CN117726457A - Risk prediction method, device, equipment and storage medium based on artificial intelligence - Google Patents
Risk prediction method, device, equipment and storage medium based on artificial intelligence Download PDFInfo
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Abstract
The application belongs to the field of artificial intelligence and the field of financial science and technology, and relates to a risk prediction method based on artificial intelligence, which comprises the following steps: judging whether a risk prediction request triggered by a user is received or not; the risk prediction request carries a super-claim contract identifier; if yes, acquiring a super-claim contract corresponding to the super-claim contract identifier; determining a population of claims contained in the super claim contract; invoking a risk prediction model to perform super-claim risk prediction processing on the claim case group, and generating a super-claim risk prediction result corresponding to the claim case body; and sending the super claim risk prediction result to the user. The application also provides an artificial intelligence-based risk prediction device, computer equipment and a storage medium. In addition, the present application relates to blockchain techniques in which error localization information may be stored. The risk analysis method and the risk analysis device can be applied to risk analysis scenes in the financial field, effectively improve risk identification efficiency of the super-claim contracts based on the use of the risk prediction model, and ensure accuracy of the generated super-claim risk prediction results.
Description
Technical Field
The application relates to the technical field of artificial intelligence development and the field of financial science and technology, in particular to a risk prediction method, a risk prediction device, computer equipment and a storage medium based on artificial intelligence.
Background
Currently, in the insurance industry, a super-claim contract generally refers to that, for some high-risk industries or customers, an insurance company needs to bear a part exceeding the amount of an accident when the amount exceeds the upper limit of the amount of the claim contracted by providing the contract in a special form for the high-risk industries or customers. Such contracts can effectively promote the development of high risk industries and customers and increase their credibility. However, the risk to be borne by the insurance company in the super-claim contract is relatively large, and thus it is necessary to perform risk identification on the super-claim contract in advance to identify whether or not the super-claim contract is at risk. The existing insurance company often relies on manual experience and rules to perform risk identification on the super-claim contracts, and has the problems of low processing efficiency and easy generation of misjudgment.
Disclosure of Invention
An object of the embodiments of the present application is to provide an artificial intelligence-based risk prediction method, apparatus, computer device and storage medium, so as to solve the technical problems that the existing insurance company performs risk recognition on a super claim contract, which often depends on manual experience and rules to perform risk recognition, and has low processing efficiency and is prone to misjudgment.
In order to solve the above technical problems, the embodiments of the present application provide an artificial intelligence based risk prediction method, which adopts the following technical scheme:
judging whether a risk prediction request triggered by a user is received or not; wherein the risk prediction request carries a super claim contract identification;
if yes, acquiring a super claim contract corresponding to the super claim contract identifier;
determining a population of claims contained in the super claim contract;
calling a preset risk prediction model;
performing super-claim risk prediction processing on the claim case group based on the risk prediction model, and generating a super-claim risk prediction result corresponding to the claim case body;
and sending the result of the super claim risk prediction to the user.
Further, the risk prediction request also carries user information of the user; before the step of obtaining the super claim contract corresponding to the super claim contract identification, the method further comprises:
acquiring user information of the user;
judging whether the user information is stored in a preset blacklist or not;
if the user information is not stored in the blacklist, acquiring target authority information corresponding to the user information based on a preset authority data table;
Judging whether the target authority information is in a preset authority range or not;
and if the target authority information is in the authority range, executing the step of acquiring the super claim contract corresponding to the super claim contract identification.
Further, the step of obtaining the super claim contract corresponding to the super claim contract identifier specifically includes:
calling a preset contract database;
querying the contract database based on the super-claim contract identification, and searching out a specified super-claim contract corresponding to the super-claim contract identification from the contract database;
and taking the appointed super-claim contract as the super-claim contract.
Further, the step of determining the claim case group contained in the super claim contract specifically includes:
acquiring a preset classification standard;
acquiring a preset treatment index;
and classifying and clustering the super-claim contracts based on the classification standard and the processing index to obtain a claim case group corresponding to the super-claim contracts.
Further, before the step of calling the preset risk prediction model, the method further includes:
acquiring initial super-claim contract data collected in advance;
performing data preprocessing on the initial super-claim contract data to obtain corresponding sample data;
Dividing the sample data into training data and test data;
calling a preset deep neural network model;
and training and optimizing the deep neural network model by using the sample data and the test data to obtain the risk prediction model meeting the preset construction requirement.
