Risk identification method, device and equipment and storage medium thereof

Abstract

The embodiment of the invention belongs to the technical field of artificial intelligence, is applied to a business handling risk identification scene, and relates to a risk identification method, device and equipment and a storage medium thereof. Performing verification input structure arrangement on the risk verification data to obtain structured data consistent with the input requirement of the pre-trained risk identification model; identifying a risk value of the target business object; and generating a business conclusion based on the risk value, and outputting the business conclusion through a risk identification model. According to the risk identification method provided by the invention, in particular in a financial insurance policy underwriting service, before model identification is carried out, verification input structure arrangement is carried out on the complicated risk verification data in advance, so that the data identified during model identification is structured data, and the insurance policy underwriting efficiency is fully improved; and in combination with the risk value, the insurance policy underwriting accuracy is improved.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and is applied to business risk identification scenarios, and in particular to a risk identification method, device, equipment and storage medium thereof. Background Art

[0002] At present, there are a large number of underwriting policies in the underwriting field of the insurance industry. The headquarter will issue underwriting policies that distinguish different products, institutions, and industries. Faced with a large amount of underwriting policy information, sales staff are unclear about certain policies when conducting business and need to seek help from the underwriters of the institution or the headquarters for policy answers or answers to questions.

[0003] Therefore, there is a possibility that the underwriter may not be clear about a certain policy, or may misunderstand it, resulting in high-risk document underwriting. Different underwriters have different understandings of risks, and different underwriters may make inconsistent judgments under the same conditions. This may cause high-quality business to be identified as high-risk business and rejected; it is also possible that high-risk business may be mistakenly judged as high-quality business by the underwriter, causing operating risks for the company, and failing to improve the efficiency and accuracy of risk identification in the policy underwriting scenario. Summary of the invention

[0004] The purpose of the embodiments of the present application is to propose a risk identification method, device, equipment and storage medium thereof to solve the problem that the existing risk identification efficiency and accuracy in the insurance policy underwriting scenario cannot be improved.

[0005] In order to solve the above technical problems, the embodiment of the present application provides a risk identification method, which adopts the following technical solution:

[0006] A risk identification method comprises the following steps:

[0007] Obtain identity information provided by the target business object;

[0008] Obtain multi-dimensional risk verification data from N authoritative data sources according to the identity information, where N is a positive integer;

[0009] The risk verification data is verified and input structured to obtain structured data consistent with the input requirements of the pre-trained risk identification model;

[0010] Inputting the structured data into the pre-trained risk identification model to identify the risk value of the target business object;

[0011] A business conclusion is generated based on the risk value, and the business conclusion is output through the risk identification model.

[0012] Furthermore, the step of obtaining multi-dimensional risk verification data from N authoritative data sources according to the identity information specifically includes:

[0013] Using the identity information as the search field, the income data, credit data, asset data, consumption data and business circle data of the target business object are obtained from the N authoritative data sources, wherein the business circle data refers to the income data, credit data, asset data, consumption data of an object having a direct business relationship with the target business object, and N is a positive integer.

[0014] Furthermore, before executing the step of structuring the risk verification data to obtain structured data consistent with the input requirements of the pre-trained risk identification model, the method further includes:

[0015] According to the preset risk identification correlation strength form, the income data, credit data, asset data, consumption data and business circle data of the target business object are cleaned, and the data with weak correlation is deleted, and only the data with strong correlation is retained as the updated risk verification data;

[0016] The step of arranging the verification input structure of the risk verification data to obtain structured data consistent with the input requirements of the pre-trained risk identification model specifically includes:

[0017] Identify the input data format requirements specified by the pre-trained risk identification model;

[0018] Based on the input data format requirement, the updated risk verification data is structured to obtain a structured arrangement result;

[0019] The structured arrangement results are used as structured data consistent with the input requirements of the pre-trained risk identification model.

