corresponding to the health nuclear questionnaire input by the user, and checking the feedback data based on a preset nuclear insurance check rule to generate a nuclear insurance result corresponding to the feedback data; if the verification result is refusal, acquiring historical application data of the applied person, and determining an insurance product to be recommended corresponding to the applied person based on the historical application data; subsequently acquiring health data of the insured person, analyzing and processing the insurance product to be recommended and the health data based on a preset product recommendation model, and outputting a target insurance product matched with the health data; and finally pushing the target insurance product to the user. The application processes the user-triggered application request based on the health database and the use of the verification rules of the application, can automatically and rapidly complete the application request of the application, avoids the condition that service personnel confirms abnormal health information items and makes final application decision according to own working experience, and improves the application processing efficiency. In addition, when the verification result corresponding to the verification request is identified as refused, the health data of the insurance applicant and the insurance product to be recommended are analyzed and processed based on the use of a preset product recommendation model, and the target insurance product matched with the health data is output, so that the suitability of the target insurance product and the insurance applicant is ensured, the targeted insurance product recommendation of the insurance applicant is realized, and the accuracy of the target insurance product recommendation is effectively improved.

Drawings

In order to more clearly illustrate the solution of the present application, a brief description will be given below of the drawings required for the description of the embodiments of the present application, it being apparent that the drawings in the following description are some embodiments of the present application, and that other drawings may be obtained from these drawings without the exercise of inventive effort for a person of ordinary skill in the art.

FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;

FIG. 2 is a flow chart of one embodiment of an artificial intelligence based method of processing insurance data in accordance with the present application;

FIG. 3 is a schematic diagram illustrating one embodiment of an artificial intelligence based insuring data processing apparatus in accordance with the present application;

FIG. 4 is a schematic structural diagram of one embodiment of a computer device in accordance with the present application.

Detailed Description

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the applications herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having" and any variations thereof in the description of the application and the claims and the description of the drawings above are intended to cover a non-exclusive inclusion. The terms first, second and the like in the description and in the claims or in the above-described figures, are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.

In order to make the person skilled in the art better understand the solution of the present application, the technical solution of the embodiment of the present application will be clearly and completely described below with reference to the accompanying drawings.

As shown in fig. 1, a system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.

The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as a web browser application, a shopping class application, a search class application, an instant messaging tool, a mailbox client, social platform software, etc., may be installed on the terminal devices 101, 102, 103.

The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablet computers, electronic book readers, MP3 players (Moving Picture Experts Group Audio Layer III, dynamic video expert compression standard audio plane 3), MP4 (Moving Picture Experts Group Audio Layer IV, dynamic video expert compression standard audio plane 4) players, laptop and desktop computers, and the like.

The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.

It should be noted that, the method for processing the application data based on the artificial intelligence provided by the embodiment of the application is generally executed by a server/terminal device, and correspondingly, the device for processing the application data based on the artificial intelligence is generally arranged in the server/terminal device.

The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.

Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.

It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

With continued reference to FIG. 2, a flowchart of one embodiment of an artificial intelligence based method of processing insurance data in accordance with the present application is shown. The order of the steps in the flowchart may be changed and some steps may be omitted according to various needs. The artificial intelligence-based insurance data processing method provided by the embodiment of the application can be applied to any scene needing insurance product recommendation, and can be applied to products in the scenes, for example, insurance product recommendation in the digital medical field. The method for processing the insuring data based on the artificial intelligence comprises the following steps:

Step S201, receiving an application request triggered by a user; wherein the application request carries personal information of the applied person.

In this embodiment, the electronic device (for example, the server/terminal device shown in fig. 1) on which the artificial intelligence-based application data processing method operates may acquire the personal information of the applied person through a wired connection manner or a wireless connection manner. It should be noted that the wireless connection may include, but is not limited to, 3G/4G/5G connection, wiFi connection, bluetooth connection, wiMAX connection, zigbee connection, UWB (ultra wideband) connection, and other now known or later developed wireless connection. The user can complete triggering of the application request by clicking an application button of the application and filling personal information of the applied person in a corresponding input box. The personal information may include name information, age information, gender information, identification card information, contact information, plan information, product information, and the like.

Step S202, a health nuclear questionnaire matched with the personal information is generated based on the personal information and a preset health database, and the health nuclear questionnaire is displayed.

