Disclosure of Invention
The embodiment of the application aims to provide a risk analysis method, a risk analysis device, computer equipment and a storage medium, which are used for solving the technical problems that the existing risk assessment method adopted by insurance enterprises is low in processing efficiency and large in workload due to dependence on manpower, and the accuracy of a generated risk assessment result cannot be guaranteed.
In order to solve the above technical problems, the embodiment of the present application provides a risk analysis method, which adopts the following technical scheme:
Acquiring risk factor data of a client based on a preset data type; wherein the number of customers includes a plurality;
Acquiring historical underwriting data of the client and historical claim settlement data of the client;
marking the risk factor data, the historical underwriting data and the historical claim settlement data based on a preset marking strategy to obtain corresponding first marking data, second marking data and third marking data;
generating a risk score for the customer based on the first, second, and third targeting data;
screening out the designated risk score with the highest numerical value corresponding to the preset percentage from all the risk scores;
And performing risk analysis on the specified risk score to generate a specified risk level of the specified client corresponding to the specified risk score.
Further, the step of generating the risk score of the customer based on the first, second and third marking data specifically includes:
calling a preset risk score calculation formula;
Calculating the first marking data, the second marking data and the third marking data based on the risk score calculation formula to obtain corresponding calculation results;
and taking the calculation result as a risk score of the client.
Further, the step of performing risk analysis on the specified risk score and generating a specified risk level of the specified client corresponding to the specified risk score specifically includes:
Acquiring a first risk score of a first customer; wherein the first client is any one of all the specified clients;
performing numerical analysis on the first risk score, and if the first risk score is detected to be in a first preset numerical range, determining that the first risk level of the first client is a high risk level;
If the first risk score is detected to be in a second preset numerical range, determining that the first risk level of the first client is a risk level;
And if the first risk score is detected to be in a third preset numerical range, determining that the first risk level of the first client is a low risk level.
Further, after the step of performing risk analysis on the specified risk score and generating a specified risk level of the specified client corresponding to the specified risk score, the method further includes:
acquiring a second risk level of a second customer; wherein the second client is any one of all the specified clients;
Calculating and generating the warranty data corresponding to the second client based on the second risk level;
And calculating and generating premium data corresponding to the second client based on the second risk level.
Further, after the step of performing risk analysis on the specified risk score and generating a specified risk level of the specified client corresponding to the specified risk score, the method further includes:
Acquiring appointed underwriting data and appointed claim settling data of a third client; wherein the third customer is any one of all the specified customers;
acquiring a second risk score and a third risk level of the third customer;
Constructing and generating an enterprise client risk image corresponding to the third client based on the specified underwriting data, the specified claim data, the second risk score and the third risk level;
the enterprise client risk profile is stored.
Further, after the step of creating an enterprise client risk representation corresponding to the third client based on the specified underwriting data, the specified claim data, the second risk score, and the third risk level, the method further includes:
Acquiring a specified enterprise client risk portrait corresponding to the specified client;
obtaining mechanism distribution information corresponding to the appointed clients;
constructing an enterprise client risk map based on the specified enterprise client risk portraits and the institution distribution information;
And storing the enterprise client risk map.
Further, after the step of performing risk analysis on the specified risk score and generating a specified risk level of the specified client corresponding to the specified risk score, the method further includes:
Analyzing the appointed risk level, and screening a fourth client with the appointed risk level being a high risk level from the appointed clients;
Acquiring client information of the fourth client;
generating corresponding risk early warning information based on the client information;
acquiring contact information of a target processor;
and based on the contact information, sending the risk early warning information to the target processing personnel.