Further, the step of performing data preprocessing on the initial claim contract data to obtain corresponding sample data specifically includes:
performing data cleaning processing on the initial super claim contract data to obtain corresponding first data;
performing data interpolation processing on the first data to obtain corresponding second data;
normalizing the second data to obtain corresponding third data;
and taking the third data as the sample data.
Further, after the step of training and optimizing the deep neural network model by using the sample data and the test data to obtain the risk prediction model meeting the preset construction requirement, the method further includes:
acquiring a preset model storage identifier;
determining a target storage area matched with the model storage identification from a plurality of storage areas contained in a blockchain;
And storing the risk prediction model into a target storage area.
In order to solve the above technical problems, the embodiments of the present application further provide an artificial intelligence based risk prediction apparatus, which adopts the following technical scheme:
the first judging module is used for judging whether a risk prediction request triggered by a user is received or not; wherein the risk prediction request carries a super claim contract identification;
the first acquisition module is used for acquiring the super-claim contract corresponding to the super-claim contract identifier if yes;
a first determining module for determining a population of claims contained in the super claim contract;
the first calling module is used for calling a preset risk prediction model;
the prediction module is used for carrying out super-claim risk prediction processing on the claim case group based on the risk prediction model and generating a super-claim risk prediction result corresponding to the claim case body;
and the sending module is used for sending the super claim risk prediction result to the user.
In order to solve the above technical problems, the embodiments of the present application further provide a computer device, which adopts the following technical schemes:
judging whether a risk prediction request triggered by a user is received or not; wherein the risk prediction request carries a super claim contract identification;
If yes, acquiring a super claim contract corresponding to the super claim contract identifier;
determining a population of claims contained in the super claim contract;
calling a preset risk prediction model;
performing super-claim risk prediction processing on the claim case group based on the risk prediction model, and generating a super-claim risk prediction result corresponding to the claim case body;
and sending the result of the super claim risk prediction to the user.
In order to solve the above technical problems, embodiments of the present application further provide a computer readable storage medium, which adopts the following technical solutions:
judging whether a risk prediction request triggered by a user is received or not; wherein the risk prediction request carries a super claim contract identification;
if yes, acquiring a super claim contract corresponding to the super claim contract identifier;
determining a population of claims contained in the super claim contract;
calling a preset risk prediction model;
performing super-claim risk prediction processing on the claim case group based on the risk prediction model, and generating a super-claim risk prediction result corresponding to the claim case body;
and sending the result of the super claim risk prediction to the user.
Compared with the prior art, the embodiment of the application has the following main beneficial effects:
The embodiment of the application firstly judges whether a risk prediction request triggered by a user is received or not; wherein the risk prediction request carries a super claim contract identification; if yes, acquiring a super claim contract corresponding to the super claim contract identifier; then determining a population of claims contained in the super claim contract; then calling a preset risk prediction model; subsequently, carrying out super-claim risk prediction processing on the claim case group based on the risk prediction model, and generating a super-claim risk prediction result corresponding to the claim case body; and finally, sending the super claim risk prediction result to the user. After receiving a risk prediction request triggered by a user, the embodiment of the application firstly obtains a super-claim contract corresponding to the super-claim contract identifier from the risk prediction request, determines a claim case group contained in the super-claim contract, and then automatically and intelligently carries out super-claim risk prediction processing on the claim case group based on the risk prediction model so as to quickly and accurately generate a super-claim risk prediction result corresponding to the claim case body and send the super-claim risk prediction result to the user. Compared with a processing mode of risk identification by artificial experience and rules, the risk prediction model based on the risk prediction model effectively improves the processing efficiency of risk identification of the super-claim contracts and ensures the accuracy of the generated super-claim risk prediction result.
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For a clearer description of the solution in the present application, a brief description will be given below of the drawings that are needed in the description of the embodiments of the present application, it being obvious 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 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 risk prediction method according to the present application;
FIG. 3 is a schematic structural diagram of one embodiment of an artificial intelligence based risk prediction device according to the present application;
FIG. 4 is a schematic structural diagram of one embodiment of a computer device according to 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 and claims of the present application and in the description of the figures above are intended to cover non-exclusive inclusions. 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 present 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 better understand the technical solutions of the present application, the following description will clearly and completely describe the technical solutions in the embodiments of the present application 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 risk prediction method based on artificial intelligence provided in the embodiments of the present application is generally executed by a server/terminal device, and accordingly, the risk prediction device based on artificial intelligence is generally disposed in the server/terminal device.