[0020] Furthermore, before executing the step of inputting the structured data into the pre-trained risk identification model to identify the risk value of the target business object, the method includes:

[0021] Obtaining a risk identification document issued by a target risk control agency, wherein the risk identification document contains all key data of concern when conducting risk identification;

[0022] Structuring all the data in the risk identification file and extracting key risk identification data;

[0023] Building a risk identification strategy based on the risk identification key data;

[0024] The risk identification strategy is deployed into a pre-built risk identification model to obtain a pre-built risk identification model.

[0025] Furthermore, before executing the step of inputting the structured data into the pre-trained risk identification model to identify the risk value of the target business object, the method further includes:

[0026] Adopting a big data acquisition method, obtaining multi-dimensional risk training data from the N authoritative data sources, wherein the multi-dimensional risk training data includes income level training data, credit level training data, asset level training data, consumption level training data and business circle level training data, and N is a positive integer;

[0027] Identifying input data format requirements specified by the pre-built risk identification model;

[0028] Based on the input data format requirement, the multi-dimensional risk training data is structured to obtain a structured arrangement result;

[0029] The structured organization result is input into the pre-built risk identification model, and the pre-built risk identification model is trained for risk identification in combination with the risk identification strategy to obtain the data indicator ranges corresponding to different income levels, credit levels, asset levels, consumption levels and business circle levels, and obtain the pre-trained risk identification model.

[0030] Furthermore, the step of inputting the structured data into the pre-trained risk identification model to identify the risk value of the target business object specifically includes:

[0031] Parsing the structured data to extract income data, credit data, asset data, consumption data, and business circle data;

[0032] According to the income data, credit data, asset data, consumption data and business circle data, and the data indicator ranges corresponding to different income levels, credit levels, asset levels, consumption levels and business circle levels, the income level, credit level, asset level, consumption level and business circle level corresponding to the target business object are identified;

[0033] According to the risk weights corresponding to different income levels, credit levels, asset levels, consumption levels and business circle levels, the target risk weights corresponding to the income level, credit level, asset level, consumption level and business circle level corresponding to the target business object are obtained;

[0034] The target risk weights corresponding to the income level, credit level, asset level, consumption level and business circle level of the target business object are respectively added up and summed to obtain the accumulated sum value as the risk value of the target business object.

[0035] Furthermore, the step of generating a business conclusion based on the risk value and outputting the business conclusion through the risk identification model specifically includes:

[0036] By comparison, determining whether the risk value exceeds a preset risk threshold;

[0037] If the risk value exceeds a preset risk threshold, a conclusion is generated that the target business object cannot be processed in combination with a preset message generation task;

[0038] If the risk value does not exceed a preset risk threshold, a conclusion that business can be handled for the target business object is generated in combination with a preset message generation task.

[0039] In order to solve the above technical problems, the embodiment of the present application also provides a risk identification device, which adopts the following technical solution:

[0040] A risk identification device, comprising:

[0041] An identity information acquisition module is used to obtain identity information provided by a target business object;

[0042] A risk verification data acquisition module, used to obtain multi-dimensional risk verification data from N authoritative data sources according to the identity information, where N is a positive integer;

[0043] A structured processing module, used to organize the risk verification data into a verification input structure to obtain structured data consistent with the input requirements of the pre-trained risk identification model;

[0044] A risk value identification module, used to input the structured data into the pre-trained risk identification model to identify the risk value of the target business object;

[0045] A business conclusion generation output module is used to generate a business conclusion based on the risk value and output the business conclusion through the risk identification model.

[0046] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0047] A computer device includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the risk identification method described above when executing the computer-readable instructions.

[0048] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0049] A computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the risk identification method as described above.

