CN115936895A

CN115936895A - Risk assessment method, device and equipment based on artificial intelligence and storage medium

Info

Publication number: CN115936895A
Application number: CN202211441027.5A
Authority: CN
Inventors: 袁欢
Original assignee: Ping An Property and Casualty Insurance Company of China Ltd
Current assignee: Ping An Property and Casualty Insurance Company of China Ltd
Priority date: 2022-11-17
Filing date: 2022-11-17
Publication date: 2023-04-07

Abstract

The embodiment of the application belongs to the field of artificial intelligence, and relates to a risk assessment method based on artificial intelligence, which comprises the following steps: receiving a policy checking request input by a user; acquiring a first insurance policy case corresponding to the insurance policy identification information; analyzing the first insurance policy case based on the analysis rule, and screening a second insurance policy case from the first insurance policy case; acquiring target data from the second policy case; inputting the target data into a risk assessment model to obtain a target prediction risk factor corresponding to the target data; and performing risk evaluation on the second policy case based on the calculation model and the target prediction risk factor to generate a risk evaluation result. The application also provides a risk assessment device based on artificial intelligence, computer equipment and a storage medium. In addition, the application also relates to a block chain technology, and a risk assessment result can be stored in the block chain. The risk assessment method and the risk assessment system improve the processing efficiency of risk assessment of the policy case and ensure the accuracy of the risk assessment result of the policy case.

Description

Risk assessment method, device and equipment based on artificial intelligence and storage medium

Technical Field

The present application relates to the field of artificial intelligence technologies, and in particular, to a risk assessment method, apparatus, device, and storage medium based on artificial intelligence.

Background

Currently, the importance of the insurance industry is increasing due to the proliferation of the various risks faced by different industries. At present, more and more enterprises need to apply insurance to related businesses. Therefore, it is very important for insurance companies to efficiently and accurately evaluate business risks.

In the prior art, insurance companies generally perform risk assessment on insurance policy cases through professional underwriting personnel, however, an underwriting mode based on manual risk assessment requires a large number of professional underwriters, and with the increase of insurance traffic, gaps of underwriters increase, so that underwriters aging and user experience are easily affected. Moreover, the manual underwriting efficiency is low, the evaluation is easily influenced by subjective judgment of underwriters, and the accuracy of the risk evaluation result of the policy case cannot be guaranteed.

Disclosure of Invention

An object of the embodiments of the present application is to provide a risk assessment method and apparatus based on artificial intelligence, a computer device, and a storage medium, so as to solve the problem that an existing underwriting method based on artificial risk assessment requires a large number of professional underwriters, and as insurance traffic increases, gaps of the underwriters increase, which easily affects underwriting timeliness and user experience. And the manual underwriting efficiency is low, the influence of subjective judgment of underwriters is easy to occur, and the accuracy of the risk evaluation result of the policy case cannot be guaranteed.

In order to solve the above technical problem, an embodiment of the present application provides a risk assessment method based on artificial intelligence, which adopts the following technical scheme:

receiving a policy checking request input by a user; wherein the policy checking request carries policy identification information;

acquiring a plurality of first policy cases corresponding to the policy identification information;

analyzing the first policy case based on a preset analysis rule, and screening out a second policy case which accords with the analysis rule from all the first policy cases;

acquiring target data corresponding to a preset data type from the second policy case;

inputting the target data into a pre-trained risk assessment model, and outputting a plurality of target prediction risk factors corresponding to the target data through the risk assessment model;

and performing risk assessment on the second policy case based on a preset calculation model and each target prediction risk factor to generate a risk assessment result corresponding to the second policy case.

Further, the analysis rule includes a plurality of analysis rules, and the step of analyzing the first policy case based on a preset analysis rule and selecting a second policy case meeting the analysis rule from all the first policy cases specifically includes:

for a specified policy case, acquiring specified data corresponding to each analysis rule in the specified policy case; wherein the designated policy case is any one policy case of all the first policy cases;

analyzing corresponding specified data based on each analysis rule respectively, and judging whether all the specified data accord with the corresponding analysis rule;

and if so, taking the appointed policy case as the second policy case.