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 flow chart of one embodiment of an artificial intelligence based risk prediction method according to 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 risk prediction method based on the artificial intelligence can be applied to any scene requiring risk analysis of the super claim contracts, and then the risk prediction method based on the artificial intelligence can be applied to products of the scenes, such as risk analysis of the super claim contracts in the field of financial insurance. The risk prediction method based on artificial intelligence comprises the following steps:
Step S201, judging whether a risk prediction request triggered by a user is received; wherein the risk prediction request carries a super-claim contract identification.
In this embodiment, the electronic device (e.g., the server/terminal device shown in fig. 1) on which the risk prediction method based on artificial intelligence operates may acquire the risk prediction request 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 risk prediction request is a request triggered by a user and used for performing risk prediction on the super-claim contract corresponding to the super-claim contract identification. In the insurance industry, a super-claim contract generally refers to a contract for which an insurance company provides a special form for some high risk industry or customer, and when an accident occurs and the amount of claim is over the upper limit of the amount of claim contracted, the insurance company needs to bear the portion exceeding the amount. Such contracts can effectively promote the development of high risk industries and customers and increase the credibility of insurance companies.
And step S202, if yes, acquiring a super-claim contract corresponding to the super-claim contract identification.
In this embodiment, the above implementation process of obtaining the super claim contract corresponding to the super claim contract identifier will be described in further detail in the following specific embodiments, which will not be described herein.
Step S203, determining a group of claims cases contained in the super claim contract.
In this embodiment, the foregoing specific implementation procedure for determining the claim population included in the super claim contract will be described in further detail in the following specific embodiments, which are not described herein.
Step S204, calling a preset risk prediction model.
In this embodiment, the risk prediction model is generated by training a preset deep neural network model according to pre-collected initial super-claim contract data. For the specific construction process of the risk prediction model, this application will be described in further detail in the following specific embodiments, which will not be described herein.
And step S205, performing super-claim risk prediction processing on the claim case group based on the risk prediction model, and generating a super-claim risk prediction result corresponding to the claim case body.
In this embodiment, the claim case group may be input into the risk prediction model, and the risk prediction model may perform a super-claim risk prediction process on the claim case group, and output a super-claim risk prediction result corresponding to the claim case body. Wherein, the content of the super-claim risk prediction result comprises the existence of the super-claim risk or the absence of the super-claim risk. In addition, the cost allocation of the super claim contracts can be further carried out in a manner based on a grouping thought and an adaptive algorithm, the super claim risks are classified and allocated, the cost allocation proportion of each client in the claim case group is calculated, and the safe storage and transparent disclosure of information are realized through a block chain technology. Specifically, K-means clustering or hierarchical clustering algorithms can be used to divide the claims into different risk levels, and then cost sharing is performed according to the ratio of each customer in the risk level. Meanwhile, in order to control the risk, various restrictions and policies such as excess premium, excess claim, etc. may be set. Finally, secure storage and transparent disclosure of information is achieved through the use of blockchain technology. Information and cost sharing records of various claims are stored on the blockchain through mechanisms such as intelligent contracts and the like, and are disclosed and shared to related parties. Therefore, the allocation efficiency and the allocation precision can be effectively improved, and disputes are avoided.
And step S206, sending the super claim risk prediction result to the user.
In this embodiment, the user information of the user may be obtained, and the communication information of the user may be queried according to the user information, and then the risk prediction result of the claim may be sent to the communication terminal corresponding to the user according to the communication information. Wherein the user information may include a name of the user. The communication information may include a telephone number or a mail address.
Firstly, judging whether a risk prediction request triggered by a user is received or not; wherein the risk prediction request carries a super claim contract identification; if yes, acquiring a super claim contract corresponding to the super claim contract identifier; then determining a population of claims contained in the super claim contract; then calling a preset risk prediction model; subsequently, carrying out super-claim risk prediction processing on the claim case group based on the risk prediction model, and generating a super-claim risk prediction result corresponding to the claim case body; and finally, sending the super claim risk prediction result to the user. After receiving a risk prediction request triggered by a user, the method can acquire a super-claim contract corresponding to the super-claim contract identifier from the risk prediction request and determine a claim case group contained in the super-claim contract, and then automatically and intelligently conduct super-claim risk prediction processing on the claim case group based on the risk prediction model so as to quickly and accurately generate a super-claim risk prediction result corresponding to the claim case body and send the super-claim risk prediction result to the user. Compared with a processing mode of risk identification by artificial experience and rules, the risk prediction model based on the risk prediction model effectively improves the processing efficiency of risk identification of the super-claim contracts and ensures the accuracy of the generated super-claim risk prediction result.
