Risk identification method and device, storage medium and electronic equipment

Abstract

The invention discloses a risk identification method and device, a storage medium and electronic equipment. In the method, the server can extract the key information of the target service and determine the rule matched with the target service from the rule base, so that the risk control model can perform risk identification on the target service according to the key information of the target service and the rule matched with the target service, thereby realizing automation of service risk identification, and improving the risk identification efficiency. And the risk identification speed and efficiency are improved. And each neural network model can be coordinated and dispatched by constructing a risk identification agent, so that a risk identification task for the target service can be efficiently executed, and the identification speed is accelerated. Meanwhile, efficient fine tuning of a large language model is supported, and optimization complexity and cost are reduced. In the continuous identification process, the rule base and historical information are dynamically supplemented, and support is provided for follow-up risk identification.

Description

Technical Field

[0001] The present specification relates to the fields of computer technology and artificial intelligence, and in particular to a risk identification method, device, storage medium and electronic device. Background Art

[0002] With the continuous development of artificial intelligence technology, artificial intelligence models can also be used to identify potential risks that may exist in business processes, and then confirm corresponding risk control measures based on the identified potential risks to ensure the accurate implementation of business processes.

[0003] For example, in the e-commerce field, artificial intelligence models are used to analyze users' personal information, such as their purchasing patterns, device information, and Internet Protocol Address (IP address), to identify potential risks and ensure normal payment for users. For another example, in the manufacturing field, manufacturers use artificial intelligence models to analyze potential risks in the supply chain based on logistics information in the supply chain and information such as raw material prices, to ensure the continuity of product manufacturing and cost stability.

[0004] At present, traditional potential risk identification mainly relies on manual review or traditional machine learning. Although manual review can accurately evaluate new risk types, it cannot meet the rapidly growing business volume. Although traditional machine learning can improve review efficiency, it cannot accurately identify new risk types. It is necessary to continuously update the machine learning model according to business processes and new risk types, resulting in a waste of resources. Summary of the invention

[0005] The embodiments of this specification provide a risk identification method, device, storage medium and electronic device to partially solve the problems existing in the above-mentioned prior art.

[0006] The embodiments of this specification adopt the following technical solutions:

[0007] This manual provides a risk identification method, including:

[0008] Acquire business information to be analyzed of the target business, where the business information to be analyzed is used to characterize the business process involved in the target business;

[0009] Determining key information corresponding to the target business from the business information to be analyzed;

[0010] Determine a rule matching the target business from a preset rule library, and load it as a target rule;

[0011] Determining, according to the loaded target rule, a prompt statement for risk identification according to the target rule;

[0012] The prompt statement and the key information are input into a preset risk control model to identify risks of the target business through the risk control model.

[0013] Optionally, the service information to be analyzed includes a service text to be analyzed;

[0014] Determining key information corresponding to the target business from the business information to be analyzed specifically includes:

[0015] Extracting initial key information from the business text to be analyzed;

[0016] Using the initial key information, historical business information matching the business text to be analyzed is screened out from a preset database;

[0017] According to the historical business information, key information corresponding to the target business is determined from the business text to be analyzed.

[0018] Optionally, according to the historical business information, determining key information corresponding to the target business from the business information to be analyzed specifically includes:

[0019] Determining key information corresponding to the historical business information;

[0020] According to the matching result between the key information corresponding to the historical business information and the initial key information, the key information corresponding to the target business is determined from the business information to be analyzed.

[0021] Optionally, the service information to be analyzed includes a service picture to be analyzed;

[0022] Determining key information corresponding to the target business from the business information to be analyzed specifically includes:

[0023] Image recognition is performed on the business image to be analyzed to determine key information corresponding to the target business from text information recognized in the business image to be analyzed.

[0024] Optionally, the method further comprises:

[0025] The business information to be analyzed, the risk identification result corresponding to the target business, and the key information corresponding to the target business are correspondingly saved.

[0026] Optionally, the method further comprises:

[0027] Based on the risk identification results obtained after risk identification for the target business, a risk assessment report for the target business is generated and displayed.

