CN114820165A - Flow monitoring method, equipment and medium based on identification analysis - Google Patents
Flow monitoring method, equipment and medium based on identification analysis Download PDFInfo
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Abstract
The application discloses an individual transaction supervision method, equipment and a medium based on identification analysis, wherein the method comprises the following steps: acquiring individual information of a target trading individual, and acquiring account data of the target trading individual according to the individual information; generating individual identification of the target trading individual by an identification analysis technology according to the individual information of the target trading individual; binding the individual identification and account data corresponding to the individual identification, and storing the individual identification and the account data in a supervision platform; acquiring account data corresponding to the individual identification through the individual identification, and performing format normalization processing; determining a transaction relation network between the target transaction individual and other transaction individuals according to the transaction time and the transaction object in the flow data set; determining an early warning threshold value of the target transaction individual according to the individual information of the target transaction individual; and determining the flow anomaly level of the target transaction individual according to the flow data set, the early warning threshold value and the transaction relation network.
Description
Technical Field
The application relates to the field of financial supervision, in particular to a streamline supervision method, equipment and medium based on identification analysis.
Background
The financial system is one of the important pillars of modern economic development, with the networking and informatization development of the financial system, the capital flow is accelerated, and the daily transaction flow of a financial transaction institution such as a bank can reach the order of millions or even tens of millions. Pipelining refers to the deposit and withdrawal transaction records for an account. The transaction records can be divided into personal water flow and public water flow, namely personal accounts and company accounts according to the different properties of the account types.
In massive transaction data, due to the fact that the flow information among banks is split, account numbers of different accounts in different banks are different in identification, and a single transaction individual may have multiple accounts, when illegal transaction behaviors such as illegal fund transfer, fraud and illegal funding are monitored, the account identifications of different banks need to be exchanged among the same transaction individual, and then all flow information of a target transaction individual can be obtained. This results in a low level of supervision over illegal transactions such as illegal fund transfer, fraud, illegal funding, and the like, and it is difficult to achieve effective supervision over the flow of individual transactions.
Disclosure of Invention
In order to solve the above problems, the present application provides a method, a device, and a medium for monitoring a pipeline based on identifier resolution, where the method includes:
acquiring individual information of a target trading individual, and acquiring account data of the target trading individual according to the individual information; generating individual identification of the target trading individual by an identification analysis technology according to the individual information of the target trading individual; binding the individual identification and the account data corresponding to the individual identification, and storing the bound individual identification and the account data in a supervision platform; acquiring the account data corresponding to the individual identification through the individual identification, and performing format normalization processing on the account data to obtain a running data set of the target transaction individual; the flow data set at least comprises transaction time, transaction objects, transaction amount and fund outflow direction; determining a transaction relation network between the target transaction individual and other transaction individuals according to the transaction time and the transaction object in the flow data set; determining an early warning threshold value of the target transaction individual according to the individual information of the target transaction individual; and determining the running abnormal grade of the target transaction individual according to the running data set, the early warning threshold value and the transaction relation network.
In one example, the determining an early warning threshold of the target transaction individual according to the individual information of the target transaction individual specifically includes: acquiring individual information of the target trading individual through the individual identification of the target trading individual; the individual information includes at least: the operation property, the operation scale and the asset limit of the target transaction individual; determining the early warning threshold value according to the individual information and by the following formula: q ═ k 1 A+k 2 B + C; wherein Q is the early warning threshold; k is 1 、k 2 Is a preset coefficient, and the size of the preset coefficient is determined by the operation property of the transaction individual; the A is the operation scale of the target trading individual, and the B is the asset limit of the target trading individual; and C is a preset constant.
In one example, the determining a transaction relationship network between the target transaction individual and other transaction individuals according to the transaction time and the transaction object in the chronological data set specifically includes: determining the transaction time and the transaction frequency of the other transaction individuals respectively transacting with the target transaction individual according to the transaction time and the transaction object in the flow data set; generating a frequency time relation network of the target trading individual and the other trading individuals according to the trading time; wherein, in the frequency time relationship network, the distance between any other trading individual and the target trading individual is: d ═k 3 E-k 4 F + G; wherein D is the distance between any other trading individual and the target trading individual; k is 3 And k is said 4 Is a preset coefficient, and the size of the preset coefficient is determined by the number of other trading individuals and the size of the running water data set; the E is the transaction time of the first transaction of any other transaction individual and the target transaction individual, and the earlier the transaction time of the first transaction is, the smaller the E is; f is the transaction number between any other transaction individual and the target transaction individual; and G is a preset constant.
