CN117035433A - Illegal funds transfer customer identification method and device - Google Patents
Illegal funds transfer customer identification method and device Download PDFInfo
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Abstract
The invention discloses a method and a device for identifying illegal funds transfer clients, and relates to the technical field of big data, wherein the method comprises the following steps: determining standard deviation and offset degree information of transaction characteristic data corresponding to each transaction index according to historical transaction data and a plurality of transaction indexes of a customer to be identified in a preset time period; determining illegal funds transfer transaction risks of the clients to be identified according to the standard deviation and the offset degree; determining a customer group to which the customer belongs according to the customer characteristic data of the customer to be identified and the transaction characteristic data corresponding to the transaction indexes; according to historical transaction data of all clients in a client group to which the clients belong in a preset time period, determining the P value of the client group to which the clients belong; and verifying illegal funds transfer transaction risk information of the customer to be identified according to the P value to obtain an identification result that the customer to be identified is an illegal funds transfer customer. The invention can improve the accuracy of illegal funds transfer customer identification.
Description
Technical Field
The invention relates to the technical field of big data, in particular to a method and a device for identifying illegal funds transfer clients.
Background
This section is intended to provide a background or context to the embodiments of the invention that are recited in the claims. The description herein is not admitted to be prior art by inclusion in this section.
At present, the illegal funds transfer client identification method of each large financial institution is mostly realized by constructing an illegal funds transfer client identification model, the existing illegal funds transfer client identification model mainly comprises a rule model, and the characteristic threshold value of the rule model is generally fixed, for example, the characteristic transaction of total type: the rule model cannot be flexibly changed, so that the transaction information of a plurality of illegal funds transfer clients is not in the rule condition and cannot be identified and pre-warned by the model; and the updating iteration period of the rule model is long, the rule model is in a missing state for a long time, and illegal funds transfer clients cannot be accurately identified.
Disclosure of Invention
The embodiment of the invention provides an illegal funds transfer client identification method, which is used for accurately identifying illegal funds transfer clients, and comprises the following steps:
acquiring client characteristic data of a client to be identified and historical transaction data in a preset time period;
Determining standard deviation and offset degree information of transaction characteristic data corresponding to each transaction index of a customer to be identified according to historical transaction data of the customer to be identified in a preset time period and a plurality of preset transaction indexes;
determining illegal funds transfer transaction risk information of the customer to be identified according to standard deviation and offset degree information of transaction characteristic data corresponding to a plurality of transaction indexes of the customer to be identified;
determining a customer group to which the customer belongs according to the customer characteristic data of the customer to be identified and the transaction characteristic data corresponding to the transaction indexes;
according to historical transaction data of all clients in a client group to which the clients belong in a preset time period, determining the P value of the client group to which the clients belong;
and verifying illegal funds transfer transaction risk information of the customer to be identified according to the P value of the customer group to which the customer belongs to obtain an identification result that the customer to be identified is the illegal funds transfer customer.
The embodiment of the invention also provides an illegal funds transfer client identification device which is used for accurately identifying illegal funds transfer clients, and the device comprises:
the acquisition module is used for acquiring the client characteristic data of the client to be identified and the historical transaction data in a preset time period;
The first processing module is used for determining standard deviation and offset degree information of transaction characteristic data corresponding to each transaction index of the customer to be identified according to historical transaction data of the customer to be identified in a preset time period and a plurality of preset transaction indexes;
the second processing module is used for determining illegal funds transfer transaction risk information of the customer to be identified according to standard deviation and offset degree information of transaction characteristic data corresponding to a plurality of transaction indexes of the customer to be identified;
the group determining module is used for determining a customer group to which the customer belongs according to the customer characteristic data of the customer to be identified and the transaction characteristic data corresponding to the transaction indexes;
the third processing module is used for determining the P value of the customer group to which the customer belongs according to the historical transaction data of all the customers in the customer group to which the customer belongs in a preset time period;
and the result determining module is used for verifying illegal funds transfer transaction risk information of the clients to be identified according to the P value of the client group to which the clients belong to, so as to obtain the identification result that the clients to be identified are illegal funds transfer clients.
The embodiment of the invention also provides computer equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the illegal funds transfer client identification method when executing the computer program.
The embodiment of the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the illegal funds transfer client identification method when being executed by a processor.
