CN112016788A - Risk control strategy generation and risk control method and device and electronic equipment - Google Patents
Risk control strategy generation and risk control method and device and electronic equipment Download PDFInfo
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Abstract
The invention discloses a risk control strategy generation method, a risk control device and electronic equipment, wherein the risk control strategy generation method comprises the following steps: acquiring historical sample data of a target service; inputting the sample data and a preset expected value into a preset strategy generation model for training; and when the output value of the strategy generation model reaches a preset expected value, generating a corresponding target risk control strategy. According to the invention, the wind control strategy meeting the conditions is generated by setting the expected value output by the strategy, and the automatic selection and combination of the strategies are realized, so that the wind control strategy can be automatically optimized, and the efficiency and effect of generating the wind control strategy are improved; meanwhile, the wind control strategy generation method does not need manual interference, only needs manual local optimization on the strategy aiming at part of specific users, and improves the timeliness of generating the wind control strategy.
Description
Technical Field
The invention relates to the field of computer information processing, in particular to a risk control strategy generation method, a risk control strategy generation device, a risk control method, an electronic equipment generation device, an electronic equipment generation method, an electronic equipment generation device, and a computer readable medium.
Background
With the rapid development of information technology and internet technology, online services are rapidly developed and widely applied, such as payment services, registration services, marketing services, and the like. However, with the rapid development of online services, it is often the case that some lawbreakers perform fraudulent activities in online services by various means. Therefore, how to improve the security of the online service is getting more and more attention and attention.
The current risk rule is established through three steps of sample extraction, data analysis and strategy establishment. This process is subject to the knowledge of the rule maker and the analysis tools, resulting in inefficiencies. In order to identify a high-risk user or a high-risk transaction behavior related to fraud in the online service in time and improve the security of the online service, a risk control strategy is often deployed. At present, most of wind control strategies need to be completed in a manual mode when the wind control strategies are generated, time is long, and accuracy is poor. Therefore, how to efficiently and accurately generate a wind control strategy for each service becomes a technical problem which needs to be solved urgently at present.
Disclosure of Invention
In order to solve the problems of low efficiency and poor effect of generating a wind control strategy in the prior art, the invention provides a risk control strategy generation and risk control method, a risk control device and electronic equipment.
One aspect of the present invention provides a method for generating a wind control policy, including:
acquiring historical data of a target service;
inputting the historical data and a preset expected value into a preset strategy generation model for training;
and when the output value of the strategy generation model reaches a preset expected value, generating a target risk control strategy corresponding to the target service.
According to a preferred embodiment of the present invention, the acquiring historical data of the target service further includes:
and acquiring historical risk control data of the target service as a training sample, wherein the historical risk control data comprises service type data, credit granting state data and risk state data.
According to a preferred embodiment of the present invention, the inputting the historical data and the preset expected value into a preset strategy generation model for training further includes:
inputting the historical data and a preset strategy effect expected value into the strategy generation model;
and analyzing, simulating and calculating according to the historical data to obtain strategy effect values corresponding to different strategies.
According to a preferred embodiment of the present invention, the inputting the historical data and the preset expected value of the policy effect into the policy generation model further includes:
setting a weight for a risk control index in the historical data;
and carrying out weighted combination on any number of risk control indexes and inputting the risk control indexes into the strategy generation model.
According to a preferred embodiment of the present invention, when the output value of the policy generation model reaches a preset expected value, generating a target risk control policy corresponding to the target service further includes:
comparing strategy effect values corresponding to different strategies output by the strategy generation model with the strategy effect expected values;
and screening out the risk control strategy corresponding to the strategy effect value closest to the strategy effect expected value as the risk control strategy of the target service.
According to a preferred embodiment of the invention, the method further comprises:
and adjusting the risk control strategy according to the historical data of different users, and associating the adjusted risk control strategy with the corresponding user.