In some alternative implementations, step S202 includes the steps of:
and acquiring user information of the user.
In this embodiment, the user information may include information such as a user name or ID for identifying the user.
And judging whether the user information is stored in a preset blacklist or not.
In this embodiment, the blacklist is list data previously constructed and storing user information of an illegal user. Information matching may be performed by using the user information of the user with the user information of all illegal users contained in the blacklist. If it is detected that information matched with the user information of the user does not exist in the user information of all illegal users contained in the blacklist, judging that the user information of the user is not stored in the blacklist, otherwise, judging that the user information of the user is stored in the blacklist.
And if the user information is not stored in the blacklist, acquiring target authority information corresponding to the user information based on a preset authority data table.
In this embodiment, the authority data table is a data table that is built in advance and stores authority information of a plurality of clients, and the authority information of a client is stored in the authority data table in such a manner that the client information of the client is used as index information.
And judging whether the target authority information is in a preset authority range or not.
In the present embodiment, the above-described authority range is an authority range section required for a risk processing operation corresponding to a super claim contract. The value of the authority range is not particularly limited, and can be set according to the actual authority service requirement.
And if the target authority information is in the authority range, executing the step of acquiring the super claim contract corresponding to the super claim contract identification.
In this embodiment, if the target authority information is within the authority range, it is determined that the user has a service authority to perform a risk processing operation of a super claim contract.
The method comprises the steps of obtaining user information of a user; then judging whether the user information is stored in a preset blacklist or not; if the user information is not stored in the blacklist, acquiring target authority information corresponding to the user information based on a preset authority data table; subsequently judging whether the target authority information is in a preset authority range or not; and if the target authority information is in the authority range, executing the step of acquiring the super claim contract corresponding to the super claim contract identification. Before the risk prediction request of the claim contract triggered by the user is processed, the blacklist and the authority data table are intelligently used for carrying out identity verification and authority verification on the user based on the user information of the user, and response processing on the risk prediction request is executed only when the user is detected to pass the identity verification and the authority verification at the same time, so that adverse effects caused by the response to the risk prediction request triggered by an illegal user or an unauthorized user are avoided, and the processing normalization and the processing intelligence of the risk prediction request are effectively improved.
In some alternative implementations of the present embodiment, step S202 includes the steps of:
and calling a preset contract database.
In this embodiment, the contract database is pre-constructed and stores the super-claim contracts provided for various high-risk industries or clients by insurance enterprises, and a corresponding super-claim contract identifier is pre-generated for mapping different super-claim contracts, wherein the super-claim contracts and the super-claim contract identifiers have a data association relationship, and the super-claim contract identifiers have uniqueness.
And inquiring the contract database based on the super-claim contract identification, and searching out a specified super-claim contract corresponding to the super-claim contract identification from the contract database.
In this embodiment, the contract database is queried based on the super-claim contract identifier, a specified super-claim contract identifier matched with the super-claim contract identifier is determined from the contract database, and a specified super-claim contract associated with the specified super-claim contract identifier is extracted from the contract database.
And taking the appointed super-claim contract as the super-claim contract.
The method comprises the steps of calling a preset contract database; then, inquiring the contract database based on the super-claim contract identification, and searching out a specified super-claim contract corresponding to the super-claim contract identification from the contract database; the specified super claim contract is then taken as the super claim contract. According to the method and the system for searching the contract database, the contract database can be queried based on the contract database identifier, so that the contract corresponding to the contract identifier can be quickly searched from the contract database, and the acquisition efficiency and the acquisition intelligence of the contract are improved.
In some alternative implementations, step S203 includes the steps of:
and obtaining a preset classification standard.
In this embodiment, the classification criteria may specifically include factors such as insurance products, customer groups, insurance types, damaged article types, and lost amounts. The insurance products comprise various life insurance, car insurance, property insurance and the like. Customer complexes include individuals, businesses, government agencies, and the like.
And acquiring a preset treatment index.
In this embodiment, the processing indexes may specifically include indexes such as the number of claims and the amount of payouts.
And classifying and clustering the super-claim contracts based on the classification standard and the processing index to obtain a claim case group corresponding to the super-claim contracts.