In order to solve the above technical problems, the embodiment of the present application further provides a risk analysis device, which adopts the following technical scheme:
the first acquisition module is used for acquiring risk factor data of the client based on a preset data type; wherein the number of customers includes a plurality;
the second acquisition module is used for acquiring the historical underwriting data of the client and acquiring the historical claim settlement data of the client;
the first processing module is used for performing marking processing on the risk factor data, the historical underwriting data and the historical claim settlement data based on a preset marking strategy to obtain corresponding first marking data, second marking data and third marking data;
a first generation module for generating a risk score for the customer based on the first, second, and third marking data;
the screening module is used for screening out the appointed risk score with the highest numerical value corresponding to the preset percentage from all the risk scores;
And the second generation module is used for carrying out risk analysis on the specified risk score and generating a specified risk level of the specified client corresponding to the specified risk score.
In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical schemes:
Acquiring risk factor data of a client based on a preset data type; wherein the number of customers includes a plurality;
Acquiring historical underwriting data of the client and historical claim settlement data of the client;
marking the risk factor data, the historical underwriting data and the historical claim settlement data based on a preset marking strategy to obtain corresponding first marking data, second marking data and third marking data;
generating a risk score for the customer based on the first, second, and third targeting data;
screening out the designated risk score with the highest numerical value corresponding to the preset percentage from all the risk scores;
And performing risk analysis on the specified risk score to generate a specified risk level of the specified client corresponding to the specified risk score.
In order to solve the above technical problems, an embodiment of the present application further provides a computer readable storage medium, which adopts the following technical schemes:
Acquiring risk factor data of a client based on a preset data type; wherein the number of customers includes a plurality;
Acquiring historical underwriting data of the client and historical claim settlement data of the client;
marking the risk factor data, the historical underwriting data and the historical claim settlement data based on a preset marking strategy to obtain corresponding first marking data, second marking data and third marking data;
generating a risk score for the customer based on the first, second, and third targeting data;
screening out the designated risk score with the highest numerical value corresponding to the preset percentage from all the risk scores;
And performing risk analysis on the specified risk score to generate a specified risk level of the specified client corresponding to the specified risk score.
Compared with the prior art, the embodiment of the application has the following main beneficial effects:
Firstly, acquiring risk factor data of a client based on a preset data type; then, acquiring historical underwriting data of the client and historical claim settlement data of the client; performing marking processing on the risk factor data, the historical underwriting data and the historical claim settlement data based on a preset marking strategy to obtain corresponding first marking data, second marking data and third marking data; generating a risk score of the customer based on the first, second and third marking data; further screening out the appointed risk score with the highest numerical value corresponding to the preset percentage from all the risk scores; and finally, carrying out risk analysis on the specified risk score to generate a specified risk grade of the specified client corresponding to the specified risk score. According to the embodiment of the application, the risk factor data of the client is obtained based on the preset data type, the historical underwriting data and the historical claim settlement data of the client are obtained, then the risk factor data, the historical underwriting data and the historical claim settlement data are subjected to marking processing based on the preset marking strategy so as to automatically generate the risk score of the client, and the designated risk score with the highest numerical value corresponding to the preset percentage, which is screened from the risk score, is subjected to risk analysis, so that the designated risk grade of the designated client corresponding to the designated risk score is automatically and accurately generated, the processing efficiency of the risk analysis of the client is effectively improved, the processing workload of the risk analysis of the client is reduced, and the data accuracy of the generated risk grade is ensured.
Detailed Description
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the applications herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having" and any variations thereof in the description of the application and the claims and the description of the drawings above are intended to cover a non-exclusive inclusion. The terms first, second and the like in the description and in the claims or in the above-described figures, are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
In order to make the person skilled in the art better understand the solution of the present application, the technical solution of the embodiment of the present application will be clearly and completely described below with reference to the accompanying drawings.
As shown in fig. 1, a system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as a web browser application, a shopping class application, a search class application, an instant messaging tool, a mailbox client, social platform software, etc., may be installed on the terminal devices 101, 102, 103.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablet computers, electronic book readers, MP3 players (Moving Picture Experts Group Audio Layer III, dynamic video expert compression standard audio plane 3), MP4 (Moving Picture Experts Group Audio Layer IV, dynamic video expert compression standard audio plane 4) players, laptop and desktop computers, and the like.