Further, the step of performing risk assessment on the second policy case based on a preset calculation model and each target predicted risk factor to generate a risk assessment result corresponding to the second policy case specifically includes:

calling the calculation model, and calculating the risk value of the second policy case based on each target prediction risk factor;

generating a target risk level corresponding to the second policy case based on the risk value;

and taking the target risk grade as a risk evaluation result of the second policy case.

Further, the step of calculating the risk value of the second policy case based on each of the target predicted risk factors specifically includes:

acquiring weight values corresponding to the target prediction risk factors respectively;

calculating each target prediction risk factor based on a preset calculation formula corresponding to each weight value to obtain a corresponding calculated value;

and taking the calculated value as a risk value of the second policy case.

Further, before the step of inputting the target data into a risk assessment model trained in advance and outputting a plurality of target predicted risk factors corresponding to the target data through the risk assessment model, the method further includes:

acquiring specified data corresponding to the data types in a preset number of historical policy cases and preset predicted risk factors;

calling a preset initial model;

respectively taking the designated data of each historical policy case as the input of the initial model, taking each predicted risk factor as the output of the initial model, and training the initial model;

acquiring a convergence value of the initial model in a training process;

and when the convergence value is smaller than a preset convergence threshold value, judging that the training of the initial model is finished, and taking the initial model obtained after training as the risk assessment model.

Further, after the step of using the initial model obtained after training as the risk assessment model, the method further includes:

determining a preset time period;

acquiring a target historical policy case in the preset time period;

and updating the risk assessment model based on the target historical policy.

Further, after the step of performing risk assessment on the second policy case based on the preset calculation model and each target predicted risk factor, and generating a risk assessment result corresponding to the second policy case, the method further includes:

calling a preset strategy library;

inquiring a processing strategy corresponding to the risk assessment result from the strategy library;

and correspondingly processing the second policy case based on the processing strategy.

In order to solve the above technical problem, an embodiment of the present application further provides a risk assessment device based on artificial intelligence, which adopts the following technical scheme:

the receiving module is used for receiving a policy checking request input by a user; wherein the policy checking request carries policy identification information;

the first obtaining module is used for obtaining a plurality of first insurance policy cases corresponding to the insurance policy identification information;

the analysis module is used for analyzing the first policy case based on a preset analysis rule and screening out a second policy case which accords with the analysis rule from all the first policy cases;

the second acquisition module is used for acquiring target data corresponding to a preset data type from the second policy case;

the output module is used for inputting the target data into a pre-trained risk assessment model and outputting a plurality of target prediction risk factors corresponding to the target data through the risk assessment model;

and the evaluation module is used for carrying out risk evaluation on the second policy case based on a preset calculation model and each target prediction risk factor to generate a risk evaluation result corresponding to the second policy case.

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

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

acquiring a plurality of first insurance policy cases corresponding to the insurance policy identification information;

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

after receiving a policy checking request input by a user, acquiring a plurality of first policy cases corresponding to the policy identification information; then analyzing the first policy case based on a preset analysis rule, and screening out a second policy case which accords with the analysis rule from all the first policy cases; then acquiring target data corresponding to a preset data type from the second policy case; subsequently inputting the target data into a pre-trained risk assessment model, and outputting a plurality of target prediction risk factors corresponding to the target data through the risk assessment model; and finally, performing risk assessment on the second policy case based on a preset calculation model and each target prediction risk factor to generate a risk assessment result corresponding to the second policy case. According to the embodiment of the application, the first policy case is analyzed based on the analysis rule, so that the second policy case meeting the analysis rule is screened out from all the first policy cases, the subsequent risk assessment model only needs to carry out risk assessment processing on the screened second policy case, the processing workload of carrying out risk assessment on the policy case is effectively reduced, the processing efficiency of risk assessment on the policy case is improved, and the accuracy of the risk assessment result of the policy case is ensured.

Drawings

In order to more clearly illustrate the solution of the present application, the drawings needed for describing the embodiments of the present application will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and that other drawings can be obtained by those skilled in the art without inventive effort.