[0028] This specification provides an industry risk identification device, including:

[0029] An acquisition module, used for acquiring the to-be-analyzed business information of the target business, wherein the to-be-analyzed business information is used for characterizing the business process involved in the target business;

[0030] A determination module, used to determine key information corresponding to the target business from the business information to be analyzed;

[0031] A loading module, used to determine a rule matching the target business from a preset rule library, and load it as a target rule;

[0032] A generating module, configured to determine, based on the loaded target rule, a prompt statement for risk identification according to the target rule;

[0033] The identification module is used to input the prompt statement and the key information into a preset risk control model to identify the risks of the target business through the risk control model.

[0034] Optionally, the service information to be analyzed includes a service text to be analyzed;

[0035] The determination module is specifically used to extract initial key information from the business text to be analyzed;

[0036] Using the initial key information, historical business information matching the business text to be analyzed is screened out from a preset database;

[0037] According to the historical business information, key information corresponding to the target business is determined from the business text to be analyzed.

[0038] This specification provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned risk identification method is implemented.

[0039] An electronic device provided in this specification includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned risk identification method when executing the program.

[0040] At least one of the above technical solutions adopted in the embodiments of this specification can achieve the following beneficial effects:

[0041] In the embodiments of the present specification, after first obtaining the business information to be analyzed of the target business, the key information corresponding to the target business is determined from the business information to be analyzed, wherein the business information to be analyzed is used to characterize the business process involved in the target business, and then the rules matching the target business can be determined from the preset rule library, loaded as the target rules, and based on the loaded target rules, the prompt statements for risk identification according to the target rules are determined, and the prompt statements and the key information are input into the preset risk control model to identify the risks of the target business through the risk control model.

[0042] In this method, the server can extract the key information of the target business and determine the rules that match the target business from the rule base, so that the risk control model can identify the risks of the target business based on the key information of the target business and the rules that match the target business, thereby realizing the automation of business risk identification and improving the speed and efficiency of risk identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The drawings described herein are used to provide a further understanding of this specification and constitute a part of this specification. The exemplary embodiments and descriptions of this specification are used to explain this specification and do not constitute an improper limitation on this specification.

[0044] In the figure:

[0045] Figure 1 A schematic diagram of a risk identification method provided in an embodiment of this specification;

[0046] Figure 2 A schematic diagram of the screening process of the historical business information provided in this manual;

[0047] Figure 3 A schematic diagram of the risk identification process provided in this manual;

[0048] Figure 4 A schematic diagram of the structure of a risk identification device provided in an embodiment of this specification;

[0049] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this specification. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solutions and advantages of this specification more clear, the technical solutions of this specification will be clearly and completely described below in combination with the specific embodiments of this specification and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of them. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this specification.

[0051] In the business risk control scenario, whenever a business platform has new business needs or whenever the existing business of the business platform changes, it is necessary to identify the risks of the new business processes or the changed business processes, so as to identify the possible risks of the new business processes or the changed business processes (such as: data theft, fraud, data security and compliance risks), and effectively prevent and control the risks to ensure the stable operation of the business processes. Therefore, it is particularly important to efficiently and accurately identify the risks of the new business processes or the changed business processes.

[0052] The technical solutions provided by the embodiments of this specification are described in detail below in conjunction with the accompanying drawings.

[0053] Figure 1 A flow chart of a risk identification method provided in an embodiment of this specification includes:

[0054] S100: Acquire to-be-analyzed business information of a target business, where the to-be-analyzed business information is used to characterize a business process involved in the target business.

[0055] In this specification, when the R&D personnel of the business platform need to submit a new business process or update the current business process of the business platform, they can first take the business containing the new business process or the business with changed business process as the target business, and upload the business information of the target business to the business platform, so that the business platform can obtain the business information uploaded by the user as the business information to be analyzed of the target business, and then the business platform can identify risks of the target business based on the business information to be analyzed.

[0056] Among them, the above-mentioned business information to be analyzed is used to characterize the business processes involved in the target business. The above-mentioned business information to be analyzed may include business texts to be analyzed. The above-mentioned business texts to be analyzed may be product requirements documents (Product Requirements Document, PRD) of data. The product requirements document here may include: business process requirements, business goals of business processes, functions of business processes, user interfaces involved in business processes, and performance standards used in business processes and other information.

