Business risk assessment method, device and equipment and storage medium thereof

Abstract

The invention belongs to the technical field of service early warning, and relates to a service risk assessment method, device and equipment and a storage medium thereof.The method comprises the steps that a risk assessment message is obtained and analyzed, and identification information and risk data cache information of a target risk user are obtained; performing differentiated marking processing on the risk data corresponding to the target risk user; a risk task processing component is called in combination with the distinguishing marking result, and risk task processing is carried out; according to the risk task processing result and the cache information, updating risk data corresponding to the target risk user to obtain latest storage data; and re-evaluating the latest storage data according to a preset evaluation strategy to obtain a latest risk evaluation message, and pushing the latest risk evaluation message to all target receiving ends. The risk level of the user can be continuously concerned in an integrated, real-time and dynamic manner, and financial online services or business providers can reasonably avoid business risks according to the risk level change of the user.

Description

Technical Field

[0001] The present application relates to the field of business early warning technology, and is applied to business risk assessment scenarios during online financial business processing, and relates to a business risk assessment method, apparatus, device, and storage medium thereof. Background Art

[0002] Risk assessment and handling are the most effective ways to reduce risks. Most enterprise risks are not explosive, but phased and predictable.

[0003] Under the conditions of big data, each business monitoring system of a financial enterprise will generate tens of thousands of data. Although there are many traditional logs or other professional tools that can help enterprises analyze this data, the current analysis still remains in the form of separate, simple charts and simple list queries. The association of these data, the extraction of key data, and the establishment of data association relationships all rely on manual work. It is impossible to continuously monitor and evaluate the user's risk level in a comprehensive, real-time, and dynamic manner, which is not conducive to the reasonable risk avoidance of related financial businesses. Summary of the Invention

[0004] The purpose of the embodiments of the present application is to propose a business risk assessment method, device, equipment and storage medium thereof to solve the problem that the existing technology is unable to comprehensively, real-timely and dynamically monitor the user's risk level and conduct continuous assessment of the user's risk level in financial business risk assessment, which is not conducive to the reasonable risk avoidance of related financial businesses.

[0005] In a first aspect, the embodiments of the present application provide a business risk assessment method, which adopts the following technical solution:

[0006] A business risk assessment method comprises the following steps:

[0007] Obtaining a risk assessment message, wherein the risk assessment message is a risk assessment message given for the data stored in the multi-source cache library;

[0008] Parsing the risk assessment message to obtain identification information of the target risk user and risk data cache information, wherein the cache information includes cache library address information, cache library name information, cache table name information, and row and column position information of the risk data in the cache table;

[0009] Performing differential tokenization processing on the risk data corresponding to the target risk user according to the cached information to obtain a differential tokenization result;

[0010] In combination with the distinguishing marking result, calling a preset risk task processing component to process the risk task and obtain a risk task processing result;

[0011] updating the risk data corresponding to the target risk user according to the risk task processing result and the cache information, and obtaining the latest stored data at the corresponding position of the cache information after the update;

[0012] The latest stored data is re-evaluated according to a preset evaluation strategy to obtain a latest risk assessment message, and the latest risk assessment message is pushed to all target receiving ends.

[0013] In a second aspect, the embodiments of the present application further provide a business risk assessment device, which adopts the following technical solution:

[0014] A business risk assessment device, comprising:

[0015] A risk assessment message acquisition module, configured to acquire a risk assessment message, wherein the risk assessment message is a risk assessment message given for the data stored in the multi-source cache library;

[0016] A risk assessment message parsing module is used to parse the risk assessment message to obtain identification information of the target risk user and risk data cache information, wherein the cache information includes cache library address information, cache library name information, cache table name information, and row and column position information of the risk data in the cache table;

[0017] A risk data distinguishing marking module is used to perform distinguishing marking processing on the risk data corresponding to the target risk user according to the cached information to obtain a distinguishing marking result;

[0018] A risk task processing module is used to call a preset risk task processing component based on the distinguishing mark result, perform risk task processing, and obtain a risk task processing result;

[0019] A risk data updating module is used to update the risk data corresponding to the target risk user according to the risk task processing result and the cache information, and obtain the latest stored data at the corresponding position of the cache information after the update;

[0020] The updated data re-evaluation module is used to re-evaluate the latest stored data according to a preset evaluation strategy, obtain the latest risk assessment message, and push the latest risk assessment message to all target receiving ends.

[0021] In a third aspect, an embodiment of the present application further provides a computer device that adopts the following technical solution:

[0022] A computer device includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the business risk assessment method described above when executing the computer-readable instructions.

[0023] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0024] A computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the business risk assessment method as described above.