According to a preferred embodiment of the invention, the method further comprises:
and verifying whether the strategy effect value output by the adjusted risk control strategy is larger than the strategy effect value output by the risk control strategy before adjustment according to the historical data of different users.
A second aspect of the present invention provides a risk control method, comprising:
judging whether the business data has risks according to a risk control strategy;
wherein the risk control strategy is generated by the risk control strategy generation method.
A third aspect of the present invention provides a risk control policy generation apparatus, including:
the data acquisition module is used for acquiring historical data of the target service;
the data training module is used for inputting the historical data and a preset expected value into a preset strategy generation model for training;
and the strategy generation module is used for generating a target risk control strategy corresponding to the target service when the output value of the strategy generation model reaches a preset expected value.
According to a preferred embodiment of the present invention, the data acquisition module further comprises:
and the sample data acquisition unit is used for acquiring historical risk control data of the target service as a training sample, wherein the historical risk control data comprises service type data, credit granting state data and risk state data.
According to a preferred embodiment of the present invention, the data training module further comprises:
the data input unit is used for inputting the historical data and a preset strategy effect expected value into the strategy generation model;
and the data output unit is used for carrying out analysis simulation calculation according to the historical data to obtain strategy effect values corresponding to different strategies.
According to a preferred embodiment of the present invention, the data input unit further comprises:
the data adjusting unit is used for setting weight for the risk control indexes in the historical data;
and the data combination unit is used for performing weighted combination on any number of risk control indexes and inputting the risk control indexes into the strategy generation model.
According to a preferred embodiment of the present invention, the policy generation module further comprises:
the effect comparison unit is used for comparing the strategy effect values corresponding to different strategies output by the strategy generation model with the strategy effect expected values;
and the data screening unit is used for screening out the risk control strategy corresponding to the strategy effect value closest to the strategy effect expected value as the risk control strategy of the target service.
According to a preferred embodiment of the invention, the device further comprises:
and the strategy adjusting module is used for adjusting the risk control strategy according to the historical data of different users and associating the adjusted risk control strategy with the corresponding user.
According to a preferred embodiment of the invention, the device further comprises:
and the strategy checking module is used for verifying whether the strategy effect value output by the adjusted risk control strategy is larger than the strategy effect value output by the risk control strategy before adjustment according to the historical data of different users.
A fourth aspect of the present invention provides a risk control device comprising:
the judging module is used for judging whether the business data has risks according to the risk control strategy;
wherein the risk control strategy is generated by the risk control strategy generation method.
A fifth aspect of the present invention provides an electronic apparatus, wherein the electronic apparatus includes: a processor; and the number of the first and second groups,
a memory storing computer executable instructions that, when executed, cause the processor to perform any of the methods.
A sixth aspect of the invention provides a computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement any of the methods.
The technical scheme of the invention has the following beneficial effects:
according to the method, the wind control strategy meeting the conditions is generated by setting the expected value output by the strategy, and the automatic selection and combination of the strategies are realized, so that the wind control strategy can be automatically optimized, and the efficiency and the effect of generating the wind control strategy are improved; meanwhile, the wind control strategy generation method does not need manual interference, only needs manual local optimization on the strategy aiming at part of specific users, and improves the timeliness of generating the wind control strategy.
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In order to make the technical problems solved by the present invention, the technical means adopted and the technical effects obtained more clear, the following will describe in detail the embodiments of the present invention with reference to the accompanying drawings. It should be noted, however, that the drawings described below are only drawings of exemplary embodiments of the invention, from which other embodiments can be derived by those skilled in the art without inventive step.
FIG. 1 is a schematic flow chart of a risk control strategy generation method of the present invention;
FIG. 2 is a schematic flow chart of a risk control method of the present invention;
FIG. 3 is a schematic diagram of a risk control policy generation apparatus according to the present invention;
FIG. 4 is a schematic diagram of a risk control strategy generation electronic device architecture framework of the present invention;
FIG. 5 is a schematic diagram of a computer-readable storage medium of the present invention.
