CN111951008A - Risk prediction method and device, electronic equipment and readable storage medium - Google Patents
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Abstract
The present application relates to the field of electronic devices, and in particular, to a method, an apparatus, an electronic device, and a readable storage medium for risk prediction, where the method includes: by collecting the transaction information of the target user; determining risk influence factor information according to the target user transaction information; inputting the risk influence factor information into a risk prediction model; the risk prediction model is a convolutional neural network model loaded with a group intelligent optimization algorithm; and outputting a risk prediction result by a risk prediction model, wherein the risk prediction result is used for representing whether the target user is a risk user. The risk prediction scheme based on the CNN model loaded with the BSA algorithm disclosed by the embodiment of the application can be used for rapidly, efficiently and accurately predicting whether a user is a risk user.
Description
Technical Field
The present application relates to the field of information security technologies, and in particular, to a risk prediction method, an apparatus, an electronic device, and a readable storage medium.
Background
In order to draw a new user in fierce competition, the consumption habits of the user are developed, various types of marketing activities and subsidy activities are developed, and a group of black users concentrating on the marketing activities, namely, a so-called wool party, is promoted while the benefits are brought to normal users. At present, under the great interest temptation, the speed of upgrading manipulations and technologies of wool weeding wool is faster and faster, the traditional wind control system based on expert rules is difficult to follow the iteration of the manipulations of wool, and the traditional wind control system can be used for wind control detection aiming at the linear overground line rules only after the wool party has gained a profit. Thus, a vicious circle of "win-deployment rule-win-adjustment rule twice by changing technique of thinning wool" is easily formed, and the risk detection and prevention of the behavior of thinning wool cannot be fundamentally performed. In the prior art, a scheme for discovering malicious users or IP by adopting a statistical method or a neural network is adopted, but the defects of low network black product data identification efficiency, high false alarm rate, high missing report rate and the like exist.
Disclosure of Invention
The present application aims to solve at least one of the above technical drawbacks. The technical scheme adopted by the application is as follows:
in a first aspect, an embodiment of the present application provides a risk prediction method, where the method includes:
collecting target user transaction information;
determining risk influence factor information according to the target user transaction information;
inputting the risk influence factor information into a risk prediction model; the risk prediction model is a convolutional neural network model loaded with a group intelligent optimization algorithm;
and outputting a risk prediction result by a risk prediction model, wherein the risk prediction result is used for representing whether the target user is a risk user.
Optionally, the group intelligent optimization algorithm is a bird group optimization algorithm.
Optionally, the constructing of the risk prediction model comprises:
loading a bird group optimization algorithm into the convolutional neural network model, and determining parameters of the convolutional neural network model according to the bird group optimization algorithm;
acquiring sample transaction information to train the neural network model loaded with the bird swarm optimization algorithm;
and determining the trained neural network model loaded with the bird swarm optimization algorithm as a risk prediction model according to a preset training rule.
Optionally, the bird swarm optimization algorithm is a bird swarm optimization algorithm with boundary constraint conditions added.
Optionally, the preset rule includes at least one of:
determining the convolutional neural network model loaded with the bird swarm optimization algorithm as a risk prediction model when the training iteration number reaches a preset threshold value;
or the like, or, alternatively,
inputting sample transaction data to a convolutional neural network model loaded with a bird swarm optimization algorithm, and calculating a sample fitness value after iterative training for a preset number of times;
and if the sample fitness value accords with a preset fitness value, stopping training, and determining the convolutional neural network model loaded with the bird swarm optimization algorithm as a risk prediction model.
Optionally, the method comprises: and collecting transaction data of the target user in real time.
Second aspect an embodiment of the present invention provides a risk prediction apparatus, where the apparatus includes: the device comprises an acquisition module, a determination module, an input module, a prediction module and a storage module, wherein:
the acquisition module is used for acquiring the transaction information of the target user;
the determining module is used for determining risk influence factor information according to the target user transaction information;
the input module is used for inputting the risk influence factor information into a risk prediction model;
the storage module is used for storing a risk prediction model, wherein the risk prediction model is a convolutional neural network model loaded with a group intelligent optimization algorithm;
and the prediction module is used for controlling a risk prediction model to output a risk prediction result, wherein the risk prediction result is used for representing whether the target user is a risk user.
Optionally, the group intelligent optimization algorithm is a bird group optimization algorithm added with boundary constraint conditions.