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

FIG. 2 is a flow diagram of one embodiment of an artificial intelligence based risk assessment method according to the present application;

FIG. 3 is a schematic block diagram of one embodiment of an artificial intelligence based risk assessment device according to the present application;

FIG. 4 is a schematic block 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 application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "including" and "having," and any variations thereof, in the description and claims of this application and the description of the above figures are intended to cover non-exclusive inclusions. The terms "first," "second," and the like in the description and claims of this application or in the above-described drawings are used for distinguishing between different objects and not for describing a particular order.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase 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. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.

In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

As shown in fig. 1, the system architecture 100 may include

terminal devices

101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the

terminal devices

101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

The user may use the

terminal devices

101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. Various communication client applications, such as a web browser application, a shopping application, a search 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 smart phones, tablet computers, e-book readers, MP3 players (Mov I picture ExpertsGroup Aud I o Layer I, mpeg compression standard audio Layer 3), MP4 players (Mov I ng P I ctu re experts G roup Aud I o Layer I V, mpeg compression standard audio Layer 4), laptop portable computers, 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 assessment method based on artificial intelligence provided in the embodiments of the present application is generally executed by a server/terminal device, and accordingly, a risk assessment apparatus 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 diagram of one embodiment of an artificial intelligence based risk assessment method according to the present application is shown. The risk assessment method based on artificial intelligence comprises the following steps:

step S201, receiving a policy checking request input by a user; wherein, the policy checking request carries policy identification information.

In this embodiment, the electronic device (for example, the server/terminal device shown in fig. 1) on which the risk assessment method based on artificial intelligence operates may obtain the policy checking request through a wired connection manner or a wireless connection manner. It should be noted that the wireless connection means may include, but is not limited to, a 3G/4G/5G connection, a wfi connection, a bluetooth connection, a wimax connection, a zi gbee connection, a UWB (u l tra W i deband) connection, and other wireless connection means now known or developed in the future. The policy checking request is a request triggered by a user and used for performing risk assessment on the policy case corresponding to the policy identification information carried in the policy checking request. The policy identification information may include a name of a policy case or id information of the policy case.

Step S202, a plurality of first insurance policy cases corresponding to the insurance policy identification information are obtained.

In this embodiment, by extracting the policy identification information from the policy checking request, a plurality of first policy cases corresponding to the policy identification information can be queried from the policy database created in advance. The policy database stores a plurality of policy identification information and policy cases corresponding to the policy identification information one by one.

Step S203, analyzing the first policy case based on a preset analysis rule, and screening out a second policy case meeting the analysis rule from all the first policy cases.

In this embodiment, the number of the analysis rules may include a plurality of analysis rules, for example, a content authenticity rule, a validity rule, a responsibility rule, and the like. And the third policy case which does not accord with the analysis rule and is contained in the first policy case can be directly regarded as a risk policy case. In addition, the specific implementation process of analyzing the first policy case based on the preset analysis rule and screening out the second policy case meeting the analysis rule from all the first policy cases is further described in detail in the following specific embodiments, and will not be described herein.

Step S204, acquiring target data corresponding to a preset data type from the second policy case.

In this embodiment, the data types may be set according to actual business usage requirements, and may include, for example, a first data type corresponding to enterprise data, a second data type corresponding to insured data, a third data type corresponding to insurable data, and so on.

Step S205, inputting the target data into a risk assessment model trained in advance, and outputting a plurality of target predicted risk factors corresponding to the target data through the risk assessment model.

In this embodiment, the type of the target predicted risk factor may be set according to actual business usage requirements, and may include, for example, insurance risk ratio, policy and payment ratio, and the like. In the actual implementation process, a person skilled in the art can flexibly add or delete the predicted risk factors according to actual use requirements. The specific implementation process corresponding to the training and generating process of the risk assessment model is further described in detail in the following specific embodiments, and is not set forth herein in any greater way.

And S206, performing risk assessment on the second policy case based on a preset calculation model and each target prediction risk factor to generate a risk assessment result corresponding to the second policy case.

In this embodiment, a specific implementation process of performing risk assessment on the second policy case based on the preset calculation model and each target predicted risk factor to generate a risk assessment result corresponding to the second policy case is described in further detail in the following specific embodiment, and will not be described in detail herein.