In the embodiment of the invention, the client characteristic data of the client to be identified and the historical transaction data in a preset time period are obtained; determining standard deviation and offset degree information of transaction characteristic data corresponding to each transaction index of a customer to be identified according to historical transaction data of the customer to be identified in a preset time period and a plurality of preset transaction indexes; determining illegal funds transfer transaction risk information of the customer to be identified according to standard deviation and offset degree information of transaction characteristic data corresponding to a plurality of transaction indexes of the customer to be identified; determining a customer group to which the customer belongs according to the customer characteristic data of the customer to be identified and the transaction characteristic data corresponding to the transaction indexes; according to historical transaction data of all clients in a client group to which the clients belong in a preset time period, determining the P value of the client group to which the clients belong; and verifying illegal funds transfer transaction risk information of the customer to be identified according to the P value of the customer group to which the customer belongs to obtain an identification result that the customer to be identified is the illegal funds transfer customer. Compared with the existing illegal funds transfer customer identification method, the method has the advantages that firstly, the illegal funds transfer transaction risk information of the customer is preliminarily determined through the standard deviation and the offset degree information of the transaction characteristic data corresponding to each transaction index of the customer; and then determining a customer group to which the customer belongs, and further verifying illegal funds transfer transaction risk information of the customer through a P value of the customer group to which the customer belongs to obtain a final identification result of the illegal funds transfer customer, so that different judgment standards can be provided for different customers from two aspects of customer self comparison and group comparison, and the illegal funds transfer customer can be accurately identified.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. In the drawings:
FIG. 1 is a flow chart of a method for identifying an illegal funds-transfer customer in accordance with an embodiment of the invention;
FIG. 2 is a flow chart of a further method of identifying an illegal funds-transfer customer in accordance with an embodiment of the invention;
FIG. 3 is a flow chart of a further method for identifying an illegal funds-transfer customer in accordance with an embodiment of the invention;
FIG. 4 is a schematic diagram of an illegal funds-transfer customer identification device according to an embodiment of the invention;
fig. 5 is a schematic diagram of a computer device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention will be described in further detail with reference to the accompanying drawings. The exemplary embodiments of the present invention and their descriptions herein are for the purpose of explaining the present invention, but are not to be construed as limiting the invention.
In the description of the present specification, the terms "comprising," "including," "having," "containing," and the like are open-ended terms, meaning including, but not limited to. The description of the reference terms "one embodiment," "a particular embodiment," "some embodiments," "for example," etc., means that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. The order of steps involved in the embodiments is illustrative of the practice of the application, and is not limited and may be suitably modified as desired.
Based on the defect that an illegal funds transfer customer identification scheme exists by constructing an illegal funds transfer customer identification model in the prior art, the embodiment of the application provides the illegal funds transfer customer identification scheme which has different standards for different customers, so that the illegal funds transfer customers can be accurately identified.
It should be noted that, in the technical scheme of the application, the acquisition, storage, use, processing and the like of the data all conform to the relevant regulations of national laws and regulations.
Fig. 1 is a flow chart of a method for identifying illegal funds-transfer customers according to an embodiment of the application, as shown in fig. 1, the method includes the following steps:
step 101, acquiring customer characteristic data of a customer to be identified and historical transaction data in a preset time period;
step 102, determining standard deviation and offset degree information of transaction characteristic data corresponding to each transaction index of a customer to be identified according to historical transaction data of the customer to be identified in a preset time period and a plurality of preset transaction indexes;
step 103, determining illegal funds transfer transaction risk information of the customer to be identified according to standard deviation and offset degree information of transaction characteristic data corresponding to a plurality of transaction indexes of the customer to be identified;
step 104, determining a customer group to which the customer belongs according to the customer characteristic data of the customer to be identified and the transaction characteristic data corresponding to the transaction indexes;
step 105, determining the P value of the customer group to which the customer belongs according to the historical transaction data of all the customers in the customer group to which the customer belongs in a preset time period;
And 106, verifying illegal funds transfer transaction risk information of the customer to be identified according to the P value of the customer group to which the customer belongs, and obtaining an identification result that the customer to be identified is the illegal funds transfer customer.
In the embodiment of the invention, the client characteristic data of the client to be identified and the historical transaction data in a preset time period are obtained; determining standard deviation and offset degree information of transaction characteristic data corresponding to each transaction index of a customer to be identified according to historical transaction data of the customer to be identified in a preset time period and a plurality of preset transaction indexes; determining illegal funds transfer transaction risk information of the customer to be identified according to standard deviation and offset degree information of transaction characteristic data corresponding to a plurality of transaction indexes of the customer to be identified; determining a customer group to which the customer belongs according to the customer characteristic data of the customer to be identified and the transaction characteristic data corresponding to the transaction indexes; according to historical transaction data of all clients in a client group to which the clients belong in a preset time period, determining the P value of the client group to which the clients belong; and verifying illegal funds transfer transaction risk information of the customer to be identified according to the P value of the customer group to which the customer belongs to obtain an identification result that the customer to be identified is the illegal funds transfer customer. Compared with the existing illegal funds transfer customer identification method, the method has the advantages that firstly, the illegal funds transfer transaction risk information of the customer is preliminarily determined through the standard deviation and the offset degree information of the transaction characteristic data corresponding to each transaction index of the customer; and then determining a customer group to which the customer belongs, and further verifying illegal funds transfer transaction risk information of the customer through a P value of the customer group to which the customer belongs to obtain a final identification result of the illegal funds transfer customer, so that different judgment standards can be provided for different customers from two aspects of customer self comparison and group comparison, and the illegal funds transfer customer can be accurately identified.