[0025] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0026] The business risk assessment method described in the embodiment of the present application obtains a risk assessment message; parses the risk assessment message to obtain the identification information and risk data cache information of the target risk user; performs differential marking processing on the risk data corresponding to the target risk user based on the cache information to obtain a differential marking result; calls a preset risk task processing component in combination with the differential marking result to perform risk task processing to obtain a risk task processing result; updates the risk data corresponding to the target risk user based on the risk task processing result and the cache information to obtain the latest stored data at the corresponding position of the cache information after the update; re-evaluates the latest stored data according to the preset assessment strategy to obtain the latest risk assessment message, and pushes the latest risk assessment message to all target receiving terminals. It can continuously monitor the risk level of users in a comprehensive, real-time and dynamic manner, which helps financial online services or business providers to reasonably avoid business risks based on changes in user risk levels. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the solutions in this application, a brief introduction will be given below to the drawings required for use in the description of the embodiments of this application. Obviously, the drawings described below are some embodiments of this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0028] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0029] Figure 2 is a flow chart of an embodiment of a business risk assessment method according to the present application;

[0030] Figure 3 yes Figure 2A flowchart of a specific embodiment of step 202 is shown;

[0031] Figure 4 yes Figure 2 A flowchart of a specific embodiment of step 203 is shown;

[0032] Figure 5 yes Figure 2 A flowchart of a specific embodiment of step 204 is shown;

[0033] Figure 6 This is a schematic structural diagram of an embodiment of a business risk assessment device according to the present application;

[0034] Figure 7 It is a structural diagram of an embodiment of a computer device according to the present application. DETAILED DESCRIPTION

[0035] Unless otherwise defined, all technical and scientific terms used herein have the same meanings as commonly understood by those skilled in the art to which this application belongs. The terms used in the specification of the application are for the purpose of describing specific embodiments only and are not intended to limit this application. The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of this application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0036] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0037] 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.

[0038] like Figure 1 As shown, system architecture 100 may include a terminal device 101, a network 102, and a server 103. Terminal device 101 may be a laptop computer 1011, a tablet computer 1012, or a mobile phone 1013. Network 102 is a medium for providing a communication link between terminal device 101 and server 103. Network 102 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0039] The user can use the terminal device 101 to interact with the server 103 via 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.

[0040] The terminal device 101 can be various electronic devices with a display screen and supporting web browsing. In addition to the laptop computer 1011, tablet computer 1012 or 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) player, a laptop computer and a desktop computer, etc.

[0041] The server 103 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal device 101 .

[0042] It should be noted that the business risk assessment method provided in the embodiment of the present application is generally executed by a server, and accordingly, a business risk assessment device is generally set in the server.

[0043] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0044] Continue to refer Figure 2 , shows a flow chart of an embodiment of a business risk assessment method according to the present application. The business risk assessment method includes the following steps:

[0045] Step 201: Obtain a risk assessment message, wherein the risk assessment message is a risk assessment message given for data stored in a multi-source cache library.

[0046] In this embodiment, the risk assessment message specifically refers to a risk assessment message for each customer generated based on transaction data stored in multiple cache repositories in a multi-source transaction system. Specifically, the multiple cache repositories can be multiple cache platforms, multiple cache databases, etc.

[0047] By generating the risk assessment message, different risk levels can be assigned to different transaction users during financial transactions, especially banking transactions and transfer transactions, so as to facilitate the subsequent reasonable allocation of risk monitoring resources for users with different risk levels.

[0048] Step 202: parse the risk assessment message to obtain identification information of the target risk user and risk data cache information.

[0049] The cache information includes cache library address information, cache library name information, cache table name information, and row and column position information of risk data in the cache table.

[0050] In this embodiment, the identification information of the target risk user, in addition to basic identity differentiation identification information, may also include risk level differentiation identification information pre-specified for risk level differentiation. For example, the risk level is divided into five levels, namely: high risk, medium-high risk, medium risk, medium-low risk and low risk.

[0051] By parsing the risk assessment message, the risk level identification information and risk data cache information of the target risk user are obtained, so that the subsequent risk task processing component can perform targeted processing on the risk data.

[0052] Step 203: Based on the cache information, the risk data corresponding to the target risk user is subjected to a distinguishing tokenization process to obtain a distinguishing token result.

[0053] In this embodiment, the risk data corresponding to the target risk user is differentiated and marked based on the cache information to obtain a differentiated marking result. More emphasis is placed on distinguishing the cache libraries that process the risk data for different risk users, so as to facilitate subsequent cross-cache library processing of the risk data.

[0054] Step 204: Based on the distinguishing mark result, a preset risk task processing component is called to process the risk task and obtain a risk task processing result.

[0055] Specifically, the preset risk task processing component pre-sets risk users with different risk levels, as well as risk processing strategies corresponding to the risk data corresponding to risk users with different risk levels. For example, different risk level users correspond to different re-risk assessment cycles, or different risk level users correspond to different risk processing methods, etc.

[0056] By combining the distinguishing marking result, calling the preset risk task processing component, performing risk task processing, and obtaining the risk task processing result, the risk task processing component is utilized to perform risk data processing in combination with the cache information corresponding to the risk data in the distinguishing marking result.

[0057] Step 205: updating the risk data corresponding to the target risk user according to the risk task processing result and the cache information, and obtaining the latest stored data at the corresponding position of the cache information after the update.