In this embodiment, the health database is a database that is built in advance and stores a plurality of health care questions, and each health care question is marked with a factor information label that matches itself. The specific implementation process of generating the health core questionnaire matched with the personal information based on the personal information and the preset health database will be described in further detail in the following specific embodiments, which will not be described herein.

Step S203, receiving feedback data corresponding to the health kernel questionnaire input by the user.

In this embodiment, the feedback data refers to a feedback answer input by the user for each question included in the health core questionnaire.

Step S204, checking the feedback data based on a preset check rule, and generating a check result corresponding to the feedback data.

In this embodiment, the feedback data is verified based on the verification rule of the kernel protection, and if the feedback data meets the verification rule of the kernel protection, a kernel protection result that the kernel protection passes is generated; and if the feedback data does not accord with the check rule of the check, generating a refused check result. Wherein the verification rules comprise basic application rules and evaluation rules, the basic insurance policy is a basic rule corresponding to the insurance policy formulated by the insurance company, the evaluation rule is based on a summary of the processing results of the application sheet that has been processed in recent years. When determining the rating rule, the working experience of the service processor of the selected processed application policy is taken as one of factors of main references, so that the obtained rating rule has the effect of more comprehensive consideration and higher accuracy.

Step S205, if the verification result is refusal, acquiring historical application data of the applied person, and determining an insurance product to be recommended corresponding to the applied person based on the historical application data; wherein the number of insurance products to be recommended includes a plurality of insurance products.

In this embodiment, product matching may be performed on all the insurance products in the preset insurance product library based on the historical application data of the applied person, so as to obtain the insurance product matched with the historical application data, and the insurance product is used as the insurance product to be recommended. The product matching process may refer to a similar process of the product, and when the similarity reaches a certain threshold, the matching condition is considered to be met, and the higher the similarity is, the higher the matching degree is.

Step S206, acquiring health data of the insured person, analyzing and processing the insurance product to be recommended and the health data based on a preset product recommendation model, and outputting a target insurance product matched with the health data;

in this embodiment, the method includes the steps of obtaining the health data of the insured person, analyzing the insurance product to be recommended and the health data based on a preset product recommendation model, and outputting a specific implementation process of the target insurance product matched with the health data.

Step S207, pushing the target insurance product to the user.

In this embodiment, the target insurance product may be pushed to the user terminal of the user by acquiring terminal information of the user and based on the terminal information.

After receiving an application request triggered by a user, firstly generating a health nuclear questionnaire matched with personal information based on the personal information in the application request and a preset health database, and displaying the health nuclear questionnaire; then receiving feedback data corresponding to the health nuclear questionnaire input by the user, and checking the feedback data based on a preset nuclear insurance check rule to generate a nuclear insurance result corresponding to the feedback data; if the verification result is refusal, acquiring historical application data of the applied person, and determining an insurance product to be recommended corresponding to the applied person based on the historical application data; subsequently acquiring health data of the insured person, analyzing and processing the insurance product to be recommended and the health data based on a preset product recommendation model, and outputting a target insurance product matched with the health data; and finally pushing the target insurance product to the user. The application processes the user-triggered application request based on the health database and the use of the verification rules of the application, can automatically and rapidly complete the application request of the application, avoids the condition that service personnel confirms abnormal health information items and makes final application decision according to own working experience, and improves the application processing efficiency. In addition, when the verification result corresponding to the verification request is identified as refused, the health data of the insurance applicant and the insurance product to be recommended are analyzed and processed based on the use of a preset product recommendation model, and the target insurance product matched with the health data is output, so that the suitability of the target insurance product and the insurance applicant is ensured, the targeted insurance product recommendation of the insurance applicant is realized, and the accuracy of the target insurance product recommendation is effectively improved.

In some alternative implementations, step S202 includes the steps of:

and acquiring a preset factor, and acquiring target information matched with the preset factor from the personal information.

In this embodiment, the predetermined factors may include age, sex, and the like. The target information refers to age information and sex information of the insured person.

And calling the health database.

And acquiring the target health maintenance problem matched with the target information from the health database.

In this embodiment, the first health care questions corresponding to the age information are obtained from the health database; acquiring a second health maintenance problem corresponding to the gender information from the health database; and integrating the first health maintenance problem and the second health maintenance problem to obtain the target health maintenance problem.