The method of identifying an illegal funds-transfer customer shown in figure 1 is described in detail below.
In the above step 101, customer characteristic data of the customer to be identified and historical transaction data within a preset period of time may be acquired.
In particular implementations, the customer characteristic data may include basic attribute characteristics of the customer, such as age, gender, occupation, industry, province, etc. of the customer; customer asset information, such as asset totals, etc., may also be included. Each historical transaction data may include a transaction amount, a transaction time, a transaction opponent, a transaction type, a transaction zone, and the like. Specifically, the preset time period may be approximately one week, approximately one month, or the like, and may be set according to a specific scene, which is not limited herein.
It should be noted that, in order to ensure accuracy of identification results of illegal funds transfer customers and save computing resources, historical transaction data of customers may be preprocessed. Specifically, transactions automatically completed by the system in the historical transaction data can be filtered, such as settlement transactions, accumulation fund extraction transactions and the like, and transactions without obvious anomalies or transactions without direct relation are filtered.
In the step 102, the dynamic change of the transaction of the customer is determined according to the historical transaction data of the customer to be identified in the preset time period.
In specific implementation, a plurality of transaction indexes can be preset, transaction characteristic data corresponding to each transaction index is extracted from historical transaction data of a customer to be identified in a preset time period, and then standard deviation and offset degree information of the transaction characteristic data corresponding to each transaction index are calculated. The offset degree information may be an offset amount or an offset rate. The offset may be obtained by dividing the maximum value of the trade feature data corresponding to each trade index by the average value of the trade feature data corresponding to the trade index.
The preset transaction indexes can include transaction amount, night transaction amount, transaction times, public-private transaction times, transaction opponent number, cross-border transaction number, cross-regional transaction number and the like. The trade index may be set according to a specific scenario, without limitation.
In the step 103, it may be determined that the customer to be identified is at risk of illegal funds transfer transaction based on the dynamic change of the customer to be identified's own transaction, that is, the standard deviation and the offset degree information of the transaction characteristic data corresponding to each transaction index of the customer to be identified.
In the implementation, the standard deviation can reflect the fluctuation condition of data, for example, the larger the standard deviation of the transaction amount is, the larger the change of the transaction amount is, and the higher the risk of illegal funds transfer transaction is; or the larger the standard deviation of the transaction times is, the higher the risk of illegal funds transfer transaction exists for the client can be also stated; the degree of deviation information may reflect the degree to which the data deviates from the normal level, and if the degree of deviation is greater, the higher the likelihood of an abnormal transaction, the higher the risk that the customer has an illegal funds transfer transaction, etc. Specifically, the standard deviation threshold value and the offset degree threshold value can be preset, standard deviation and offset degree information of transaction characteristic data corresponding to each transaction index of the customer to be identified are compared with the corresponding threshold values, and the risk of illegal funds transfer transaction of the customer to be identified is determined according to the comparison result.
For example, if the offset of the transaction characteristic data corresponding to a certain transaction index is greater than 100, it indicates that the customer to be identified is at risk of illegal funds transfer transaction.
In the embodiment of the present invention, after the historical transaction data of the customer to be identified in the preset time period is obtained in the step 101, the risk of illegal funds transfer transaction of the customer to be identified can also be determined through the distribution condition of the data.
In one embodiment, to further improve the accuracy of the determined risk information of the illegal funds-transfer transaction for the customer to be identified, after the step 103, the method may further include:
acquiring historical transaction data of a customer to be identified in a plurality of continuous preset time periods;
according to historical transaction data of a customer to be identified in a plurality of continuous preset time periods and a plurality of preset transaction indexes, determining a moving average difference or a moving average standard deviation of transaction characteristic data corresponding to each transaction index;
and updating illegal funds transfer transaction risk information of the customer to be identified according to the moving average difference or the moving average standard deviation of the transaction characteristic data corresponding to each transaction index.
In the implementation, historical transaction data of the customer to be identified in a plurality of continuous time periods can be obtained, namely, historical transaction data of the customer to be identified in a plurality of periods are obtained, and the average value or standard deviation of the transaction characteristic data corresponding to each transaction index in each period is calculated; and calculating the moving average difference or the moving average standard deviation of the transaction characteristic data corresponding to each transaction index according to the average value or the standard deviation of the transaction characteristic data corresponding to each transaction index in a plurality of periods. And updating and correcting illegal funds transfer transaction risk information of the transaction data to be identified according to the moving average difference or the moving average standard deviation of the transaction characteristic data corresponding to each transaction index.