The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.
It should be noted that, the risk analysis method provided by the embodiment of the present application is generally executed by a server/terminal device, and accordingly, the risk analysis device 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 a risk analysis 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 analysis method provided by the embodiment of the application can be applied to any scene needing to carry out risk assessment of enterprise clients, and can be applied to products of the scenes, for example, risk assessment of enterprise clients in the field of financial insurance. The risk analysis method comprises the following steps:
Step S201, acquiring risk factor data of a client based on a preset data type; wherein the number of customers includes a plurality.
In this embodiment, the electronic device (e.g., the server/terminal device shown in fig. 1) on which the risk analysis method operates may acquire the risk factor data 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. In the business scenario of risk assessment of enterprise clients of financial insurance, the data types comprise types of external public opinion, investment assets, sai Misi rating, bankruptcy belief losing and the like, and the corresponding risk factor data comprise external public opinion data, investment asset data, sai Misi rating data and bankruptcy belief losing data.
Step S202, acquiring historical underwriting data of the client and acquiring historical claim settlement data of the client.
In this embodiment, the historical underwriting data of the client and the historical claim settlement data of the client may be queried from a preset insurance database according to the client information of the client. The insurance database is a database which is built in advance and stores the underwriting data and the claim settling data of each customer. The client information may refer to a client name of the client.
And step 203, performing marking processing on the risk factor data, the historical underwriting data and the historical claim settlement data based on a preset marking strategy to obtain corresponding first marking data, second marking data and third marking data.
In this embodiment, the content of the marking policy may include: the grades of the external public opinion are 7 grades, 1 to 7 are used for representing important negative early warning, the smaller the number is, the lower the public opinion influence is, if the number is smaller than 4, the positive public opinion is represented, and the weight coefficient is 0.2; investment assets are less than 100 ten thousand and 1, more than 100 ten thousand and less than 500 ten thousand and 2, and the like, 1 is added every more than 500 ten thousand, and the weight coefficient is 0.1; the Saigis grading grade is A-D with 12 grades, which is 1-12, the larger the number is, the lower the grade is, the poor the financial condition is, and the weight coefficient is 0.2; if the bankruptcy belief loss data exist, directly adding 20 minutes; the weight coefficient is 0.2; underwriting data: the guard is less than 100 ten thousand and 1, more than 100 ten thousand and less than 500 ten thousand are 2,1 is added to every 500 ten thousand, and the weight coefficient is 0.1; and (3) claim data: every 10 ten thousand+1 of claims are settled, the weight coefficient is 0.2.
Step S204, generating a risk score of the customer based on the first, second and third marking data.
In this embodiment, the foregoing specific implementation process of generating the risk score of the customer based on the first marking data, the second marking data and the third marking data will be described in further detail in the following specific embodiments, which will not be described herein.
Step S205, the designated risk score with the highest value corresponding to the preset percentage is screened from all the risk scores.
In this embodiment, the value of the preset percentage is not specifically limited, and may be set according to the actual service requirement, for example, may be set to 20%. If the preset percentage is 20%, the risk score of the first 20% with the highest value is selected from all the risk scores to be used as the specified risk score.
And S206, performing risk analysis on the specified risk score to generate a specified risk level of the specified client corresponding to the specified risk score.
In this embodiment, the risk analysis is performed on the specified risk score, and a specific implementation process of generating the specified risk level of the specified client corresponding to the specified risk score is described in further detail in the following specific embodiment, which will not be described herein.