[0050] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0051] The risk identification method described in the embodiment of the present application obtains the identity information provided by the target business object; obtains multi-dimensional risk verification data based on the identity information; verifies the input structure of the risk verification data to obtain structured data consistent with the input requirements of the pre-trained risk identification model; inputs the structured data into the pre-trained risk identification model to identify the risk value of the target business object; generates business conclusions based on the risk value, and outputs the business conclusions through the risk identification model. The risk identification method described in the present application, especially in the financial insurance policy underwriting business, before model identification, verifies the input structure of the complicated risk verification data in advance, so that the data identified during model identification is structured data, which fully improves the efficiency of insurance policy underwriting, more quickly identifies business risks, and improves the accuracy of insurance policy underwriting in combination with the risk value. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0053] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0054] Figure 2 is a flow chart of an embodiment of a risk identification method according to the present application;

[0055] Figure 3 yes Figure 2 A flowchart of a specific embodiment of step 203 is shown;

[0056] Figure 4 It is a flowchart of a specific embodiment of the steps of constructing a risk identification strategy in the risk identification method described in this application;

[0057] Figure 5is a flowchart of a specific embodiment of the steps of performing model training in the risk identification method described in the present application;

[0058] Figure 6 yes Figure 2 A flowchart of a specific embodiment of step 204 is shown;

[0059] Figure 7 is a schematic structural diagram of an embodiment of a risk identification device according to the present application;

[0060] Figure 8 It is a structural diagram of an embodiment of a computer device according to the present application. DETAILED DESCRIPTION

[0061] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0062] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0063] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0064] like Figure 1 As shown, the system architecture 100 may include a terminal device 101, a network 102 and a server 103. The terminal device 101 may be a laptop 1011, a tablet computer 1012 or a mobile phone 1013. The network 102 is used to provide a medium for a communication link between the terminal device 101 and the server 103. The network 102 may include various connection types, such as wired, wireless communication links or optical fiber cables.

[0065] The user can use the terminal device 101 to interact with the server 103 through the network 102 to receive or send messages, etc. Various communication client applications can be installed on the terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0066] The terminal device 101 can be any electronic device with a display screen and supporting web browsing. In addition to a laptop computer 1011, a tablet computer 1012 or a mobile phone 1013, the terminal device 101 can also be an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 (Moving Picture Experts Group Audio Layer IV), a laptop computer, a desktop computer, etc.

[0067] The server 103 may be a server that provides various services, such as a background server that provides support for a web page displayed on the terminal device 101 .

[0068] It should be noted that the risk identification method provided in the embodiment of the present application is generally executed by a terminal device, and accordingly, the risk identification device is generally arranged in the terminal device.

[0069] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0070] Continue to refer Figure 2 , shows a flow chart of an embodiment of a risk identification method according to the present application. The risk identification method comprises the following steps:

[0071] Step 201, obtaining identity information provided by the target business object;

[0072] Specifically, the target business object includes an individual object or a company object. When the target business object is an individual object, the identity information includes personal name information and personal ID number information; when the target business object is a company object, the identity information includes company name information and company unified social credit code information.

[0073] By obtaining the identity information provided by the target business object, it is convenient to subsequently combine the identity information to obtain the risk verification data of the target business object.

[0074] Step 202, obtaining multi-dimensional risk verification data from N authoritative data sources according to the identity information, where N is a positive integer;

[0075] In this embodiment, the authoritative data source refers to a data source provided by a government department or a relevant authoritative institution, for example, an income data acquisition platform and a credit data acquisition platform provided by a banking regulatory agency.

[0076] By using identity information to obtain multi-dimensional risk verification data from N authoritative data sources, it not only ensures the authority of the risk verification data, but also ensures the accuracy of subsequent risk identification results.

[0077] Step 203, arranging the risk verification input structure of the risk verification data to obtain structured data consistent with the input requirements of the pre-trained risk identification model;

[0078] By arranging the verification input structure of the risk verification data, structured data consistent with the input requirements of the pre-trained risk identification model is obtained, ensuring that the model can quickly identify and process data during subsequent model processing, thereby improving model processing efficiency.

[0079] Step 204: input the structured data into the pre-trained risk identification model to identify the risk value of the target business object;

[0080] The pre-trained risk identification model is used to identify the risk value of the target business object, so as to more accurately perform business risk control in combination with the risk value and improve the accuracy of risk identification.