In this way, by further comparing the transaction conditions of the customer to be identified in a plurality of periods, and correcting the information of the risk of illegal funds transfer transaction of the customer to be identified, which is judged in step 103, according to the difference of the plurality of periods, the risk of illegal funds transfer transaction of the customer to be identified can be comprehensively identified, and the accuracy of the risk of illegal funds transfer transaction of the identified customer is improved.
In step 104, a determination may be made as to whether the customer is at risk for an illegal funds transfer transaction from the perspective of the customer base. First, a customer group to which a customer belongs may be determined according to the customer characteristic data and transaction characteristic data corresponding to a plurality of transaction indexes.
In particular, the customer groups to which the customers belong can be combined and positioned according to the basic attribute characteristics and the transaction characteristic data of the customers.
In the step 105 and the step 106, the P value of the customer group to which the customer belongs may be determined according to the historical transaction data in the preset time period of all the customers in the customer group to which the customer belongs; and then, verifying illegal funds transfer transaction risk information of the customer to be identified according to the P value of the customer group to which the customer belongs to obtain an identification result that the customer to be identified is the illegal funds transfer customer.
In specific implementation, the statistical meaning of the P value is an estimation method of the true degree of the result (which can represent the overall), the professional P value is a decreasing index of the credibility of the result, and the greater the P value is, the less the association of variables in the sample can be considered as the reliable index of the association of the variables in the overall. If the P value is small, the probability of occurrence of the original hypothesis is small, and if the occurrence is caused, the reason for rejecting the original hypothesis is reasonable according to the principle of small probability, and the smaller the P value is, the more sufficient the reason for rejecting the original hypothesis is. Thus, the smaller the P value, the more pronounced the result. But whether the test results are "significant", "moderately significant" or "highly significant" needs to be addressed depending on the magnitude of the P value and the actual problem.
In the embodiment of the invention, the P value of the customer group to which the customer belongs is used for verifying the illegal funds transfer transaction risk information of the customer to be identified determined in the step 103, specifically, if the P value is smaller than the preset threshold, the illegal funds transfer transaction risk information of the customer to be identified can be considered as false, so that the identification accuracy problem caused by the fact that the characteristic threshold of the illegal funds transfer customer identification model constructed in the prior art is relatively fixed is solved to a certain extent by forming the illegal funds transfer transaction risk judgment basis of the customer through the transaction difference condition of the customer and the transaction difference condition between the customer and the customer group, realizing the difference of each customer condition and forming different illegal funds transfer transaction risk judgment standards of the customer.
In addition, in order to further improve the recognition accuracy, in the embodiment of the present invention, after determining the P value of the customer group to which the customer belongs according to the historical transaction data of all the customers in the customer group to which the customer belongs in the preset time period in the step 105, the method may further include:
classifying all the clients in the client group to which the clients belong according to the historical transaction data of all the clients in the client group to which the clients belong in a plurality of continuous preset time periods to obtain a plurality of sub groups;
according to historical transaction data of all clients in each subgroup in a plurality of preset time periods continuously, determining the P value of each subgroup;
and verifying the P value of the customer group to which the customer belongs according to the P values of the plurality of sub-groups.
In one embodiment, verifying the P value of the customer base to which the customer belongs based on the P values of the plurality of sub-bases includes:
and comparing the P value of each sub-group with the P value of the customer group to which the customer belongs, and determining the validity of the P value of the customer group to which the customer belongs according to the comparison result.
In the implementation, the clients in the client group to which the clients belong can be divided again on the basis of the client group to which the clients belong, and specifically, the clients in the client group to which the clients belong are further classified based on the transaction conditions of the clients in the client group to which the clients belong in a plurality of continuous preset time periods (a plurality of periods) to obtain a plurality of sub-groups. Specifically, the clients in the client group to which the clients belong may be further classified according to the transaction scale, the amount of the opponent of the transaction, the frequency of the public transaction, and the like in a plurality of time periods. And then, calculating the P value of each sub-group, comparing the P value of each sub-group with the P value of the client group to which the client belongs, and determining the validity of the P value of the client group to which the client belongs according to the comparison result.
Alternatively, P values of the multiple sub-groups may be compared in pairs, and a more multi-level basis may be formed by comparing the groups.