[0057] For a risk identification method provided in this specification, it can be a designated device such as a server set up on a business platform, or it can refer to a terminal device such as a desktop computer, a laptop computer, etc. For the sake of ease of description, the following only takes the server as the execution entity to describe a risk identification method provided in this specification.

[0058] S102: Determine key information corresponding to the target business from the business information to be analyzed.

[0059] Furthermore, after acquiring the to-be-analyzed business information of the target business, the server may pre-process the to-be-analyzed business information to obtain the pre-processed to-be-analyzed business information, and further may extract the initial key information from the pre-processed to-be-analyzed business information.

[0060] Among them, the server may perform preprocessing methods on the business information to be analyzed, such as: standardizing the document format (for example: if the business information to be analyzed contains business text to be analyzed in the format of HyperText Markup Language (HTML), it is necessary to convert the HTML format used by the business text to be analyzed into the standard format), cleaning irrelevant noise data (for example: removing noise data irrelevant to risk identification, such as page numbers and extra spaces contained in the business text to be analyzed), etc.

[0061] In the above content, there may be multiple methods for the server to extract the initial key information from the business information to be analyzed, for example: the server performs semantic analysis on the business text to be analyzed contained in the business information to be analyzed to extract the initial key information from the business text to be analyzed. For another example: the server inputs the business text to be analyzed contained in the business information to be analyzed into a preset large language model for extracting initial key information, so as to extract the initial key information from the business information to be analyzed through the preset large language model for extracting initial key information.

[0062] In the above content, the initial key information extracted from the business information to be analyzed can be used to reflect what users of the target business can do through the target business (which may include the specific process steps performed by the user when doing this thing), as well as the impact on the operations of users using the target business.

[0063] For example, when the target business is a payment business, users can make payments through the payment business, and users can improve payment efficiency by using the payment business.

[0064] For another example, when the target service is an online course browsing service, users can learn courses in fields such as programming and design through the online course browsing service, and users can improve their learning efficiency by using the online course browsing service.

[0065] Furthermore, after extracting the initial key information from the business information to be analyzed, the server can filter out historical business information that matches the business information to be analyzed from a preset database through the initial key information, as shown in the following example. Figure 2 shown.

[0066] Figure 2 This is a schematic diagram of the screening process of the historical business information provided in this manual.

[0067] Combination Figure 2 It can be seen that the server can determine the key information corresponding to each historical business information, and then obtain each historical business information from the preset knowledge base, and determine the historical business information that matches the target business from each historical business information based on the matching result between the key information corresponding to each historical business information and the initial key information.

[0068] The above historical business information is used to characterize the business process involved in the historical business.

[0069] In the above content, the method for the server to determine the matching result between the key information corresponding to the historical business information and the initial key information can be that the server determines the matching result between the key information corresponding to the historical business information and the initial key information based on the similarity between the key information corresponding to the historical business information and the initial key information.

[0070] For example, if the similarity between the key information corresponding to the historical business information and the initial key information exceeds a preset threshold, it can be determined that the matching result between the key information corresponding to the historical business information and the initial key information is a match.

[0071] It can be seen from the above content that the initial key information extracted by the server from the business information to be analyzed only contains the summary of the business information to be analyzed but not the entire content of the business information to be analyzed. Therefore, the accuracy of the historical business information matching the target business determined based on the initial key information is low.

[0072] Based on this, in order to further improve the accuracy of the historical business information determined to match the target business, the server can also perform feature extraction on the business information to be analyzed to determine the feature representation corresponding to the business information to be analyzed, and then determine the historical business information matching the target business based on the matching results between the feature representation corresponding to the business information to be analyzed and the feature representation corresponding to the historical business information.

[0073] The matching result between the feature representation corresponding to the business information to be analyzed and the feature representation corresponding to the historical business information may be determined according to the similarity between the feature representation corresponding to the business information to be analyzed and the feature representation corresponding to the historical business information.

[0074] Furthermore, the server may determine the key information corresponding to the target business according to the business information to be analyzed, the initial key information corresponding to the business information to be analyzed, and the historical business information matching the target business.