Firstly, acquiring risk factor data of a client based on a preset data type; then, acquiring historical underwriting data of the client and historical claim settlement data of the client; performing marking processing on the risk factor data, the historical underwriting data and the historical claim settlement data based on a preset marking strategy to obtain corresponding first marking data, second marking data and third marking data; generating a risk score of the customer based on the first, second and third marking data; further screening out the appointed risk score with the highest numerical value corresponding to the preset percentage from all the risk scores; and finally, carrying out risk analysis on the specified risk score to generate a specified risk grade of the specified client corresponding to the specified risk score. According to the application, the risk factor data of the client is obtained based on the preset data type, the historical underwriting data and the historical claim settlement data of the client are obtained, then the risk factor data, the historical underwriting data and the historical claim settlement data are subjected to marking processing based on the preset marking strategy so as to automatically generate the risk score of the client, and then the specified risk score with the highest numerical value corresponding to the preset percentage, which is screened from the risk scores, is subjected to risk analysis, so that the specified risk grade of the specified client corresponding to the specified risk score is automatically and accurately generated, the processing efficiency of the risk analysis of the client is effectively improved, the processing workload of the risk analysis of the client is reduced, and the data accuracy of the generated risk grade is ensured.
In some alternative implementations of the present embodiment, step S204 includes the steps of:
And calling a preset risk score calculation formula.
In this embodiment, the risk score calculation formula is a weighted sum formula of marking data applied to risk factor data, marking data of underwriting data, and marking data of claim data constructed according to an actual risk determination rule. The risk factor data may include external public opinion data, investment assets, saigis ratings, bankruptcy belief loss data. The weight values of the external public opinion data, the investment assets, the Saigis rating, the bankruptcy and trust loss data, the underwriting data and the claim settlement data are not particularly limited, and can be set according to actual use requirements. The weights of the external public opinion data, investment assets, saigis ratings, bankruptcy belief loss data, underwriting data, and claim settlement data are preferably set to 0.2,0.1,0.2,0.2,0.1,0.2, respectively.
And calculating the first marking data, the second marking data and the third marking data based on the risk score calculation formula to obtain corresponding calculation results.
In this embodiment, the weights of the first marking data, the second marking data, and the third marking data, and the external public opinion data, the investment asset, the Saigis rating, the bankruptcy and loss trust data, the underwriting data, and the claim settlement data may be substituted into the above risk score calculation formula to perform calculation processing, so as to obtain the corresponding calculation result.
And taking the calculation result as a risk score of the client.
The method comprises the steps of calling a preset risk score calculation formula; then, calculating the first marking data, the second marking data and the third marking data based on the risk score calculation formula to obtain corresponding calculation results; and taking the calculation result as a risk score of the client. According to the application, the first marking data, the second marking data and the third marking data are calculated by calling the preset risk score calculation formula, so that the risk score of the customer can be rapidly and accurately generated, the calculation efficiency of the risk score is improved, and the data accuracy of the generated risk score is ensured.
In some alternative implementations, step S206 includes the steps of:
A first risk score for a first customer is obtained.
In this embodiment, the first client is any one of all specified clients.
And carrying out numerical analysis on the first risk score, and if the first risk score is detected to be in a first preset numerical range, determining that the first risk level of the first client is a high risk level.
In this embodiment, the value of the first preset numerical range is not specifically limited, and may be selected according to the actual service setting requirement of the high risk determination. The first preset numerical range is larger than the second preset numerical range, and the second preset numerical range is larger than the third preset numerical range.
And if the first risk score is detected to be in the second preset numerical range, determining that the first risk level of the first client is a risk level.
In this embodiment, the value of the second preset numerical range is not specifically limited, and may be selected according to the service setting requirement of the actual risk determination.
And if the first risk score is detected to be in a third preset numerical range, determining that the first risk level of the first client is a low risk level.
In this embodiment, the value of the third preset value range is not specifically limited, and may be selected according to the actual service setting requirement of the low risk determination.
The method comprises the steps of obtaining a first risk score of a first customer; then carrying out numerical analysis on the first risk score, and if the first risk score is detected to be in a first preset numerical range, determining that the first risk level of the first client is a high risk level; if the first risk score is detected to be in a second preset numerical range, determining that the first risk level of the first client is a risk level; and if the first risk score is detected to be in the third preset numerical range, determining that the first risk level of the first client is a low risk level. According to the method and the device for generating the specified risk grade, the specified risk grade of the specified client corresponding to the specified risk score can be quickly and accurately generated by carrying out risk analysis on the specified risk score according to the use of the preset numerical range, so that the generation efficiency of the specified risk grade is improved, and the data accuracy of the generated specified risk grade is ensured.
