Business data management and business processing method and device, equipment and medium

Abstract

The invention relates to the technical field of financial science and technology and health medical treatment, and discloses a business data management and business processing method and device, equipment and a medium, and the method comprises the steps: carrying out the encryption storage of customer business data, and carrying out the access control based on a preset authority distribution strategy; receiving and identifying a service request category; when the request category is risk assessment, calling customer personal information and historical business data, inputting the customer personal information and the historical business data into a pre-trained risk prediction model, obtaining a risk prediction result, and recommending a product; when the request category is a claim settlement request, calling claim settlement service data for automatic auditing and processing; and when the request category is health management service, calling health management service data and analyzing by using the pre-training model to obtain health condition assessment and disease risk prediction results. According to the method and the system, secure storage and efficient processing of the data can be realized, the data security is enhanced, a plurality of service systems are integrated, efficient collaboration of the service systems is realized, and the overall service processing efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of business data management and processing, and the technical field of financial technology and health care, and particularly relates to a business data management and business processing method, device, equipment and medium. BACKGROUND

[0002] With the rapid development of information technology, the Internet technology and computer technology enable information to be quickly spread through the Internet, so that the business information or business data of various businesses can be stored in the Internet, so that business processing can be realized based on the Internet. For the insurance industry, the traditional insurance industry is undergoing digital transformation under the influence of information technology development, and the business data management and business processing mode is also constantly innovating. With the acceleration of the digital transformation of the insurance industry, the business data management and processing mode faces systematic challenges.

[0003] The inventors realize that for the insurance industry, the existing platform or system has the following significant limitations:

[0004] (1) Data security and permission control defects: the existing insurance industry lacks effective protection of customer data, and the access control of customer data is not strict, such as business staff can access all customer data at any time, resulting in high risk of data leakage;

[0005] (2) System coordination obstacles: information islands are serious between multiple systems, and data standards are not unified in the sub-systems of underwriting, claims, and health management. Cross-business collaboration needs to be manually connected, which is low in efficiency and time-consuming, and lacks real-time performance. Data update delay leads to mismatch between risk assessment and actual customer status, making it difficult to accurately hit customer needs. SUMMARY

[0006] The present application provides a business data management and business processing method, device, computer equipment and medium to solve the technical problems of high risk of customer data leakage in the existing insurance industry and serious data information islands between multiple systems, which makes it difficult to implement cross-system business.

[0007] In a first aspect, a business data management and business processing method is provided, comprising:

[0008] The acquired business data of the customer is encrypted and stored, and the access permission of the business data is controlled based on a preset permission allocation strategy. The business data includes at least one of personal information data, insurance business data, claims business data, health management business data, and historical business data;

[0009] A business request is received, and the category of the business request is identified;

[0010] When the category of the service request is risk assessment on the target customer, personal information data and historical service data of the target customer are called and input to a pre-trained risk prediction model to obtain a risk prediction result, and product recommendation is performed according to the risk assessment result;

[0011] When the category of the service request is a claim request, claim service data corresponding to the claim request is called, and the claim service data is automatically audited, and claim service processing is automatically performed according to the audit result;

[0012] When the category of the service request is health management service, health management service data corresponding to the health management service is called, and a pre-trained analysis model is used to analyze the health management service data to obtain a health condition assessment result and a disease risk prediction result.

[0013] In a second aspect, a service data management and service processing apparatus is provided, comprising:

[0014] A data storage and permission control module is configured to encrypt and store the obtained service data of the customer, and perform access permission control on the service data based on a pre-set permission allocation strategy, the service data comprising at least one of personal information data, insurance service data, claim service data, health management service data, and historical service data;

[0015] A receiving module is configured to receive a service request and identify the category of the service request;

[0016] A first processing module is configured to, when the category of the service request is risk assessment on a target customer, call personal information data and historical service data of the target customer and input them to a pre-trained risk prediction model to obtain a risk prediction result, and perform product recommendation according to the risk assessment result;

[0017] A second processing module is configured to, when the category of the service request is a claim request, call claim service data corresponding to the claim request, and automatically audit the claim service data, and automatically perform claim service processing according to the audit result;

[0018] A third processing module is configured to, when the category of the service request is health management service, call health management service data corresponding to the health management service, and use a pre-trained analysis model to analyze the health management service data to obtain a health condition assessment result and a disease risk prediction result.