In this embodiment, a preset questionnaire template may be obtained; then constructing a question number sequence corresponding to all the target health maintenance questions one by one; and filling all the target health care questions into the questionnaire template based on the question number sequence to obtain a health care questionnaire. The questionnaire template is a template file constructed according to an actual questionnaire generation request. In addition, the generation of the question number sequence of the target health care problem is not particularly limited, and the question number sequence can be set according to actual use requirements, or randomly generated, and the like.

The method comprises the steps of obtaining a preset factor and obtaining target information matched with the preset factor from the personal information; then calling the health database; then, acquiring a target health maintenance problem matched with the target information from the health database; and constructing the health nuclear questionnaire based on the target health nuclear questionnaire. According to the application, the corresponding target information is extracted from the personal information based on the use of the preset factors, and further the target health care problem matched with the target information is obtained from the health database based on the use of the health database, so that the required health care questionnaire is automatically and rapidly constructed according to the target health care problem, and the generation efficiency and the generation intelligence of the health care questionnaire are improved. And the health nuclear questionnaire can be dynamically constructed according to the specific factors of the insured person, so that the suitability of the health nuclear questionnaire to the insured person is effectively improved.

In some alternative implementations of the present embodiment, step S206 includes the steps of:

and acquiring the health data of the insured person from a preset database based on the personal information.

In this embodiment, the preset database is a pre-constructed database storing health data of clients, and the health data of the insured person can be queried from the database according to personal information of the insured person. In the medical application scenario in the digital medical field, the health data may include personal health files, prescription data, medical treatment data, examination reports, and the like.

And calling a preset constructed product recommendation model.

In this embodiment, health data of a sample user and insurance product data of the sample user may be obtained as a training data set, the health data of the sample user is input as a deep learning model, the insurance product data of the sample user is output as the deep learning model, and the deep learning is performed through a decision tree machine algorithm to construct and obtain a product recommendation model. The deep learning model can specifically adopt a decision tree model, wherein the decision tree is a probability analysis method, and in machine learning, the decision tree is used as a prediction model for representing a mapping relation between object attributes and object values. Each node in the tree represents an object, each bifurcation path represents some possible attribute value, and each leaf node corresponds to the value of the object represented by the path traversed from the root node to that leaf node.

And inputting the insurance products to be recommended and the health data into the product recommendation model, and generating matching values between the insurance products to be recommended and the health data through the product recommendation model.

In this embodiment, after the insurance product to be recommended and the health data are input into the product recommendation model, the product recommendation model calculates the matching degree between each insurance product to be recommended and the health data, so as to obtain the matching value between each insurance product to be recommended and the health data.

And screening the specified insurance products with the largest matching values from all the insurance products to be recommended.

In this embodiment, the specified insurance product corresponding to the specified matching value may be obtained by screening the specified matching value with the highest value from all the matching values, and further obtaining the insurance product corresponding to the specified matching value from all the insurance products to be recommended

And taking the appointed insurance product as the target insurance product.

Based on the personal information, health data of the insured person are obtained from a preset database; then calling a preset constructed product recommendation model; inputting the insurance products to be recommended and the health data into the product recommendation model, and generating matching values between the insurance products to be recommended and the health data through the product recommendation model; and screening out the appointed insurance product with the largest matching value from all the insurance products to be recommended, and taking the appointed insurance product as the target insurance product. After the health data of the insurance applicant is obtained based on the use of the preset database, the insurance product to be recommended and the health data are analyzed and processed based on the use of the product recommendation model, and the final target insurance product with the highest matching value with the health data in the insurance product to be recommended is output.

In some alternative implementations, after step S204, the electronic device may further perform the following steps:

and if the nuclear protection result is refusal, establishing communication connection with a preset human nuclear platform.

In this embodiment, the above-mentioned human kernel platform is a pre-constructed platform for assisting a kernel-protecting person in performing artificial kernel protection.

And generating a underwriting task corresponding to the user based on the feedback data and the health underwriting questionnaire.

In this embodiment, the feedback data and the health kernel questionnaire may be filled into the task template by acquiring a task template, so as to generate a kernel-preserving task corresponding to the user. The task template is a template file constructed according to the actual kernel-preserving task generation requirement.

And determining a target auditor, and acquiring communication information of the target auditor based on the human-core platform.