Detailed Description
Exemplary embodiments of the present invention will now be described more fully with reference to the accompanying drawings. The exemplary embodiments, however, may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these exemplary embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the invention to those skilled in the art. The same reference numerals denote the same or similar elements, components, or parts in the drawings, and thus their repetitive description will be omitted.
Features, structures, characteristics or other details described in a particular embodiment do not preclude the fact that the features, structures, characteristics or other details may be combined in a suitable manner in one or more other embodiments in accordance with the technical idea of the invention.
In describing particular embodiments, the present invention has been described with reference to features, structures, characteristics or other details that are within the purview of one skilled in the art to provide a thorough understanding of the embodiments. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific features, structures, characteristics, or other details.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
It will be understood that, although the terms first, second, third, etc. may be used herein to describe various elements, components, or sections, these terms should not be construed as limiting. These phrases are used to distinguish one from another. For example, a first device may also be referred to as a second device without departing from the spirit of the present invention.
The term "and/or" and/or "includes any and all combinations of one or more of the associated listed items.
In order that the objects, technical solutions and advantages of the present invention will become more apparent, the present invention will be further described in detail with reference to the accompanying drawings in conjunction with the following specific embodiments.
Fig. 1 is a schematic flow chart of a risk control policy generation method according to the present invention. As shown in fig. 1, the method includes:
s101, obtaining historical data of the target service.
Specifically, the target service may be any type of service such as a payment service, a shopping service, a registration service, a marketing service, a credit evaluation service, and the like. The target service may be any service that needs to generate a wind control policy, a specific service type of the target service is not limited in the embodiment of this specification, and the historical data may be historical risk control data, and includes a plurality of wind control indicators: business type, credit status, risk status, repayment presentation, etc.
Generally, when a wind control policy is deployed for a target service, monitoring is performed on one or more aspects of the target service, for example, monitoring is performed on service equipment or a user of the target service. In the embodiments of the present specification, an object monitored using a wind control policy is referred to as a wind control subject. For example, if the generated wind control policy is to monitor the risk of the device, the target wind control subject in step 101 is the device; if the generated wind control policy is to monitor the user, the target wind control subject in step 101 is the user. Of course, in a specific implementation, the target wind control main body may be one or more, and a specific number thereof may be set based on the wind control policy that needs to be generated, which is not limited in this specification. In specific implementation, the historical service data may be obtained from a service database of the target service or a service platform executing the target service.
In this embodiment of the present specification, if an event corresponding to a certain piece of acquired historical service data is a risk event, a risk state of the historical service data may be marked as a risk sample, and if an event corresponding to a certain piece of acquired historical service data is a non-risk event, a risk state of the historical service data may be marked as a safety sample.
For example, in one embodiment, if a certain piece of historical service data is acquired, the following is performed:
XXXX year XX month XX day XX: XX, the user A obtains one ten thousand yuan of credit amount from the financial institution through XX equipment, and the repayment time exceeds the repayment deadline by XX days; if the event is a risk event, the risk status of the historical business data can be marked as a risk sample.
And S102, inputting the historical data and a preset expected value into a preset strategy generation model for training.
Specifically, the acquired historical data in the above embodiment is input into a preset policy generating model, where each wind control indicator in the historical data may be used as an input feature of the policy generating model, and a value corresponding to the wind control indicator is input feature data, and a preset policy effect expected value is also input into the policy generating model.
The policy effect expected value is a result value expected by a business person through a policy to be generated, the policy generation model continuously adjusts the generated wind control policy according to a large amount of input data through a plurality of analysis methods or combinations, continuously fits the expected value through simulation calculation, and outputs an effect value correspondingly each time one policy is obtained, the closer the output effect value is to the policy effect expected value, the more the corresponding policy meets the requirements of the business person, for example, the policy effect expected value is set to a risk effect, the corresponding policy effect expected value can be set to 100, which is equivalent to an ideal state without risk, when the effect value output by the policy generation model is lower, the higher the description risk is, and when the effect value output by the policy generation model is closer to 100, the lower the description risk is the corresponding policy risk is.