[0075] Specifically, the server can input the business information to be analyzed, the initial key information corresponding to the business information to be analyzed, and the historical business information matching the target business into a preset key information extraction large language model, so that the key information extraction large language model will use the historical business information matching the target business, and the initial key information corresponding to the business information to be analyzed, as the context information corresponding to the business information to be analyzed, and then can determine the key information corresponding to the target business from the business information to be analyzed based on the initial key information corresponding to the business information to be analyzed and the historical business information matching the target business.

[0076] Among them, the above-mentioned key information may include attribute information such as actions that have a significant impact on the core functions, user experience or business goal realization of the target business in the process of executing the business processes involved in the target business (that is, the operations that users need to perform in the process of using the target business, or the processes that the target business automatically executes to achieve specific functions), needs, subjects, platforms, products, etc.

[0077] In addition, the above-mentioned key information may also include the explanatory text output by the predicted large language model to characterize why the above-mentioned attribute information is determined as key information (taking the above-mentioned attribute information as an action as an example, the explanatory text here is used to explain why this action belongs to key information), and the classification result of what category the above-mentioned attribute information belongs to (taking the above-mentioned attribute information as an action as an example, this classification result can be one of: user interaction action, system processing action, business process action, system automation action).

[0078] In addition, the above-mentioned business information to be analyzed may also include business images to be analyzed, and these business images to be analyzed may include conceptual diagrams reflecting the business processes involved in the target business, flowcharts reflecting the business processes involved in the target business, design diagrams reflecting the business processes involved in the target business, etc.

[0079] Therefore, the server may also perform image recognition on the business image to be analyzed, so as to determine key information corresponding to the target business from text information recognized in the business image to be analyzed.

[0080] Specifically, the server can preprocess the business image to be analyzed to obtain a preprocessed business image to be analyzed, and then determine the key information corresponding to the target business from the text information identified in the business image to be analyzed based on the pixel value of each pixel contained in the preprocessed business image to be analyzed.

[0081] The method by which the server pre-processes the service image to be analyzed may include: denoising, brightness and contrast adjustment, sharpening, size normalization, and the like.

[0082] Of course, the server can also input the business image to be analyzed into a preset image recognition model to perform image recognition on the business image to be analyzed through the preset image recognition model, so as to determine the key information corresponding to the target business from the text information recognized in the business image to be analyzed.

[0083] Furthermore, after the server determines the key information corresponding to the target business from the text information identified in the business image to be analyzed, it can also compare it with the key information corresponding to the target business obtained from the business text to be analyzed, so as to merge the key information corresponding to the target business determined from the text information identified in the business image to be analyzed with the key information corresponding to the target business obtained from the business text to be analyzed according to the comparison result.

[0084] Specifically, if it is determined according to the comparison results that there is a deviation between the key information corresponding to the target business determined from the text information identified in the business image to be analyzed and the key information corresponding to the target business obtained from the business text to be analyzed, then the key information corresponding to the target business can be re-determined from the text information identified in the business image to be analyzed and the key information corresponding to the target business obtained from the business text to be analyzed.

[0085] Of course, the server can also, when it is determined based on the comparison results that there is a deviation between the key information corresponding to the target business determined from the text information identified in the business image to be analyzed and the key information corresponding to the target business obtained from the business text to be analyzed, merge the key information corresponding to the target business determined from the text information identified in the business image to be analyzed with the key information corresponding to the target business obtained from the business text to be analyzed according to the weight of the key information corresponding to the target business determined from the text information identified in the business image to be analyzed and the weight of the key information corresponding to the target business obtained from the business text to be analyzed.

[0086] S104: Determine a rule matching the target service from a preset rule library, take the rule as the target rule, and load it.

[0087] S106: Determine, based on the loaded target rule, a prompt statement for performing risk identification according to the target rule.

[0088] Furthermore, after determining the key information corresponding to the target business, the server can determine the rules that match the target business from the preset rule library according to the identification information corresponding to the target business, and load them as the target rules. Then, based on the loaded target rules, the server can determine the prompt statements for risk identification according to the target rules.

[0089] Among them, the rules in the above-mentioned rule library can be set according to actual needs and adjusted according to historical business information. The rules here can be used to perform logical judgment on the target business according to the above-mentioned key information through conditional judgment statements to determine the classification labels corresponding to the risks that may be involved in the business processes involved in the target business, and the risk levels corresponding to the risks that may be involved in the business processes involved in the target business.