In this embodiment, the selection of the target auditor is not specifically limited, and the selection may be performed according to actual service requirements, for example, the target auditor may be the currently-idle auditor, or the auditor with the highest evaluation score for the individual, and so on. Wherein, the communication information can refer to mail address.

In this embodiment, through the rule configuration of the newly added transfer person's nucleus, insurance service personnel can dynamically configure which products, channels, ages and other factors, and which channels or ages of the list hit the self-checking rule and then do not give refusal to protect the conclusion, and give the conclusion of the transfer person's nucleus, and then enter the person's nucleus workbench to carry out expert checking and protection, and the insurance service personnel can still be under protection after the checking and protection pass. Compared with the old process, the new process improves the underwriting conversion rate through the differentiated underwriting strategy.

If the check result is detected to be refused, firstly establishing communication connection with a preset human-check platform; then, based on the feedback data and the health nuclear questionnaire, generating a nuclear insurance task corresponding to the user; then determining a target auditor, and acquiring communication information of the target auditor based on the human-core platform; and then, based on the communication information, sending the underwriting task to a target auditor, so as to audit the underwriting task by the target auditor, and generating a designated underwriting result corresponding to the underwriting task. After determining that the content of the underwriting result is refused, the application also executes a differentiated underwriting strategy, generates the underwriting task corresponding to the user based on the feedback data and the health underwriting questionnaire, and then sends the underwriting task to a target auditor, so that the target auditor carries out audit processing on the underwriting task to generate a designated underwriting result corresponding to the underwriting task, thereby realizing secondary audit on the underwriting request based on the actual service requirement by manpower, improving the intelligence and accuracy of the underwriting processing, and being beneficial to improving the underwriting conversion rate.

In some optional implementations of this embodiment, after the step of sending the underwriting task to a target auditor based on the communication information to audit the underwriting task by the target auditor to generate a specified underwriting result corresponding to the underwriting task, the electronic device may further execute the following steps:

and receiving the specified underwriting result returned by the human kernel platform.

In this embodiment, the contents of the above specified underwriting result include pass (subject/except) or all refusal.

And if the specified underwriting result is all refusal, refusing to respond to the application request.

And generating corresponding reminding information based on the verification result.

In this embodiment, the reminding information corresponding to the user may be generated by acquiring a reminding information template and filling the verification result into the reminding information template. The reminding information template is an information template which is constructed in advance according to actual refusal and reminding requirements.

The application receives the appointed underwriting result returned by the human kernel platform; if the specified underwriting results are all refused, refusing to respond to the application request; then generating corresponding reminding information based on the verification result; and subsequently, the reminding information is sent to a user terminal corresponding to the user. When the appointed underwriting result returned by the human kernel platform is received, if the appointed underwriting result is judged to be all refused, the response to the application request is refused, so that the situation of error processing for underwriting the insuring cases which cannot be applied is avoided, and the correct operation of the underwriting process is ensured. In addition, corresponding reminding information is intelligently generated based on the verification result and is sent to a user terminal corresponding to the user, so that refused reminding of the user is performed, the user can perform subsequent corresponding processing based on the reminding information, and the user's application experience is improved.

In some optional implementations of this embodiment, after step S203, the electronic device may further perform the following steps:

and acquiring a preset disease code.

In this embodiment, the disease code is specifically an ICD10 disease code.

And performing code matching processing on each topic contained in the health nuclear questionnaire fed back by the user based on the disease codes to obtain corresponding code matching data.

In this embodiment, a natural language processing technology (Natural Language Processing, NLP) may be adopted to code each topic included in the health kernel questionnaire fed back by the user with the disease code, so as to obtain corresponding code matching data, so as to increase accuracy of disease classification of the client by improving sample size. And the customer disease risk is labeled through a differentiated underwriting strategy, so that the underwriting conversion rate is further improved.

And storing the code pair data.

In this embodiment, the storage manner of the code data is not limited, and for example, a blockchain storage manner, a cloud storage manner, a local database storage manner, and the like may be adopted.

The application obtains the preset disease code; performing code matching processing on each topic contained in the health nuclear questionnaire fed back by the user based on the disease codes to obtain corresponding code matching data; and subsequently storing the code matching data. After the user finishes the feedback corresponding to the health nuclear questionnaire, the application intelligently carries out code matching processing on each question contained in the health nuclear questionnaire fed back by the user based on the disease code to obtain code matching data, and stores the code data so as to convert each question in the health nuclear questionnaire into the corresponding code for storage, thereby preparing a nuclear insurance output model which can be built by self and can introduce common diseases and high risk diseases in future, and being beneficial to improving the data utilization rate of the feedback data of the health nuclear questionnaire.