In order to make the output policy more satisfactory, the data may be processed before the historical data is input into the policy generation model, for example, the data may be weighted and combined according to the wind control indicator, for example: the wind control indexes comprise passing rate, credit line, risk loss and profit income, the weight is distributed to the four wind control indexes respectively according to needs, if the risk loss is emphasized, the weight of the risk loss is set to be higher than the rest three items, if the profit income is emphasized, the weight of the profit income is set to be higher than the rest three items, and the output result of the strategy generation model is also influenced by the weight of the wind control indexes.
In this embodiment, the index type corresponding to the wind control index may be a categorical type, a numerical type, a boolean type, and the like. Aiming at the category index, the set data processing rule can be used for counting the times in a period of time, the number of duplicate removal statistics and the like; for the numerical index, the set data processing rule can carry out summation, average value, maximum value, minimum value, variance, standard deviation and the like on historical service data in a period of time; for the boolean index, the set data processing rule may be to sum, average, or the like historical traffic data over a period of time. The specific value of the period of time may be set according to an actual application scenario, for example, the period of time may be 1 minute or three months, and the embodiment of the present specification does not limit the specific value of the period of time. In this embodiment of the present specification, the wind control indicators with higher correlation may be combined and combined into one input feature input policy generation model.
After the wind control indexes are processed, any number of wind control indexes can be combined to form combined characteristics as input characteristics of the strategy generation model, and the formed wind control strategy is more targeted and better meets the requirements of business personnel.
S103, when the output value of the strategy generation model reaches a preset expected value, generating a target risk control strategy corresponding to the target service.
Specifically, the strategy generation model in the above embodiment is continuously trained to finally obtain an output value closest to the expected value of the strategy effect, and when the service personnel selects the secondary output value, the strategy generation model automatically generates and extracts the corresponding wind control strategy, which is the most target wind control strategy. After the target wind control strategy is obtained, the wind control strategy can be verified, for example, a group of data is randomly selected from historical data of the target service, after a strategy generation model with the target wind control strategy is input, whether the obtained risk effect value is higher than a risk effect value corresponding to the original wind control strategy is checked, if so, the target wind control strategy is superior to the original wind control strategy, for example, a group of data of a user in the target service is selected, the risk effect value obtained by executing according to the original wind control strategy is lower, the historical data is displayed as a risk sample, the risk effect value obtained by executing according to the target wind control strategy is higher, and the historical data is displayed as a safety sample.
When the generated target wind control strategy is verified, whether the wind control strategy needs to be optimized can be judged through one or more of the following evaluation indexes: risk coverage, strategy accuracy, user disturbance rate, and the like.
Generating a risk control policy may be understood as a set of wind control rules set for different target wind control subjects, for example, if the policy effect expectation is set as a risk effect according to the above embodiment, the set wind control rules may be:
setting an ideal risk effect value as 100, and directly passing services with the risk effect value higher than 80 points of a target wind control main body; the risk effect value of the target wind control subject is manually checked based on the business between the 50-point value and the 80-point value; and the business with the risk effect value of the target wind control subject lower than 50 points can directly refuse to execute.
Preferably, when a specific client is faced, the common wind control strategy is not completely suitable for the client, the prominent wind control indexes of the client can be weighted and input into the strategy generation model as input features to adjust the obtained wind control strategy, and the specific part in the wind control strategy can be manually optimized to meet the requirements of the two parties to obtain a global optimal result.
According to the risk control strategy generation method provided by the embodiment of the invention, the wind control strategy meeting the conditions is generated by setting the expected value output by the strategy, and the automatic selection and combination of the strategies are realized, so that the wind control strategy can be automatically optimized, the efficiency and the effect of generating the wind control strategy are improved, a large amount of time and labor cost are saved, the interference of human experience is avoided, and the accuracy and the comprehensiveness of the generated wind control strategy can be improved.