In one example, the determining the level of supervision of the target transaction individual according to the running data set, the early warning threshold, and the transaction relationship network specifically includes: determining the transaction object and the transaction amount in the chronological dataset of the target transaction individual; if the transaction amount exceeds the amount early warning threshold value of the target transaction individual or the other transaction individuals, determining the running data as suspicious running data; and determining outliers of the pipeline data according to the following formula:
wherein H is the outlier of the pipelined data, Q is the outlier of the pipelined data x For the amount of the transaction, said Q 1 And said Q 2 Early warning threshold values of both transaction parties respectively; and determining the supervision level of the target transaction individual according to a preset running water abnormal level division rule and the abnormal value.
In one example, the method further comprises: determining the flow data of a first transaction of the target transaction individual and a second transaction adjacent to the first transaction according to the flow data set; if the interval time between the first transaction and the second transaction exceeds a silent account judgment threshold value and the transaction amount of the first transaction or the second transaction exceeds the early warning threshold value, the level of the running water abnormity of the target transaction individual is improved.
In one example, after the binding the individual identifier and the account data corresponding to the individual identifier and storing the bound individual identifier and the account data in a supervision platform, the method further includes: determining account opening operation of the target trading individual, and acquiring account opening information of the target trading individual; acquiring account data of a newly opened account of the target transaction individual according to the account opening information, binding the account data of the newly opened account with the individual identification of the target transaction individual, and storing the bound account data in the supervision platform; determining the seller operation of the target transaction individual, and acquiring the seller information of the target transaction individual; and storing the sales information in the supervision platform.
In one example, the method further comprises: acquiring transaction times and transaction time between the target transaction individual and the other transaction individuals according to the flow data set; and determining the transaction frequency between the target transaction individual and the other transaction individuals within preset time according to the transaction times and the transaction time.
In one example, the method further comprises: acquiring individual information of the target trading individual and the other trading individuals through the individual identification, and determining overseas trading individuals according to the individual information; and if the transaction frequency of the target transaction individual and the overseas transaction individual within the preset time is determined to be higher than a preset frequency early warning threshold value and the transaction content does not accord with the operating property of the target transaction individual, the running water abnormity level of the target transaction individual is improved.
The application also provides a flowing water supervisory equipment based on identification is analytic, includes: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to: acquiring individual information of a target trading individual, and acquiring account data of the target trading individual according to the individual information; generating individual identification of the target trading individual by an identification analysis technology according to the individual information of the target trading individual; binding the individual identification and the account data corresponding to the individual identification, and storing the bound individual identification and the account data in a supervision platform; acquiring the account data corresponding to the individual identification through the individual identification, and performing format normalization processing on the account data to obtain a running data set of the target transaction individual; the flow data set at least comprises transaction time, transaction objects, transaction amount and fund outflow direction; determining a transaction relation network between the target transaction individual and other transaction individuals according to the transaction time and the transaction object in the flow data set; determining an early warning threshold value of the target transaction individual according to the individual information of the target transaction individual; and determining the running abnormal level of the target transaction individual according to the running data set, the early warning threshold and the transaction relationship network, and if the running abnormal level exceeds a preset threshold, judging that the target transaction individual is running abnormally.
The present application further provides a non-volatile computer storage medium storing computer-executable instructions configured to: acquiring individual information of a target trading individual, and acquiring account data of the target trading individual according to the individual information; generating individual identification of the target trading individual by an identification analysis technology according to the individual information of the target trading individual; binding the individual identification and the account data corresponding to the individual identification, and storing the bound individual identification and the account data in a supervision platform; acquiring the account data corresponding to the individual identification through the individual identification, and performing format normalization processing on the account data to obtain a running data set of the target transaction individual; the flow data set at least comprises transaction time, transaction objects, transaction amount and fund outflow direction; determining a transaction relation network between the target transaction individual and other transaction individuals according to the transaction time and the transaction object in the flow data set; determining an early warning threshold value of the target transaction individual according to the individual information of the target transaction individual; and determining the running abnormal level of the target transaction individual according to the running data set, the early warning threshold and the transaction relationship network, and if the running abnormal level exceeds a preset threshold, judging that the target transaction individual is running abnormally.