In this embodiment, the splitting criteria are used to classify the claim cases in the super claim contracts, so as to obtain a plurality of classification results. And then clustering the multiple classification results based on the processing indexes of the multiple classification results to determine a representative claim population from the multiple classification results.
The method comprises the steps of obtaining a preset classification standard; then obtaining a preset treatment index; and classifying and clustering the super-claim contracts based on the classification standards and the processing indexes to obtain the claim case groups corresponding to the super-claim contracts. According to the method and the device, the super claim contracts are classified and clustered based on the acquired classification standards and the acquired processing indexes, so that the claim case groups contained in the super claim contracts can be rapidly and accurately determined, and the generation efficiency and the generation accuracy of the claim case groups are improved.
In some alternative implementations, before step S204, the electronic device may further perform the following steps:
initial super-claim contract data collected in advance is obtained.
In this embodiment, the meta-claim contract data within the preset history period may be acquired as the above-described initial meta-claim contract data. The value of the preset historical time period is not particularly limited, and may be set according to actual service usage requirements, for example, may be set within the first 2 years from the current time.
And carrying out data preprocessing on the initial super-claim contract data to obtain corresponding sample data.
In this embodiment, the specific implementation process of performing the data preprocessing on the initial super claim contract data to obtain the corresponding sample data will be described in further detail in the following specific embodiments, which will not be described herein.
The sample data is divided into training data and test data.
In this embodiment, corresponding data may be randomly extracted from the sample data according to a preset dividing ratio, and the corresponding data may be respectively used as training data and test data. The value of the above-mentioned dividing ratio is not particularly limited, and may be set to 7:3, for example.
And calling a preset deep neural network model.
In this embodiment, the deep neural network model may specifically use a deep learning model such as a multi-layer perceptron (MLP) or a Long Short Term Memory (LSTM) to obtain higher prediction accuracy and cost control capability.
And training and optimizing the deep neural network model by using the sample data and the test data to obtain the risk prediction model meeting the preset construction requirement.
In this embodiment, the training and optimizing process of the deep neural network model using the sample data and the test data may refer to a training and constructing process of an existing deep neural network model. In addition, the above construction requirement refers to a case where the model evaluation index of the risk prediction model to be constructed satisfies a preset index threshold. The value of the index threshold is not limited, and may be set according to actual use requirements.
The method comprises the steps of obtaining initial super-claim contract data collected in advance; then, carrying out data preprocessing on the initial super claim contract data to obtain corresponding sample data; dividing the sample data into training data and test data; subsequently calling a preset deep neural network model; and finally, training and optimizing the deep neural network model by using the sample data and the test data to obtain the risk prediction model meeting the preset construction requirement. According to the method and the device, the data preprocessing is carried out through the pre-collected initial super claim contract data, so that the sample data and the test data are used for training and optimizing the preset deep neural network model, the risk prediction model meeting the preset construction requirement is obtained, the construction process of the risk prediction model is completed, the model effect and the prediction processing accuracy of the generated risk prediction model are effectively guaranteed, and the construction efficiency of the risk prediction model is improved.
In some optional implementations of the present embodiment, the performing data preprocessing on the initial super-claim contract data to obtain corresponding sample data includes the following steps:
and carrying out data cleaning processing on the initial super claim contract data to obtain corresponding first data.
In the present embodiment, the above-described data cleaning process may include a process of cleaning an error value, an abnormal value, noise data, or the like.
And performing data interpolation processing on the first data to obtain corresponding second data.
In this embodiment, the first data may be subjected to a data interpolation process by a linear interpolation method or a lagrangian interpolation method to obtain corresponding second data
And carrying out normalization processing on the second data to obtain corresponding third data.
In this embodiment, the normalization processing is further performed on the second data by using a normalization formula, so as to obtain corresponding third data.
And taking the third data as the sample data.
The method comprises the steps of performing data cleaning processing on initial super claim contract data to obtain corresponding first data; then, performing data interpolation processing on the first data to obtain corresponding second data; then, carrying out normalization processing on the second data to obtain corresponding third data; the third data is then taken as the sample data. According to the method and the device, the data cleaning process, the data interpolation process and the normalization process are carried out on the initial super claim contract data, so that sample data meeting the construction requirements of the risk prediction model can be obtained rapidly and accurately, and the construction efficiency of the risk prediction model can be effectively improved when the sample data is used for model construction of the risk prediction model in the follow-up process.