Fig. 2 is a schematic flow chart of a risk control method according to the present invention. As shown in fig. 2, the method may include: judging whether the business data has risks according to a wind control strategy; the wind control strategy is generated by the wind control strategy generation method of any one of the embodiments.
In this embodiment, the service data is imported into the wind control policy, and the category of the service data may be obtained, for example: a risk free category, a risk present category, a high risk present category, etc. In this way, risk control can be performed, reducing the losses due to risk.
Those skilled in the art will appreciate that all or part of the steps to implement the above-described embodiments are implemented as programs (computer programs) executed by a computer data processing apparatus. When the computer program is executed, the method provided by the invention can be realized. Furthermore, the computer program may be stored in a computer readable storage medium, which may be a readable storage medium such as a magnetic disk, an optical disk, a ROM, a RAM, or a storage array composed of a plurality of storage media, such as a magnetic disk or a magnetic tape storage array. The storage medium is not limited to centralized storage, but may be distributed storage, such as cloud storage based on cloud computing.
Embodiments of the apparatus of the present invention are described below, which may be used to perform method embodiments of the present invention. The details described in the device embodiments of the invention should be regarded as complementary to the above-described method embodiments; reference is made to the above-described method embodiments for details not disclosed in the apparatus embodiments of the invention.
Those skilled in the art will appreciate that the modules in the above-described embodiments of the apparatus may be distributed as described in the apparatus, and may be correspondingly modified and distributed in one or more apparatuses other than the above-described embodiments. The modules of the above embodiments may be combined into one module, or further split into multiple sub-modules.
Fig. 3 is a schematic diagram of an architecture of a risk control policy generation apparatus 300 according to the present invention, where the apparatus 300 includes:
a data obtaining module 301, configured to obtain historical data of a target service;
a data training module 302, configured to input the historical data and a preset expected value into a preset strategy generation model for training;
a policy generating module 303, configured to generate a target risk control policy corresponding to the target service when an output value of the policy generating model reaches a preset expected value.
According to a preferred embodiment of the present invention, the data obtaining module 301 further comprises:
and the sample data acquisition unit is used for acquiring historical risk control data of the target service as a training sample, wherein the historical risk control data comprises service type data, credit granting state data and risk state data.
According to a preferred embodiment of the present invention, the data training module 302 further comprises:
the data input unit is used for inputting the historical data and a preset strategy effect expected value into the strategy generation model;
and the data output unit is used for carrying out analysis simulation calculation according to the historical data to obtain strategy effect values corresponding to different strategies.
According to a preferred embodiment of the present invention, the data input unit further comprises:
the data adjusting unit is used for setting weight for the risk control indexes in the historical data;
and the data combination unit is used for performing weighted combination on any number of risk control indexes and inputting the risk control indexes into the strategy generation model.
According to a preferred embodiment of the present invention, the policy generation module 303 further comprises:
the effect comparison unit is used for comparing the strategy effect values corresponding to different strategies output by the strategy generation model with the strategy effect expected values;
and the data screening unit is used for screening out the risk control strategy corresponding to the strategy effect value closest to the strategy effect expected value as the risk control strategy of the target service.
According to a preferred embodiment of the present invention, the apparatus 300 further comprises:
and the strategy adjusting module is used for adjusting the risk control strategy according to the historical data of different users and associating the adjusted risk control strategy with the corresponding user.
According to a preferred embodiment of the present invention, the apparatus 300 further comprises:
and the strategy checking module is used for verifying whether the strategy effect value output by the adjusted risk control strategy is larger than the strategy effect value output by the risk control strategy before adjustment according to the historical data of different users.
Furthermore, an embodiment of the present invention also provides a risk control device, including:
the judging module is used for judging whether the business data has risks according to the wind control strategy; the wind control strategy is generated by a wind control strategy generation method of any embodiment.
The specific details of the implementation process of the functions and actions of each module in the device are found in the implementation process of the corresponding step in the risk control method, and are not described herein again.