[0081] Step 205: Generate a business conclusion based on the risk value, and output the business conclusion through the risk identification model.

[0082] Business conclusions are generated in combination with the risk value, and the business conclusions are output through the risk identification model, which realizes the input of risk verification data into the model, and the model directly outputs the business conclusions, so as to more directly control business risk and avoid the company from suffering major business losses.

[0083] In this embodiment, the identity information provided by the target business object is obtained; based on the identity information, multi-dimensional risk verification data is obtained; the risk verification data is verified and inputted into a structure to obtain structured data consistent with the input requirements of the pre-trained risk identification model; the structured data is inputted into the pre-trained risk identification model to identify the risk value of the target business object; a business conclusion is generated based on the risk value, and the business conclusion is outputted through the risk identification model. The risk identification method described in this application, especially in the financial insurance policy underwriting business, before model identification, verifies the input structure of the complicated risk verification data in advance, so that the data identified during model identification is structured data, which fully improves the efficiency of insurance policy underwriting, more quickly identifies business risks, and improves the accuracy of insurance policy underwriting in combination with the risk value.

[0084] In this embodiment, the step of obtaining multi-dimensional risk verification data from N authoritative data sources according to the identity information specifically includes:

[0085] Using the identity information as the search field, the income data, credit data, asset data, consumption data and business circle data of the target business object are obtained from the N authoritative data sources, wherein the business circle data refers to the income data, credit data, asset data, consumption data of an object having a direct business relationship with the target business object, and N is a positive integer.

[0086] By obtaining the income data, credit data, asset data, consumption data and business circle data of the target business object from the N authoritative data sources, the authority and multi-dimensionality of the risk verification data are guaranteed, thereby realizing risk identification of the target business object from multiple dimensions.

[0087] In this embodiment, before executing the step of verifying the input structure of the risk verification data to obtain structured data consistent with the input requirements of the pre-trained risk identification model, the method also includes: according to a preset risk identification correlation strength form, the income data, credit data, asset data, consumption data and business circle data of the target business object are cleaned, and data with weak correlation is deleted, and only data with strong correlation is retained as updated risk verification data.

[0088] By cleaning the income data, credit data, asset data, consumption data and business circle data of the target business object, deleting the data with weak correlation, and retaining only the data with strong correlation as the updated risk verification data, the model avoids introducing too much data with weak correlation when identifying risks, and only uses data with strong correlation, thereby improving the efficiency and accuracy of risk identification.

[0089] Continue to refer Figure 3 , in some optional implementations, Figure 3 yes Figure 2 The flowchart of a specific embodiment of step 203 shown includes the following steps:

[0090] Step 301, identifying the input data format requirements specified by the pre-trained risk identification model;

[0091] Step 302, based on the input data format requirement, the updated risk verification data is structured to obtain a structured arrangement result;

[0092] Step 303: Use the structured sorting results as structured data consistent with the input requirements of the pre-trained risk identification model.

[0093] By arranging the verification input structure of the risk verification data, structured data consistent with the input requirements of the pre-trained risk identification model is obtained, ensuring that the model can quickly identify and process data during subsequent model processing, thereby improving model processing efficiency.

[0094] Continue to refer Figure 4 In some optional implementations, before executing step 204, a step of constructing a risk identification strategy is also included. Figure 4 The flowchart of a specific embodiment of the steps of constructing a risk identification strategy in the risk identification method described in the present application includes the following steps:

[0095] Step 401, obtaining a risk identification document issued by a target risk control institution, wherein the risk identification document contains all key data concerned when performing risk identification;

[0096] Step 402, structuring all data in the risk identification file and extracting key risk identification data;

[0097] Step 403, constructing a risk identification strategy based on the risk identification key data;

[0098] Step 404: deploy the risk identification strategy into the pre-built risk identification model to obtain the pre-built risk identification model.

