Business risk detection method and device, nonvolatile storage medium and electronic equipment

Abstract

The invention discloses a business risk detection method and device, a nonvolatile storage medium and electronic equipment. The method comprises the following steps: receiving a detection instruction for carrying out risk detection on a target service, the detection instruction comprising a service type of the target service; in response to the detection instruction, displaying a risk detection feature corresponding to the target service according to a predetermined corresponding relationship between the service type and the risk detection feature; receiving a selection instruction, and determining selected risk detection features; and calling a risk detection module corresponding to the selected risk detection feature, and carrying out risk detection on the target service. The technical problem that financial business risks cannot be simply and quickly detected in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of computer program development, and in particular to a business risk detection method, device, non-volatile storage medium and electronic device. Background Art

[0002] At present, the execution logic contained in most review products is relatively similar. Multiple review factors are nested and interdependent, and the calling order is strongly coupled. For example, calling them one by one is a waste of resources. At the same time, the distribution changes of traditional risk avoidance review engines rely on developers to modify the underlying code implementation, resulting in a high maintenance cost, and the inability to quickly respond to business demands, and even the dilemma of individual functions being unable to be realized. Distributing the review engine by restarting the system is very likely to cause monitoring vulnerabilities. Such review distribution is cumbersome and inflexible, and emergency monitoring is difficult to take effect in real time.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiments of the present invention provide a business risk detection method, device, non-volatile storage medium and electronic device to at least solve the technical problem in the prior art that financial business risks cannot be detected simply and quickly.

[0005] According to one aspect of an embodiment of the present invention, a business risk detection method is provided, comprising: receiving a detection instruction for performing risk detection on a target business, wherein the detection instruction includes the business type of the target business; in response to the detection instruction, displaying risk detection features corresponding to the target business based on a predetermined correspondence between the business type and the risk detection features; receiving a selection instruction to determine the selected risk detection features; and calling a risk detection module corresponding to the selected risk detection features to perform risk detection on the target business.

[0006] Optionally, the correspondence between business types and risk detection features is determined in the following manner: obtaining historical detection features for risk detection of historical businesses, and historical business types corresponding to the historical businesses; determining multiple historical business sets based on the historical detection features, wherein the number of historical businesses in each of the multiple historical business sets that have the same historical detection features exceeds a predetermined number; determining the same historical detection features of each of the multiple historical business sets; determining the correspondence between business types and risk detection features based on the same historical detection features of each of the multiple historical business sets, and the historical business types corresponding to the historical businesses.

[0007] Optionally, before calling the risk detection module corresponding to the selected risk detection feature to perform risk detection on the target business, it also includes: determining the detection conditions corresponding to the selected risk detection feature; and generating a risk detection module corresponding to the selected risk detection feature based on the detection conditions corresponding to the selected risk detection feature.

[0008] Optionally, a modification instruction for modifying a detection condition value is received, wherein the modification instruction includes an identifier of a risk detection feature module to be modified; and in response to the modification instruction, the value of the detection condition included in the risk detection feature module to be modified is modified.

[0009] Optionally, a risk detection module corresponding to the selected risk detection feature is called to perform risk detection on the target business, including: when multiple risk detection features are selected, using the risk detection modules corresponding to the multiple selected risk detection features in turn to perform risk detection on the target business, and outputting the detection results respectively; when the detection results output by the risk detection modules corresponding to the multiple selected risk detection features include abnormal detection results, determining that the risk detection result of the target business is abnormal.

[0010] Optionally, the target risk detection module in the risk detection module corresponding to each of the multiple selected risk detection features performs risk detection on the target business and outputs the target detection result: determines the value of the target risk feature corresponding to the target risk detection module in the target business; when the value of the target risk feature meets the target detection condition in the target risk detection module, determines that the target detection result is normal; when the value of the target risk feature does not meet the target detection condition in the target risk detection module, determines that the target detection result is abnormal.

[0011] According to another aspect of an embodiment of the present invention, a business risk detection device is provided, including: a receiving module, used to receive a detection instruction for performing risk detection on a target business, wherein the detection instruction includes the business type of the target business; a display module, used to respond to the detection instruction and display the risk detection feature corresponding to the target business according to a predetermined correspondence between the business type and the risk detection feature; a determination module, used to receive a selection instruction and determine the selected risk detection feature; and a detection module, used to call a risk detection module corresponding to the selected risk detection feature to perform risk detection on the target business.