[0019] In a third aspect, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above service data management and service processing method when executing the computer program.

[0020] In a fourth aspect, a computer-readable storage medium is provided, which stores a computer program. The computer program is executed by a processor to implement the steps of the business data management and business processing method.

[0021] In the scheme implemented by the business data management and business processing method, the device, the computer device and the storage medium, the decentralized data storage center and transmission network are constructed based on the blockchain technology, and the multi-level permission control strategy is combined, so that the problem of insufficient data privacy protection is effectively solved, and the data security is significantly improved. The hybrid model constructed by the gradient boosting decision tree and the neural network is used for risk assessment, which can process structured and unstructured data at the same time, and greatly improves the accuracy of risk assessment. The OCR technology and the natural language processing technology are used for automatic auditing of the claim business data, which simplifies the claim process, improves the claim efficiency and reduces the waiting time of customers. The health data of customers is collected in real time by the intelligent health detection equipment and analyzed, so that the personalized health management service is realized, and the health risk management is effectively promoted. The system architecture integrating the data storage and data transmission, the risk assessment business, the claim business and the health management business breaks the information island, realizes the efficient cooperation of the business systems and improves the overall business processing efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the description of the embodiments of the present application. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0023] Figure 1 is an application environment schematic diagram of the business data management and business processing method in an embodiment of the present application;

[0024] Figure 2 is a flow schematic diagram of the business data management and business processing method in an embodiment of the present application;

[0025] Figure 3 is a structure schematic diagram of the business data management and business processing device in an embodiment of the present application;

[0026] Figure 4 is a structure schematic diagram of the computer device in an embodiment of the present application. DETAILED DESCRIPTION

[0027] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those ordinarily skilled in the art without creative efforts should fall into the scope of the present application.

[0028] The service data management and service processing method provided by the embodiments of the present application can be applied in the application environment as shown in Figure 1 , wherein the client is used for inputting countable service data by a customer and showing information such as product recommendation, service processing result, health condition evaluation result and disease risk prediction result to the customer. The client communicates with the server through a network. Specifically, a transmission network is constructed between the client and the server through blockchain technology, the client transmits the service data of the customer to the server through the transmission network, the server stores the obtained service data of the customer in an encrypted manner, and access permission control is performed on the service data based on a preset permission allocation strategy. The service data includes at least one of personal information data, insurance business data, claim business data, health management business data and historical business data. A service request is received, and the category of the service request is identified. When the category of the service request is risk evaluation on a target customer, the personal information data and the historical business data of the target customer are called and input into a pre-trained risk prediction model to obtain a risk prediction result, and product recommendation is performed according to the risk evaluation result. When the category of the service request is a claim request, the claim business data corresponding to the claim request is called, and the claim business data is automatically audited, and automatic claim business processing is performed according to the audit result. When the category of the service request is a health management service, the health management business data corresponding to the health management service is called, and a pre-trained analysis model is used to analyze the health management business data to obtain a health condition evaluation result and a disease risk prediction result. The client can be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers and portable wearable devices. The server can be implemented by an independent server or a server cluster composed of multiple servers. The present application will be described in detail through specific embodiments.

[0029] Please refer to Figure 2 , Figure 2 is a flowchart of the service data management and service processing method provided by the embodiments of the present application, which includes the following steps:

[0030] S10: The acquired business data of the customer is encrypted and stored, and access control is performed on the business data based on a preset permission allocation strategy. The business data includes at least one of personal information data, insurance business data, claim business data, health management business data, and historical business data.

[0031] It should be noted that the personal information data includes the identity information and contact information of the customer and the like, which is filled in and saved when the customer registers for the first time; the insurance business data includes data information related to the insurance project currently participated by the customer, such as the policy clause, financial information, etc.; the claim business data includes data information uploaded by the customer and related to the claim business, such as medical documents, diagnosis reports, etc.; the health management business data mainly includes health monitoring data of the customer collected through a wearable device, electronic health records of the customer, disease history of the customer, and the like; and the historical business data includes data such as past insurance, claim records, and policy change records of the customer.

[0032] Specifically, when the customer registers for the first time, the personal information data of the customer can be collected, and a data storage center dedicated to the customer is constructed based on the personal information data. Then, all business data of the customer is stored in the data storage center and encrypted, which can be encrypted by using a high-strength encryption algorithm such as AES-256 encryption algorithm to ensure the security of the data during storage. Then, according to a preset permission allocation strategy, access control is performed on the business data of the customer, so as to limit the users who can access the business data, and further reduce the risk of leakage of the business data of the customer.