In some optional implementations of this embodiment, after the step of training and optimizing the deep neural network model using the sample data and the test data to obtain the risk prediction model that meets a preset construction requirement, the electronic device may further execute the following steps:
and acquiring a preset model storage identifier.
In this embodiment, the blockchain is divided into a plurality of storage areas in one-to-one correspondence in advance according to a plurality of storage identifiers. The storage identifier may include a model storage identifier, a data table storage identifier, a picture storage identifier, a file storage identifier, and so on. Each storage area is used for storing data corresponding to the storage identification.
A target memory region matching the model memory identity is determined from a plurality of memory regions contained in the blockchain.
In this embodiment, the target storage area may be obtained by screening out the storage areas of the tag and the model storage identifier from the plurality of storage areas included in the blockchain.
And storing the risk prediction model into a target storage area.
The method comprises the steps of obtaining a preset model storage identifier; then determining a target storage area matched with the model storage identification from a plurality of storage areas contained in the blockchain; and storing the risk prediction model into a target storage area. According to the method and the device, the sample data and the test data are used for training and optimizing the deep neural network model, after the risk prediction model meeting the preset construction requirement is obtained, the risk prediction model is stored to the target storage area matched with the model storage identification in the blockchain according to the model storage identification corresponding to the risk prediction model intelligently, the storage normalization and the storage intelligence of the risk prediction model are effectively improved, the required model can be quickly taken out from the target storage area, and accordingly the model calling efficiency is improved.
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 invention.
It is emphasized that to further ensure the privacy and security of the above-described super-claim risk prediction results, the above-described super-claim risk prediction results may also be stored in nodes of a blockchain.
The blockchain referred to in the application 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.
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.
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 shown in fig. 2, the present application provides an embodiment of an artificial intelligence-based risk prediction apparatus, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and the apparatus may be specifically applied to various electronic devices.
As shown in fig. 3, the risk prediction apparatus 300 based on artificial intelligence according to the present embodiment includes: the system comprises a first judging module 301, a first acquiring module 302, a first determining module 303, a first calling module 304, a predicting module 305 and a sending module 306. Wherein:
a first judging module 301, configured to judge whether a risk prediction request triggered by a user is received; wherein the risk prediction request carries a super claim contract identification;
a first obtaining module 302, configured to obtain, if yes, a super claim contract corresponding to the super claim contract identifier;
a first determining module 303, configured to determine a population of claims cases included in the super claim contract;
the first invoking module 304 is configured to invoke a preset risk prediction model;
a prediction module 305, configured to perform a super-claim risk prediction process on the claim case group based on the risk prediction model, and generate a super-claim risk prediction result corresponding to the claim case body;
And the sending module 306 is configured to send the result of the risk prediction of the claim to the user.
In this embodiment, the operations performed by the above modules or units are respectively corresponding to the steps of the artificial intelligence-based risk prediction method in the foregoing embodiment, and are not described herein again.
In some optional implementations of this embodiment, the artificial intelligence based risk prediction apparatus further includes:
the second acquisition module is used for acquiring the user information of the user;
the second judging module is used for judging whether the user information is stored in a preset blacklist or not;
the third acquisition module is used for acquiring target authority information corresponding to the user information based on a preset authority data table if the user information is not stored in the blacklist;
the third judging module is used for judging whether the target authority information is in a preset authority range or not;
and the execution module is used for executing the step of acquiring the super claim contract corresponding to the super claim contract identification if the target authority information is in the authority range.
In this embodiment, the operations performed by the above modules or units are respectively corresponding to the steps of the artificial intelligence-based risk prediction method in the foregoing embodiment, and are not described herein again.
In some optional implementations of this embodiment, the first obtaining module 302 includes:
the calling sub-module is used for calling a preset contract database;
the query sub-module is used for querying the contract database based on the super-claim contract identification, and searching out a specified super-claim contract corresponding to the super-claim contract identification from the contract database;
and the first determination submodule is used for taking the specified super-claim contract as the super-claim contract.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the artificial intelligence-based risk prediction method in the foregoing embodiment, and are not described herein again.
In some alternative implementations of the present embodiment, the first determining module 303 includes:
the first acquisition sub-module is used for acquiring a preset classification standard;
the second acquisition submodule is used for acquiring preset processing indexes;
and the first processing sub-module is used for classifying and clustering the super-claim contracts based on the classification standards and the processing indexes to obtain the claim case groups corresponding to the super-claim contracts.
In this embodiment, the operations performed by the above modules or units are respectively corresponding to the steps of the artificial intelligence-based risk prediction method in the foregoing embodiment, and are not described herein again.