[0012] According to another aspect of an embodiment of the present invention, a non-volatile storage medium is provided. The non-volatile storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing any one of the business risk detection methods.

[0013] According to another aspect of an embodiment of the present invention, there is provided an electronic device, comprising: one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement any one of the business risk detection methods.

[0014] According to yet another aspect of an embodiment of the present invention, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, any one of the above-mentioned business risk detection methods is implemented.

[0015] In an embodiment of the present invention, by receiving a detection instruction for performing risk detection on a target business, wherein the detection instruction includes the business type of the target business; in response to the detection instruction, displaying the risk detection feature corresponding to the target business according to a predetermined correspondence between the business type and the risk detection feature; receiving a selection instruction to determine the selected risk detection feature; calling the risk detection module corresponding to the selected risk detection feature to perform risk detection on the target business, the technical problem that the prior art cannot simply and quickly detect the risks of financial business is solved, thereby achieving the technical effect of improving the efficiency and accuracy of financial business risk detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0017] Figure 1 is a flow chart of a business risk detection method provided according to an embodiment of the present invention;

[0018] Figure 2 is a schematic diagram of a business risk detection device provided according to an embodiment of the present invention;

[0019] Figure 3 is a schematic diagram of an electronic device provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0021] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0022] According to an embodiment of the present invention, a method embodiment of business risk detection is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0023] Figure 1 is a flow chart of a business risk detection method according to an embodiment of the present invention. Figure 1 As shown, the method comprises the following steps:

[0024] Step S102, receiving a detection instruction for performing risk detection on a target business, wherein the detection instruction includes a business type of the target business;

[0025] In this step, when performing risk detection on a specific business, the business operator will send a "detection instruction". This detection instruction contains the "business type" of the target business. The business type refers to the category or nature of the target business, such as insurance business, payment business, credit business, etc.

[0026] Step S104, in response to the detection instruction, displaying the risk detection features corresponding to the target business according to the predetermined correspondence between the business type and the risk detection features;

[0027] In this step, based on historical data and business demand analysis, a database or model of "the correspondence between business types and risk detection features" is pre-established in the review system. When the business operator issues a "detection instruction" in the system, the review system will immediately respond to this instruction and determine the risk detection features that match the target business type based on the correspondence.

[0028] In an optional embodiment, the correspondence between business types and risk detection features is determined in the following manner: obtaining historical detection features for risk detection of historical businesses, and historical business types corresponding to the historical businesses; determining multiple historical business sets based on the historical detection features, wherein the number of historical businesses in each of the multiple historical business sets that have the same historical detection features exceeds a predetermined number; determining the same historical detection features of each of the multiple historical business sets; determining the correspondence between business types and risk detection features based on the same historical detection features of each of the multiple historical business sets, and the historical business types corresponding to the historical businesses.

[0029] Optionally, collect historical detection features used in the historical review process and the business types of historical businesses corresponding to these features. Historical detection features include various review rules and parameters, such as transaction amount, transaction frequency, customer credit score, etc., while historical business types cover all business categories processed by the system, such as insurance claims, payment transactions, credit approval, etc. Based on the collected historical detection features, historical businesses are divided into multiple historical business sets. The businesses in each set have similar detection features, and the number of occurrences of these features exceeds a predetermined number. In each historical business set, the system identifies the detection features with the highest frequency of occurrence, which are called the same historical detection features in the set. They are common risk points faced by the businesses in the set. Based on the same historical detection features of the historical business set and the historical types of the businesses in the set, a correspondence between business types and risk detection features is constructed.

[0030] In an optional embodiment, before calling the risk detection module corresponding to the selected risk detection feature to perform risk detection on the target business, it also includes: determining the detection conditions corresponding to the selected risk detection feature; and generating a risk detection module corresponding to the selected risk detection feature based on the detection conditions corresponding to the selected risk detection feature.

[0031] Optionally, detection conditions are rules and thresholds used to implement risk detection, which define how to identify and quantify risks. For example, if the selected risk detection feature is "the user's current transaction cost is less than 1 or greater than or equal to 2,000 yuan", then the corresponding detection conditions include a numerical range check for the transaction amount and a comparison of the transaction history of a specific user. Based on the determined detection conditions, a dedicated module or component is constructed to perform specific risk detection tasks.