[0033] Further, in some embodiments, the step of encrypting and storing the acquired business data of the customer in step S10 specifically includes:

[0034] A decentralized data storage center and transmission network are constructed based on blockchain technology, the business data of the customer is transmitted by using the transmission network, and the business data is stored in the data storage center.

[0035] Specifically, first, a decentralized data storage center and transmission network are constructed based on blockchain technology. The blockchain technology adopts a distributed ledger structure to ensure that data cannot be tampered with once written, and a consensus mechanism to ensure data consistency and reliability. Then, the business data of the customer is transmitted by using the transmission network, and the business data is stored in the data storage center. In the transmission process, the data is encrypted by using an asymmetric encryption algorithm to ensure that even if the data is intercepted, it cannot be decrypted. The data storage center uses sharding storage technology to store the customer data on different nodes, improving data security and access efficiency.

[0036] Further, in order to strengthen the control of access rights, in some embodiments, the preset right allocation strategy includes a role-based access control strategy, a behavior pattern-based access control strategy and a time-based access control strategy.

[0037] The role-based access control strategy includes allocating data access rights to the access person according to the role of the access person. For example, a customer can only access the business data of the customer himself, a customer manager can only access the basic information of the customers he is responsible for, a claims officer can only access the business data related to claims, and a risk assessment teacher can access the risk-related data of the customer. By giving different access rights to different roles, the access person can be prevented from accessing data other than business requirements as much as possible under the condition that the business can be normally completed, thereby reducing the possibility of data leakage.

[0038] The behavior pattern-based access control strategy includes limiting the data access rights of the access person according to the occurrence time and scene of the access behavior of the access person. For example, when it is detected that the access person downloads a large amount of customer data at a non-working time or an irregular place or a non-specific scene, the system will automatically limit the access rights, thereby reducing the possibility of data leakage.

[0039] The time-based access control strategy includes setting the business access valid time according to the business requirements, and allocating data access rights to the access person according to the access valid time. For example, a customer manager is temporarily authorized to access specific customer data, and the access right is valid only within a specific time period; a claims officer is authorized to access claims business data during the process of claims business, and the access right is automatically invalidated after the completion of the claims business.

[0040] The above-mentioned role-based access control strategy, behavior pattern-based access control strategy and time-based access control strategy can comprehensively limit the access rights of the access person from the role, access behavior and access time of the access person, and can prevent the access person from accessing the business data of the customer by improper means, thereby greatly reducing the possibility of leakage of the business data of the customer.

[0041] Further, in some embodiments, after the step of controlling the access rights of the business data based on the preset right allocation strategy, the business data management and business processing method further includes:

[0042] Abnormality detection and analysis are performed on the access behavior of the access person, and the data access rights of the access person are frozen when it is confirmed that the access behavior of the access person is abnormal.

[0043] Specifically, the embodiment not only adopts a preset permission allocation strategy to limit the access permission of the access person, but also needs to perform abnormality detection analysis on the access behavior of the access person, and freeze the data access permission of the access person when confirming that the access behavior of the access person is abnormal (such as high-frequency access in an abnormal time period, access beyond the permission range, etc.).

[0044] The abnormality detection analysis can be implemented in a manner of machine learning algorithm, user behavior portrait, etc. For example, the machine learning algorithm can be trained by using historical data to establish a normal behavior mode baseline of the access person, and the abnormal behavior deviating from the baseline is identified by using the machine learning algorithm. When the behavior portrait manner is used, the behavior portrait of the user can be constructed based on the historical data, and when it is detected that the access behavior of the access person deviates from the normal access behavior, a pre-warning mechanism is triggered. When the abnormal behavior is detected, the data access permission of the access person is immediately frozen, and an alarm is sent to the system administrator.

[0045] Further, in some embodiments, when the access person performs data access, a risk score can be generated based on the behavior characteristics of the access person and environmental factors, the behavior characteristics include whether the access person has access behavior beyond the access permission range, historical access behavior, historical access violation behavior, etc., and the environmental factors include the type of access device used by the access person, the access network environment, etc. The risk assessment can be implemented by constructing a machine learning model and using the machine learning model.