As shown in fig. 2, in another method for identifying an illegal funds transfer client according to the embodiment of the present invention, after determining a client group to which a client belongs according to the client feature data of the client to be identified and the transaction feature data corresponding to a plurality of transaction indexes in step 104, the method may further include:
step 201, determining standard deviation of transaction characteristic data corresponding to each transaction index in a customer group to which the customer belongs according to historical transaction data and a plurality of preset transaction indexes of all customers in the customer group to which the customer belongs in a preset time period;
step 202, comparing the standard deviation of the transaction characteristic data corresponding to each transaction index of the customer to be identified with the standard deviation of the transaction characteristic data corresponding to each transaction index in the customer group to which the customer belongs, and verifying the illegal funds transfer transaction risk information of the customer to be identified according to the comparison result to obtain the identification result that the customer to be identified is the illegal funds transfer customer.
In the implementation, the illegal funds transfer transaction risk information of the customer to be identified can be verified through the standard deviation of the transaction characteristic data corresponding to each transaction index in the customer group to which the customer belongs, so that the identification result that the customer to be identified is the illegal funds transfer customer can be obtained. Specifically, if the error of the standard deviation of the transaction characteristic data corresponding to each transaction index in the customer group to which the customer belongs and the standard deviation of the transaction characteristic data corresponding to each transaction index of the customer to be identified is smaller than a preset error threshold, the illegal funds transfer transaction risk information of the customer to be identified can be considered to be accurate, and at this time, the illegal funds transfer transaction risk information of the customer to be identified is taken as the identification result of the customer to be identified as the illegal funds transfer customer. If the error is not smaller than the preset error threshold, the illegal funds transfer transaction risk information of the customer to be identified is considered to be inaccurate, and a result opposite to the illegal funds transfer transaction risk information of the customer to be identified can be used as an identification result of the customer to be identified as the illegal funds transfer customer, or further judgment, such as calculation of a P value, and the like, can be performed.
As shown in fig. 3, in another method for identifying an illegal funds transfer client according to the embodiment of the present invention, after determining a client group to which a client belongs according to the client feature data of the client to be identified and the transaction feature data corresponding to a plurality of transaction indexes in step 104, the method may further include:
step 301, obtaining the number of illegal funds transfer clients in a client group to which the client belongs according to the identification information of the illegal funds transfer clients of all the clients in the client group to which the client belongs;
step 302, determining the proportion of illegal funds transfer clients in the client group to which the clients belong according to the number of illegal funds transfer clients in the client group to which the clients belong;
and 303, verifying illegal funds transfer transaction risk information of the customer to be identified according to the proportion of the illegal funds transfer customers in the customer group to which the customer belongs, and obtaining an identification result that the customer to be identified is the illegal funds transfer customer.
In the specific implementation, all clients in the client group to which the clients belong carry the identification of whether the clients are illegal funds transfer clients, and the number of the illegal funds transfer clients in the client group to which the clients belong can be counted according to the identification of whether the clients are illegal funds transfer clients carried by the clients, so that the proportion of the illegal funds transfer clients in the client group to which the clients belong is obtained; and then, judging the accuracy of illegal funds transfer transaction risk information of the client to be identified according to the proportion of the illegal funds transfer clients in the client group to which the client belongs, and obtaining the identification result that the client to be identified is the illegal funds transfer client. For example, if the proportion of the illegal funds transfer clients in the client group to which the clients belong is greater than a preset proportion threshold, if the illegal funds transfer transaction risk information of the client to be identified is that the client has illegal funds transfer transaction risk, the illegal funds transfer transaction risk information of the client to be identified can be considered to be accurate.
In summary, the illegal funds transfer client identification method can preliminarily determine whether the client is an illegal funds transfer transaction risk client by utilizing the change condition of the transaction information of the client; and then, determining the customer group to which the customer belongs, carrying out further risk judgment from the illegal funds transfer transaction risk of the customer group, and verifying whether the preliminarily determined customer is the illegal funds transfer transaction risk customer, thereby obtaining the identification result of the final illegal funds transfer customer. Therefore, different judgment standards of different clients can be dynamically determined, illegal funds transfer clients can be identified in an omnibearing manner, missing part of illegal funds transfer clients is avoided, and the identification accuracy of the illegal funds transfer clients is improved.
The embodiment of the invention also provides an illegal funds transfer customer identification device, as described in the following embodiment. Because the principle of the device for solving the problem is similar to that of the illegal funds transfer client identification method, the implementation of the device can refer to the implementation of the illegal funds transfer client identification method, and the repetition is omitted.