[0099] Through the risk identification documents issued by the target risk control agency, key risk identification data are extracted and a risk identification strategy is constructed to facilitate risk identification according to the requirements of the target risk control agency, ensuring the basis and standardization of risk identification.

[0100] Continue to refer Figure 5 In some optional implementations, a model training step is also included before executing step 204. Figure 5 The flowchart of a specific embodiment of the steps of model training in the risk identification method described in the present application includes the following steps:

[0101] Step 501, using a big data acquisition method to acquire multi-dimensional risk training data from the N authoritative data sources, wherein the multi-dimensional risk training data includes income level training data, credit level training data, asset level training data, consumption level training data and business circle level training data, and N is a positive integer;

[0102] Step 502, identifying the input data format requirements specified by the pre-built risk identification model;

[0103] Step 503, based on the input data format requirement, the multi-dimensional risk training data is structured and sorted to obtain a structured sorting result;

[0104] Step 504, input the structured sorting result into the pre-constructed risk identification model, and perform risk identification training on the pre-constructed risk identification model in combination with the risk identification strategy to obtain the data indicator ranges corresponding to different income levels, credit levels, asset levels, consumption levels and business circle levels, and obtain the pre-trained risk identification model.

[0105] Specifically, the purpose of risk identification training for the pre-built risk identification model is to train the data indicator ranges corresponding to different income levels, credit levels, asset levels, consumption levels and business circle levels, so as to facilitate the subsequent identification of the income level, credit level, asset level, consumption level and business circle level corresponding to the target business object.

[0106] By combining N authoritative data sources, we obtain multi-dimensional risk training data and conduct risk identification model training, ensuring that the trained risk identification model is authoritative and highly available.

[0107] Continue to refer Figure 6 , in some optional implementations, Figure 6 yes Figure 2 The flowchart of a specific embodiment of step 204 shown includes the following steps:

[0108] Step 601, parsing the structured data to obtain income data, credit data, asset data, consumption data and business circle data;

[0109] Step 602, identifying the income level, credit level, asset level, consumption level and business circle level corresponding to the target business object based on the income data, credit data, asset data, consumption data and business circle data, and the data indicator ranges corresponding to different income levels, credit levels, asset levels, consumption levels and business circle levels;

[0110] Step 603, according to the risk weights corresponding to different income levels, credit levels, asset levels, consumption levels and business circle levels, the target risk weights corresponding to the income level, credit level, asset level, consumption level and business circle level corresponding to the target business object are obtained;

[0111] Step 604, cumulatively summing the target risk weights corresponding to the income level, credit level, asset level, consumption level and business circle level of the target business object, and obtaining the cumulative sum value as the risk value of the target business object.

[0112] A pre-trained risk identification model is used to identify the income level, credit level, asset level, consumption level and business circle level corresponding to the target business object, so as to more accurately perform business risk control in combination with the income level, credit level, asset level, consumption level and business circle level, thereby improving the accuracy of risk identification.

[0113] In this embodiment, the step of generating a business conclusion based on the risk value and outputting the business conclusion through the risk identification model specifically includes: judging whether the risk value exceeds a preset risk threshold by comparison; if the risk value exceeds the preset risk threshold, generating a conclusion that the business cannot be processed for the target business object in combination with a preset message generation task; if the risk value does not exceed the preset risk threshold, generating a conclusion that the business can be processed for the target business object in combination with the preset message generation task.

[0114] Business conclusions are generated in combination with the risk value, and the business conclusions are output through the risk identification model, which realizes the input of risk verification data into the model, and the model directly outputs the business conclusions, so as to more directly control business risk and avoid the company from suffering major business losses.