After receiving a policy checking request input by a user, the policy checking method and the policy checking system can firstly acquire a plurality of first policy cases corresponding to the policy identification information; then analyzing the first policy case based on a preset analysis rule, and screening out a second policy case which accords with the analysis rule from all the first policy cases; then acquiring target data corresponding to a preset data type from the second policy case; subsequently inputting the target data into a pre-trained risk assessment model, and outputting a plurality of target prediction risk factors corresponding to the target data through the risk assessment model; and finally, performing risk assessment on the second policy case based on a preset calculation model and each target prediction risk factor to generate a risk assessment result corresponding to the second policy case. According to the method and the device, the first insurance policy cases are analyzed based on the analysis rules, the second insurance policy cases meeting the analysis rules are screened out from all the first insurance policy cases, the subsequent risk assessment model only needs to carry out risk assessment processing on the screened second insurance policy cases, the processing workload of carrying out risk assessment on the insurance policy cases is effectively reduced, the processing efficiency of risk assessment on the insurance policy cases is improved, and the accuracy of the risk assessment results of the insurance policy cases is guaranteed.

In some optional implementation manners, the analysis rule includes a plurality of rules, and step S203 includes the following steps:

for a specified policy case, acquiring specified data corresponding to each analysis rule in the specified policy case; wherein the designated policy case is any one policy case among all the first policy cases.

In this embodiment, the analysis rule may be a commonality rule manually defined for the insurance category. The analysis rules include a plurality of analysis rules, and specifically may include content authenticity rules, validity rules, and responsibility rules. The content authenticity rules comprise basic information auditing of an insured, auditing of tampering data and auditing of editing data, including but not limited to consistency, integrity and time validity of each item of auditing data; the validity rules are mainly used for checking whether the accident occurs within the validity period of the insurance policy; the responsibility rules can include report information audit, application information audit, name audit, inspection result audit, invoice audit and the like. The specified data includes first data corresponding to the content authenticity rule, second data corresponding to the validity rule, and third data corresponding to the liability rule in the specified policy case.

And analyzing the corresponding specified data based on each analysis rule, and judging whether all the specified data accord with the corresponding analysis rule.

In this embodiment, for a specific policy case, it may be sequentially analyzed whether the first data meets the requirement of the content authenticity rule, whether the second data meets the requirement of the validity rule, and whether the third data meets the requirement of the liability rule. If it is not

And if so, taking the appointed policy case as the second policy case.

In this embodiment, if the designated data in the designated policy case all conform to the corresponding analysis rules, that is, the first data, the second data, and the third data all conform to the corresponding content authenticity rules, validity rules, and responsibility rules, respectively, it is determined that the designated policy case passes the preliminary examination based on the analysis rules.

According to the method and the device, after a plurality of first insurance policy cases corresponding to insurance policy identification information are acquired, the first insurance policy cases are analyzed based on the preset analysis rules, so that the second insurance policy cases meeting the analysis rules are screened out from all the first insurance policy cases, and the dependency of a risk assessment model on training data can be effectively reduced. And the subsequent risk assessment model only needs to carry out risk assessment processing on the screened second policy case, so that the processing workload of carrying out risk assessment on the policy case is effectively reduced, and the processing efficiency of the risk assessment on the policy case is improved.

In some optional implementations of this embodiment, step S206 includes the following steps:

and calling the calculation model, and calculating the risk value of the second policy case based on each target prediction risk factor.

In this embodiment, the specific implementation process of invoking the calculation model and calculating the risk value of the second policy case based on each of the target predicted risk factors is described in further detail in the following specific embodiments, and will not be described in detail herein.

Generating a target risk level corresponding to the second policy case based on the risk value.

In this embodiment, a plurality of risk levels, for example, a, B, and C, may be preset, each risk level corresponds to a numerical range, and when a risk value obtained by calculating the target predicted risk factor based on the calculation model falls into which numerical range, which risk level corresponds to the second policy case, that is, the target risk level, is determined.