[0046] S11: receiving a service request and identifying the category of the service request.

[0047] Specifically, the service request includes but is not limited to a customer-initiated insurance business request and a claim business request, a risk assessment business request and a health management business request generated by the system after the initial entry of the business data of the customer, etc. Different service requests correspond to different service processing modes.

[0048] S12: When the category of the service request is to perform risk assessment on the target customer, the personal information data and the historical business data of the target customer are called and input into a pre-trained risk prediction model to obtain a risk prediction result, and product recommendation is performed according to the risk assessment result.

[0049] It should be noted that in the insurance industry, the risk assessment business can be performed in multiple scenarios, for example, when a customer first enters the system, risk assessment analysis can be performed according to the business data submitted by the customer; it can also be performed when the customer initiates an insurance request. Risk assessment of business data submitted by the customer. Specifically, when performing risk assessment, first obtain the permission to call the business data of the target customer, then call the personal information data and historical business data of the target customer from the data storage center, then input the personal information data and historical business data into the pre-trained risk prediction model, analyze and predict the personal information data and historical business data through the risk prediction model, obtain the risk prediction result, and then provide personalized product recommendations and services to the target customer according to the risk prediction result. In this embodiment, a risk-product matching matrix can be pre-constructed, for example, Table 1 below shows an example of a risk-product matching matrix:

[0050] Table 1

[0051]

[0052] Further, in this embodiment, the risk prediction model is constructed based on a machine learning algorithm. Specifically, the risk prediction model includes a hybrid model constructed based on gradient boosting decision trees and neural networks, specifically including a decision tree module, a neural network module, and a fusion layer. The decision tree module is used to analyze and predict the structured data in the personal information data and historical business data to obtain a first risk assessment score. The structured data includes quantifiable information such as customer age, income, occupation, and medical history. The neural network module is used to analyze and predict the unstructured data in the personal information data and historical business data to obtain a second risk assessment score. The unstructured data includes health report text, medical images, and physical examination report images. The fusion layer is used to weight and fuse the first risk assessment score and the second risk assessment score to obtain the risk prediction result. The fusion layer is constructed based on the Stacking strategy. Stacking is an advanced integration technology that intelligently combines multiple base model predictions by training a meta-model. It utilizes model diversity to learn complex combination patterns, and can typically significantly improve prediction performance.

[0053] S13: When the category of the business request is a claim request, the claim business data corresponding to the claim request is called and automatically audited, and the claim business processing is automatically performed according to the audit result.

[0054] Specifically, when the category of the business request is a claim request, this embodiment can automatically adjust the claim business data corresponding to the claim request, and automatically audit the claim business data, and automatically perform claim business processing according to the audit result. It realizes the automatic processing of the whole process of the claim business, thereby greatly improving the processing efficiency of the claim business.

[0055] Further, step S13 specifically includes:

[0056] 1. Obtain access permission to the claim business data corresponding to the claim request.

[0057] Specifically, before conducting the claim business, access permission needs to be applied first, and then the claim business data corresponding to the claim request is accessed and obtained on the premise of obtaining access permission, to ensure that data access complies with privacy protection regulations.

[0058] 2. Retrieve the claim business data from the data storage center, and identify the text information data in the claim business data based on OCR technology.

[0059] Specifically, OCR technology can extract text information from scanned copies, photos and other images, support multiple document formats and multi-language recognition, and thus can obtain text information data from image materials such as medical bills, medical records and diagnosis reports.

[0060] 3. Preprocess the text information data, and convert the unstructured data in the text information data into structured data.

[0061] Specifically, preprocessing includes steps such as text cleaning, standardization and classification labeling. After preprocessing the text information data, the unstructured data in the text information data is converted into structured data for subsequent analysis and processing.

[0062] 4. Extract key data from the text information data using natural language processing technology.

[0063] Specifically, the natural language processing technology is used to extract key data from the text information data, including accident description, loss amount, medical diagnosis and other information.

[0064] 5. Perform risk assessment using a pre-trained claim risk assessment model and key data to obtain a claim risk assessment result.

[0065] 6. Perform claim processing based on the claim risk assessment result.

[0066] Specifically, the claim risk assessment model is trained based on historical claim data and can identify potential fraud risks and abnormal claim patterns. Based on the claim risk assessment result, low-risk cases are automatically approved and enter the compensation process, medium-risk cases (such as cases of cost overruns and treatment items not matching insurance clauses) are transferred to manual review, and high-risk cases (such as cases involving fraud) are marked for investigation and notified to professional investigators to intervene.