[0090] For ease of understanding, the following only takes the rules for matching transaction services as an example to illustrate the rules set in the rule base, and this specification does not limit other specific rules set in the rule base.

[0091] For example, if the target business is a transaction business, the rule may be "IF (transaction amount > 10,000) AND (user's geographic location does not match the usual address) THEN classification label = "abnormal transaction" risk level = "high" ELSE IF (transaction time is outside business hours) THEN classification label = "transaction during outside business hours" risk level = "medium" ELSE IF (unverified login attempts exist in the user account) THEN classification label = "potential unauthorized access" risk level = "low" ELSE classification label = "normal transaction" risk level = "none" END IF".

[0092] The above prompt statement can be, for example: "The risk control rule of the business is "IF (transaction amount>10000) AND (user's geographical location does not match the usual address) THEN classification label = "abnormal transaction" risk level = "high" ELSE IF (transaction time is outside business hours) THEN classification label = "non-business hours transaction" risk level = "medium" ELSE IF (user account has unverified login attempts) THEN classification label = "potential unauthorized access" risk level = "low" ELSE classification label = "normal transaction" risk level = "none" END IF", please judge whether the target business corresponding to the key information entered involves risks during the execution process according to this rule, and inform where risks may occur".

[0093] S108: Input the prompt statement and the key information into a preset risk control model to identify risks of the target business through the risk control model.

[0094] In this specification, the server can input prompt statements and key information into a preset risk control model to identify risks of the target business through the risk control model and obtain risk identification results. The server can also save the business information to be analyzed, the risk identification results corresponding to the target business, and the key information corresponding to the target business in a preset structured format.

[0095] Furthermore, the server may also generate and display a risk assessment report for the target business based on the risk identification results obtained after risk identification for the target business.

[0096] Specifically, the server can input the risk identification results obtained after risk identification for the target business into the preset large language generation model to generate a risk assessment report for the target business based on the risk identification results obtained after risk identification for the target business through the preset large language generation model.

[0097] Among them, the above-mentioned risk assessment report may include classification labels corresponding to the risks that may be involved in the business processes involved in the target business, risk levels corresponding to the risks that may be involved in the business processes involved in the target business, key information corresponding to the risks that may be involved in the business processes involved in the target business, and solutions corresponding to the risks that may be involved in the business processes involved in the target business.

[0098] In actual application scenarios, since the types of risks involved in the execution of the business corresponding to the business information to be analyzed obtained by the server are constantly changing, it is difficult for fixed risk identification methods to identify new risk scenarios that may exist in the business information to be analyzed.

[0099] Based on this, the server obtains the business information to be analyzed uploaded by the user, and the method for identifying the risk of the target business according to the business information to be analyzed of the target business can also be that the server starts a preset risk identification intelligent agent, and configures the computing resources and operation permissions required for the operation of the above-mentioned risk identification intelligent agent, and configures the receiving parameters for the above-mentioned risk identification intelligent agent, and then the configured risk identification intelligent agent can obtain the business information to be analyzed uploaded by the user, and identify the risk of the target business according to the obtained business information to be analyzed, as shown in the following example. Figure 3 shown.

[0100] Figure 3 This is a flowchart of the risk identification process provided in this manual.

[0101] Combination Figure 3 It can be seen that the server can obtain the business information to be analyzed of the target business through the risk identification intelligent agent, and extract the initial key information from the business information to be analyzed by scheduling the preset initial key information extraction large language model, and then determine the historical business information matching the target business from various historical business information based on the initial key information of the business information to be analyzed, and determine the key information corresponding to the target business through the key information extraction large language model based on the initial key information of the business information to be analyzed, the historical business information matching the target business, and the business information to be analyzed.

[0102] Furthermore, the risk identification agent can determine the rules and prompt statements that match the target business, and schedule the risk control model to identify the risks of the target business based on the key information and prompt statements of the target business to obtain the risk identification results of the target business.

[0103] Furthermore, the risk identification agent can also obtain feedback information after manual review corresponding to each historical business information, and adjust the above-mentioned rule base and each neural network model used in the risk identification process of the target business based on the deviation between the feedback information and the risk identification results output by the risk control model for each historical business information.