According to the method and the device, the risk value of the second policy case is calculated based on each predicted risk factor by calling the calculation model, so that the risk evaluation result corresponding to the second policy case can be quickly and accurately generated based on the obtained risk value, and the processing efficiency of the risk evaluation of the policy case is improved.

In some optional implementations, the step of calculating the risk value of the second policy case based on each of the target predicted risk factors includes the steps of:

and acquiring weight values respectively corresponding to the target prediction risk factors.

In this embodiment, the value of the weight value is not specifically limited, and may be set according to actual service requirements.

And calculating each target prediction risk factor based on a preset calculation formula corresponding to each weight value to obtain a corresponding calculated value.

In this embodiment, the preset calculation formula may be a weighted summation formula corresponding to the weight value, and after the target predicted risk factors are obtained, the weighted summation corresponding to the target predicted risk factors may be performed by using the weight values corresponding to the target predicted risk factors based on the preset calculation formula, so as to obtain corresponding calculated values.

And taking the calculated value as a risk value of the second policy case.

According to the method and the device, the weighted values corresponding to the predicted risk factors are obtained, and then the predicted risk factors can be calculated and processed based on the calculation formula corresponding to the weighted values, so that the risk value of the second policy case can be quickly obtained, the follow-up risk evaluation result corresponding to the second policy case can be quickly and accurately generated based on the obtained risk value, and the processing efficiency of the risk evaluation of the policy case is improved.

In some optional implementations, before step S205, the electronic device may further perform the following steps:

and acquiring specified data corresponding to the data types in a preset number of historical policy cases and preset predicted risk factors.

In this embodiment, the value of the preset number is not specifically limited, and may be set according to actual service requirements.

And calling a preset initial model.

In this embodiment, the type of the initial model is not particularly limited, and may be set according to actual business requirements.

And respectively taking the specified data of each historical policy case as the input of the initial model, taking each predicted risk factor as the output of the initial model, and training the initial model.

And acquiring a convergence value of the initial model in a training process.

And when the convergence value is smaller than a preset convergence threshold value, judging that the initial model training is finished, and taking the initial model obtained after training as the risk assessment model.

In this embodiment, the convergence threshold may be set according to actual use requirements, which is not particularly limited. The smaller the convergence value of the risk assessment model is, the greater the training difficulty is, and the more reliable the predicted risk factor is output when subsequently performing risk assessment on the policy-preserving case.

According to the method and the device, the initial model is trained to generate the required risk assessment model by acquiring the designated data corresponding to the data types in the historical policy case with the preset number and the preset prediction risk factors, so that the policy case can be predicted to obtain the corresponding target prediction risk factors based on the obtained risk assessment model in the follow-up process, and then the policy case can be accurately subjected to risk assessment based on the target prediction risk factors.

In some optional implementation manners of this embodiment, after the step of using the trained initial model as the risk assessment model, the electronic device may further perform the following steps:

a preset time period is determined.

In this embodiment, the value of the preset time period is not specifically limited, and may be set according to actual service requirements, for example, the preset time period may be within the previous year from the current time.

And acquiring a target historical policy keeping case in the preset time period.

In this embodiment, the target historical policy case may be obtained by querying a policy database.

And updating the risk assessment model based on the target historical policy.

In this embodiment, after the risk assessment model is obtained through training, the risk assessment model is updated by using the target historical policy, which is beneficial to improving the performance of the risk assessment model.

According to the method and the device, the risk assessment model is updated based on the acquired data of the target historical policy case in the preset time period, so that the method and the device are beneficial to dealing with a new business mode, and the prediction processing performance of the risk assessment model is improved.

In some optional implementation manners of this embodiment, after step S206, the electronic device may further perform the following steps:

and calling a preset strategy library.

In this embodiment, the policy library is pre-created and stores different risk levels and processing policies corresponding to the risk levels, respectively. Different risk levels correspond to different processing strategies, and specific contents of the processing strategies can be flexibly set according to actual business requirements without specific limitations. For example, the processing policy corresponding to the high risk level may be set to deny the application, the processing policy corresponding to the medium risk level may be set to allow the application but add a certain premium, and the processing policy corresponding to the low risk level may be set to allow the application.