[0067] In addition, in this embodiment, after completing the claim settlement business audit, the system can also notify the customer of the claim settlement audit result through SMS, telephone or email, etc. to ensure that the customer can learn the claim settlement progress in time.

[0068] S14: When the category of the service request is health management service, the health management business data corresponding to the health management service is called, and the health management business data is analyzed by using the pre-trained analysis model to obtain the health status evaluation result and the disease risk prediction result.

[0069] The embodiment integrates health detection, intelligent inquiry, and health care for the elderly, and provides customers with comprehensive health management services.

[0070] Further, the above step S14 specifically includes:

[0071] 1. Real-time health data of the customer is collected by the smart health detection device worn by the customer, and is transmitted to the data storage center based on a transmission network, and the data storage center also stores historical health data, health questionnaire data and insurance business data of the customer, and the real-time health data, the historical health data, the health questionnaire data and the insurance business data constitute the health management business data.

[0072] Specifically, the smart health detection device includes smart bracelets, smart watches and other wearable devices, which can monitor physiological indicators such as heart rate, blood pressure, blood oxygen and sleep quality. When uploading the data collected by the smart health detection device, the transmission network constructed based on the blockchain technology is used for transmission, so as to realize encrypted uploading. The historical health data includes but is not limited to the electronic health record (EHR) of the customer, including past medical history, medication record, physical examination report, etc. The health questionnaire data can collect information such as eating habits, lifestyle, family medical history of the customer through the questionnaire built in the system.

[0073] 2. Access permission for accessing the health management business data is obtained.

[0074] Specifically, before the health management business is performed, the access permission needs to be applied first, and then the health management business data corresponding to the health management business is accessed and obtained under the premise of obtaining the access permission, so as to ensure that the data access conforms to the privacy protection regulations.

[0075] 3. The health management business data of the customer is called from the data storage center, and the health management business data is input into the health status evaluation model to obtain the health status evaluation result and the disease risk prediction result.

[0076] Specifically, the health condition assessment model is trained based on a large amount of medical data and health indicators, and can comprehensively analyze various health indicators of the customer to generate an overall health score and specific health suggestions. The disease risk prediction result includes the health risks that the customer is likely to have in the future and the probability thereof, and the system generates a personalized health management plan and prevention suggestions according to the prediction result.

[0077] It should be noted that, before the health management business data is input into the health condition assessment model, the health management business data is subjected to data cleaning and standardization processing. The health condition assessment model is constructed based on a machine learning algorithm (such as logistic regression, random forest, neural network, etc.). The health condition assessment result can be divided into three levels of “healthy”, “sub-healthy” and “high risk” according to the risk score. The disease risk prediction result includes the prediction of the diseases that the customer is likely to have in the future (such as hypertension, diabetes, cardiovascular disease, etc.), and the calculation of the probability of having the diseases.

[0078] Further, the embodiment can also make personalized suggestions for the customer according to the health condition assessment result. For example: dietary suggestions: according to the nutritional needs and health condition of the customer, recommend suitable dietary plan (such as low-salt diet, high-fiber diet, etc.); exercise suggestions: according to the exercise habits and health level of the customer, design scientific exercise plan (such as exercise frequency, intensity, duration, etc. per week); work and rest suggestions: according to the sleep data and living habits of the customer, provide optimized work and rest schedule (such as early to bed and early to rise, avoid staying up late, etc.); health monitoring suggestions: for high-risk customers, suggest regular monitoring of key health indicators (such as blood pressure, blood sugar, heart rate, etc.), and provide monitoring frequency and method. Similarly, the embodiment can also make personalized suggestions according to the disease risk prediction result, for example: for customers with high risk of high blood sugar, suggest controlling diet, increasing exercise, and regularly monitoring blood sugar; for customers with high risk of cardiovascular disease, suggest quitting smoking and limiting alcohol, maintaining a healthy weight, and regularly checking heart health.