[0058] Specifically, after obtaining the risk task processing result, the latest data after risk task processing is updated and stored in the cache information according to the cache information where the risk data was located before risk processing, thereby obtaining the latest stored data at the corresponding position of the cache information.

[0059] Step 206: re-evaluate the latest stored data according to a preset evaluation strategy to obtain a latest risk assessment message, and push the latest risk assessment message to all target receiving terminals.

[0060] Specifically, after a risk data processing is performed, the latest stored data is re-evaluated according to a preset evaluation strategy to obtain the latest risk assessment message, so as to dynamically update the risk status of the target user, which helps financial online services or business providers to reasonably avoid business risks based on changes in the user's risk level.

[0061] In this embodiment, the business risk assessment method is applied to the business risk assessment scenario during financial online business processing, which not only realizes the identification of user risk levels across multiple source cache libraries, but also realizes the risk data processing across multiple source cache libraries. In addition, combined with the processing results of the risk task processing component, risk assessment is performed again, which can continuously monitor the user's risk level in real time and dynamically, and help financial online service or business providers to reasonably avoid business risks based on changes in user risk levels.

[0062] In this embodiment, a risk assessment message is obtained; the risk assessment message is parsed to obtain the identification information and risk data cache information of the target risk user; based on the cache information, the risk data corresponding to the target risk user is differentiated and marked to obtain a differentiated marking result; in combination with the differentiated marking result, a preset risk task processing component is called to perform risk task processing to obtain a risk task processing result; based on the risk task processing result and the cache information, the risk data corresponding to the target risk user is updated to obtain the latest stored data at the corresponding position of the cache information after the update; the latest stored data is re-evaluated according to the preset evaluation strategy to obtain the latest risk assessment message, and the latest risk assessment message is pushed to all target receiving terminals. The ability to continuously monitor the risk level of users in a comprehensive, real-time and dynamic manner helps financial online services or business providers to reasonably avoid business risks based on changes in the risk level of users.

[0063] Continue to refer Figure 3 , Figure 3 yes Figure 2 The flowchart of a specific embodiment of step 202 shown includes the following steps:

[0064] Step 301: Identify users whose risk levels reach a preset first risk level as target risk users by parsing the risk assessment message, wherein the identification information includes risk level information;

[0065] Specifically, by parsing the risk assessment message, the risk level information corresponding to all users is obtained, and then combined with the preset first risk level, all users whose risk levels reach the preset first risk level are identified as the target risk users. For example, the preset first risk level is a high risk level.

[0066] Step 302: parsing the risk assessment message, comparing the parsed result of the current risk assessment message with the parsed results of N previous risk assessment messages, and identifying users whose risk levels have reached a preset second risk level for N+1 consecutive times as target risk users, where N is a positive integer and the preset first risk level is higher than the preset second risk level;

[0067] Specifically, after parsing the risk assessment message and obtaining the risk level information corresponding to all users, for users whose risk level does not reach the preset first risk level, the second target risk user identification method is adopted, that is, the parsing result of this risk assessment message is compared with the parsing results of N previous risk assessment messages, and users whose risk level reaches the preset second risk level for N+1 consecutive times are identified as the target risk users. For example: the preset second risk level is a medium-high risk level, which ensures that high-risk level users and long-term medium-high risk level users in the multi-source cache library are fully identified, which is convenient for the company to rationalize and avoid business risks in the future.

[0068] Step 303: Acquire the identification information and risk data cache information of the target risk user, and construct an association mapping relationship between the risk user identification information and the risk data cache information.

[0069] Specifically, the risk data includes multiple business data related to the target risk user in specific financial business, such as financial transaction data, loan application data, etc. It can be understood that after the user is identified as the target risk user, the relevant business data or associated data retained in the enterprise's cache are all identified as risk data. By parsing the risk assessment message, the cache information of the risk data is also obtained, and by constructing an association mapping relationship between the risk user identification information and the risk data cache information, the enterprise can subsequently continue to conduct risk level assessments and risk data processing on the target risk user, realize comprehensive, real-time, and dynamic continuous monitoring of the user's risk level and conduct continuous assessment of the user's risk level, and assist the enterprise in reasonably avoiding business risks.

[0070] In this embodiment, after executing the step of parsing the risk assessment message to obtain the identification information and risk data cache information of the target risk user, the method also includes: if the risk level identification information of a user is a null value, marking the user as a new user; performing an initial risk level assessment on the new user according to the preset new user risk assessment cycle and the risk data cache information corresponding to the new user.

[0071] Specifically, if the risk level identification information of the target risk user is a null value, the user is marked as a new user, that is, the risk level assessment of the target risk user has not been performed yet. Therefore, the risk level of the new user can be initially assessed based on the preset new user risk assessment cycle and the risk data cache information corresponding to the new user. For example, the preset new user risk assessment cycle is to complete the first risk level assessment within 10 days after the user registers for the service.