Fig. 4 is a schematic structural diagram of an illegal funds-transfer customer identification device according to an embodiment of the invention, as shown in fig. 4, the device may include:
An acquisition module 401, configured to acquire customer characteristic data of a customer to be identified and historical transaction data within a preset period of time;
a first processing module 402, configured to determine standard deviation and offset degree information of transaction characteristic data corresponding to each transaction index of a customer to be identified according to historical transaction data of the customer to be identified in a preset time period and a plurality of preset transaction indexes;
a second processing module 403, configured to determine illegal funds transfer transaction risk information of the customer to be identified according to standard deviation and offset degree information of transaction feature data corresponding to a plurality of transaction indexes of the customer to be identified;
the group determining module 404 is configured to determine, according to the customer characteristic data of the customer to be identified and the transaction characteristic data corresponding to the plurality of transaction indexes, a customer group to which the customer belongs;
a third processing module 405, configured to determine a P value of a customer group to which a customer belongs according to historical transaction data of all customers in the customer group to which the customer belongs in a preset time period;
and the result determining module 406 is configured to verify the risk information of the illegal funds transfer transaction of the customer to be identified according to the P value of the customer group to which the customer belongs, so as to obtain an identification result that the customer to be identified is the illegal funds transfer customer.
In one embodiment, the apparatus may further include a fourth processing module, configured to, after the third processing module determines the P value of the customer base to which the customer belongs according to historical transaction data of all customers in the customer base to which the customer belongs in a preset time period:
classifying all the clients in the client group to which the clients belong according to the historical transaction data of all the clients in the client group to which the clients belong in a plurality of continuous preset time periods to obtain a plurality of sub groups;
according to historical transaction data of all clients in each subgroup in a plurality of preset time periods continuously, determining the P value of each subgroup;
and verifying the P value of the customer group to which the customer belongs according to the P values of the plurality of sub-groups.
In one embodiment, the fourth processing module may be further configured to:
and comparing the P value of each sub-group with the P value of the customer group to which the customer belongs, and determining the validity of the P value of the customer group to which the customer belongs according to the comparison result.
In one embodiment, the apparatus may further include an updating module, configured to, after the second processing module determines the risk information of the illegal funds transfer transaction of the customer to be identified according to the standard deviation and the offset degree information of the transaction characteristic data corresponding to the plurality of transaction indexes of the customer to be identified:
Acquiring historical transaction data of a customer to be identified in a plurality of continuous preset time periods;
according to historical transaction data of a customer to be identified in a plurality of continuous preset time periods and a plurality of preset transaction indexes, determining a moving average difference or a moving average standard deviation of transaction characteristic data corresponding to each transaction index;
and updating illegal funds transfer transaction risk information of the customer to be identified according to the moving average difference or the moving average standard deviation of the transaction characteristic data corresponding to each transaction index.
In one embodiment, the apparatus may further include a fifth processing module configured to, after the group determining module determines the group of clients to which the client belongs according to the client characteristic data of the client to be identified and the transaction characteristic data corresponding to the plurality of transaction indexes:
determining standard deviation of transaction characteristic data corresponding to each transaction index in a customer group to which the customer belongs according to historical transaction data and a plurality of preset transaction indexes of all customers in the customer group to which the customer belongs in a preset time period;
and comparing the standard deviation of the transaction characteristic data corresponding to each transaction index of the customer to be identified with the standard deviation of the transaction characteristic data corresponding to each transaction index in the customer group to which the customer belongs, and verifying the illegal funds transfer transaction risk information of the customer to be identified according to the comparison result to obtain the identification result that the customer to be identified is the illegal funds transfer customer.
In one embodiment, the above apparatus may further include a sixth processing module, configured to, after the group determining module determines, according to the client characteristic data of the client to be identified and the transaction characteristic data corresponding to the plurality of transaction indexes, a client group to which the client belongs:
acquiring the number of illegal funds transfer clients in a client group to which the client belongs according to the identification information of the illegal funds transfer clients of all the clients in the client group to which the client belongs;
determining the proportion of illegal funds transfer clients in the client group to which the client belongs according to the number of the illegal funds transfer clients in the client group to which the client belongs;
and verifying illegal funds transfer transaction risk information of the client to be identified according to the proportion of the illegal funds transfer clients in the client group to which the client belongs, so as to obtain an identification result that the client to be identified is the illegal funds transfer client.
An embodiment of the present invention further provides a computer device, and fig. 5 is a schematic diagram of a computer device in the embodiment of the present invention, where the computer device 500 includes a memory 510, a processor 520, and a computer program 530 stored in the memory 510 and capable of running on the processor 520, and the processor 520 implements the illegal funds transfer client identifying method when executing the computer program 530.
The embodiment of the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the illegal funds transfer client identification method when being executed by a processor.