[0079] The business data management and business processing method of the embodiment of the present application effectively solves the problem of insufficient data privacy protection by constructing a decentralized data storage center and transmission network based on blockchain technology, combined with a multi-level permission control strategy, significantly improving data security; the hybrid model constructed by gradient boosting decision tree and neural network for risk assessment can process both structured and unstructured data, significantly improving the accuracy of risk assessment; using OCR technology and natural language processing technology to automatically audit claim settlement business data simplifies the claim settlement process, improves claim settlement efficiency, and reduces customer waiting time; real-time collection and analysis of customer health data through intelligent health detection equipment realizes personalized health management services, effectively promoting health risk management; the integration of data storage and data transmission, risk assessment business, claim settlement business, and health management business system architecture breaks down information silos, enabling efficient collaboration between business systems, and improving overall business processing efficiency.

[0080] It should be understood that the size of the serial number of each step in the above embodiment does not mean the order of execution, and the execution order of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiment of the present application.

[0081] In an embodiment, a business data management and business processing apparatus is provided, which corresponds one-to-one to the business data management and business processing method described above. As shown in the figure, the business data management and business processing apparatus includes a data storage and permission control module 10, a receiving module 11, a first processing module 12, a second processing module 13, and a third processing module 14. Figure 3

[0082] The data storage and permission control module 10 is configured to encrypt and store the obtained business data of the customer, and control the access permission of the business data based on a preset permission allocation strategy, wherein the business data includes at least one of personal information data, insurance business data, claim settlement business data, health management business data, and historical business data.

[0083] The receiving module 11 is configured to receive a business request and identify the category of the business request.

[0084] The first processing module 12 is configured to, when the category of the business request is risk assessment of a target customer, input the personal information data and historical business data of the target customer into a pre-trained risk prediction model to obtain a risk prediction result, and perform product recommendation according to the risk assessment result.

[0085] ​The second processing module 13 is configured to, when the category of the service request is a claim request, call claim service data corresponding to the claim request, automatically audit the claim service data, and automatically perform claim service processing according to an audit result.

[0086] The third processing module 14 is configured to, when the category of the service request is a health management service, call health management service data corresponding to the health management service, and analyze the health management service data by using a pre-trained analysis model to obtain a health condition evaluation result and a disease risk prediction result.

[0087] Optionally, the data storage and permission control module 10 performs an operation of encrypting and storing the obtained service data of the customer, and specifically includes: constructing a decentralized data storage center and a transmission network based on a blockchain technology, transmitting the service data of the customer by using the transmission network, and storing the service data in the data storage center.

[0088] Optionally, the preset permission allocation strategy includes a role-based access control strategy, a behavior pattern-based access control strategy, and a time-based access control strategy; the role-based access control strategy includes allocating data access permissions to an access person according to a role of the access person; the behavior pattern-based access control strategy includes limiting data access permissions of the access person according to a time and a scene of occurrence of an access behavior of the access person; and the time-based access control strategy includes setting a service access valid time length according to a service requirement, and allocating data access permissions to the access person according to the access valid time length.

[0089] Optionally, after the data storage and permission control module 10 performs the operation of controlling access permissions of the service data based on the preset permission allocation strategy, the data storage and permission control module 10 is further configured to: perform abnormality detection analysis on an access behavior of the access person, and freeze data access permissions of the access person when it is confirmed that the access behavior of the access person is abnormal.

[0090] Optionally, the risk prediction model includes a hybrid model constructed based on a gradient boosting decision tree and a neural network, the risk prediction model includes a decision tree module, a neural network module, and a fusion layer, the decision tree module is configured to analyze and predict structured data in the personal information data and the historical service data to obtain a first risk evaluation score, the neural network module is configured to analyze and predict unstructured data in the personal information data and the historical service data to obtain a second risk evaluation score, and the fusion layer is configured to perform weighted fusion on the first risk evaluation score and the second risk evaluation score to obtain a risk prediction result.

[0091] Optionally, the second processing module 13 performs the operation of retrieving claim settlement business data corresponding to the claim settlement request, automatically auditing the claim settlement business data, and automatically performing claim settlement business processing according to the auditing result, specifically including: obtaining access permission of the claim settlement business data corresponding to the claim settlement request; retrieving the claim settlement business data from the data storage center, and identifying the text information data in the claim settlement business data based on the OCR technology; preprocessing the text information data, and converting the unstructured data in the text information data into structured data; extracting key data from the text information data by using the natural language processing technology; performing risk assessment by using the pre-trained claim settlement risk assessment model and the key data, to obtain a claim settlement risk assessment result; and performing claim settlement processing based on the claim settlement risk assessment result.