[0072] Continue to refer Figure 4 , Figure 4 yes Figure 2 The flowchart of a specific embodiment of step 203 shown includes the following steps:

[0073] Step 401: Identify the cache address information, cache name information, cache table name information, and row and column position information of each risk data in the corresponding cache table through the cache information.

[0074] Step 402: Perform preliminary distinguishing and marking processing on the target risk data based on the cache address information, cache name information, cache table name information, and row and column position information of each risk data in the corresponding cache table to obtain a preliminary marking result;

[0075] Specifically, when performing preliminary distinguishing mark processing, the cache address information, cache name information, cache table name information, and row and column position information of each risk data in the corresponding cache table can be directly set as the preliminary marking result;

[0076] Step 403: Perform a distinguishing mark supplement process on the target risk data according to the risk level of the risk user corresponding to each risk data to obtain a supplementary mark result;

[0077] Specifically, when performing the distinguishing mark supplement processing, the risk level of the risk user corresponding to each risk data can be directly set as the supplementary mark result;

[0078] Step 404: Generate a distinguishing marking result corresponding to each risk data based on the preliminary marking result and the supplementary marking result.

[0079] Specifically, the distinguishing marking result may be generated by splicing the preliminary marking result and the supplementary marking result.

[0080] In this embodiment, a distinguishing mark result corresponding to each risk data is generated. The distinguishing mark result includes both the cache information of the risk data and the risk level information of the risk data, so that the subsequent risk task processing component can automatically obtain the risk data based on the cache information of the risk data, and the risk task processing component can perform risk processing and assessment based on the risk level information of the risk data.

[0081] In this embodiment, the preset risk task processing components include a pre-analysis component, a pre-acquisition component, a classification processing component, and a risk processing component.

[0082] Continue to refer Figure 5 , Figure 5 yes Figure 2 The flowchart of a specific embodiment of step 204 shown includes the following steps:

[0083] Step 501: Send the distinguishing mark result to the risk task processing component;

[0084] Step 502: parse the cached information of the risk data corresponding to all the distinguishing mark results and the risk level of the risk user corresponding to the risk data through the pre-analysis component;

[0085] Step 503: Using the cache information as an acquisition parameter of the pre-acquisition component, acquire the risk data corresponding to all the distinguishing marking results;

[0086] Step 504: using the classification processing component, classify the risk data according to the risk level of the corresponding risk user to obtain a classification processing result;

[0087] Step 505: Using the risk processing component, the risk data of different risk levels in the classification processing results are differentiated using a preset risk processing differentiation strategy, wherein the preset risk processing differentiation strategy includes adopting different risk processing measures to process the risk data corresponding to risk users of different risk levels.

[0088] In this embodiment, the preset evaluation strategy includes hierarchical evaluation and time-based evaluation. Specifically, the hierarchical evaluation includes evaluating users of different risk levels separately, and the time-based evaluation includes periodically evaluating users of different risk levels according to different evaluation cycles.

[0089] In this embodiment, the step of re-evaluating the latest stored data according to a preset evaluation strategy to obtain the latest risk assessment message includes: identifying the current risk level of the risk user corresponding to the latest stored data; based on the current risk level, screening different evaluation cycles and evaluation methods to perform risk re-evaluation to obtain the latest risk assessment message, wherein the higher the risk level, the shorter the corresponding evaluation cycle, and different risk levels correspond to different evaluation methods.

[0090] For example, risk levels are divided into five levels: high risk, medium-high risk, medium risk, medium-low risk, and low risk. The higher the risk level, the shorter the corresponding assessment period. Specifically, after a high-risk customer is last rated, it needs to be rated again after an interval of T1 months; after a medium-high risk customer is last rated, it needs to be rated again after an interval of T2 months; after a medium-risk customer is last rated, it needs to be rated again after an interval of T3 months, and so on. T1, T2, and T3 are values ​​greater than 0, and T1>T2>T3.

[0091] In this embodiment, the step of re-evaluating the latest stored data according to a preset evaluation strategy to obtain the latest risk assessment message further includes: determining whether the current risk level of the risk user corresponding to the latest stored data exceeds a preset risk level threshold; if the current risk level of the risk user corresponding to the latest stored data is higher than the preset risk level threshold, performing risk level evaluation on the risk user in a continuous evaluation mode until the latest current risk level does not exceed the risk level threshold, then switching to an irregular evaluation mode, wherein the continuous evaluation mode includes continuously evaluating the risk level of the risk user according to an evaluation period corresponding to the current risk level, continuously obtaining the latest current risk level; if the latest current risk level still exceeds the risk level threshold, continuing to evaluate the risk level of the risk user according to the evaluation period corresponding to the latest current risk level, and repeating this process; if the current risk level of the risk user corresponding to the latest stored data is not higher than the preset risk level threshold, performing risk level trigger evaluation on the risk user in an irregular evaluation mode, wherein the irregular evaluation mode includes freely performing risk level evaluation according to a preset trigger instruction.