In some alternative implementations, after step S206, the electronic device may further perform the following steps:
A second risk level of a second customer is obtained.
In this embodiment, the second client is any one of all the specified clients.
And calculating and generating the warranty data corresponding to the second client based on the second risk level.
In this embodiment, the second risk level may be substituted into a preset policy calculation formula to generate policy data corresponding to the second client. The deposit calculation formula is pre-constructed according to the actual deposit calculation requirement.
And calculating and generating premium data corresponding to the second client based on the second risk level.
In this embodiment, the premium data corresponding to the second customer may be generated by calling a preset premium calculation formula and substituting the second risk level into the premium calculation formula. The premium calculation formula is pre-constructed according to actual premium calculation requirements.
The application obtains a second risk level of a second customer; then calculating and generating the deposit data corresponding to the second client based on the second risk level; and calculating and generating premium data corresponding to the second client based on the second risk level. After the specified risk level of the specified client corresponding to the specified risk score is generated, the application can intelligently and automatically generate the premium data and the premium data of each specified client according to the specified risk level, so that the premium and the premium of the client are not required to be calculated manually, the processing workload of calculating the premium and the premium is reduced, the calculation efficiency of the premium and the premium is improved, and the accuracy of the generated premium data and premium data is ensured.
In some alternative implementations, after step S206, the electronic device may further perform the following steps:
And acquiring the specified underwriting data and the specified claim settlement data of the third client.
In this embodiment, the third client is any one of all specified clients.
And acquiring a second risk score and a third risk level of the third client.
In this embodiment, the second risk score and the third risk level of the third client may be extracted by performing a data query process on the risk score and the risk level of the third client.
And constructing and generating an enterprise client risk portrait corresponding to the third client based on the specified underwriting data, the specified claim data, the second risk score and the third risk level.
In this embodiment, the enterprise client risk portrait corresponding to the third client may be constructed by filling the specified underwriting data, specified claim data, the second risk score and the third risk level into the positions of the parameters to be filled of the corresponding field values in the preset client risk portrait template. The client risk portrait template is a template file generated according to the actual risk portrait construction requirement, and the template file comprises an underwriting field, a claim settlement field, a risk score field and a risk grade field. In addition, the risk comments and the intra-industry conditions can be combined in the enterprise client risk portraits to carry out multidimensional depiction and display of risk trends so as to improve the content richness of the enterprise client risk portraits.
The enterprise client risk profile is stored.
In this embodiment, the storage manner of the enterprise client risk image is not limited, and for example, blockchain storage, local database storage, cloud storage, and the like may be adopted.
The application obtains the appointed underwriting data and the appointed claim settlement data of the third client; then obtaining a second risk score and a third risk level of the third customer; then constructing and generating an enterprise client risk image corresponding to the third client based on the specified underwriting data, the specified claim data, the second risk score and the third risk level; the enterprise client risk profile is subsequently stored. After the specified risk score and the specified risk grade of the specified client are generated, the specified risk score, the specified risk grade, the historical underwriting data and the historical claim settlement data of the specified client are intelligently used to realize automatic and convenient construction of the enterprise client risk portrait of the specified client, and the construction efficiency of the enterprise client risk portrait is effectively improved. In addition, the constructed enterprise client risk image is stored, so that the data security of the generated enterprise client risk image is ensured.
In some optional implementations of this embodiment, after the step of creating an enterprise customer risk representation corresponding to the third customer based on the specified underwriting data, specified claim data, the second risk score, and the third risk level, the electronic device may further perform the steps of:
And acquiring a specified enterprise client risk portrait corresponding to the specified client.
In this embodiment, the specified enterprise client risk image of the specified client is stored after the specified enterprise client risk image is constructed and generated.