In the embodiment of the invention, the client characteristic data of the client to be identified and the historical transaction data in a preset time period are obtained; determining standard deviation and offset degree information of transaction characteristic data corresponding to each transaction index of a customer to be identified according to historical transaction data of the customer to be identified in a preset time period and a plurality of preset transaction indexes; determining illegal funds transfer transaction risk information of the customer to be identified according to standard deviation and offset degree information of transaction characteristic data corresponding to a plurality of transaction indexes of the customer to be identified; determining a customer group to which the customer belongs according to the customer characteristic data of the customer to be identified and the transaction characteristic data corresponding to the transaction indexes; according to historical transaction data of all clients in a client group to which the clients belong in a preset time period, determining the P value of the client group to which the clients belong; and verifying illegal funds transfer transaction risk information of the customer to be identified according to the P value of the customer group to which the customer belongs to obtain an identification result that the customer to be identified is the illegal funds transfer customer. Compared with the existing illegal funds transfer customer identification method, the method has the advantages that firstly, the illegal funds transfer transaction risk information of the customer is preliminarily determined through the standard deviation and the offset degree information of the transaction characteristic data corresponding to each transaction index of the customer; and then determining a customer group to which the customer belongs, and further verifying illegal funds transfer transaction risk information of the customer through a P value of the customer group to which the customer belongs to obtain a final identification result of the illegal funds transfer customer, so that different judgment standards can be provided for different customers from two aspects of customer self comparison and group comparison, and the illegal funds transfer customer can be accurately identified.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (14)
1. A method of identifying an illegal funds-transfer customer, comprising:
acquiring client characteristic data of a client to be identified and historical transaction data in a preset time period;
determining standard deviation and offset degree information of transaction characteristic data corresponding to each transaction index of a customer to be identified according to historical transaction data of the customer to be identified in a preset time period and a plurality of preset transaction indexes;
determining illegal funds transfer transaction risk information of the customer to be identified according to standard deviation and offset degree information of transaction characteristic data corresponding to a plurality of transaction indexes of the customer to be identified;
determining a customer group to which the customer belongs according to the customer characteristic data of the customer to be identified and the transaction characteristic data corresponding to the transaction indexes;
according to historical transaction data of all clients in a client group to which the clients belong in a preset time period, determining the P value of the client group to which the clients belong;
and verifying illegal funds transfer transaction risk information of the customer to be identified according to the P value of the customer group to which the customer belongs to obtain an identification result that the customer to be identified is the illegal funds transfer customer.
2. The method of claim 1, wherein after determining the P value of the customer base to which the customer belongs based on historical transaction data of all customers within the customer base to which the customer belongs within a preset time period, further comprising:
Classifying all the clients in the client group to which the clients belong according to the historical transaction data of all the clients in the client group to which the clients belong in a plurality of continuous preset time periods to obtain a plurality of sub groups;
according to historical transaction data of all clients in each subgroup in a plurality of preset time periods continuously, determining the P value of each subgroup;
and verifying the P value of the customer group to which the customer belongs according to the P values of the plurality of sub-groups.
3. The method of claim 2, wherein verifying the P value of the customer base to which the customer belongs based on the P values of the plurality of sub-bases comprises:
and comparing the P value of each sub-group with the P value of the customer group to which the customer belongs, and determining the validity of the P value of the customer group to which the customer belongs according to the comparison result.
4. The method of claim 1, wherein after determining the risk information of the illegal funds transfer transaction for the customer to be identified based on the standard deviation and the offset degree information of the transaction characteristic data corresponding to the plurality of transaction indicators for the customer to be identified, further comprising:
acquiring historical transaction data of a customer to be identified in a plurality of continuous preset time periods;
according to historical transaction data of a customer to be identified in a plurality of continuous preset time periods and a plurality of preset transaction indexes, determining a moving average difference or a moving average standard deviation of transaction characteristic data corresponding to each transaction index;
And updating illegal funds transfer transaction risk information of the customer to be identified according to the moving average difference or the moving average standard deviation of the transaction characteristic data corresponding to each transaction index.
5. The method of claim 1, wherein after determining the customer group to which the customer belongs based on the customer characteristic data of the customer to be identified and the transaction characteristic data corresponding to the plurality of transaction indicators, further comprising:
determining standard deviation of transaction characteristic data corresponding to each transaction index in a customer group to which the customer belongs according to historical transaction data and a plurality of preset transaction indexes of all customers in the customer group to which the customer belongs in a preset time period;
and comparing the standard deviation of the transaction characteristic data corresponding to each transaction index of the customer to be identified with the standard deviation of the transaction characteristic data corresponding to each transaction index in the customer group to which the customer belongs, and verifying the illegal funds transfer transaction risk information of the customer to be identified according to the comparison result to obtain the identification result that the customer to be identified is the illegal funds transfer customer.
6. The method of claim 1, wherein after determining the customer group to which the customer belongs based on the customer characteristic data of the customer to be identified and the transaction characteristic data corresponding to the plurality of transaction indicators, further comprising:
Acquiring the number of illegal funds transfer clients in a client group to which the client belongs according to the identification information of the illegal funds transfer clients of all the clients in the client group to which the client belongs;
determining the proportion of illegal funds transfer clients in the client group to which the client belongs according to the number of the illegal funds transfer clients in the client group to which the client belongs;
and verifying illegal funds transfer transaction risk information of the client to be identified according to the proportion of the illegal funds transfer clients in the client group to which the client belongs, so as to obtain an identification result that the client to be identified is the illegal funds transfer client.