[0092] Specifically, if the risk level threshold is a medium risk level, the current risk level of the risk user corresponding to the latest stored data is a high risk level, which obviously exceeds the medium risk level; at this time, a continuous grading mode is used to perform risk level assessment on the risk user, that is, the risk level assessment is continuously performed on the risk user according to the assessment period (T1) corresponding to the current risk level (high risk level); if after re-assessment, the current risk level of the risk user corresponding to the latest stored data changes to a medium-high risk level, the risk level assessment is continued on the risk user according to the assessment period (T2) corresponding to the current risk level (medium-high risk level) until the current risk level of the risk user corresponding to the latest stored data changes to a medium risk level, and an irregular grading mode is used to perform risk level trigger assessment on the risk user; specifically, the irregular mode, for example, is to freely perform risk level assessment according to the actual operating instructions of the assessor.

[0093] In this embodiment, after executing the step of pushing the latest risk assessment message to all target receiving ends, the method also includes: obtaining the latest risk assessment message; parsing the latest risk assessment message to obtain the latest risk user identification information and risk data cache information; and updating the distinction marking result based on the latest risk user identification information and risk data cache information.

[0094] By obtaining the latest risk assessment message; parsing the latest risk assessment message to obtain the latest risk user identification information and risk data cache information; updating the distinguishing mark result based on the latest risk user identification information and risk data cache information, so that the enterprise can continue to conduct risk level assessment and risk data processing on the target risk users in the future, realize comprehensive, real-time and dynamic continuous attention to the user's risk level and continuous assessment of the user's risk level, and assist the enterprise to reasonably avoid business risks.

[0095] In this embodiment, a risk assessment message is obtained; the risk assessment message is parsed to obtain the identification information and risk data cache information of the target risk user; based on the cache information, the risk data corresponding to the target risk user is differentiated and marked to obtain a differentiated marking result; in combination with the differentiated marking result, a preset risk task processing component is called to perform risk task processing to obtain a risk task processing result; based on the risk task processing result and the cache information, the risk data corresponding to the target risk user is updated to obtain the latest stored data at the corresponding position of the cache information after the update; the latest stored data is re-evaluated according to the preset evaluation strategy to obtain the latest risk assessment message, and the latest risk assessment message is pushed to all target receiving terminals. The ability to continuously monitor the risk level of users in a comprehensive, real-time and dynamic manner helps financial online services or business providers to reasonably avoid business risks based on changes in the risk level of users.

[0096] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. 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 achieve optimal results.

[0097] Fundamental AI technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interaction systems, and mechatronics. AI software technologies primarily encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0098] In this embodiment, a risk assessment message is obtained; the risk assessment message is parsed to obtain the identification information and risk data cache information of the target risk user; based on the cache information, the risk data corresponding to the target risk user is differentiated and marked to obtain a differentiated marking result; in combination with the differentiated marking result, a preset risk task processing component is called to perform risk task processing to obtain a risk task processing result; based on the risk task processing result and the cache information, the risk data corresponding to the target risk user is updated to obtain the latest stored data at the corresponding position of the cache information after the update; the latest stored data is re-evaluated according to the preset evaluation strategy to obtain the latest risk assessment message, and the latest risk assessment message is pushed to all target receiving terminals. The ability to continuously monitor the risk level of users in a comprehensive, real-time and dynamic manner helps financial online services or business providers to reasonably avoid business risks based on changes in the risk level of users.

[0099] Further references Figure 6 , 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 business risk assessment device. Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0100] like Figure 6 As shown, the business risk assessment device 600 of this embodiment includes: a risk assessment message acquisition module 601, a risk assessment message parsing module 602, a risk data distinguishing marking module 603, a risk task processing module 604, a risk data updating module 605 and an updated data re-evaluation module 606.

[0101] The risk assessment message acquisition module 601 is used to acquire a risk assessment message, wherein the risk assessment message is a risk assessment message given for the data stored in the multi-source cache library;

[0102] The risk assessment message parsing module 602 is configured to parse the risk assessment message to obtain identification information of the target risk user and risk data cache information, wherein the cache information includes cache address information, cache library name information, cache table name information, and row and column position information of the risk data in the cache table;

[0103] The risk data distinguishing marking module 603 is used to perform distinguishing marking processing on the risk data corresponding to the target risk user according to the cached information to obtain a distinguishing marking result;

[0104] The risk task processing module 604 is configured to call a preset risk task processing component based on the distinguishing mark result, perform risk task processing, and obtain a risk task processing result;

[0105] The risk data updating module 605 is configured to update the risk data corresponding to the target risk user according to the risk task processing result and the cache information, and obtain the latest stored data at the corresponding position of the cache information after the update;

[0106] The updated data re-evaluation module 606 is configured to re-evaluate the latest stored data according to a preset evaluation strategy, obtain the latest risk assessment message, and push the latest risk assessment message to all target receiving terminals.