Optionally, the apparatus further comprises a model building module, wherein the model building module is configured to:
loading a bird group optimization algorithm into the convolutional neural network model, and determining parameters of the convolutional neural network model according to the bird group optimization algorithm;
acquiring sample transaction information to train the neural network model loaded with the bird swarm optimization algorithm;
and determining the trained neural network model loaded with the bird swarm optimization algorithm as a risk prediction model according to a preset training rule.
Optionally, the collecting module is further configured to collect the transaction data of the target user in real time.
In a third aspect, an embodiment of the present invention provides an electronic device, including a processor and a memory;
the memory is used for storing operation instructions;
and the processor is used for executing the risk prediction method by calling the operation instruction.
In a fourth aspect, a computer-readable storage medium has stored thereon a computer program which, when executed by a processor, implements the above-described method of risk prediction.
The technical scheme provided by the embodiment of the application has the following beneficial effects: by collecting the transaction information of the target user; determining risk influence factor information according to the target user transaction information; inputting the risk influence factor information into a risk prediction model; the risk prediction model is a convolutional neural network model loaded with a group intelligent optimization algorithm; and outputting a risk prediction result by a risk prediction model, wherein the risk prediction result is used for representing whether the target user is a risk user. The risk prediction scheme disclosed by the embodiment of the application can be used for processing and predicting the user transaction data by using the convolutional neural network model of the improved bird swarm optimization algorithm, can be used for rapidly, efficiently and accurately predicting whether the user is a risk user, and has the characteristics of high accuracy rate, low false alarm rate and low cost. Avoiding unnecessary loss of risk users, such as black-producing users, to merchants or customers by utilizing marketing scheme loopholes,
drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings used in the description of the embodiments of the present application will be briefly described below.
Fig. 1 is a schematic flowchart of a risk prediction method according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a risk prediction apparatus according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of explaining the present application and are not to be construed as limiting the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term "and/or" includes all or any element and all combinations of one or more of the associated listed items.
The present application relates to a risk prediction technology, and in particular, to a scheme for performing risk prediction on a convolutional neural network model loaded with a bird swarm optimization algorithm, which may be used for detecting black birth users, and in order to more clearly describe the present application, some definitions, concepts or apparatuses that may be used in the embodiments are described below:
black product data: account number, device, phone number, location, etc. used by the fraudulent party.
And (4) black yield users: the black trading user brings great threats to the computer information system security and the network space management order by taking the internet as a medium and taking the network technology as a main means.
False transaction: the buyer and the seller do not have the commodity purchasing behavior of the actual commodity purchase.
Thinning wool: benefits are obtained using various offers.
The Bird Swarm optimization algorithm is Bird Swarm optimization algorithm, which is Bird Swarm optimization algorithm, is abbreviated as BSA algorithm in English and is a Swarm intelligence algorithm.
Convolutional Neural Networks (CNN, hereinafter, CNN model) is a kind of feed forward Neural Networks (feed forward Neural Networks) containing convolution calculation and having a deep structure, and is one of the representative algorithms of deep learning (deep learning). The convolutional neural network has a representation learning (representation learning) capability, and can perform shift-invariant classification (shift-invariant classification) on input information according to a hierarchical structure of the convolutional neural network.
The following describes the technical solutions of the present application and how to solve the above technical problems with specific embodiments in conjunction with the accompanying drawings. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
To make the purpose, technical solution and advantages of the present application clearer, fig. 1 discloses a flowchart of a risk prediction method provided by an embodiment of the present application, and as shown in fig. 1, the risk prediction method includes:
s101, collecting transaction information of a target user;
s102, determining risk influence factor information according to the target user transaction information;
s103, inputting the risk influence factor information into a risk prediction model; the risk prediction model is a convolutional neural network model loaded with a group intelligent optimization algorithm;
and S104, outputting a risk prediction result by the risk prediction model, wherein the risk prediction result is used for representing whether the target user is a risk user.
In an optional embodiment, the swarm intelligent optimization algorithm may be a bird swarm optimization algorithm, a particle swarm optimization algorithm, or other intelligent optimization algorithms.
The risk prediction method disclosed based on the embodiment is mainly realized based on a risk prediction model, so the risk prediction model and the construction of the risk prediction model are introduced in a preposed manner in the embodiment.
In the embodiment of the present application, the risk prediction mode is a convolutional neural network model loaded with a group intelligence optimization algorithm. Optionally, when the intelligent population optimization algorithm is a bird population optimization algorithm, constructing a risk prediction model scheme includes:
step 1, loading a bird group optimization algorithm into the convolutional neural network model, and determining parameters of the convolutional neural network model according to the bird group optimization algorithm, namely determining hidden layer parameters of a CNN model.