[0115] The present application obtains the identity information provided by the target business object; obtains multi-dimensional risk verification data based on the identity information; verifies the input structure of the risk verification data to obtain structured data consistent with the input requirements of the pre-trained risk identification model; inputs the structured data into the pre-trained risk identification model to identify the risk value of the target business object; generates business conclusions based on the risk value, and outputs the business conclusions through the risk identification model. The risk identification method described in the present application, especially in the financial insurance policy underwriting business, before model identification, verifies the input structure of the complicated risk verification data in advance, so that the data identified during model identification is structured data, which fully improves the efficiency of insurance policy underwriting, more quickly identifies business risks, and improves the accuracy of insurance policy underwriting in combination with the risk value.

[0116] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0117] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0118] In an embodiment of the present application, identity information provided by the target business object is obtained; based on the identity information, multi-dimensional risk verification data is obtained; the risk verification data is verified and inputted into a structure to obtain structured data consistent with the input requirements of a pre-trained risk identification model; the structured data is inputted into a pre-trained risk identification model to identify the risk value of the target business object; a business conclusion is generated based on the risk value, and the business conclusion is outputted through the risk identification model. The risk identification method described in the present application, especially in the financial insurance policy underwriting business, before model identification, verifies the input structure of the complicated risk verification data in advance, so that the data identified during model identification is structured data, which fully improves the efficiency of insurance policy underwriting, more quickly identifies business risks, and improves the accuracy of insurance policy underwriting in combination with the risk value.

[0119] Further references Figure 7 , as a response to the above Figure 2 In order to realize the method shown in the figure, the present application provides an embodiment of a risk identification device, which is similar to Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0120] like Figure 7 As shown, the risk identification device 700 described in this embodiment includes: an identity information acquisition module 701, a risk verification data acquisition module 702, a structured processing module 703, a risk value identification module 704 and a business conclusion generation and output module 705. Among them:

[0121] The identity information acquisition module 701 is used to acquire the identity information provided by the target business object;

[0122] The risk verification data acquisition module 702 is used to obtain multi-dimensional risk verification data from N authoritative data sources according to the identity information, where N is a positive integer;

[0123] The structured processing module 703 is used to organize the risk verification input structure of the risk verification data to obtain structured data consistent with the input requirements of the pre-trained risk identification model;

[0124] A risk value identification module 704 is used to input the structured data into the pre-trained risk identification model to identify the risk value of the target business object;

[0125] The business conclusion generation and output module 705 is used to generate a business conclusion based on the risk value and output the business conclusion through the risk identification model.

[0126] The present application obtains the identity information provided by the target business object; obtains multi-dimensional risk verification data based on the identity information; verifies the input structure of the risk verification data to obtain structured data consistent with the input requirements of the pre-trained risk identification model; inputs the structured data into the pre-trained risk identification model to identify the risk value of the target business object; generates business conclusions based on the risk value, and outputs the business conclusions through the risk identification model. The risk identification method described in the present application, especially in the financial insurance policy underwriting business, before model identification, verifies the input structure of the complicated risk verification data in advance, so that the data identified during model identification is structured data, which fully improves the efficiency of insurance policy underwriting, more quickly identifies business risks, and improves the accuracy of insurance policy underwriting in combination with the risk value.

[0127] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0128] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0129] To solve the above technical problems, the present application also provides a computer device. Figure 8 , Figure 8 This is a basic structural block diagram of the computer device in this embodiment.

[0130] The computer device 8 includes a memory 8a, a processor 8b, and a network interface 8c which are interconnected through a system bus. It should be noted that: Figure 8 Only a computer device 8 having a component memory 8a, a processor 8b, and a network interface 8c is shown, but it should be understood that it is not required to implement all the components shown, and more or fewer components may be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to a microprocessor, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital processor (DSP), an embedded device, etc.

[0131] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0132] The memory 8a includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, 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, disk, optical disk, etc. In some embodiments, the memory 8a can be an internal storage unit of the computer device 8, such as a hard disk or memory of the computer device 8. In other embodiments, the memory 8a can also be an external storage device of the computer device 8, such as a plug-in hard disk, smart memory card (Smart Media Card, SMC), secure digital (Secure Digital, SD) card, flash card (FlashCard), etc. equipped on the computer device 8. Of course, the memory 8a can also include both the internal storage unit of the computer device 8 and its external storage device. In this embodiment, the memory 8a is generally used to store the operating system and various application software installed on the computer device 8, such as a computer-readable instruction of a risk identification method, etc. In addition, the memory 8a can also be used to temporarily store various types of data that have been output or are to be output.