[0032] In an optional embodiment, a modification instruction for modifying a detection condition value is received, wherein the modification instruction includes an identifier of a risk detection feature module to be modified; and in response to the modification instruction, the value of the detection condition included in the risk detection feature module to be modified is modified.

[0033] Optionally, during the operation of the review system, business rules may be updated, risk thresholds may be adjusted, etc., and the values ​​of the detection conditions that have been set may need to be modified. When the system receives a modification instruction, it will respond immediately, search for a risk detection feature module that matches the identifier in the instruction, and update the "value of the detection condition" contained in the module to reflect the new risk control strategy or business needs.

[0034] Step S106, receiving a selection instruction and determining the selected risk detection feature;

[0035] In this step, after the risk detection features corresponding to the target business are determined, the detection instructions are manually selected by the business operator on the review interface or automatically generated by the system according to preset rules or algorithms, so as to select the features actually applied to the risk detection of the target business.

[0036] Step S108: calling the risk detection module corresponding to the selected risk detection feature to perform risk detection on the target business.

[0037] In this step, after selecting the risk detection features, the system will automatically identify the modules related to these features and call them to perform risk detection on the target business. For example, if "abnormal transaction amount" is selected as a risk detection feature, the system will call the corresponding module to check whether the transaction amount of the target business exceeds the preset threshold or whether it is significantly different from the historical transaction pattern.

[0038] In an optional embodiment, a risk detection module corresponding to a selected risk detection feature is called to perform risk detection on a target business, including: when multiple risk detection features are selected, using the risk detection modules corresponding to each of the multiple selected risk detection features in turn to perform risk detection on the target business, and outputting detection results respectively; when the detection results output by the risk detection modules corresponding to each of the multiple selected risk detection features include abnormal detection results, determining that the risk detection result of the target business is abnormal.

[0039] Optionally, the risk detection modules corresponding to the selected risk detection features are used in sequence. As long as any detection module identifies an abnormal situation, the system will determine the risk status of the entire target business as abnormal. For example, if the system is reviewing an insurance claim transaction, it takes into account multiple features such as the transaction amount, user historical transaction behavior, and user credit rating. As long as the detection result of one of the features indicates an abnormality, such as an abnormally high transaction amount, the system will mark the transaction as high risk, triggering further review or risk avoidance measures.

[0040] In an optional embodiment, the target risk detection module in the risk detection module corresponding to each of the multiple selected risk detection features performs risk detection on the target business and outputs the target detection result: determines the value of the target risk feature corresponding to the target risk detection module in the target business; when the value of the target risk feature meets the target detection condition in the target risk detection module, determines that the target detection result is normal; when the value of the target risk feature does not meet the target detection condition in the target risk detection module, determines that the target detection result is abnormal.

[0041] Optionally, based on the pre-defined rules and algorithms of the system, the value or state of specific risk features related to the current detection module is extracted from the data of the target business, such as transaction amount, transaction frequency, user geographic location, etc. If the value of the target risk feature "meets the target detection condition in the target risk detection module", the review system will "determine that the target detection result is normal". For example, if the detection condition is that the transaction amount is between 1 yuan and 2,000 yuan, and the actual transaction amount is 1,500 yuan, then the system's detection result will be judged as normal. If the value of the target risk feature "does not meet the target detection condition in the target risk detection module", the review system will "determine that the target detection result is abnormal". For example, if the transaction amount is 2,500 yuan, which exceeds the set detection condition range, the system will mark the transaction as abnormal, requiring further review or risk avoidance measures.

[0042] By the above-mentioned receiving a detection instruction for performing risk detection on a target business, wherein the detection instruction includes the business type of the target business; in response to the detection instruction, displaying the risk detection feature corresponding to the target business according to the correspondence between the predetermined business type and the risk detection feature; receiving a selection instruction, determining the selected risk detection feature; calling the risk detection module corresponding to the selected risk detection feature to perform risk detection on the target business, the technical problem that the prior art cannot simply and quickly detect the risks of financial business is solved, thereby achieving the technical effect of improving the efficiency and accuracy of risk detection on financial business.

[0043] Based on the above embodiments and optional embodiments, the present invention proposes an optional implementation manner.