Step 2, obtaining sample transaction information to train the neural network model loaded with the bird swarm optimization algorithm; the sample transaction information is transaction information of a plurality of users including risk users, risk influence factor information (also called characteristic value information) of the sample transaction information is extracted from the sample transaction information, an influence coefficient of the risk influence factor is calculated, and the risk influence factor is input into a CNN model loaded with a BSA algorithm for training.
Step 3, determining the trained neural network model loaded with the bird swarm optimization algorithm as a risk prediction model according to a preset training rule:
in this step, the preset training rule may be set such that the number of times of training iteration performed on the CNN model loaded with the BSA algorithm by using the sample transaction data reaches a preset threshold, for example, the number of times of training iteration 500 is equal to the preset threshold, and the model is considered to reach a stable state, that is, the convolutional neural network model loaded with the bird swarm optimization algorithm may be determined to be a risk prediction model;
optionally, the preset training rule may also be set as the following rule:
step 3-1, inputting sample transaction data to a convolutional neural network model loaded with a bird swarm optimization algorithm, iteratively training for a preset number of times, and then calculating a sample fitness value: and dividing the sample transaction data into 10 parts according to a ten-fold intersection method, wherein 9 parts are used as training data, 1 part is used as verification data, the 10 parts of training data are used for respectively training the CNN model loaded with the BSA algorithm, and the result obtained by the first 9 parts is used as a training result and is compared with the verification result of the verification data so as to adjust the model. The training process is as follows: inputting training data into a CNN model loaded with a BSA algorithm, iterating for a preset number of times, for example, iterating for 1 time, updating the position information of the bird group individuals, adjusting the parameters of a hidden layer of the model, starting iterative training, calculating the fitness value of the bird group individuals, and updating the current optimal fitness value and optimal performance.
3-2, stopping training if the sample fitness value accords with a preset fitness value, and determining that the convolutional neural network model loaded with the bird swarm optimization algorithm is a risk prediction model: and (3) when the optimal fitness value calculated in the step (3-2) accords with a preset fitness value, for example, the accuracy of the verification result of the model reaches 97%, and the recall rate of the model, namely the recall rate reaches more than 95%, determining that the model reaches a stable state.
Optionally, when the sample fitness value meets the preset fitness value, it may be determined whether the current iteration number reaches the predetermined iteration number, and if not, the iterative training is performed again.
In an optional embodiment of the present application, the bird swarm optimization algorithm is a bird swarm optimization algorithm with an increased boundary constraint condition, so that the quality of the solution and the convergence speed of the algorithm are improved, and the local optimization is avoided.
The following describes a basic implementation flow of risk prediction based on the risk prediction model constructed in the above embodiments:
step 1, collecting a target user transaction message in real time;
step 2, determining risk influence factor information according to the target user transaction information: classifying the transaction messages collected in real time in the step 1, extracting risk influence factors and calculating influence coefficients, wherein the risk influence factors include but are not limited to: the method comprises 218 risk influence factors, such as the number of login times of a target user in a preset period, the number of registered accounts of the target user (or a target terminal), the number of transactions initiated to the same object in a preset period, the number of times that a transaction amount exceeds a preset upper limit in the preset period, the latest transaction position information of the target user, the transaction number of the target user on a single transaction day, the total transaction number in a latest preset period, whether a grade transaction object and a history object, position information and a mobile phone bank signed merchant are in different places or not, and the like.
Step 3, inputting all the acquired risk influence factor information into a risk prediction model;
and 4, outputting a risk prediction result by a risk prediction model, wherein the risk prediction result is used for representing whether the target user is a risk user. For example, when the prediction result output by the risk prediction model is 1, the target user is considered as a risk user, such as a wool party, otherwise, the target user is considered as a non-risk user.
And 5, sending the risk prediction result of the target user to a third-party platform and other user analysis platforms to develop downstream applications.
The risk prediction scheme based on the CNN model loaded with the BSA algorithm, disclosed by the embodiment of the application, can be used for rapidly, efficiently and accurately predicting whether a user is a risk user or not, effectively tracing the source of the risk user and processing the risk user, and has the characteristics of high accuracy rate, low false alarm rate and low cost.
Fig. 2 shows that the present application provides a risk prediction apparatus, and as shown in fig. 2, the apparatus may mainly include: a 201 acquisition module, a 202 determination module, a 203 input module, a 204 storage module 205 prediction module and wherein:
the 201 acquisition module is used for acquiring transaction information of a target user;
the 202 determining module is configured to determine risk impact factor information according to the target user transaction information;
the 203 input module is used for inputting the risk influence factor information into a risk prediction model;
the 204 storage module is used for storing a risk prediction model, wherein the risk prediction model is a convolutional neural network model loaded with a group intelligent optimization algorithm;
the 205 prediction module is configured to control a risk prediction model to output a risk prediction result, where the risk prediction result is used to characterize whether the target user is a risk user.