The method provided by the application can be used for endowing transaction individuals such as enterprises and individual merchants with identifications through an identification analysis technology, serially connecting transaction data of different transaction accounts and agent accounts of the transaction individuals, matching the association relation of each transaction individual through an artificial intelligence algorithm, associating other transaction streams such as a pseudo account and an agent account with a main account of the transaction individual, timely discovering illegal transaction behaviors such as illegal fund transfer, fraud and illegal funding, timely early warning abnormal accounts and realizing effective supervision on the transaction individual streams.
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The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a schematic diagram of a flow supervision method based on identifier resolution in an embodiment of the present application;
fig. 2 is a schematic diagram of a pipelining supervision device based on identifier resolution in an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.
Fig. 1 is a flowchart illustrating a pipelining supervision method based on identity resolution according to one or more embodiments of the present disclosure. The process can be executed by computing equipment in the corresponding field (for example, a wind control server or an intelligent mobile terminal corresponding to the payment service, and the like), and some input parameters or intermediate results in the process allow manual intervention and adjustment to help improve the accuracy.
The analysis method according to the embodiment of the present application may be implemented by a terminal device or a server, which is not particularly limited in this application. For convenience of understanding and description, the following embodiments are described in detail by taking a server as an example.
It should be noted that the server may be a single device, or may be a system composed of multiple devices, that is, a distributed server, which is not specifically limited in this application.
As shown in fig. 1, an embodiment of the present application provides a method for pipelining supervision based on identity resolution, including:
s101: acquiring individual information of a target trading individual, and acquiring account data of the target trading individual according to the individual information.
In order to monitor the running data of the transaction individuals, firstly, the individual information of the target transaction individuals needs to be acquired; it should be noted that, the transaction individuals may refer to enterprises or individual merchants. The individual information of the enterprise can refer to the information such as the name of the enterprise, the type of the enterprise, the unified social credit code, the legal representative, the address of the enterprise, the operating range, the registered capital and the like, and the individual information of the individual merchant can be the information such as the name of the merchant, the legal representative of the merchant, the identity card number, the address and the like. After the individual information of the target transaction individual is obtained, account data of the target transaction individual needs to be obtained according to the individual information, for example, the identity information of a certain individual merchant is known, and account data of all accounts under the name of the individual merchant is obtained through the identity information of the individual merchant.
S102: and generating the individual identification of the target trading individual by an identification analysis technology according to the individual information of the target trading individual.
After the account data of the individual is acquired, in order to eliminate the flow information splitting between each bank and each country, the individual identification which can be accepted between each bank can be generated through an identification analysis technology. It should be noted that, when generating the individual identifier of the transaction individual, the transaction individual needs to actively apply for the identifier on the identifier parsing platform and fill in the individual information of the transaction individual, that is, the individual information of the transaction individual can be obtained by the way of actively applying for the transaction individual. And for the transaction individuals which may have illegal fund transfer, illegal fund collection and other behaviors, the application identification is generally not carried out, and for the transaction individuals which do not actively apply for, the individual information of the transaction individuals is obtained through an organization such as a bank and the like, and the individual identification of the transaction individuals is generated according to the individual information.
S103: and binding the individual identification and the account data corresponding to the individual identification, and storing the bound individual identification and the account data in a supervision platform.
After generating the individual identification of the target transaction individual and acquiring the account data of the target transaction individual, binding the individual identification and the account data, and storing the bound individual identification and the account data in the supervision platform. When the running data of the target transaction individual is monitored, the account data of the transaction individual can be acquired in the monitoring platform through the individual identification of the target transaction individual.
S104: acquiring the account data corresponding to the individual identification through the individual identification, and performing format normalization processing on the account data to obtain a running data set of the target transaction individual; the flow data set at least comprises transaction time, transaction objects, transaction amount and fund outflow direction.
When the target transaction individual needs to be supervised, the server may select the account data of the target transaction individual from the supervision platform. Of course, the server can also directly obtain the account data of the target transaction individual from each bank through the individual identification. The present embodiment does not limit the manner of acquiring the account data. Because account data formats in various banks may be different, format normalization processing needs to be performed on the account data to obtain a running data set of target transaction individuals in the same format. The chronological data set herein shall include at least transaction time, transaction object, transaction amount, and direction of funds flow of the targeted transaction individual when the transaction is conducted.
S105: and determining a transaction relation network between the target transaction individual and other transaction individuals according to the transaction time and the transaction object in the flow data set.