[0044] Step S1, receiving a detection instruction for performing risk detection on a target business, wherein the detection instruction includes a business type of the target business;

[0045] In this step, when performing risk detection on a specific business, the business operator will send a "detection instruction". This detection instruction contains the "business type" of the target business. The business type refers to the category or nature of the target business, such as insurance business, payment business, credit business, etc.

[0046] Step S2, in response to the detection instruction, displaying the risk detection features corresponding to the target business according to the predetermined correspondence between the business type and the risk detection features;

[0047] In this step, based on historical data and business demand analysis, a database or model of "the correspondence between business types and risk detection features" is pre-established in the review system. When the business operator issues a "detection instruction" in the system, the review system will immediately respond to this instruction and determine the risk detection features that match the target business type based on the correspondence.

[0048] In step S21, the correspondence between the business type and the risk detection feature is determined in the following manner: obtaining historical detection features for risk detection of historical businesses, and the historical business types corresponding to the historical businesses; determining multiple historical business sets based on the historical detection features, wherein the number of historical businesses in each of the multiple historical business sets that have the same historical detection features exceeds a predetermined number; determining the same historical detection features of each of the multiple historical business sets; determining the correspondence between the business type and the risk detection feature based on the same historical detection features of each of the multiple historical business sets, and the historical business types corresponding to the historical businesses.

[0049] Optionally, collect historical detection features used in the historical review process and the business types of historical businesses corresponding to these features. Historical detection features include various review rules and parameters, such as transaction amount, transaction frequency, customer credit score, etc., while historical business types cover all business categories processed by the system, such as insurance claims, payment transactions, credit approval, etc. Based on the collected historical detection features, historical businesses are divided into multiple historical business sets. The businesses in each set have similar detection features, and the number of occurrences of these features exceeds a predetermined number. In each historical business set, the system identifies the detection features with the highest frequency of occurrence, which are called the same historical detection features in the set. They are common risk points faced by the businesses in the set. Based on the same historical detection features of the historical business set and the historical types of the businesses in the set, a correspondence between business types and risk detection features is constructed.

[0050] Step S22, before calling the risk detection module corresponding to the selected risk detection feature to perform risk detection on the target business, also includes: determining the detection condition corresponding to the selected risk detection feature; generating the risk detection module corresponding to the selected risk detection feature according to the detection condition corresponding to the selected risk detection feature.

[0051] Optionally, detection conditions are rules and thresholds used to implement risk detection, which define how to identify and quantify risks. For example, if the selected risk detection feature is "the user's current transaction cost is less than 1 or greater than or equal to 2,000 yuan", then the corresponding detection conditions include a numerical range check for the transaction amount and a comparison of the transaction history of a specific user. Based on the determined detection conditions, a dedicated module or component is constructed to perform specific risk detection tasks.

[0052] Step S23, receiving a modification instruction for modifying the detection condition value, wherein the modification instruction includes an identifier of the risk detection feature module to be modified; in response to the modification instruction, modifying the value of the detection condition included in the risk detection feature module to be modified.

[0053] Optionally, during the operation of the review system, business rules may be updated, risk thresholds may be adjusted, etc., and the values ​​of the detection conditions that have been set may need to be modified. When the system receives a modification instruction, it will respond immediately, search for a risk detection feature module that matches the identifier in the instruction, and update the "value of the detection condition" contained in the module to reflect the new risk control strategy or business needs.

[0054] Step S3, receiving a selection instruction and determining the selected risk detection feature;

[0055] In this step, after the risk detection features corresponding to the target business are determined, the detection instructions are manually selected by the business operator on the review interface or automatically generated by the system according to preset rules or algorithms, so as to select the features actually applied to the risk detection of the target business.

[0056] Step S4, calling the risk detection module corresponding to the selected risk detection feature to perform risk detection on the target business, including: when multiple risk detection features are selected, using the risk detection modules corresponding to the multiple selected risk detection features in turn to perform risk detection on the target business, and outputting the detection results respectively; when the detection results output by the risk detection modules corresponding to the multiple selected risk detection features include abnormal detection results, determining that the risk detection result of the target business is abnormal.