And obtaining organization distribution information corresponding to the appointed clients.
In the present embodiment, the above-described institution distribution information is distribution information specifying customers in each financial institution.
And constructing an enterprise client risk map based on the specified enterprise client risk portraits and the organization distribution information.
In this embodiment, the specified enterprise client risk portrait and the organization distribution information may be filled into the position of the corresponding region to be filled in the preset enterprise client risk map template, so as to construct and obtain the corresponding enterprise client risk map. The client risk portrait template is a template file generated according to the actual enterprise client risk map construction requirement, and the template file comprises a storage area corresponding to the enterprise client risk portrait and a storage area corresponding to the organization distribution information.
And storing the enterprise client risk map.
In this embodiment, the storage manner of the enterprise client risk map is not limited, and for example, blockchain storage, local database storage, cloud storage and the like may be adopted.
The method comprises the steps of obtaining a specified enterprise client risk portrait corresponding to the specified client; then obtaining mechanism distribution information corresponding to the appointed clients; then constructing an enterprise client risk map based on the specified enterprise client risk portraits and the organization distribution information; and subsequently storing the enterprise client risk map. After the enterprise client risk image of the appointed client is generated, the method and the system can intelligently use the appointed enterprise client risk image and the mechanism distribution information corresponding to the appointed client to automatically and conveniently construct the corresponding enterprise client risk map, and effectively improve the construction efficiency of the enterprise client risk map. In addition, by storing the constructed enterprise client risk map, the data security of the generated enterprise client risk map can be ensured. In addition, the related users can quickly know the situation information of the enterprise risk clients contained in each organization and the enterprise client risk portraits of the enterprise risk clients by referring to the enterprise client risk map, so that the use experience of the related users is improved.
In some optional implementations of this embodiment, after step S206, the electronic device may further perform the following steps:
and analyzing the appointed risk level, and screening a fourth client with the appointed risk level being a high risk level from the appointed clients.
In this embodiment, the content analysis may be performed on the specified risk level to screen the fourth client with a specified risk level being a high risk level from the specified clients.
And acquiring the client information of the fourth client.
In this embodiment, the client information may include a client name.
And generating corresponding risk early warning information based on the client information.
In this embodiment, the client information may be filled into corresponding positions in a preset risk early-warning information template to generate corresponding risk early-warning information. The risk early warning information template is generated according to the actual business requirements of risk early warning reminding, and the content of the risk early warning information template is not limited.
And acquiring contact information of the target processing personnel.
In this embodiment, the target handler may be a business person of the organization. The contact information may include a telephone number or a mail address.
And based on the contact information, sending the risk early warning information to the target processing personnel.
In this embodiment, the risk early warning information may be sent to a contact terminal corresponding to the target processor according to the contact information.
The application screens out a fourth customer with a high risk level from the appointed customers by analyzing the appointed risk level; then obtaining the client information of the fourth client; then generating corresponding risk early warning information based on the client information; subsequently, acquiring contact information of target processing personnel; and finally, based on the contact information, sending the risk early warning information to the target processing personnel. After the specified risk grade of the specified client corresponding to the specified risk score is generated, the fourth client with the specified risk grade being high in risk grade is intelligently screened out from the specified clients, corresponding risk early warning information is generated according to the client information of the fourth client, and the risk early warning information is further sent to the target processing personnel, so that the target processing personnel can timely conduct risk investigation on the fourth client according to the obtained risk early warning information, follow-up countermeasures are conducted, and the work efficiency and the work experience of the target processing personnel are 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 the specified risk level may also be stored in a blockchain node in order to further ensure privacy and security of the specified risk level.
The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The blockchain (Blockchain), essentially a de-centralized database, is a string of data blocks that are generated in association using cryptographic methods, each of which contains information from a batch of network transactions for verifying the validity (anti-counterfeit) of its information 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. Wherein 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 expand 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 a risk analysis device, which corresponds to the embodiment of the method shown in fig. 2, and which is particularly applicable to various electronic devices.