In some optional implementations of this embodiment, the artificial intelligence based risk prediction apparatus further includes:
a fourth acquisition module for acquiring initial super-claim contract data collected in advance;
the preprocessing module is used for carrying out data preprocessing on the initial super-claim contract data to obtain corresponding sample data;
the division module is used for dividing the sample data into training data and test data;
the second calling module is used for calling a preset deep neural network model;
and the construction module is used for training and optimizing the deep neural network model by using the sample data and the test data to obtain the risk prediction model meeting the preset construction requirement.
In this embodiment, the operations performed by the above modules or units are respectively corresponding to the steps of the artificial intelligence-based risk prediction method in the foregoing embodiment, and are not described herein again.
In some optional implementations of this embodiment, the preprocessing module includes:
the screening sub-module is used for carrying out data cleaning processing on the initial super claim contract data to obtain corresponding first data;
the second processing sub-module is used for performing data interpolation processing on the first data to obtain corresponding second data;
The third processing sub-module is used for carrying out normalization processing on the second data to obtain corresponding third data;
and the second determining submodule is used for taking the third data as the sample data.
In this embodiment, the operations performed by the above modules or units are respectively corresponding to the steps of the artificial intelligence-based risk prediction method in the foregoing embodiment, and are not described herein again.
In some optional implementations of this embodiment, the artificial intelligence based risk prediction apparatus further includes:
the fifth acquisition module is used for acquiring a preset model storage identifier;
the second determining module is used for determining a target storage area matched with the model storage identification from a plurality of storage areas contained in the blockchain;
and the storage module is used for storing the risk prediction model into a target storage area.
In this embodiment, the operations performed by the above modules or units are respectively corresponding to the steps of the artificial intelligence-based risk prediction method in the foregoing embodiment, and are not described herein again.
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 based on an artificial intelligence risk prediction method. 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 executing computer readable instructions of the artificial intelligence based risk prediction method.
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.
Compared with the prior art, the embodiment of the application has the following main beneficial effects:
in the embodiment of the application, whether a risk prediction request triggered by a user is received is firstly judged; wherein the risk prediction request carries a super claim contract identification; if yes, acquiring a super claim contract corresponding to the super claim contract identifier; then determining a population of claims contained in the super claim contract; then calling a preset risk prediction model; subsequently, carrying out super-claim risk prediction processing on the claim case group based on the risk prediction model, and generating a super-claim risk prediction result corresponding to the claim case body; and finally, sending the super claim risk prediction result to the user. After receiving a risk prediction request triggered by a user, the embodiment of the application firstly obtains a super-claim contract corresponding to the super-claim contract identifier from the risk prediction request, determines a claim case group contained in the super-claim contract, and then automatically and intelligently carries out super-claim risk prediction processing on the claim case group based on the risk prediction model so as to quickly and accurately generate a super-claim risk prediction result corresponding to the claim case body and send the super-claim risk prediction result to the user. Compared with a processing mode of risk identification by artificial experience and rules, the risk prediction model based on the risk prediction model effectively improves the processing efficiency of risk identification of the super-claim contracts and ensures the accuracy of the generated super-claim risk prediction result.
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 an artificial intelligence-based risk prediction method as described above.
Compared with the prior art, the embodiment of the application has the following main beneficial effects:
in the embodiment of the application, whether a risk prediction request triggered by a user is received is firstly judged; wherein the risk prediction request carries a super claim contract identification; if yes, acquiring a super claim contract corresponding to the super claim contract identifier; then determining a population of claims contained in the super claim contract; then calling a preset risk prediction model; subsequently, carrying out super-claim risk prediction processing on the claim case group based on the risk prediction model, and generating a super-claim risk prediction result corresponding to the claim case body; and finally, sending the super claim risk prediction result to the user. After receiving a risk prediction request triggered by a user, the embodiment of the application firstly obtains a super-claim contract corresponding to the super-claim contract identifier from the risk prediction request, determines a claim case group contained in the super-claim contract, and then automatically and intelligently carries out super-claim risk prediction processing on the claim case group based on the risk prediction model so as to quickly and accurately generate a super-claim risk prediction result corresponding to the claim case body and send the super-claim risk prediction result to the user. Compared with a processing mode of risk identification by artificial experience and rules, the risk prediction model based on the risk prediction model effectively improves the processing efficiency of risk identification of the super-claim contracts and ensures the accuracy of the generated super-claim risk prediction result.