In the following, embodiments of the electronic device of the present invention are described, which may be regarded as specific physical implementations for the above-described embodiments of the method and apparatus of the present invention. Details described in the embodiments of the electronic device of the invention should be considered supplementary to the embodiments of the method or apparatus described above; for details which are not disclosed in embodiments of the electronic device of the invention, reference may be made to the above-described embodiments of the method or the apparatus.
FIG. 4 is a schematic diagram of a risk control strategy generation electronic device architecture framework of the present invention; an electronic device 400 according to this embodiment of the invention is described below with reference to fig. 4. The electronic device 400 shown in fig. 4 is only an example and should not bring any limitation to the function and the scope of use of the embodiments of the present invention.
As shown in fig. 4, electronic device 400 is embodied in the form of a general purpose computing device. The components of electronic device 400 may include, but are not limited to: at least one processing unit 410, at least one memory unit 420, a bus 430 that connects the various system components (including the memory unit 420 and the processing unit 410), a display unit 440, and the like.
Wherein the storage unit stores program code executable by the processing unit 410 to cause the processing unit 410 to perform the steps according to various exemplary embodiments of the present invention described in the above-mentioned electronic prescription flow processing method section of the present specification. For example, the processing unit 410 may perform the steps as shown in fig. 1.
The storage unit 420 may include readable media in the form of volatile storage units, such as a random access memory unit (RAM)4201 and/or a cache memory unit 4202, and may further include a read only memory unit (ROM) 4203.
The storage unit 420 may also include a program/utility 4204 having a set (at least one) of program modules 4205, such program modules 4205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The electronic device 400 may also communicate with one or more external devices 500 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 400, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 400 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 450. Also, the electronic device 400 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) via the network adapter 460. The network adapter 460 may communicate with other modules of the electronic device 400 via the bus 430. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with electronic device 400, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments of the present invention described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiment of the present invention can be embodied in the form of a software product, which can be stored in a computer-readable storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to make a computing device (which can be a personal computer, a server, or a network device, etc.) execute the above-mentioned method according to the present invention. The computer program, when executed by a data processing apparatus, enables the computer readable medium to implement the above-described method of the invention, namely: acquiring historical data of a target service; inputting the historical data and a preset expected value into a preset strategy generation model for training; and when the output value of the strategy generation model reaches a preset expected value, generating a target risk control strategy corresponding to the target service.
FIG. 5 is a schematic diagram of a computer readable storage medium of the present invention, the computer program may be stored on one or more computer readable media, as shown in FIG. 5. The computer readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
In summary, the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functionality of some or all of the components in embodiments in accordance with the invention may be implemented in practice using a general purpose data processing device such as a microprocessor or a Digital Signal Processor (DSP). The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
While the foregoing embodiments have described the objects, aspects and advantages of the present invention in further detail, it should be understood that the present invention is not inherently related to any particular computer, virtual machine or electronic device, and various general-purpose machines may be used to implement the present invention. The invention is not to be considered as limited to the specific embodiments thereof, but is to be understood as being modified in all respects, all changes and equivalents that come within the spirit and scope of the invention.
Claims (10)
1. A risk control strategy generation method is characterized by comprising the following steps:
acquiring historical data of a target service;
inputting the historical data and a preset expected value into a preset strategy generation model for training;
and when the output value of the strategy generation model reaches a preset expected value, generating a target risk control strategy corresponding to the target service.
2. The method for generating a risk control policy according to claim 1, wherein the obtaining historical data of the target service further comprises:
and acquiring historical risk control data of the target service as a training sample, wherein the historical risk control data comprises service type data, credit granting state data and risk state data.
3. The risk control strategy generation method of any of claims 1-2, wherein the inputting the historical data and a predetermined expected value into a predetermined strategy generation model for training further comprises:
inputting the historical data and a preset strategy effect expected value into the strategy generation model;
and analyzing, simulating and calculating according to the historical data to obtain strategy effect values corresponding to different strategies.