As shown in fig. 3, the risk analysis device 300 according to the present embodiment includes: a first acquisition module 301, a second acquisition module 302, a first processing module 303, a first generation module 304, a screening module 305, and a second generation module 306. Wherein:
a first obtaining module 301, configured to obtain risk factor data of a client based on a preset data type; wherein the number of customers includes a plurality;
a second obtaining module 302, configured to obtain historical underwriting data of the client, and obtain historical claim settlement data of the client;
The first processing module 303 is configured to perform marking processing on the risk factor data, the historical underwriting data, and the historical claim settlement data based on a preset marking policy, so as to obtain corresponding first marking data, second marking data, and third marking data;
a first generation module 304, configured to generate a risk score of the customer based on the first, second, and third marking data;
A screening module 305, configured to screen out a specified risk score with a highest numerical value corresponding to a preset percentage from all the risk scores;
And the second generation module 306 is configured to perform risk analysis on the specified risk score, and generate a specified risk level of the specified client corresponding to the specified risk score.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the risk analysis method in the foregoing embodiment one by one, and are not described herein again.
In some alternative implementations of the present embodiment, the first generating module 304 includes:
the calling sub-module is used for calling a preset risk score calculation formula;
The calculation sub-module is used for calculating the first marking data, the second marking data and the third marking data based on the risk score calculation formula to obtain corresponding calculation results;
and the first determination submodule is used for taking the calculation result as a risk score of the client.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the risk analysis method in the foregoing embodiment one by one, and are not described herein again.
In some alternative implementations of the present embodiment, the second generating module 306 includes:
An acquisition sub-module for acquiring a first risk score of a first customer; wherein the first client is any one of all the specified clients;
the first determining submodule is used for carrying out numerical analysis on the first risk score, and if the first risk score is detected to be in a first preset numerical range, determining that the first risk level of the first customer is a high risk level;
The second determining submodule is used for determining that the first risk level of the first customer is a risk level in a stroke if the first risk score is detected to be in a second preset numerical range;
and the third determining submodule is used for determining that the first risk level of the first customer is a low risk level if the first risk score is detected to be in a third preset numerical range.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the risk analysis method in the foregoing embodiment one by one, and are not described herein again.
In some optional implementations of this embodiment, the risk analysis device further includes:
The third acquisition module is used for acquiring a second risk level of a second client; wherein the second client is any one of all the specified clients;
The first calculation module is used for calculating and generating the deposit data corresponding to the second client based on the second risk level;
And the second calculation module is used for calculating and generating premium data corresponding to the second client based on the second risk level.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the risk analysis method in the foregoing embodiment one by one, and are not described herein again.
In some optional implementations of this embodiment, the risk analysis device further includes:
the fourth acquisition module is used for acquiring the specified underwriting data and the specified claim settlement data of the third client; wherein the third customer is any one of all the specified customers;
a fifth obtaining module, configured to obtain a second risk score and a third risk level of the third client;
a third generation module, configured to construct and generate an enterprise client risk image corresponding to the third client based on the specified underwriting data, the specified claim data, the second risk score, and the third risk level;
and the first storage module is used for storing the enterprise client risk portrait.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the risk analysis method in the foregoing embodiment one by one, and are not described herein again.
In some optional implementations of this embodiment, the risk analysis device further includes:
A sixth acquisition module, configured to acquire a specified enterprise client risk portrait corresponding to the specified client;
A seventh acquisition module, configured to acquire mechanism distribution information corresponding to the specified client;
the second processing module is used for constructing an enterprise client risk map based on the specified enterprise client risk portraits and the mechanism distribution information;
and the second storage module is used for storing the enterprise client risk map.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the risk analysis method in the foregoing embodiment one by one, and are not described herein again.