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 (such as ROM/RAM, magnetic disk, optical disk), comprising several 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 described in the embodiments of the present application.
It is apparent that the embodiments described above are only some embodiments of the present application, but not all embodiments, the preferred embodiments of the present application are given in the drawings, but not limiting the patent scope of the present application. This application may be embodied in many different forms, but rather, embodiments are provided in order to provide a more thorough understanding of the present disclosure. Although the present 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, or equivalents may be substituted for elements thereof. All equivalent structures made by the specification and the drawings of the application are directly or indirectly applied to other related technical fields, and are also within the protection scope of the application.
Claims (10)
1. An artificial intelligence-based risk prediction method is characterized by comprising the following steps:
judging whether a risk prediction request triggered by a user is received or not; wherein the risk prediction request carries a super claim contract identification;
if yes, acquiring a super claim contract corresponding to the super claim contract identifier;
determining a population of claims contained in the super claim contract;
calling a preset risk prediction model;
performing super-claim risk prediction processing on the claim case group based on the risk prediction model, and generating a super-claim risk prediction result corresponding to the claim case body;
and sending the result of the super claim risk prediction to the user.
2. The artificial intelligence based risk prediction method of claim 1, wherein the risk prediction request also carries user information of the user; before the step of obtaining the super claim contract corresponding to the super claim contract identification, the method further comprises:
acquiring user information of the user;
judging whether the user information is stored in a preset blacklist or not;
if the user information is not stored in the blacklist, acquiring target authority information corresponding to the user information based on a preset authority data table;
Judging whether the target authority information is in a preset authority range or not;
and if the target authority information is in the authority range, executing the step of acquiring the super claim contract corresponding to the super claim contract identification.
3. The artificial intelligence based risk prediction method according to claim 1, wherein the step of obtaining a super claim contract corresponding to the super claim contract identification specifically comprises:
calling a preset contract database;
querying the contract database based on the super-claim contract identification, and searching out a specified super-claim contract corresponding to the super-claim contract identification from the contract database;
and taking the appointed super-claim contract as the super-claim contract.
4. The artificial intelligence based risk prediction method according to claim 1, wherein the step of determining a population of claims cases contained in the super-claim contract, in particular, comprises:
acquiring a preset classification standard;
acquiring a preset treatment index;
and classifying and clustering the super-claim contracts based on the classification standard and the processing index to obtain a claim case group corresponding to the super-claim contracts.
5. The artificial intelligence based risk prediction method according to claim 1, further comprising, before the step of calling a preset risk prediction model:
Acquiring initial super-claim contract data collected in advance;
performing data preprocessing on the initial super-claim contract data to obtain corresponding sample data;
dividing the sample data into training data and test data;
calling a preset deep neural network model;
and training and optimizing the deep neural network model by using the sample data and the test data to obtain the risk prediction model meeting the preset construction requirement.
6. The risk prediction method based on artificial intelligence according to claim 5, wherein the step of performing data preprocessing on the initial super-claim contract data to obtain corresponding sample data specifically comprises:
performing data cleaning processing on the initial super claim contract data to obtain corresponding first data;
performing data interpolation processing on the first data to obtain corresponding second data;
normalizing the second data to obtain corresponding third data;
and taking the third data as the sample data.
7. The artificial intelligence based risk prediction method according to claim 5, further comprising, after the step of training and optimizing the deep neural network model using the sample data and the test data to obtain the risk prediction model meeting a preset construction requirement:
Acquiring a preset model storage identifier;
determining a target storage area matched with the model storage identification from a plurality of storage areas contained in a blockchain;
and storing the risk prediction model into a target storage area.
8. An artificial intelligence based risk prediction apparatus, comprising:
the first judging module is used for judging whether a risk prediction request triggered by a user is received or not; wherein the risk prediction request carries a super claim contract identification;
the first acquisition module is used for acquiring the super-claim contract corresponding to the super-claim contract identifier if yes;
a first determining module for determining a population of claims contained in the super claim contract;
the first calling module is used for calling a preset risk prediction model;
the prediction module is used for carrying out super-claim risk prediction processing on the claim case group based on the risk prediction model and generating a super-claim risk prediction result corresponding to the claim case body;
and the sending module is used for sending the super claim risk prediction result to the user.
9. A computer device comprising a memory having stored therein computer readable instructions which when executed implement the steps of the artificial intelligence based risk prediction method 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 risk prediction method of any of claims 1 to 7.
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