In some optional implementations of this embodiment, after step S204, the electronic device may further perform the following steps:

if the verification result is that the verification passes, generating the policy confirmation information of the target policy corresponding to the application request, and displaying the policy confirmation information.

In this embodiment, after generating the policy confirmation information of the target policy corresponding to the application request, a policy confirmation page may be further constructed, and the policy confirmation information may be displayed on the policy confirmation page

And judging whether a policy confirmation operation triggered by the user is received or not.

In this embodiment, the user may trigger the policy confirmation operation by clicking the confirmation application button in the policy confirmation page.

And if the policy confirmation operation is received, displaying a preset premium payment page.

In this embodiment, after the user confirms the policy, the network marketing wind control self-checking may be performed, the contract rule and the core policy may be executed, and after the self-checking passes, the subsequent step of displaying the preset premium payment page may be executed. The premium payment page is a page which is constructed in advance and comprises a cash register and is used for indicating a user to pay a policy.

If the user is detected to finish the payment operation on the premium payment page, judging whether the target policy meets the preset auditing conditions.

In this embodiment, the audit condition may be a condition generated according to an actual network marketing audit requirement, for example, may include channel code 08 and product code P205 or P206.

In this embodiment, if the print generation of the target policy is completed, the target policy may be previewed online later. If the target policy meets the above auditing conditions, further network sales auditing processing is required to be performed on the target policy, if the network sales auditing is passed, the processing of generating the target policy and performing underwriting processing on the target policy is continuously performed. If the network pin audit is not passed, the target policy is judged to be the problematic policy with problems, and refund processing is carried out through the front end, so that the situation of error processing of underwriting on the problematic policy case is avoided, and the correct operation of the underwriting process is ensured. In addition, the three-party bill issuing process can be realized through a big data technology, and meanwhile, dynamic bill issuing is carried out according to rule configuration of different factors in the insurance policy.

If the verification result is detected to be that the verification passes, generating the policy confirmation information of the target policy corresponding to the application request, and displaying the policy confirmation information; then judging whether a policy confirmation operation triggered by the user is received or not; if the policy confirmation operation is received, displaying a preset premium payment page; if the user is detected to finish the payment operation on the premium payment page, judging whether the target policy meets a preset auditing condition or not; and if the auditing conditions are not met, generating the target policy, and carrying out underwriting treatment on the target policy. The application can execute corresponding underwriting processing corresponding to the underwriting request only when the underwriting result of the underwriting request is determined to be the underwriting passing, can effectively avoid the situation of carrying out error processing on underwriting of the insurance policy cases which do not pass the underwriting, improves the processing standardization of the underwriting request, and ensures the correct operation of the underwriting flow of the underwriting cases.

It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present application.

It should be emphasized that the target insurance product may also be stored in a blockchain node in order to further ensure privacy and security of the target insurance product.

The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by computer readable instructions stored in a computer readable storage medium that, when executed, may comprise the steps of the embodiments of the methods described above. The storage medium may be a nonvolatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a random access Memory (Random Access Memory, RAM).

It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited in order and may be performed in other orders, unless explicitly stated herein. Moreover, at least some of the steps in the flowcharts of the figures may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order of their execution not necessarily being sequential, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.

With further reference to FIG. 3, as an implementation of the method of FIG. 2 described above, the present application provides an embodiment of an artificial intelligence based insuring data processing apparatus, corresponding to the method embodiment of FIG. 2, which is particularly applicable to a variety of electronic devices.

As shown in fig. 3, the artificial intelligence based application data processing apparatus 300 according to the present embodiment includes: a first receiving module 301, a first generating module 302, a second receiving module 303, a second generating module 304, a first determining module 305, a second determining module 306 and a pushing module 307. Wherein:

a first receiving module 301, configured to receive an application request triggered by a user; wherein, the application request carries personal information of the applied person;

a first generation module 302, configured to generate a health core questionnaire matched with the personal information based on the personal information and a preset health database, and display the health core questionnaire;

a second receiving module 303, configured to receive feedback data corresponding to the health kernel questionnaire input by the user;

the second generating module 304 is configured to verify the feedback data based on a preset verification rule for a kernel protection, and generate a kernel protection result corresponding to the feedback data;

The first determining module 305 is configured to obtain historical application data of the applied person if the verification result is refusal, and determine, based on the historical application data, an insurance product to be recommended corresponding to the applied person; wherein the number of insurance products to be recommended includes a plurality of insurance products;

the second determining module 306 is configured to obtain health data of the insured person, analyze the insurance product to be recommended and the health data based on a preset product recommendation model, and output a target insurance product matched with the health data;

a pushing module 307, configured to push the target insurance product to the user.