And inquiring a processing strategy corresponding to the risk assessment result from the strategy library.

In this embodiment, after the risk assessment result is obtained, a target risk level matching the risk assessment result may be determined from the policy repository, and then a processing policy corresponding to the target risk level may be queried from the policy repository.

After the risk evaluation result of the second policy case is obtained, the processing strategy corresponding to the risk evaluation result is inquired from the strategy library, and the second policy case is correspondingly processed based on the processing strategy, so that accurate risk control over the second policy case is realized, and the processing intelligence of the risk control over the second policy case is improved.

It is emphasized that, in order to further ensure the privacy and security of the risk assessment results, the risk assessment results may also be stored in the nodes of a block chain.

The block chain referred by the application is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. The block chain (B l ockcha i n), which is essentially a decentralized database, is a string of data blocks associated by using cryptography, and each data block contains information of a batch of network transactions, which is used for verifying the validity (anti-counterfeiting) of the information and generating the next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.

The embodiment of the application can acquire and process related data based on an artificial intelligence technology. The artificial intelligence (Art I f I c I a l I nte l I gene, ai) is a theory, method, technology and application system for simulating, extending and expanding human intelligence, sensing environment, acquiring knowledge and obtaining optimal results by using knowledge by using a digital computer or a machine controlled by the digital computer.

The artificial intelligence infrastructure generally includes 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 the like.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware associated with computer readable instructions, which can be stored in a computer readable storage medium, and when executed, the processes of the embodiments of the methods described above can be included. The storage medium may be a non-volatile storage medium such as a magnetic disk, an optical disk, a Read-only Memory (ROM), or a 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, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of execution is not necessarily sequential, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

With further reference to fig. 3, as an implementation of the method shown in fig. 2, the present application provides an embodiment of a risk assessment apparatus based on artificial intelligence, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and the apparatus may be applied to various electronic devices.

As shown in fig. 3, the artificial intelligence based risk assessment apparatus 300 according to the present embodiment includes: a receiving module 301, a first obtaining module 302, an analyzing module 303, a second obtaining module 304, an outputting module 305, and an evaluating module 306. Wherein:

a receiving module 301, configured to receive a policy checking request input by a user; wherein the policy checking request carries policy identification information;

a first obtaining module 302, configured to obtain a plurality of first policy cases corresponding to the policy identification information;

an analysis module 303, configured to analyze the first policy case based on a preset analysis rule, and screen out a second policy case that meets the analysis rule from all the first policy cases;

a second obtaining module 304, configured to obtain target data corresponding to a preset data type from the second policy case;

an output module 305, configured to input the target data into a risk assessment model trained in advance, and output a plurality of target predicted risk factors corresponding to the target data through the risk assessment model;

the evaluation module 306 is configured to perform risk evaluation on the second policy case based on a preset calculation model and each target predicted risk factor, and generate a risk evaluation result corresponding to the second policy case.

In this embodiment, the operations respectively executed by the modules or units correspond to the steps of the risk assessment method based on artificial intelligence in the foregoing embodiment one by one, and are not described herein again.

In some optional implementations of this embodiment, the analysis rule includes a plurality of rules, and the analysis module 303 includes:

the first obtaining submodule is used for obtaining specified data, corresponding to each analysis rule, in a specified policy case for the specified policy case; wherein the designated policy case is any one policy case of all the first policy cases;

the judgment submodule is used for analyzing corresponding specified data based on each analysis rule and judging whether all the specified data accord with the corresponding analysis rule;

and the first determining submodule is used for taking the appointed policy case as the second policy case if the first determining submodule is yes.

In some optional implementations of this embodiment, the evaluation module 306 includes:

the calculation submodule is used for calling the calculation model and calculating the risk value of the second policy case based on each target prediction risk factor;

a generation submodule for generating a target risk level corresponding to the second policy case based on the risk value;

and the second determining submodule is used for taking the target risk level as a risk evaluation result of the second policy case.

In this embodiment, the operations respectively executed by the modules or units correspond to the steps of the artificial intelligence based risk assessment method in the foregoing embodiment one to one, and are not described herein again.