[0057] Optionally, the risk detection modules corresponding to the selected risk detection features are used in sequence. As long as any detection module identifies an abnormal situation, the system will determine the risk status of the entire target business as abnormal. For example, if the system is reviewing an insurance claim transaction, it takes into account multiple features such as the transaction amount, user historical transaction behavior, and user credit rating. As long as the detection result of one of the features indicates an abnormality, such as an abnormally high transaction amount, the system will mark the transaction as high risk, triggering further review or risk avoidance measures.

[0058] In step S41, the target risk detection module in the risk detection module corresponding to each of the multiple selected risk detection features performs risk detection on the target business and outputs the target detection result: determining the value of the target risk feature corresponding to the target risk detection module in the target business; when the value of the target risk feature meets the target detection condition in the target risk detection module, determining that the target detection result is normal; when the value of the target risk feature does not meet the target detection condition in the target risk detection module, determining that the target detection result is abnormal.

[0059] Optionally, based on the pre-defined rules and algorithms of the system, the value or state of specific risk features related to the current detection module is extracted from the data of the target business, such as transaction amount, transaction frequency, user geographic location, etc. If the value of the target risk feature "meets the target detection condition in the target risk detection module", the review system will "determine that the target detection result is normal". For example, if the detection condition is that the transaction amount is between 1 yuan and 2,000 yuan, and the actual transaction amount is 1,500 yuan, then the system's detection result will be judged as normal. If the value of the target risk feature "does not meet the target detection condition in the target risk detection module", the review system will "determine that the target detection result is abnormal". For example, if the transaction amount is 2,500 yuan, which exceeds the set detection condition range, the system will mark the transaction as abnormal, requiring further review or risk avoidance measures.

[0060] The above optional implementation achieves at least the following effects: solving the technical problem in the prior art that financial business risks cannot be detected simply and quickly, thereby achieving the technical effect of improving the efficiency and accuracy of financial business risk detection.

[0061] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0062] In this embodiment, a business risk detection device is also provided, which is used to implement the above embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the terms "module" and "device" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0063] According to an embodiment of the present invention, there is also provided an apparatus embodiment for implementing the business risk detection method. Figure 2is a schematic diagram of a business risk detection device according to an embodiment of the present invention. Figure 2 As shown, the above-mentioned business risk detection device includes a receiving module 21, a display module 22, a determination module 23, and a detection module 24. The device is described below.

[0064] A receiving module 21 is used to receive a detection instruction for performing risk detection on a target business, wherein the detection instruction includes a business type of the target business;

[0065] The display module 22 is connected to the receiving module 21 and is used to respond to the detection instruction and display the risk detection characteristics corresponding to the target business according to the predetermined correspondence between the business type and the risk detection characteristics;

[0066] The determination module 23 is connected to the display module 22 and is used to receive the selection instruction and determine the selected risk detection feature;

[0067] The detection module 24 is connected to the determination module 23 and is used to call the risk detection module corresponding to the selected risk detection feature to perform risk detection on the target business.

[0068] In a business risk detection device provided by an embodiment of the present invention, a receiving module is set up to receive a detection instruction for performing risk detection on a target business, wherein the detection instruction includes the business type of the target business; a display module is used to respond to the detection instruction and display the risk detection feature corresponding to the target business according to a predetermined correspondence between the business type and the risk detection feature; a determination module is used to receive a selection instruction and determine the selected risk detection feature; and a detection module is used to call a risk detection module corresponding to the selected risk detection feature to perform risk detection on the target business, thereby solving the technical problem in the prior art that it is impossible to simply and quickly detect financial business risks, thereby achieving the technical effect of improving the efficiency and accuracy of financial business risk detection.

[0069] It should be noted that the above modules can be implemented by software or hardware. For example, for the latter, it can be implemented in the following ways: the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0070] It should be noted that the receiving module 21, the display module 22, the determination module 23, and the detection module 24 correspond to steps S102 to S108 in the embodiment, and the examples and application scenarios implemented by the modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above embodiment. It should be noted that the modules as part of the device can be run in a computer terminal.

[0071] It should be noted that the optional or preferred implementation of this embodiment can refer to the relevant description in the embodiment, which will not be repeated here.

[0072] The above-mentioned business risk detection device may also include a processor and a memory. The receiving module 21, the display module 22, the determination module 23, the detection module 24, etc. are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize corresponding functions.