[0133] The processor 8b may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip in some embodiments. The processor 8b is generally used to control the overall operation of the computer device 8. In this embodiment, the processor 8b is used to run computer-readable instructions stored in the memory 8a or process data, such as computer-readable instructions for running the risk identification method.

[0134] The network interface 8c may include a wireless network interface or a wired network interface. The network interface 8c is generally used to establish a communication connection between the computer device 8 and other electronic devices.

[0135] The computer device proposed in this embodiment belongs to the field of artificial intelligence technology and is applied to business risk identification scenarios. This application obtains the identity information provided by the target business object; obtains multi-dimensional risk verification data based on the identity information; verifies the input structure of the risk verification data to obtain structured data consistent with the input requirements of the pre-trained risk identification model; inputs the structured data into the pre-trained risk identification model to identify the risk value of the target business object; generates business conclusions based on the risk value, and outputs the business conclusions through the risk identification model. The risk identification method described in this application, especially in the financial insurance policy underwriting business, before model identification, verifies the input structure of the complicated risk verification data in advance, so that the data identified during model identification is structured data, which fully improves the efficiency of insurance policy underwriting, more quickly identifies business risks, and improves the accuracy of insurance policy underwriting in combination with the risk value.

[0136] The present application also provides another implementation, namely, providing a computer-readable storage medium, wherein the computer-readable storage medium stores computer-readable instructions, and the computer-readable instructions can be executed by a processor to enable the processor to perform the steps of the risk identification method as described above.

[0137] The computer-readable storage medium proposed in this embodiment belongs to the field of artificial intelligence technology and is applied to business risk identification scenarios. This application obtains the identity information provided by the target business object; obtains multi-dimensional risk verification data based on the identity information; verifies the input structure of the risk verification data to obtain structured data consistent with the input requirements of the pre-trained risk identification model; inputs the structured data into the pre-trained risk identification model to identify the risk value of the target business object; generates business conclusions based on the risk value, and outputs the business conclusions through the risk identification model. The risk identification method described in this application, especially in the financial insurance policy underwriting business, before model identification, verifies the input structure of the complicated risk verification data in advance, so that the data identified during model identification is structured data, which fully improves the efficiency of insurance policy underwriting, more quickly identifies business risks, and improves the accuracy of insurance policy underwriting in combination with the risk value.

[0138] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0139] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A risk identification method, characterized in that: The steps include: Obtain identity information provided by the target business object; Obtain multi-dimensional risk verification data from N authoritative data sources according to the identity information, where N is a positive integer; Structuring the input of the risk verification data to obtain structured data consistent with the input requirements of the pre-trained risk identification model; Inputting the structured data into the pre-trained risk identification model to identify the risk value of the target business object; A business conclusion is generated based on the risk value, and the business conclusion is output through the risk identification model.

2. The risk identification method according to claim 1, characterized in that: The step of obtaining multi-dimensional risk verification data from N authoritative data sources according to the identity information specifically includes: Using the identity information as the search field, the income data, credit data, asset data, consumption data and business circle data of the target business object are obtained from the N authoritative data sources, wherein the business circle data refers to the income data, credit data, asset data, consumption data of an object having a direct business relationship with the target business object, and N is a positive integer.