In an optional embodiment, the swarm intelligence optimization algorithm is a bird swarm optimization algorithm with boundary constraint conditions added.
In an optional embodiment, the apparatus further comprises a model building module, wherein the model building module is configured to:
loading a bird group optimization algorithm into the convolutional neural network model, and determining parameters of the convolutional neural network model according to the bird group optimization algorithm;
acquiring sample transaction information to train the neural network model loaded with the bird swarm optimization algorithm;
and determining the trained neural network model loaded with the bird swarm optimization algorithm as a risk prediction model according to a preset training rule.
In an optional embodiment, the acquisition module is further configured to acquire the transaction data of the target user in real time
It is understood that the above modules of the risk prediction apparatus in the present embodiment have functions of implementing the corresponding steps of the method in the embodiment shown in fig. 1. The function can be realized by hardware, and can also be realized by executing corresponding software by hardware. The hardware or software includes one or more modules corresponding to the functions described above. The modules can be software and/or hardware, and each module can be implemented independently or by integrating a plurality of modules. For the functional description of each module, reference may be specifically made to the corresponding description of the method in the embodiment shown in fig. 1, and details are not repeated here.
The embodiment of the application provides an electronic device, which comprises a processor and a memory;
a memory for storing operating instructions;
and the processor is used for executing the risk prediction method provided by any embodiment of the application by calling the operation instruction.
As an example, fig. 3 shows a schematic structural diagram of an electronic device to which an embodiment of the present application is applicable, and as shown in fig. 3, the electronic device 2000 includes: a processor 2001 and a memory 2003. Wherein the processor 2001 is coupled to a memory 2003, such as via a bus 2002. Optionally, the electronic device 2000 may also include a transceiver 2004. It should be noted that the transceiver 2004 is not limited to one in practical applications, and the structure of the electronic device 2000 is not limited to the embodiment of the present application.
The processor 2001 is applied to the embodiment of the present application to implement the method shown in the above method embodiment. The transceiver 2004 may include a receiver and a transmitter, and the transceiver 2004 is applied to the embodiments of the present application to implement the functions of the electronic device of the embodiments of the present application to communicate with other devices when executed.
The Processor 2001 may be a CPU (Central Processing Unit), general Processor, DSP (Digital Signal Processor), ASIC (Application Specific Integrated Circuit), FPGA (Field Programmable Gate Array) or other Programmable logic device, transistor logic device, hardware component, or any combination thereof. Which may implement or perform the various illustrative logical blocks, modules, and circuits described in connection with the disclosure. The processor 2001 may also be a combination of computing functions, e.g., comprising one or more microprocessors, DSPs and microprocessors, and the like.
The Memory 2003 may be a ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, a RAM (Random Access Memory) or other type of dynamic storage device that can store information and instructions, an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical Disc storage, optical Disc storage (including Compact Disc, laser Disc, optical Disc, digital versatile Disc, blu-ray Disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited to these.
Optionally, the memory 2003 is used for storing application program code for performing the disclosed aspects, and is controlled in execution by the processor 2001. The processor 2001 is configured to execute the application program code stored in the memory 2003 to implement the risk prediction method provided in any of the embodiments of the present application.
The electronic device provided by the embodiment of the application is applicable to any embodiment of the method, and is not described herein again.
The embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the computer program implements the risk prediction method shown in the above method embodiment.
The computer-readable storage medium provided in the embodiments of the present application is applicable to any of the embodiments of the foregoing method, and is not described herein again.
According to the risk prediction scheme provided by the embodiment of the application, transaction information of a target user is collected; determining risk influence factor information according to the target user transaction information; inputting the risk influence factor information into a risk prediction model; the risk prediction model is a convolutional neural network model loaded with a group intelligent optimization algorithm; and outputting a risk prediction result by a risk prediction model, wherein the risk prediction result is used for representing whether the target user is a risk user. According to the risk prediction scheme disclosed by the embodiment of the application, the convolutional neural network model of the improved bird swarm optimization algorithm is used for processing and predicting the user transaction data, whether the user is a risk user can be rapidly, efficiently and accurately predicted, and the method has the characteristics of high accuracy rate, low false alarm rate and low cost. The risk users, such as black-producing users, are prevented from bringing unnecessary loss to merchants or customers by utilizing the marketing scheme loopholes.