[0107] This application obtains a risk assessment message; parses the risk assessment message to obtain the identification information and risk data cache information of the target risk user; performs differential marking processing on the risk data corresponding to the target risk user based on the cache information to obtain a differential marking result; calls a preset risk task processing component in combination with the differential marking result to perform risk task processing to obtain a risk task processing result; updates the risk data corresponding to the target risk user based on the risk task processing result and the cache information to obtain the latest stored data at the corresponding position of the cache information after the update; re-evaluates the latest stored data according to the preset assessment strategy to obtain the latest risk assessment message, and pushes the latest risk assessment message to all target receiving terminals. It can continuously monitor the risk level of users in a comprehensive, real-time and dynamic manner, which helps financial online services or business providers to reasonably avoid business risks based on changes in user risk levels.

[0108] In this embodiment, the risk assessment message parsing module 602 includes a first target risk user identification unit, a second target risk user identification unit, and an association mapping construction processing unit.

[0109] a first target risk user identification unit, configured to identify a user whose risk level reaches a preset first risk level as the target risk user by parsing the risk assessment message, wherein the identification information includes risk level information; and

[0110] a second target risk user identification unit, configured to parse the risk assessment message, compare the parsing result of the current risk assessment message with the parsing results of N previous risk assessment messages, and identify a user whose risk level reaches a preset second risk level for N+1 consecutive times as the target risk user, where N is a positive integer and the preset first risk level is higher than the preset second risk level;

[0111] The association mapping construction processing unit is configured to obtain the identification information of the target risk user and the risk data cache information, and to construct an association mapping relationship between the risk user identification information and the risk data cache information.

[0112] In this embodiment, the service risk assessment device 600 further includes a new user identification module and a new user initial assessment module.

[0113] A new user identification module, configured to mark a user as a new user if the risk level identification information of the user is null;

[0114] The new user initial assessment module is used to perform an initial risk level assessment on the new user according to a preset new user risk assessment cycle and the risk data cache information corresponding to the new user.

[0115] In this embodiment, the risk data distinguishing mark module 603 includes a cache information identification unit, a preliminary mark processing unit, a supplementary mark processing unit and a distinguishing mark generating unit.

[0116] A cache information identification unit is used to identify the cache address information, cache library name information, cache table name information and row and column position information of each risk data in the corresponding cache table through the cache information;

[0117] A preliminary marking processing unit is used to perform preliminary distinguishing marking processing on the target risk data according to the cache address information, cache library name information, cache table name information and row and column position information of each risk data in the corresponding cache table, and obtain preliminary marking results;

[0118] A supplementary marking processing unit is used to perform a distinguishing mark supplementary processing on the target risk data according to the risk level of the risk user corresponding to each risk data to obtain a supplementary marking result;

[0119] The distinguishing mark generating unit is used to generate a distinguishing mark result corresponding to each risk data based on the preliminary mark result and the supplementary mark result.

[0120] In this embodiment, the risk task processing module 604 includes a distinguishing mark result sending unit, a pre-analysis unit, a pre-acquisition unit, a classification processing unit and a risk processing unit.

[0121] a distinguishing mark result sending unit, configured to send the distinguishing mark result to the risk task processing component;

[0122] A pre-analysis unit, configured to parse the cached information of the risk data corresponding to all the distinguishing marking results and the risk level of the risk user corresponding to the risk data through the pre-analysis component;

[0123] A pre-acquisition unit, configured to use the cache information as an acquisition parameter of the pre-acquisition component to acquire risk data corresponding to all distinguishing marking results;

[0124] a classification processing unit, configured to classify the risk data according to the risk level of the corresponding risk user using the classification processing element to obtain a classification processing result;

[0125] The risk processing unit is used to use the risk processing component to differentiate the risk data of different risk levels in the classification processing results using a preset risk processing differentiation strategy, wherein the preset risk processing differentiation strategy includes adopting different risk processing measures to process the risk data corresponding to risk users of different risk levels.

[0126] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware via computer-readable instructions. The computer-readable instructions can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes in the above-described method embodiments. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0127] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown in sequence as indicated by the arrows, these steps are not necessarily executed 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 some of the steps in the flowcharts 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 portion of the sub-steps or stages of other steps.

[0128] To solve the above technical problems, the present application also provides a computer device. Figure 7 , Figure 7 This is a basic structural block diagram of the computer device in this embodiment.

[0129] The computer device 7 includes a memory 7a, a processor 7b, and a network interface 7c that are interconnected via a system bus. Figure 7 Only a computer device 7 having components of a memory 7a, a processor 7b, and a network interface 7c is shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead. Among them, those skilled in the art will 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 signal processor (DSP), an embedded device, etc.

[0130] The computer device may be a desktop computer, notebook computer, PDA, cloud server, etc. The computer device may interact with the user via a keyboard, mouse, remote control, touchpad, or voice control device.