[0092] Optionally, the third processing module 14 performs the operation of retrieving health management business data corresponding to the health management service, and analyzing the health management business data by using a pre-trained analysis model to obtain a health status assessment result and a disease risk prediction result, specifically including: collecting real-time health data of the customer by using a smart health detection device worn by the customer, and transmitting the real-time health data to the data storage center for storage based on a transmission network, the data storage center also storing historical health data, health questionnaire data and insurance business data of the customer, and the real-time health data, the historical health data, the health questionnaire data and the insurance business data constitute the health management business data; obtaining access permission of the health management business data; retrieving the health management business data of the customer from the data storage center, and inputting the health management business data into the health status assessment model to obtain the health status assessment result and the disease risk prediction result.

[0093] The specific limitations of the business data management and business processing apparatus can be referred to the limitations of the intelligent question answering method in the above, which will not be repeated here. Each module in the above business data management and business processing apparatus can be realized by software, hardware and combinations thereof in whole or in part. Each module can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operation corresponding to each module.

[0094] In one embodiment, a computer device is provided, and an internal structure diagram of the computer device can be as shown in FIG. 1. Figure 4The computer device includes a processor, a memory, a network interface and a database connected by a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile and / or volatile storage medium, an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is configured to communicate with an external client through a network connection. The computer program, when executed by the processor, implements the following steps:

[0095] The obtained business data of the customer is encrypted and stored, and access permission control is performed on the business data based on a preset permission allocation strategy. The business data includes at least one of personal information data, insurance business data, claim business data, health management business data, and historical business data.

[0096] The business request is received, and the category of the business request is identified.

[0097] When the category of the business request is risk assessment of the target customer, the personal information data and the historical business data of the target customer are called and input into a pre-trained risk prediction model to obtain a risk prediction result, and product recommendation is performed according to the risk assessment result.

[0098] When the category of the business request is a claim request, the claim business data corresponding to the claim request is called, and the claim business data is automatically audited, and the claim business processing is automatically performed according to the audit result.

[0099] When the category of the business request is health management service, the health management business data corresponding to the health management service is called, and a pre-trained analysis model is used to analyze the health management business data to obtain a health status evaluation result and a disease risk prediction result.

[0100] In one embodiment, a computer readable storage medium is provided, and the computer readable storage medium stores a computer program. The computer program, when executed by a processor, implements the following steps:

[0101] The obtained business data of the customer is encrypted and stored, and access permission control is performed on the business data based on a preset permission allocation strategy. The business data includes at least one of personal information data, insurance business data, claim business data, health management business data, and historical business data.

[0102] The business request is received, and the category of the business request is identified.

[0103] When the category of the business request is risk assessment of the target customer, personal information data and historical business data of the target customer are called and input into a pre-trained risk prediction model to obtain a risk prediction result, and product recommendation is performed according to the risk assessment result;

[0104] When the category of the business request is a claim request, claim business data corresponding to the claim request is called, and the claim business data is automatically audited, and claim business processing is automatically performed according to the audit result;

[0105] When the category of the business request is health management service, health management business data corresponding to the health management service is called, and a pre-trained analysis model is used to analyze the health management business data to obtain a health condition assessment result and a disease risk prediction result.

[0106] It should be noted that the functions or steps that the computer readable storage medium or the computer device can achieve described above can correspond to the related description in the foregoing method embodiments, and will not be described again here to avoid repetition.

[0107] A person of ordinary skill in the art can understand that all or part of the processes in the foregoing method embodiments can be completed by a computer program instructing related hardware, and the computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, the computer program can include the processes of the foregoing method embodiments. Any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memory. The non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. The volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0108] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is exemplified, and in actual application, the above-mentioned functions can be completed by different functional units and modules according to needs, 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.

[0109] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A business data management and business process method, characterized by, The method comprises the following steps: encrypting and storing the obtained business data of the customer, and performing access permission control on the business data based on a preset permission allocation strategy, wherein the business data comprises at least one of personal information data, insurance business data, claim business data, health management business data and historical business data; receiving a business request and identifying the category of the business request; when the category of the business request is risk assessment of a target customer, inputting the personal information data and the historical business data of the target customer into a pre-trained risk prediction model to obtain a risk prediction result, and performing product recommendation according to the risk assessment result; when the category of the business request is a claim request, calling the claim business data corresponding to the claim request, automatically auditing the claim business data, and automatically performing claim business processing according to the auditing result; when the category of the business request is a health management service, calling the health management business data corresponding to the health management service, and analyzing the health management business data by using a pre-trained analysis model to obtain a health condition assessment result and a disease risk prediction result.