7. An illegal funds-transfer customer identification device, comprising:
the acquisition module is used for acquiring the client characteristic data of the client to be identified and the historical transaction data in a preset time period;
the first processing module is used for determining standard deviation and offset degree information of transaction characteristic data corresponding to each transaction index of the customer to be identified according to historical transaction data of the customer to be identified in a preset time period and a plurality of preset transaction indexes;
the second processing module is used for determining illegal funds transfer transaction risk information of the customer to be identified according to standard deviation and offset degree information of transaction characteristic data corresponding to a plurality of transaction indexes of the customer to be identified;
The group determining module is used for determining a customer group to which the customer belongs according to the customer characteristic data of the customer to be identified and the transaction characteristic data corresponding to the transaction indexes;
the third processing module is used for determining the P value of the customer group to which the customer belongs according to the historical transaction data of all the customers in the customer group to which the customer belongs in a preset time period;
and the result determining module is used for verifying illegal funds transfer transaction risk information of the clients to be identified according to the P value of the client group to which the clients belong to, so as to obtain the identification result that the clients to be identified are illegal funds transfer clients.
8. The apparatus of claim 7, further comprising a fourth processing module for, after the third processing module determines the P value for the customer base to which the customer belongs based on historical transaction data for all customers within the customer base to which the customer belongs over a predetermined period of time:
classifying all the clients in the client group to which the clients belong according to the historical transaction data of all the clients in the client group to which the clients belong in a plurality of continuous preset time periods to obtain a plurality of sub groups;
according to historical transaction data of all clients in each subgroup in a plurality of preset time periods continuously, determining the P value of each subgroup;
And verifying the P value of the customer group to which the customer belongs according to the P values of the plurality of sub-groups.
9. The apparatus of claim 8, wherein the fourth processing module is further to:
and comparing the P value of each sub-group with the P value of the customer group to which the customer belongs, and determining the validity of the P value of the customer group to which the customer belongs according to the comparison result.
10. The apparatus of claim 7, further comprising an updating module for determining illegal funds transfer transaction risk information for the customer to be identified after the second processing module determines the illegal funds transfer transaction risk information for the customer to be identified based on standard deviation and offset information for the transaction characteristic data corresponding to the plurality of transaction indicators for the customer to be identified.
Acquiring historical transaction data of a customer to be identified in a plurality of continuous preset time periods;
according to historical transaction data of a customer to be identified in a plurality of continuous preset time periods and a plurality of preset transaction indexes, determining a moving average difference or a moving average standard deviation of transaction characteristic data corresponding to each transaction index;
and updating illegal funds transfer transaction risk information of the customer to be identified according to the moving average difference or the moving average standard deviation of the transaction characteristic data corresponding to each transaction index.
11. The apparatus of claim 7, further comprising a fifth processing module for the group determination module to determine the group of customers to which the customer belongs after determining the group of customers to which the customer belongs based on the customer characteristic data of the customer to be identified and the transaction characteristic data corresponding to the plurality of transaction indicators:
determining standard deviation of transaction characteristic data corresponding to each transaction index in a customer group to which the customer belongs according to historical transaction data and a plurality of preset transaction indexes of all customers in the customer group to which the customer belongs in a preset time period;
and comparing the standard deviation of the transaction characteristic data corresponding to each transaction index of the customer to be identified with the standard deviation of the transaction characteristic data corresponding to each transaction index in the customer group to which the customer belongs, and verifying the illegal funds transfer transaction risk information of the customer to be identified according to the comparison result to obtain the identification result that the customer to be identified is the illegal funds transfer customer.
12. The apparatus of claim 7, further comprising a sixth processing module for, after the group determination module determines the customer group to which the customer belongs based on the customer characteristic data of the customer to be identified and the transaction characteristic data corresponding to the plurality of transaction indicators:
Acquiring the number of illegal funds transfer clients in a client group to which the client belongs according to the identification information of the illegal funds transfer clients of all the clients in the client group to which the client belongs;
determining the proportion of illegal funds transfer clients in the client group to which the client belongs according to the number of the illegal funds transfer clients in the client group to which the client belongs;
and verifying illegal funds transfer transaction risk information of the client to be identified according to the proportion of the illegal funds transfer clients in the client group to which the client belongs, so as to obtain an identification result that the client to be identified is the illegal funds transfer client.
13. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of claims 1 to 6 when executing the computer program.
14. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program which, when executed by a processor, implements the method of any of claims 1 to 6.
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