[0131] The memory 7a includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), 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 memory 7a may be an internal storage unit of the computer device 7, such as the hard disk or memory of the computer device 7. In other embodiments, the memory 7a may also be an external storage device of the computer device 7, such as a plug-in hard disk equipped on the computer device 7, a smart memory card (SMC), a secure digital (SD) card, a flash memory card, etc. Of course, the memory 7a may also include both the internal storage unit of the computer device 7 and its external storage device. In this embodiment, the memory 7a is generally used to store the operating system and various application software installed on the computer device 7, such as computer-readable instructions of a business risk assessment method. In addition, the memory 7a can also be used to temporarily store various types of data that have been output or are to be output.

[0132] In some embodiments, the processor 7b may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor 7b is generally used to control the overall operation of the computer device 7. In this embodiment, the processor 7b is used to execute computer-readable instructions stored in the memory 7a or process data, such as computer-readable instructions for executing the business risk assessment method.

[0133] The network interface 7c may include a wireless network interface or a wired network interface. The network interface 7c is generally used to establish a communication connection between the computer device 7 and other electronic devices.

[0134] The computer device proposed in this embodiment belongs to the field of business early warning technology and is applied to business risk assessment scenarios during financial online business processing. This application obtains a risk assessment message; parses the risk assessment message to obtain the identification information of the target risk user and the risk data cache information; performs differential marking processing on the risk data corresponding to the target risk user according to the cache information to obtain a differential marking result; calls a preset risk task processing component in combination with the differential marking result to perform risk task processing and obtain a risk task processing result; updates the risk data corresponding to the target risk user according to the risk task processing result and the cache information to obtain the latest stored data at the corresponding position of the cache information after the update; re-evaluates the latest stored data according to the preset evaluation strategy to obtain the latest risk assessment message, and pushes the latest risk assessment message to all target receiving ends. It can continuously pay attention to the risk level of users in a comprehensive, real-time and dynamic manner, which helps financial online services or business providers to reasonably avoid business risks based on changes in user risk levels.

[0135] The present application also provides another embodiment, 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 a business risk assessment method as described above.

[0136] The computer-readable storage medium proposed in this embodiment belongs to the field of business early warning technology and is applied to business risk assessment scenarios during financial online business processing. This application obtains a risk assessment message; parses the risk assessment message to obtain the identification information of the target risk user and the risk data cache information; performs differential marking processing on the risk data corresponding to the target risk user according to the cache information to obtain a differential marking result; combines the differential marking result to call a preset risk task processing component to perform risk task processing to obtain a risk task processing result; updates the risk data corresponding to the target risk user according to the risk task processing result and the cache information to obtain the latest stored data at the corresponding position of the cache information after the update; re-evaluates the latest stored data according to the preset evaluation strategy to obtain the latest risk assessment message, and pushes the latest risk assessment message to all target receiving ends. It can continuously pay attention to the risk level of users in a comprehensive, real-time and dynamic manner, which helps financial online services or business providers to reasonably avoid business risks based on changes in user risk levels.

[0137] 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 the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this 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 enabling 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.

[0138] Obviously, the embodiments described above are only some of the embodiments of the present application, rather than all of the 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 has been 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 embodiments, or to make equivalent replacements for 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 also within the scope of patent protection of this application. The non-company software tools or components that appear in the embodiments of this application are merely examples and do not represent actual use.

Claims

1. A business risk assessment method, characterized in that: The steps include: Obtaining a risk assessment message, wherein the risk assessment message is a risk assessment message given for the data stored in the multi-source cache library; Parsing the risk assessment message to obtain identification information of the target risk user and risk data cache information, wherein the cache information includes cache library address information, cache library name information, cache table name information, and row and column position information of the risk data in the cache table; Performing differential tokenization processing on the risk data corresponding to the target risk user according to the cached information to obtain a differential tokenization result; In combination with the distinguishing marking result, calling a preset risk task processing component to process the risk task and obtain a risk task processing result; updating the risk data corresponding to the target risk user according to the risk task processing result and the cache information, and obtaining the latest stored data at the corresponding position of the cache information after the update; The latest stored data is re-evaluated according to a preset evaluation strategy to obtain a latest risk assessment message, and the latest risk assessment message is pushed to all target receiving ends.

2. The business risk assessment method according to claim 1, characterized in that: The step of parsing the risk assessment message to obtain identification information of the target risk user and risk data cache information specifically includes: By parsing the risk assessment message, identifying users whose risk levels reach a preset first risk level as the target risk users, wherein the identification information includes risk level information; and By parsing the risk assessment message, comparing the parsed result of the current risk assessment message with the parsed results of the N previous risk assessment messages, identifying users whose risk levels have reached a preset second risk level for N+1 consecutive times as the target risk users, where N is a positive integer and the preset first risk level is higher than the preset second risk level; The identification information and risk data cache information of the target risk user are obtained, and an association mapping relationship between the risk user identification information and the risk data cache information is constructed.

3. The business risk assessment method according to any one of claims 1 or 2, characterized in that: After executing the step of parsing the risk assessment message to obtain identification information of the target risk user and risk data cache information, the method further includes: If the risk level identification information of a user is null, mark the user as a new user; An initial risk level assessment is performed on the new user according to a preset new user risk assessment cycle and the risk data cache information corresponding to the new user.