Because the flow data set comprises the transaction time, the transaction object, the transaction amount and the fund outflow direction of the target transaction individual when the transaction is carried out, the transaction relationship network between the target transaction individual and other transaction individuals can be determined through the flow data set. The method is characterized in that a plurality of proxy accounts are required for illegal fund transfer or illegal fund collection activities, wherein the proxy accounts refer to accounts registered by borrowing other identification cards, and other transaction individuals who are transacted closely with a target transaction account can be clearly known by establishing a transaction relationship network.
S106: and determining an early warning threshold value of the target transaction individual according to the individual information of the target transaction individual.
In the supervision, attention needs to be paid to transaction individuals with possible illegal behaviors, and therefore, multiple early warning thresholds of target transaction individuals need to be determined according to individual information of the target transaction individuals. It should be noted that, it is not certain that the target transaction individual whose index exceeds the warning threshold value has an illegal action, but may have an illegal action, and important supervision is needed. For example, the business nature of the target trading individual is a convenience store, but a large amount of trading occurs, and at this time, the target trading individual is likely to perform illegal behaviors, and needs to be heavily supervised.
S107: and determining the running abnormal grade of the target transaction individual according to the running data set, the early warning threshold value and the transaction relation network.
When judging whether the target trading individual is abnormal in running, the running abnormality level of the target trading individual is determined according to the running data set, the early warning threshold and the trading relation network. If the level of the running water anomaly is too high, the target trading individual can be judged to have the running water anomaly.
In one embodiment, when the early warning threshold of the target trading individual is determined by the individual information of the target trading individual, such as the information of the operational property, the operational scale, the asset limit, the credit status, etc., of the target trading individual needs to be acquired by the individual identifier of the target trading individual. Then, the early warning threshold value of the trading individual is determined according to the operation property, the operation scale and the asset limit of the trading individual, and the following formula can be specifically adopted: q ═ k 1 A+k 2 B + C; . Wherein Q is an early warning threshold; k is a radical of 1 、k 2 Is a preset coefficient, and the size of the preset coefficient is determined by the operation property of the trading individual; a is the operation scale of the target trading individual, B is the asset limit of the target trading individual; c is a preset constant.
In one embodiment, when determining the transaction relationship network between the target transaction individual and the other transaction individuals according to the transaction time and the transaction object, first, the transaction time and the transaction frequency of the other transaction individuals performing transactions with the target transaction individual are determined according to the transaction time information and the transaction object information in the pipelined data set. Then, the distance between other trading individuals and the target trading individual in the trading relationship network taking the target trading individual as the center is calculated through the following formula: d ═ k 3 E-k 4 F + G; wherein D is the distance between any other transaction individual and the target transaction individual; k is a radical of 3 And k 4 Is a preset coefficient and the size of the preset coefficient is determined by the number of other transaction individuals and the running numberSize determination of the data set; e is the transaction time of the first transaction of any other transaction individual and the target transaction individual, and the earlier the transaction time of the first transaction is, the smaller E is; f is the transaction frequency between any other transaction individual and the target transaction individual; and G is a preset constant. Therefore, the trading individuals with the earlier trading and the larger trading times are closer to the target trading individual, namely the trading relation between each other trading individual and the target trading individual can be reflected by the distance in the trading relation network. Meanwhile, as part of transaction individuals may have proxy accounts, the difference between the transaction relationship network taking the proxy accounts as target transaction individuals and other transaction relationship networks can be obviously seen through the transaction relationship network: if the target trading individual is the proxy account, the number of other trading individuals in the corresponding trading relation network is less, and the other trading individuals are closer to the proxy account.
In one embodiment, when determining the supervision level of a target transaction individual according to a running data set, an early warning threshold and a transaction relationship network, first, a transaction amount and a transaction object corresponding to each piece of running data in the running data set need to be determined, and if the transaction amount exceeds the amount early warning threshold of any one of two transaction parties, the running data is determined to be suspicious running data. Meanwhile, the abnormal value of the running water data can be determined according to the following formula:
In one embodiment, when the water data is monitored, if an account which has not been transacted for a long time suddenly transfers a large amount or transfers a large amount, the account is also likely to have illegal activities. Thus, the chronological data of a first transaction of the targeted trading individual and a second transaction adjacent to the first transaction can be determined from the chronological data set. And if the interval time between the first transaction and the second transaction exceeds a silent account judgment threshold value, namely the target transaction individual is a silent account, and the transaction amount of the first transaction or the second transaction exceeds an early warning threshold value, the level of the running exception of the target transaction individual is improved.