[0073] The processor includes a kernel, which retrieves the corresponding program unit from the memory. There can be one or more kernels. The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one storage chip.

[0074] An embodiment of the present invention provides a non-volatile storage medium on which a program is stored. When the program is executed by a processor, a business risk detection method is implemented.

[0075] like Figure 3 As shown, an embodiment of the present invention provides an electronic device, the electronic device 10 includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, the following steps are implemented: the memory is used to store a computer program, wherein when the computer program is executed by the processor, the processor implements the above-mentioned business risk detection method. The device in this article may be a server, a PC, etc.

[0076] The present invention also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program that initializes the following method steps: computer instructions are executed by a processor to perform the above-mentioned business risk detection method.

[0077] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention 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.

[0078] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks 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.

[0079] 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.

[0080] 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.

[0081] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0082] The memory may include non-permanent memory 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.

[0083] 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 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.

[0084] 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.

[0085] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present invention 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 codes.

[0086] The above are only embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A business risk detection method, characterized in that: include: Receiving a detection instruction for performing risk detection on a target business, wherein the detection instruction includes a business type of the target business; In response to the detection instruction, displaying the risk detection feature corresponding to the target business according to the predetermined correspondence between the business type and the risk detection feature; receiving a selection instruction and determining a selected risk detection feature; A risk detection module corresponding to the selected risk detection feature is called to perform risk detection on the target business.

2. The method according to claim 1, characterized in that The correspondence between the business type and the risk detection feature is determined in the following manner: Obtaining historical detection features for risk detection of historical business, and historical business types corresponding to the historical business; Determine a plurality of historical service sets according to the historical detection feature, wherein the number of historical services in each of the plurality of historical service sets having the same historical detection feature exceeds a predetermined number; Determining the same historical detection features of each of the multiple historical service sets; According to the same historical detection features of the multiple historical business sets and the historical business types corresponding to the historical businesses, the corresponding relationship between the business types and the risk detection features is determined.

3. The method according to claim 1, characterized in that The correspondence between the business type and the risk detection feature is determined in the following manner: Determining a detection condition corresponding to the selected risk detection feature; According to the detection condition corresponding to the selected risk detection feature, a risk detection module corresponding to the selected risk detection feature is generated.

4. The method according to claim 3, characterized in that Also includes: Receiving a modification instruction for modifying a detection condition value, wherein the modification instruction includes an identifier of a risk detection feature module to be modified; In response to the modification instruction, the value of the detection condition included in the risk detection feature module to be modified is modified.

5. The method according to claim 1, characterized in that The calling of the risk detection module corresponding to the selected risk detection feature to perform risk detection on the target business includes: In the case where there are multiple selected risk detection features, risk detection modules corresponding to the multiple selected risk detection features are used in sequence to perform risk detection on the target business, and the detection results are output respectively; In a case where the detection results output by the risk detection modules corresponding to the multiple selected risk detection features include abnormal detection results, it is determined that the risk detection result of the target business is abnormal.

6. The method according to claim 5, characterized in that The target risk detection module in the risk detection modules corresponding to the multiple selected risk detection features respectively performs risk detection on the target business using the following steps and outputs the target detection result: Determining a value of a target risk feature within the target business corresponding to the target risk detection module; When the value of the target risk feature meets the target detection condition in the target risk detection module, determining that the target detection result is normal; When the value of the target risk feature does not meet the target detection condition in the target risk detection module, it is determined that the target detection result is abnormal.

7. A business risk detection device, characterized in that: include: A receiving module, configured to receive a detection instruction for performing risk detection on a target business, wherein the detection instruction includes a business type of the target business; A display module, configured to display the risk detection feature corresponding to the target business in response to the detection instruction according to a predetermined correspondence between the business type and the risk detection feature; A determination module, used to receive a selection instruction and determine the selected risk detection feature; The detection module is used to call the risk detection module corresponding to the selected risk detection feature to perform risk detection on the target business.

8. A non-volatile storage medium, characterized in that: The non-volatile storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the business risk detection method described in any one of claims 1 to 6.

9. An electronic device, characterized in that: include: One or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the business risk detection method described in any one of claims 1 to 6.

10. A computer program product comprising computer instructions, characterized in that: The computer instructions are executed by the processor to implement the business risk detection method described in any one of claims 1 to 6.

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