In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the method for processing the secure data based on artificial intelligence in the foregoing embodiment, which is not described herein again.

In some alternative implementations of the present embodiment, the first generating module 302 includes:

the first acquisition sub-module is used for acquiring a preset factor and acquiring target information matched with the preset factor from the personal information;

the first calling sub-module is used for calling the health database;

The second acquisition sub-module is used for acquiring the target health maintenance problem matched with the target information from the health database;

and the construction submodule is used for constructing the health nuclear questionnaire based on the target health nuclear questionnaire.

In some alternative implementations of the present embodiment, the second determining module 306 includes:

the third acquisition sub-module is used for acquiring the health data of the insured person from a preset database based on the personal information;

the second calling sub-module is used for calling a preset and constructed product recommendation model;

the generation sub-module is used for inputting the insurance products to be recommended and the health data into the product recommendation model, and generating matching values between the insurance products to be recommended and the health data through the product recommendation model;

the screening submodule is used for screening out the appointed insurance product with the largest matching value from all the insurance products to be recommended;

and the determining submodule is used for taking the appointed insurance product as the target insurance product.

In some optional implementations of the present embodiment, the artificial intelligence based application data processing apparatus further comprises:

the creation module is used for establishing communication connection with a preset human-kernel platform if the kernel protection result is refusal;

the third generation module is used for generating a nuclear protection task corresponding to the user based on the feedback data and the health nuclear protection questionnaire;

the first acquisition module is used for determining a target auditor and acquiring communication information of the target auditor based on the human-core platform;

the first sending module is used for sending the underwriting task to a target auditor based on the communication information so as to audit the underwriting task through the target auditor and generate a designated underwriting result corresponding to the underwriting task.

the third receiving module is used for receiving the appointed underwriting result returned by the human kernel platform;

the first processing module is used for refusing to respond to the application request if the specified underwriting result is all refused;

a fourth generation module, configured to generate corresponding alert information based on the core security result;

and the second sending module is used for sending the reminding information to a user terminal corresponding to the user.

the second acquisition module is used for acquiring a preset disease code;

the second processing module is used for carrying out code matching processing on each topic contained in the health nuclear questionnaire after the user feedback based on the disease codes to obtain corresponding code matching data;

And the storage module is used for storing the code matching data.

a fifth generation module, configured to generate, if the result of the underwriting is that the underwriting passes, policy confirmation information of a target policy corresponding to the application request, and display the policy confirmation information;

the first judging module is used for judging whether the policy confirmation operation triggered by the user is received or not;

the display module is used for displaying a preset premium payment page if the policy confirmation operation is received;

the second judging module is used for judging whether the target policy meets preset auditing conditions or not if the user is detected to finish payment operation on the premium payment page;

and the third processing module is used for generating the target policy if the auditing conditions are not met, and carrying out underwriting processing on the target policy.

In order to solve the technical problems, the embodiment of the application also provides computer equipment. Referring specifically to fig. 4, fig. 4 is a basic structural block diagram of a computer device according to the present embodiment.

The computer device 4 comprises a memory 41, a processor 42, a network interface 43 communicatively connected to each other via a system bus. It should be noted that only computer device 4 having components 41-43 is shown in the figures, but it should be understood that not all of the illustrated components are required to be implemented and that more or fewer components may be implemented instead. It will be appreciated by those skilled in the art that the computer device herein is a device capable of automatically performing numerical calculations and/or information processing in accordance with predetermined or stored instructions, the hardware of which includes, but is not limited to, microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASICs), programmable gate arrays (fields-Programmable Gate Array, FPGAs), digital processors (Digital Signal Processor, DSPs), embedded devices, etc.

The computer equipment can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing equipment. The computer equipment can perform man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch pad or voice control equipment and the like.