[0104] The above-mentioned feedback information after manual review may be obtained by the R&D personnel randomly selecting at least part of the historical business information from various historical business information according to a specified ratio and reviewing the selected at least part of the historical business information.

[0105] The above-mentioned risk identification agent can be a pre-set software program that can adjust each neural network model used in the risk identification process according to the feedback information of the identification results of historical business information during the process of risk identification of the business information to be analyzed, and coordinate and schedule each adjusted neural network model to perform risk identification on the target business.

[0106] It should be noted that the server can coordinate and schedule various neural network models by building a risk identification agent, so that complex risk identification tasks can be efficiently executed by the intelligent system, thereby improving the speed of risk identification. It can also achieve efficient fine-tuning of parameters when optimizing large language models to adapt to new types of risks, reducing optimization complexity and computing costs. In the process of risk identification of continuously generated target businesses, the preset rule base and historical business information can be supplemented, thereby providing support for subsequent risk identification.

[0107] From the above content, it can be seen that the server can extract the key information of the target business and determine the rules that match the target business from the rule base, so that the risk control model can identify the risks of the target business based on the key information of the target business and the rules that match the target business, thereby realizing the automation of business risk identification and improving the speed and efficiency of risk identification.

[0108] In addition, the server can also combine historical business information as supplementary information for the target business, thereby effectively improving the accuracy and comprehensiveness of risk identification for the target business.

[0109] The above is a risk identification method provided in an embodiment of this specification. Based on the same idea, this specification also provides corresponding devices, storage media and electronic devices.

[0110] Figure 4 A schematic diagram of a risk identification device provided in an embodiment of this specification, the device comprising:

[0111] An acquisition module 401 is used to acquire the to-be-analyzed business information of the target business, where the to-be-analyzed business information is used to characterize the business process involved in the target business;

[0112] A determination module 402 is used to determine key information corresponding to the target business from the business information to be analyzed;

[0113] A loading module 403 is used to determine a rule matching the target business from a preset rule library, and load it as a target rule;

[0114] A generating module 404 is used to determine, according to the loaded target rule, a prompt statement for risk identification according to the target rule;

[0115] The identification module 405 is used to input the prompt statement and the key information into a preset risk control model to identify the risks of the target business through the risk control model.

[0116] Optionally, the service information to be analyzed includes a service text to be analyzed;

[0117] The determination module 402 is specifically used to extract initial key information from the business text to be analyzed; through the initial key information, filter out historical business information matching the business text to be analyzed from a preset database; and determine the key information corresponding to the target business from the business text to be analyzed based on the historical business information.

[0118] Optionally, the determination module 402 is specifically used to determine key information corresponding to the historical business information; and determine key information corresponding to the target business from the business information to be analyzed according to a matching result between the key information corresponding to the historical business information and the initial key information.

[0119] Optionally, the service information to be analyzed includes a service picture to be analyzed;

[0120] The determination module 402 is specifically configured to perform image recognition on the service image to be analyzed, so as to determine key information corresponding to the target service from text information recognized in the service image to be analyzed.

[0121] Optionally, the identification module 405 is specifically used to store the business information to be analyzed, the risk identification result corresponding to the target business, and the key information corresponding to the target business in a corresponding manner.

[0122] Optionally, the identification module 405 is specifically configured to generate and display a risk assessment report for the target business according to a risk identification result obtained after risk identification for the target business.

[0123] This specification also provides a computer-readable storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, it can be used to perform the above Figure 1 A risk identification method is provided.

[0124] based on Figure 1 A risk identification method is shown, and the embodiment of this specification also provides Figure 5 The structural diagram of the electronic device shown in FIG. Figure 5 At the hardware level, the electronic device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and may also include other hardware required for the business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above Figure 1 A risk identification method described.