In some optional implementations of this embodiment, the calculation submodule includes:

an obtaining unit configured to obtain weight values corresponding to the respective target predicted risk factors;

the calculation unit is used for calculating each target prediction risk factor based on a preset calculation formula corresponding to each weight value to obtain a corresponding calculation value;

and the determining unit is used for taking the calculated value as the risk value of the second policy case.

In this embodiment, the operations respectively executed by the modules or units correspond to the steps of the artificial intelligence based risk assessment method in the foregoing embodiment one by one, and are not described herein again.

In some optional implementations of this embodiment, the artificial intelligence based risk assessment apparatus further includes:

the third acquisition module is used for acquiring specified data corresponding to the data types in a preset number of historical policy cases and preset predicted risk factors;

the first calling module is used for calling a preset initial model;

the training module is used for respectively taking the specified data of each historical policy case as the input of the initial model, taking each predicted risk factor as the output of the initial model and training the initial model;

the fourth acquisition module is used for acquiring a convergence value of the initial model in a training process;

and the first determining module is used for judging that the initial model training is finished when the convergence value is smaller than a preset convergence threshold value, and taking the initial model obtained after training as the risk assessment model.

the second determining module is used for determining a preset time period;

a fifth obtaining module, configured to obtain a target historical policy case within the preset time period;

and the updating module is used for updating the risk assessment model based on the target historical policy.

the second calling module is used for calling a preset strategy library;

the query module is used for querying the processing strategy corresponding to the risk evaluation result from the strategy library;

and the processing module is used for correspondingly processing the second policy case based on the processing strategy.

In order to solve the technical problem, an embodiment of the present application further provides a computer device. Referring to fig. 4, fig. 4 is a block diagram of a basic structure 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 is noted that only computer device 4 having components 41-43 is shown, but it is understood that not all of the shown components are required to be implemented, and that more or fewer components may be implemented instead. AS will be understood by those skilled in the art, the computer device herein is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware includes, but is not limited to, a microprocessor, an application specific integrated circuit (App I cat I on Spec I C I integrated C I rcu I, AS ic), a programmable Gate array (F I l D-programmable ab l Gate Ar ray, FPGA), a digital Processor (D I ta l S I gna l Processor, DSP), an embedded device, and the like.

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

The memory 41 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 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 Memory Card (SMC), a Secure digital (Secure D i g i ta l, SD) Card, a flash memory Card (F l ash Card), and the like, which are provided on the computer device 4. Of course, the memory 41 may also include both internal and external storage devices of the computer device 4. In this embodiment, the memory 41 is generally used for storing an operating system and various application software installed on the computer device 4, such as computer readable instructions of the artificial intelligence based risk assessment method. Further, the memory 41 may also 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 (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip in some embodiments. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is configured to execute computer readable instructions stored in the memory 41 or process data, such as computer readable instructions for executing the artificial intelligence based risk assessment method.

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

in the embodiment of the application, after a policy checking request input by a user is received, a plurality of first policy cases corresponding to the policy identification information are obtained; then analyzing the first policy case based on a preset analysis rule, and screening out a second policy case which accords with the analysis rule from all the first policy cases; then acquiring target data corresponding to a preset data type from the second policy case; subsequently, the target data are input into a pre-trained risk assessment model, and a plurality of target prediction risk factors corresponding to the target data are output through the risk assessment model; and finally, performing risk assessment on the second policy case based on a preset calculation model and each target prediction risk factor to generate a risk assessment result corresponding to the second policy case. According to the method and the device, the first insurance policy cases are analyzed based on the analysis rules, the second insurance policy cases meeting the analysis rules are screened out from all the first insurance policy cases, the subsequent risk assessment model only needs to carry out risk assessment processing on the screened second insurance policy cases, the processing workload of carrying out risk assessment on the insurance policy cases is effectively reduced, the processing efficiency of risk assessment on the insurance policy cases is improved, and the accuracy of the risk assessment results of the insurance policy cases is guaranteed.