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
The foregoing is only a partial embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (12)
1. A method of risk prediction, the method comprising:
collecting target user transaction information;
determining risk influence factor information according to the target user transaction information;
inputting the risk influence factor information into a risk prediction model; the risk prediction model is a convolutional neural network model loaded with a group intelligent optimization algorithm;
and outputting a risk prediction result by a risk prediction model, wherein the risk prediction result is used for representing whether the target user is a risk user.
2. The risk prediction method of claim 1, wherein the swarm intelligence optimization algorithm is a bird swarm optimization algorithm.
3. The risk prediction method of claim 2, wherein the constructing of the risk prediction model comprises:
loading a bird group optimization algorithm into the convolutional neural network model, and determining parameters of the convolutional neural network model according to the bird group optimization algorithm;
acquiring sample transaction information to train the neural network model loaded with the bird swarm optimization algorithm;
and determining the trained neural network model loaded with the bird swarm optimization algorithm as a risk prediction model according to a preset training rule.
4. The risk prediction method of claim 3, wherein the bird swarm optimization algorithm is a bird swarm optimization algorithm that adds a boundary constraint.
5. The risk prediction method according to claim 3 or 4, wherein the preset rules comprise at least one of:
determining the convolutional neural network model loaded with the bird swarm optimization algorithm as a risk prediction model when the training iteration number reaches a preset threshold value;
or the like, or, alternatively,
inputting sample transaction data to a convolutional neural network model loaded with a bird swarm optimization algorithm, and calculating a sample fitness value after iterative training for a preset number of times;
and if the sample fitness value accords with a preset fitness value, stopping training, and determining the convolutional neural network model loaded with the bird swarm optimization algorithm as a risk prediction model.
6. The risk prediction method according to claims 1-4, characterized in that the method comprises: and collecting transaction data of the target user in real time.
7. A risk prediction device, the device comprising: the device comprises an acquisition module, a determination module, an input module, a prediction module and a storage module, wherein:
the acquisition module is used for acquiring the transaction information of the target user;
the determining module is used for determining risk influence factor information according to the target user transaction information;
the input module is used for inputting the risk influence factor information into a risk prediction model;
the storage module is used for storing a risk prediction model, wherein the risk prediction model is a convolutional neural network model loaded with a group intelligent optimization algorithm;
and the prediction module is used for controlling a risk prediction model to output a risk prediction result, wherein the risk prediction result is used for representing whether the target user is a risk user.
8. The risk prediction device of claim 7, wherein the swarm intelligence optimization algorithm is a bird swarm optimization algorithm that adds boundary constraints.
9. The risk prediction device of claim 8, further comprising a model building module, wherein the model building module is configured to:
loading a bird group optimization algorithm into the convolutional neural network model, and determining parameters of the convolutional neural network model according to the bird group optimization algorithm;
acquiring sample transaction information to train the neural network model loaded with the bird swarm optimization algorithm;
and determining the trained neural network model loaded with the bird swarm optimization algorithm as a risk prediction model according to a preset training rule.
10. The risk prediction device of claims 7-9, wherein the collection module is further configured to collect target user transaction data in real-time.
11. An electronic device comprising a processor and a memory;
the memory is used for storing operation instructions;
the processor is used for executing the method of any one of claims 1-6 by calling the operation instruction.
12. A computer-readable storage medium, characterized in that the storage medium has stored thereon a computer program which, when being executed by a processor, carries out the method of any one of claims 1-6.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112766975A (en) * | 2021-01-20 | 2021-05-07 | 中信银行股份有限公司 | Risk detection method and device, electronic equipment and readable storage medium |
| CN113344453A (en) * | 2021-07-05 | 2021-09-03 | 湖南快乐阳光互动娱乐传媒有限公司 | Risk monitoring method, device, system, storage medium and equipment |
| CN113420941A (en) * | 2021-07-16 | 2021-09-21 | 湖南快乐阳光互动娱乐传媒有限公司 | Risk prediction method and device for user behavior |
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2020
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112766975A (en) * | 2021-01-20 | 2021-05-07 | 中信银行股份有限公司 | Risk detection method and device, electronic equipment and readable storage medium |
| CN113344453A (en) * | 2021-07-05 | 2021-09-03 | 湖南快乐阳光互动娱乐传媒有限公司 | Risk monitoring method, device, system, storage medium and equipment |
| CN113420941A (en) * | 2021-07-16 | 2021-09-21 | 湖南快乐阳光互动娱乐传媒有限公司 | Risk prediction method and device for user behavior |
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