In some optional implementations of this embodiment, the risk analysis device further includes:
The analysis module is used for analyzing the appointed risk level and screening a fourth client with the appointed risk level being a high risk level from the appointed clients;
an eighth obtaining module, configured to obtain client information of the fourth client;
a fourth generation module, configured to generate corresponding risk early warning information based on the client information;
a ninth acquisition module, configured to acquire contact information of a target processor;
And the sending module is used for sending the risk early warning information to the target processing personnel based on the contact information.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the risk analysis method in the foregoing embodiment one by one, 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 calculation and/or information processing according to a preset or stored instruction, and its hardware includes, but is not limited to, a microprocessor, an Application SPECIFIC INTEGRATED Circuit (ASIC), a Programmable gate array (Field-Programmable GATE ARRAY, FPGA), a digital Processor (DIGITAL SIGNAL Processor, DSP), an embedded device, and the like.
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 memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory 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 of a risk analysis 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 computer readable instructions for executing the risk analysis 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, firstly, risk factor data of a client is obtained based on a preset data type; then, acquiring historical underwriting data of the client and historical claim settlement data of the client; performing marking processing on the risk factor data, the historical underwriting data and the historical claim settlement data based on a preset marking strategy to obtain corresponding first marking data, second marking data and third marking data; generating a risk score of the customer based on the first, second and third marking data; further screening out the appointed risk score with the highest numerical value corresponding to the preset percentage from all the risk scores; and finally, carrying out risk analysis on the specified risk score to generate a specified risk grade of the specified client corresponding to the specified risk score. According to the embodiment of the application, the risk factor data of the client is obtained based on the preset data type, the historical underwriting data and the historical claim settlement data of the client are obtained, then the risk factor data, the historical underwriting data and the historical claim settlement data are subjected to marking processing based on the preset marking strategy so as to automatically generate the risk score of the client, and the designated risk score with the highest numerical value corresponding to the preset percentage, which is screened from the risk score, is subjected to risk analysis, so that the designated risk grade of the designated client corresponding to the designated risk score is automatically and accurately generated, the processing efficiency of the risk analysis of the client is effectively improved, the processing workload of the risk analysis of the client is reduced, and the data accuracy of the generated risk grade is ensured.
The present application also provides another embodiment, namely, a computer-readable storage medium storing computer-readable instructions executable by at least one processor to cause the at least one processor to perform the steps of the risk analysis 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, firstly, risk factor data of a client is obtained based on a preset data type; then, acquiring historical underwriting data of the client and historical claim settlement data of the client; performing marking processing on the risk factor data, the historical underwriting data and the historical claim settlement data based on a preset marking strategy to obtain corresponding first marking data, second marking data and third marking data; generating a risk score of the customer based on the first, second and third marking data; further screening out the appointed risk score with the highest numerical value corresponding to the preset percentage from all the risk scores; and finally, carrying out risk analysis on the specified risk score to generate a specified risk grade of the specified client corresponding to the specified risk score. According to the embodiment of the application, the risk factor data of the client is obtained based on the preset data type, the historical underwriting data and the historical claim settlement data of the client are obtained, then the risk factor data, the historical underwriting data and the historical claim settlement data are subjected to marking processing based on the preset marking strategy so as to automatically generate the risk score of the client, and the designated risk score with the highest numerical value corresponding to the preset percentage, which is screened from the risk score, is subjected to risk analysis, so that the designated risk grade of the designated client corresponding to the designated risk score is automatically and accurately generated, the processing efficiency of the risk analysis of the client is effectively improved, the processing workload of the risk analysis of the client is reduced, and the data accuracy of the generated risk grade is ensured.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present application.
It is apparent that the above-described embodiments are only some embodiments of the present application, but not all embodiments, and the preferred embodiments of the present application are shown in the drawings, which do not limit the scope of the patent claims. This application may be embodied in many different forms, but rather, embodiments are provided in order to provide a thorough and complete understanding of the present disclosure. Although the application has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described in the foregoing description, or equivalents may be substituted for elements thereof. All equivalent structures made by the content of the specification and the drawings of the application are directly or indirectly applied to other related technical fields, and are also within the scope of the application.