4. The risk control strategy generation method of any of claims 1-3, wherein the inputting the historical data and a preset strategy effect expected value into the strategy generation model further comprises:
setting a weight for a risk control index in the historical data;
and carrying out weighted combination on any number of risk control indexes and inputting the risk control indexes into the strategy generation model.
5. The risk control strategy generation method according to any one of claims 1 to 4, wherein the generating a target risk control strategy corresponding to the target service when the output value of the strategy generation model reaches a preset desired value further comprises:
comparing strategy effect values corresponding to different strategies output by the strategy generation model with the strategy effect expected values;
and screening out the risk control strategy corresponding to the strategy effect value closest to the strategy effect expected value as the risk control strategy of the target service.
6. A risk control method, comprising:
judging whether the business data has risks according to a risk control strategy;
wherein the risk control strategy is generated by the risk control strategy generation method of any one of claims 1-7.
7. A risk control policy generation apparatus, comprising:
the data acquisition module is used for acquiring historical data of the target service;
the data training module is used for inputting the historical data and a preset expected value into a preset strategy generation model for training;
and the strategy generation module is used for generating a target risk control strategy corresponding to the target service when the output value of the strategy generation model reaches a preset expected value.
8. A risk control device, comprising:
the judging module is used for judging whether the business data has risks according to the risk control strategy;
wherein the risk control strategy is generated by the risk control strategy generation method of any one of claims 1-7.
9. An electronic device, wherein the electronic device comprises:
a processor; and the number of the first and second groups,
a memory storing computer-executable instructions that, when executed, cause the processor to perform the method of any of claims 1-5.
10. A computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement the method of any of claims 1-5.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112529702A (en) * | 2021-02-05 | 2021-03-19 | 北京淇瑀信息科技有限公司 | User credit granting strategy allocation method and device and electronic equipment |
| CN112784420A (en) * | 2021-01-26 | 2021-05-11 | 支付宝(杭州)信息技术有限公司 | Simulation evaluation method, device and equipment for wind control strategy |
| CN113052516A (en) * | 2021-05-31 | 2021-06-29 | 深圳高灯计算机科技有限公司 | Wind control method, system and equipment based on stream type calculation |
| CN113313575A (en) * | 2021-06-08 | 2021-08-27 | 支付宝(杭州)信息技术有限公司 | Method and device for determining risk identification model |
| CN113392921A (en) * | 2021-06-29 | 2021-09-14 | 深圳市魔数智擎人工智能有限公司 | Data-driven wind control strategy rule generation method and system |
| CN113610132A (en) * | 2021-07-29 | 2021-11-05 | 上海淇玥信息技术有限公司 | User equipment identification method and device and computer equipment |
| CN113657710A (en) * | 2021-07-08 | 2021-11-16 | 数云科际(深圳)技术有限公司 | Wind control early warning method, system, equipment and computer readable storage medium |
| CN114019901A (en) * | 2021-11-04 | 2022-02-08 | 北京安盟信息技术股份有限公司 | Method and device for integrally controlling information and production safety risk of numerical control machine tool |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6876992B1 (en) * | 2000-11-28 | 2005-04-05 | Willis North America Inc. | Method and system for risk control optimization |
| CN109002949A (en) * | 2017-06-06 | 2018-12-14 | 阿里巴巴集团控股有限公司 | A kind of method and device of air control strategy configuration and business air control |