[0125] Of course, in addition to software implementation, this specification does not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc., that is to say, the executor of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0126] In the 1990s, improvements to a technology could be clearly distinguished as hardware improvements (for example, improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the method flow). However, with the development of technology, many improvements to the method flow today can be regarded as direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that an improvement in a method flow cannot be implemented using a hardware entity module. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is such an integrated circuit whose logical function is determined by the user's programming of the device. Designers can "integrate" a digital system on a PLD by programming it themselves, without having to ask a chip manufacturer to design and produce a dedicated integrated circuit chip. Moreover, nowadays, instead of manually making integrated circuit chips, this kind of programming is mostly implemented by "logic compiler" software, which is similar to the software compiler used when developing and writing programs, and the original code before compilation must also be written in a specific programming language, which is called hardware description language (HDL). There is not only one HDL, but many kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also know that it is only necessary to program the method flow slightly in the above-mentioned hardware description languages ​​and program it into the integrated circuit, and then it is easy to obtain the hardware circuit that implements the logic method flow.

[0127] The controller can be implemented in any appropriate manner, for example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing a computer-readable program code (such as software or firmware) that can be executed by the (micro)processor, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in a purely computer-readable program code manner, the controller can be implemented in the form of a logic gate, a switch, an application-specific integrated circuit, a programmable logic controller, and an embedded microcontroller by logically programming the method steps. Therefore, this controller can be considered as a hardware component, and the devices included therein for implementing various functions can also be regarded as structures within the hardware component. Or even, the devices for implementing various functions can be regarded as both software modules for implementing the method and structures within the hardware component.

[0128] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0129] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0130] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0131] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0132] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0133] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0134] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0135] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0136] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0137] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0138] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems or computer program products. Therefore, this specification may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0139] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0140] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0141] The above description is only an embodiment of the present specification and is not intended to limit the present specification. For those skilled in the art, the present specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification shall be included in the scope of the claims of the present specification.

Claims

1. A risk identification method, comprising: Acquire business information to be analyzed of the target business, where the business information to be analyzed is used to characterize the business process involved in the target business; Determining key information corresponding to the target business from the business information to be analyzed; Determine a rule matching the target business from a preset rule library, and load it as a target rule; Determining, according to the loaded target rule, a prompt statement for risk identification according to the target rule; The prompt statement and the key information are input into a preset risk control model to identify risks of the target business through the risk control model.

2. The method according to claim 1, wherein the service information to be analyzed includes a service text to be analyzed; Determining key information corresponding to the target business from the business information to be analyzed specifically includes: Extracting initial key information from the business text to be analyzed; Using the initial key information, historical business information matching the business text to be analyzed is screened out from a preset database; According to the historical business information, key information corresponding to the target business is determined from the business text to be analyzed.

3. The method according to claim 2, wherein, based on the historical business information, key information corresponding to the target business is determined from the business information to be analyzed, specifically comprising: Determining key information corresponding to the historical business information; According to the matching result between the key information corresponding to the historical business information and the initial key information, the key information corresponding to the target business is determined from the business information to be analyzed.

4. The method according to claim 1, wherein the service information to be analyzed includes a service picture to be analyzed; Determining key information corresponding to the target business from the business information to be analyzed specifically includes: Image recognition is performed on the business image to be analyzed to determine key information corresponding to the target business from text information recognized in the business image to be analyzed.

5. The method of claim 1, further comprising: The business information to be analyzed, the risk identification result corresponding to the target business, and the key information corresponding to the target business are correspondingly saved.

6. The method of claim 1, further comprising: Based on the risk identification results obtained after risk identification for the target business, a risk assessment report for the target business is generated and displayed.

7. A risk identification device, comprising: An acquisition module, used for acquiring business information to be analyzed of a target business, wherein the business information to be analyzed is used for characterizing a business process involved in the target business; A determination module, used to determine key information corresponding to the target business from the business information to be analyzed; A loading module, used to determine a rule matching the target business from a preset rule library, and load it as a target rule; A generating module, configured to determine, based on the loaded target rule, a prompt statement for risk identification according to the target rule; The identification module is used to input the prompt statement and the key information into a preset risk control model to identify the risks of the target business through the risk control model.

8. The device according to claim 7, wherein the service information to be analyzed includes a service text to be analyzed; The determination module is specifically used to extract initial key information from the business text to be analyzed; through the initial key information, filter out historical business information matching the business text to be analyzed from a preset database; and determine key information corresponding to the target business from the business text to be analyzed based on the historical business information.

9. A computer-readable storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 6 when executing the program.

Images