The present application further provides another embodiment, which is to provide a computer-readable storage medium storing computer-readable instructions executable by at least one processor to cause the at least one processor to perform the steps of the artificial intelligence based risk assessment method as described above.

in the embodiment of the application, after a policy checking request input by a user is received, a plurality of first policy cases corresponding to the policy identification information are obtained; then analyzing the first policy case based on a preset analysis rule, and screening out a second policy case which accords with the analysis rule from all the first policy cases; then acquiring target data corresponding to a preset data type from the second policy case; subsequently inputting the target data into a pre-trained risk assessment model, and outputting a plurality of target prediction risk factors corresponding to the target data through the risk assessment model; and finally, performing risk assessment on the second policy case based on a preset calculation model and each target prediction risk factor to generate a risk assessment result corresponding to the second policy case. The first insurance policy case is analyzed based on the analysis rule, the second insurance policy case which accords with the analysis rule is screened out from all the first insurance policy cases, the subsequent risk assessment model only needs to carry out risk assessment processing on the screened second insurance policy case, the processing workload of carrying out risk assessment on the insurance policy case is effectively reduced, the processing efficiency of risk assessment on the insurance policy case is improved, and the accuracy of the risk assessment result of the insurance policy case is guaranteed.

Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application or portions thereof that contribute to the prior art may be embodied in the form of a software product, where the computer software product is stored in a storage medium (such as a ROM/RAM, a magnetic disk, and an optical disk), and includes several instructions for enabling a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present application.

It is to be understood that the above-described embodiments are merely illustrative of some, but not restrictive, of the broad invention, and that the appended drawings illustrate preferred embodiments of the invention and do not limit the scope of the invention. This application is capable of embodiments in many different forms and is provided for the purpose of enabling a thorough understanding of the disclosure of the application. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to one skilled in the art that the present application may be practiced without modification or with equivalents of some of the features described in the foregoing embodiments. All equivalent structures made by using the contents of the specification and the drawings of the present application are directly or indirectly applied to other related technical fields and are within the protection scope of the present application.

Claims

1. A risk assessment method based on artificial intelligence is characterized by comprising the following steps:

2. The artificial intelligence based risk assessment method according to claim 1, wherein the analysis rules include a plurality of analysis rules, the step of analyzing the first policy case based on the preset analysis rules and selecting a second policy case meeting the analysis rules from all the first policy cases comprises:

analyzing corresponding specified data based on each analysis rule, and judging whether all the specified data accord with the corresponding analysis rule;

and if so, taking the appointed policy case as the second policy case.

3. The artificial intelligence based risk assessment method according to claim 1, wherein the step of performing risk assessment on the second policy-preserving case based on a preset calculation model and each of the target predicted risk factors to generate a risk assessment result corresponding to the second policy-preserving case specifically comprises:

4. The artificial intelligence based risk assessment method according to claim 3, wherein said step of calculating a risk value of said second policy case based on each of said target predicted risk factors specifically comprises:

acquiring weight values respectively corresponding to the target prediction risk factors;

and taking the calculated value as a risk value of the second policy case.

5. The artificial intelligence based risk assessment method according to claim 1, wherein before the step of inputting the target data into a pre-trained risk assessment model and outputting a plurality of target predicted risk factors corresponding to the target data by the risk assessment model, further comprising:

calling a preset initial model;

acquiring a convergence value of the initial model in a training process;

6. The artificial intelligence based risk assessment method according to claim 5, further comprising, after said step of using the trained initial model as the risk assessment model:

determining a preset time period;

acquiring a target historical policy case in the preset time period;

and updating the risk assessment model based on the target historical policy.

7. The artificial intelligence based risk assessment method according to claim 1, wherein after the step of performing risk assessment on the second policy-preserving case based on the preset calculation model and each of the target predicted risk factors, and generating a risk assessment result corresponding to the second policy-preserving case, the method further comprises:

calling a preset strategy library;

8. A risk assessment device based on artificial intelligence, comprising:

9. A computer device comprising a memory having computer readable instructions stored therein and a processor that when executed performs the steps of the artificial intelligence based risk assessment method of any one of claims 1-7.

10. A computer-readable storage medium having computer-readable instructions stored thereon which, when executed by a processor, implement the steps of the artificial intelligence based risk assessment method of any one of claims 1 to 7.