| CN109034660A (en) * | 2018-08-22 | 2018-12-18 | 平安科技（深圳）有限公司 | Based on the determination method and relevant apparatus of the risk control strategy of prediction model |
| CN109472609A (en) * | 2018-11-09 | 2019-03-15 | 阿里巴巴集团控股有限公司 | A kind of air control method for determining reason and device |
| WO2019091177A1 (en) * | 2017-11-10 | 2019-05-16 | 阿里巴巴集团控股有限公司 | Risk identification model building method, apparatus and device and risk identification method, apparatus and device |
| CN110443618A (en) * | 2019-07-10 | 2019-11-12 | 阿里巴巴集团控股有限公司 | The generation method and device of air control strategy |
| CN111046425A (en) * | 2019-12-12 | 2020-04-21 | 支付宝(杭州)信息技术有限公司 | Method and device for risk identification by combining multiple parties |
| CN111209930A (en) * | 2019-12-20 | 2020-05-29 | 上海淇玥信息技术有限公司 | Method and device for generating credit granting strategy and electronic equipment |
-
2020
- 2020-07-14 CN CN202010677247.2A patent/CN112016788A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6876992B1 (en) * | 2000-11-28 | 2005-04-05 | Willis North America Inc. | Method and system for risk control optimization |
| CN109002949A (en) * | 2017-06-06 | 2018-12-14 | 阿里巴巴集团控股有限公司 | A kind of method and device of air control strategy configuration and business air control |
| WO2019091177A1 (en) * | 2017-11-10 | 2019-05-16 | 阿里巴巴集团控股有限公司 | Risk identification model building method, apparatus and device and risk identification method, apparatus and device |
| CN109034660A (en) * | 2018-08-22 | 2018-12-18 | 平安科技（深圳）有限公司 | Based on the determination method and relevant apparatus of the risk control strategy of prediction model |
| CN109472609A (en) * | 2018-11-09 | 2019-03-15 | 阿里巴巴集团控股有限公司 | A kind of air control method for determining reason and device |
| CN110443618A (en) * | 2019-07-10 | 2019-11-12 | 阿里巴巴集团控股有限公司 | The generation method and device of air control strategy |
| CN111046425A (en) * | 2019-12-12 | 2020-04-21 | 支付宝(杭州)信息技术有限公司 | Method and device for risk identification by combining multiple parties |
| CN111209930A (en) * | 2019-12-20 | 2020-05-29 | 上海淇玥信息技术有限公司 | Method and device for generating credit granting strategy and electronic equipment |
Non-Patent Citations (1)
| Title |
|---|
| 邢巍;余锦河;曹肖悦;江帆;: "基于数据分析的业务风险防控研究", 现代商业, no. 09, pages 15 - 17 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112784420A (en) * | 2021-01-26 | 2021-05-11 | 支付宝(杭州)信息技术有限公司 | Simulation evaluation method, device and equipment for wind control strategy |
| CN112529702A (en) * | 2021-02-05 | 2021-03-19 | 北京淇瑀信息科技有限公司 | User credit granting strategy allocation method and device and electronic equipment |
| CN113052516A (en) * | 2021-05-31 | 2021-06-29 | 深圳高灯计算机科技有限公司 | Wind control method, system and equipment based on stream type calculation |
| CN113052516B (en) * | 2021-05-31 | 2022-01-04 | 深圳高灯计算机科技有限公司 | Wind control method, system and equipment based on stream type calculation |
| CN113313575A (en) * | 2021-06-08 | 2021-08-27 | 支付宝(杭州)信息技术有限公司 | Method and device for determining risk identification model |
| CN113392921A (en) * | 2021-06-29 | 2021-09-14 | 深圳市魔数智擎人工智能有限公司 | Data-driven wind control strategy rule generation method and system |
| CN113657710A (en) * | 2021-07-08 | 2021-11-16 | 数云科际(深圳)技术有限公司 | Wind control early warning method, system, equipment and computer readable storage medium |
| CN113610132A (en) * | 2021-07-29 | 2021-11-05 | 上海淇玥信息技术有限公司 | User equipment identification method and device and computer equipment |
| CN114019901A (en) * | 2021-11-04 | 2022-02-08 | 北京安盟信息技术股份有限公司 | Method and device for integrally controlling information and production safety risk of numerical control machine tool |
| CN114019901B (en) * | 2021-11-04 | 2022-07-01 | 北京安盟信息技术股份有限公司 | Method and device for integrally controlling information and production safety risk of numerical control machine tool |
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