3. The risk identification method according to claim 1, characterized in that: Before executing the step of arranging the risk verification input structure of the risk verification data to obtain structured data consistent with the input requirements of the pre-trained risk identification model, the method further includes: According to the preset risk identification correlation strength form, the income data, credit data, asset data, consumption data and business circle data of the target business object are cleaned, and the data with weak correlation is deleted, and only the data with strong correlation is retained as the updated risk verification data; The step of arranging the verification input structure of the risk verification data to obtain structured data consistent with the input requirements of the pre-trained risk identification model specifically includes: Identify the input data format requirements specified by the pre-trained risk identification model; Based on the input data format requirement, the updated risk verification data is structured to obtain a structured arrangement result; The structured arrangement results are used as structured data consistent with the input requirements of the pre-trained risk identification model.

4. The risk identification method according to claim 1, characterized in that: Before executing the step of inputting the structured data into the pre-trained risk identification model to identify the risk value of the target business object, the method includes: Obtaining a risk identification document issued by a target risk control agency, wherein the risk identification document contains all key data of concern when conducting risk identification; Structuring all the data in the risk identification file and extracting key risk identification data; Building a risk identification strategy based on the risk identification key data; The risk identification strategy is deployed into a pre-built risk identification model to obtain a pre-built risk identification model.

5. The risk identification method according to claim 4, characterized in that: Before executing the step of inputting the structured data into the pre-trained risk identification model to identify the risk value of the target business object, the method further includes: Adopting a big data acquisition method, obtaining multi-dimensional risk training data from the N authoritative data sources, wherein the multi-dimensional risk training data includes income level training data, credit level training data, asset level training data, consumption level training data and business circle level training data, and N is a positive integer; Identifying input data format requirements specified by the pre-built risk identification model; Based on the input data format requirement, the multi-dimensional risk training data is structured to obtain a structured arrangement result; The structured organization result is input into the pre-built risk identification model, and the pre-built risk identification model is trained for risk identification in combination with the risk identification strategy to obtain the data indicator ranges corresponding to different income levels, credit levels, asset levels, consumption levels and business circle levels, and obtain the pre-trained risk identification model.

6. The risk identification method according to claim 5, characterized in that: The step of inputting the structured data into the pre-trained risk identification model to identify the risk value of the target business object specifically includes: Parsing the structured data to extract income data, credit data, asset data, consumption data, and business circle data; According to the income data, credit data, asset data, consumption data and business circle data, and the data indicator ranges corresponding to different income levels, credit levels, asset levels, consumption levels and business circle levels, the income level, credit level, asset level, consumption level and business circle level corresponding to the target business object are identified; According to the risk weights corresponding to different income levels, credit levels, asset levels, consumption levels and business circle levels, the target risk weights corresponding to the income level, credit level, asset level, consumption level and business circle level corresponding to the target business object are obtained; The target risk weights corresponding to the income level, credit level, asset level, consumption level and business circle level of the target business object are respectively added up and summed to obtain the accumulated sum value as the risk value of the target business object.

7. The risk identification method according to claim 1 or 6, characterized in that: The step of generating a business conclusion based on the risk value and outputting the business conclusion through the risk identification model specifically includes: By comparison, determining whether the risk value exceeds a preset risk threshold; If the risk value exceeds a preset risk threshold, a conclusion is generated that the target business object cannot be processed in combination with a preset message generation task; If the risk value does not exceed a preset risk threshold, a conclusion that business can be handled for the target business object is generated in combination with a preset message generation task.

8. A risk identification device, characterized in that: include: An identity information acquisition module is used to obtain identity information provided by a target business object; A risk verification data acquisition module, used to obtain multi-dimensional risk verification data from N authoritative data sources according to the identity information, where N is a positive integer; A structured processing module, used to organize the risk verification data into a verification input structure to obtain structured data consistent with the input requirements of the pre-trained risk identification model; A risk value identification module, used to input the structured data into the pre-trained risk identification model to identify the risk value of the target business object; A business conclusion generation output module is used to generate a business conclusion based on the risk value and output the business conclusion through the risk identification model.

9. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the risk identification method according to any one of claims 1 to 7 when executing the computer-readable instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by a processor, the steps of the risk identification method according to any one of claims 1 to 7 are implemented.

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