2. The business data management and processing method of claim 1, wherein, The method of encrypting and storing the obtained business data of the customer comprises the following steps: constructing a decentralized data storage center and a transmission network based on a blockchain technology, transmitting the business data of the customer by using the transmission network, and storing the business data in the data storage center.

3. The business data management and processing method of claim 1, wherein, The preset permission allocation strategy comprises a role-based access control strategy, a behavior pattern-based access control strategy and a time-based access control strategy; the role-based access control strategy comprises assigning data access permissions to an access person according to the role of the access person; the behavior pattern-based access control strategy comprises limiting the data access permissions of the access person according to the occurrence time and scene of the access behavior of the access person; the time-based access control strategy comprises setting a business access valid time length according to business requirements, and assigning data access permissions to the access person according to the access valid time length.

4. The service data management and service processing method according to claim 3, characterized by, After the access permission control on the business data based on the preset permission allocation strategy, the method further comprises the following steps: abnormality detection analysis on the access behavior of the access person, and freezing the data access permissions of the access person when it is confirmed that the access behavior of the access person is abnormal.

5. The business data management and processing method of claim 1, wherein, The risk prediction model comprises a hybrid model constructed based on a gradient boosting decision tree and a neural network, and the risk prediction model comprises a decision tree module, a neural network module and a fusion layer, the decision tree module is used for analyzing and predicting the structured data in the personal information data and the historical business data to obtain a first risk assessment score, the neural network module is used for analyzing and predicting the unstructured data in the personal information data and the historical business data to obtain a second risk assessment score, and the fusion layer is used for weighted fusion of the first risk assessment score and the second risk assessment score to obtain the risk prediction result.

6. The business data management and processing method of claim 1, wherein, The method of calling the claim business data corresponding to the claim request, automatically auditing the claim business data, and automatically performing claim business processing according to the auditing result comprises the following steps: obtain access permission of the claim business data corresponding to the claim request; call the claim business data from the data storage center, and identify the text information data in the claim business data based on an OCR technology; pre-process the text information data, and convert unstructured data in the text information data into structured data; extract key data from the text information data by using a natural language processing technology; perform risk assessment by using a pre-trained claim risk assessment model and the key data, and obtain a claim risk assessment result; perform claim processing based on the claim risk assessment result.

7. The business data management and processing method of claim 1, wherein, The health management business data corresponding to the health management service is called, and a pre-trained analysis model is used to analyze the health management business data to obtain a health condition assessment result and a disease risk prediction result, including: Real-time health data of a client is collected in real time by using a smart health detection device worn by the client, and is transmitted to the data storage center for storage based on the transmission network. The data storage center also stores historical health data, health questionnaire data, and insurance business data of the client. The real-time health data, the historical health data, the health questionnaire data, and the insurance business data constitute the health management business data. Access permission of the health management business data is obtained. The health management business data of the client is called from the data storage center, and the health management business data is input into a health condition assessment model to obtain a health condition assessment result and a disease risk prediction result.

8. A service data management and service processing apparatus characterized by comprising: including: The data storage and permission control module is configured to encrypt and store the obtained business data of the client, and control access permission of the business data based on a pre-set permission allocation strategy. The business data includes at least one of personal information data, insurance business data, claim business data, health management business data, and historical business data. The receiving module is configured to receive a business request and identify a category of the business request. The first processing module is configured to, when the category of the business request is risk assessment of a target client, call personal information data and historical business data of the target client and input the data into a pre-trained risk prediction model to obtain a risk prediction result, and perform product recommendation according to the risk assessment result. The second processing module is configured to, when the category of the business request is a claim request, call claim business data corresponding to the claim request, automatically audit the claim business data, and automatically perform claim business processing according to an audit result. The third processing module is configured to, when the category of the business request is a health management service, call health management business data corresponding to the health management service, and analyze the health management business data by using a pre-trained analysis model to obtain a health condition assessment result and a disease risk prediction result.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the steps of the business data management and business processing method in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program, when executed by a processor, implements the steps of the business data management and business process method according to any one of claims 1 to 7.

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