4. The business risk assessment method according to claim 1, characterized in that: The step of performing differential token processing on the risk data corresponding to the target risk user according to the cached information to obtain a differential token result specifically includes: Through the cache information, identify the cache address information, cache library name information, cache table name information and row and column position information of each risk data in the corresponding cache table; Based on the cache address information, cache name information, cache table name information, and row and column position information of each risk data, the target risk data is preliminarily marked to obtain a preliminary marking result; According to the risk level of the risk user corresponding to each risk data, the target risk data is distinguished and marked to obtain a supplementary marking result; Based on the preliminary marking result and the supplementary marking result, a distinguishing marking result corresponding to each risk data is generated.

5. The business risk assessment method according to claim 1, characterized in that: The preset risk task processing component includes a pre-analysis component, a pre-acquisition component, a classification processing component, and a risk processing component. The step of combining the distinguishing marking result, calling the preset risk task processing component, performing risk task processing, and obtaining the risk task processing result specifically includes: Sending the distinguishing marking result to the risk task processing component; The pre-analysis component parses the cached information of the risk data corresponding to all the distinguishing mark results and the risk level of the risk user corresponding to the risk data; Using the cache information as an acquisition parameter of the pre-acquisition component, acquiring the risk data corresponding to all the distinguishing marking results; Using the classification processing component, the risk data is classified according to the risk level of the corresponding risk user to obtain a classification processing result; The risk processing component is used to differentiate risk data of different risk levels in the classification processing results using a preset risk processing differentiation strategy, wherein the preset risk processing differentiation strategy includes adopting different risk processing measures to process risk data corresponding to risk users of different risk levels.

6. The business risk assessment method according to claim 1, characterized in that: The preset assessment strategy includes hierarchical assessment and time-based assessment. Specifically, the hierarchical assessment includes separately assessing users of different risk levels, and the time-based assessment includes periodically assessing users of different risk levels according to different assessment cycles. The step of re-evaluating the latest stored data according to the preset assessment strategy to obtain the latest risk assessment message includes: Identify the current risk level of the risk user corresponding to the latest stored data; According to the current risk level, different assessment cycles and assessment methods are selected to perform risk reassessment to obtain the latest risk assessment message, wherein the higher the risk level, the shorter the corresponding assessment cycle, and different risk levels correspond to different assessment methods; The step of re-evaluating the latest stored data according to a preset evaluation strategy to obtain the latest risk assessment message further includes: Determining whether the current risk level of the risk user corresponding to the latest stored data exceeds a preset risk level threshold; If the current risk level of the risk user corresponding to the latest stored data is higher than the preset risk level threshold, the risk level of the risk user is evaluated in a continuous evaluation mode until the latest current risk level does not exceed the risk level threshold, and then the system switches to an irregular evaluation mode, wherein the continuous evaluation mode includes continuously evaluating the risk level of the risk user according to the evaluation period corresponding to the current risk level, continuously obtaining the latest current risk level, and if the latest current risk level still exceeds the risk level threshold, continuing to evaluate the risk level of the risk user according to the evaluation period corresponding to the latest current risk level, and repeating this process. If the current risk level of the risk user corresponding to the latest stored data is not higher than the preset risk level threshold, an irregular grading mode is used to trigger a risk level assessment on the risk user, wherein the irregular grading mode includes freely performing a risk level assessment according to a preset trigger instruction.

7. The business risk assessment method according to claim 1, characterized in that: After executing the step of pushing the latest risk assessment message to all target receiving terminals, the method further includes: Obtain the latest risk assessment report; Parsing the latest risk assessment message to obtain the latest risk user identification information and risk data cache information; The distinguishing mark result is updated according to the latest risk user identification information and risk data cache information.

8. A business risk assessment device, characterized in that: include: A risk assessment message acquisition module, configured to acquire a risk assessment message, wherein the risk assessment message is a risk assessment message given for the data stored in the multi-source cache library; A risk assessment message parsing module is used to parse the risk assessment message to obtain identification information of the target risk user and risk data cache information, wherein the cache information includes cache library address information, cache library name information, cache table name information, and row and column position information of the risk data in the cache table; A risk data distinguishing marking module is used to perform distinguishing marking processing on the risk data corresponding to the target risk user according to the cached information to obtain a distinguishing marking result; A risk task processing module is used to call a preset risk task processing component based on the distinguishing mark result, perform risk task processing, and obtain a risk task processing result; A risk data updating module is used to update the risk data corresponding to the target risk user according to the risk task processing result and the cache information, and obtain the latest stored data at the corresponding position of the cache information after the update; The updated data re-evaluation module is used to re-evaluate the latest stored data according to a preset evaluation strategy, obtain the latest risk assessment message, and push the latest risk assessment message to all target receiving ends.

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

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the business risk assessment method according to any one of claims 1 to 7.

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