The memory 41 includes at least one type of readable storage medium including flash memory, hard disk, multimedia card, card memory (e.g., SD or DX memory, etc.), random Access Memory (RAM), static Random Access Memory (SRAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), programmable Read Only Memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the storage 41 may be an internal storage unit of the computer device 4, such as a hard disk or a memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like, which are provided on the computer device 4. Of course, the memory 41 may also comprise both an internal memory unit of the computer device 4 and an external memory device. In this embodiment, the memory 41 is typically used to store an operating system and various application software installed on the computer device 4, such as computer readable instructions for an artificial intelligence-based method of applying and processing data. Further, the memory 41 may be used to temporarily store various types of data that have been output or are to be output.

The processor 42 may be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is configured to execute computer readable instructions stored in the memory 41 or process data, such as computer readable instructions for executing the artificial intelligence based method of processing the application data.

The network interface 43 may comprise a wireless network interface or a wired network interface, which network interface 43 is typically used for establishing a communication connection between the computer device 4 and other electronic devices.

in the embodiment of the application, the user-triggered application request is processed based on the health database and the use of the verification rules of the application, so that the application request can be automatically and rapidly processed, the condition that service personnel confirm abnormal health information items and make final verification decisions according to own working experience is avoided, and the processing efficiency of the application is improved. In addition, when the verification result corresponding to the verification request is identified as refused, the health data of the insurance applicant and the insurance product to be recommended are analyzed and processed based on the use of a preset product recommendation model, and the target insurance product matched with the health data is output, so that the suitability of the target insurance product and the insurance applicant is ensured, the targeted insurance product recommendation of the insurance applicant is realized, and the accuracy of the target insurance product recommendation is effectively improved.

The present application also provides another embodiment, namely, a computer-readable storage medium storing computer-readable instructions executable by at least one processor to cause the at least one processor to perform the steps of the artificial intelligence-based method of insuring data processing as described above.

From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present application.

It is apparent that the above-described embodiments are only some embodiments of the present application, but not all embodiments, and the preferred embodiments of the present application are shown in the drawings, which do not limit the scope of the patent claims. This application may be embodied in many different forms, but rather, embodiments are provided in order to provide a thorough and complete understanding of the present disclosure. Although the application has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described in the foregoing description, or equivalents may be substituted for elements thereof. All equivalent structures made by the content of the specification and the drawings of the application are directly or indirectly applied to other related technical fields, and are also within the scope of the application.

Claims

1. An artificial intelligence-based application data processing method is characterized by comprising the following steps:

pushing the target insurance product to the user.

2. The method for processing the application data based on the artificial intelligence according to claim 1, wherein the step of generating a health core questionnaire matched with the personal information based on the personal information and a preset health database specifically comprises the following steps:

invoking the health database;

3. The method for processing the insurance data based on artificial intelligence according to claim 1, wherein the steps of obtaining the health data of the insured person, analyzing and processing the insurance product to be recommended and the health data based on a preset product recommendation model, and outputting a target insurance product matched with the health data specifically comprise:

calling a preset constructed product recommendation model;

and taking the appointed insurance product as the target insurance product.

4. The method for processing the secured data based on the artificial intelligence according to claim 1, further comprising, after the step of verifying the feedback data based on the preset verification rule for the secured data and generating the secured result corresponding to the feedback data:

5. The method for processing the application data based on the artificial intelligence according to claim 4, further comprising, after the step of transmitting the underwriting task to a target auditor based on the communication information to audit the underwriting task by the target auditor to generate a specified underwriting result corresponding to the underwriting task:

6. The artificial intelligence based insurance data processing method according to claim 1, further comprising, after said step of receiving feedback data corresponding to said health kernel questionnaire entered by said user:

acquiring a preset disease code;

and storing the code pair data.

7. The method for processing the secured data based on the artificial intelligence according to claim 1, further comprising, after the step of verifying the feedback data based on the preset verification rule for the secured data and generating the secured result corresponding to the feedback data:

8. An artificial intelligence based application data processing apparatus, comprising:

9. A computer device comprising a memory having stored therein computer readable instructions which when executed implement the steps of the artificial intelligence based method of insuring data processing of any of claims 1 to 7.

10. A computer readable storage medium having stored thereon computer readable instructions which when executed by a processor implement the steps of the artificial intelligence based method of insuring data processing of any of claims 1 to 7.