In one embodiment, each individual identifier represents one transaction individual, but there are multiple accounts under the name of the transaction individual, and the transaction individual may have an account opening and canceling operation, so after the bound individual identifier and the account data are stored in the supervision platform, if it is found that the target transaction individual has an account opening operation, account opening information of the target transaction individual needs to be acquired; and acquiring account data of a newly opened account of the target transaction individual according to the account opening information, binding the account data of the newly opened account with the individual identification of the target transaction individual, and storing the bound account data and the individual identification in the supervision platform. Similarly, if the target transaction individual is determined to have the sales operation, the sales information of the target transaction individual is also acquired and stored in the supervision platform. By updating the account information under the individual identification name at intervals, the target transaction individual can be prevented from escaping from supervision through account opening operation.
In one embodiment, in the monitoring, in order to comprehensively consider the transaction time and the transaction times, the transaction frequency of the target transaction individual and the transaction frequency of the other transaction individuals should be counted, that is, the transaction times and the transaction times between the target transaction individual and the other transaction individuals are obtained according to the pipelining data set, and then the transaction frequency between the target transaction individual and the other transaction individuals within the preset time is determined according to the transaction times and the transaction times.
Further, after the transaction frequency of the target transaction individual is determined, if the target transaction individual is found to have frequent transactions with the overseas transaction individual and the transaction content does not conform to the operation content of the target transaction individual, the target transaction individual can be considered to have a running exception, that is, the individual information of the target transaction individual and other transaction individuals is obtained through the individual identification, and the overseas transaction individual outside is determined according to the individual information; and if the transaction frequency of the target transaction individual and the overseas transaction individual within the preset time is determined to be higher than the preset frequency early warning threshold value and the transaction content is not consistent with the operation property of the target transaction individual, the running water abnormity level of the target transaction individual is improved.
As shown in fig. 2, an embodiment of the present application further provides a pipelining supervision device based on identity resolution, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to: acquiring individual information of a target trading individual, and acquiring account data of the target trading individual according to the individual information; generating individual identification of the target trading individual by an identification analysis technology according to the individual information of the target trading individual; binding the individual identification and the account data corresponding to the individual identification, and storing the bound individual identification and the account data in a supervision platform; acquiring the account data corresponding to the individual identification through the individual identification, and performing format normalization processing on the account data to obtain a running data set of the target transaction individual; the flow data set at least comprises transaction time, transaction objects, transaction amount and fund outflow direction; determining a transaction relation network between the target transaction individual and other transaction individuals according to the transaction time and the transaction object in the flow data set; determining an early warning threshold value of the target transaction individual according to the individual information of the target transaction individual; and determining the running abnormal grade of the target transaction individual according to the running data set, the early warning threshold value and the transaction relation network.
An embodiment of the present application further provides a non-volatile computer storage medium storing computer-executable instructions, where the computer-executable instructions are configured to: acquiring individual information of a target trading individual, and acquiring account data of the target trading individual according to the individual information; generating individual identification of the target trading individual by an identification analysis technology according to the individual information of the target trading individual; binding the individual identification and the account data corresponding to the individual identification, and storing the bound individual identification and the account data in a supervision platform; acquiring the account data corresponding to the individual identification through the individual identification, and performing format normalization processing on the account data to obtain a running data set of the target transaction individual; the flow data set at least comprises transaction time, transaction objects, transaction amount and fund outflow direction; determining a transaction relation network between the target transaction individual and other transaction individuals according to the transaction time and the transaction object in the flow data set; determining an early warning threshold value of the target transaction individual according to the individual information of the target transaction individual; and determining the running abnormal grade of the target transaction individual according to the running data set, the early warning threshold value and the transaction relation network.
The embodiments in the present application are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the device and media embodiments, the description is relatively simple as it is substantially similar to the method embodiments, and reference may be made to some descriptions of the method embodiments for relevant points.
The device and the medium provided by the embodiment of the application correspond to the method one to one, so the device and the medium also have the similar beneficial technical effects as the corresponding method, and the beneficial technical effects of the method are explained in detail above, so the beneficial technical effects of the device and the medium are not repeated herein.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application 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 application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A pipelining supervision method based on identification analysis is characterized by comprising the following steps:
acquiring individual information of a target trading individual, and acquiring account data of the target trading individual according to the individual information;
generating individual identification of the target trading individual by an identification analysis technology according to the individual information of the target trading individual;
binding the individual identification and the account data corresponding to the individual identification, and storing the bound individual identification and the account data in a supervision platform;
acquiring the account data corresponding to the individual identification through the individual identification, and performing format normalization processing on the account data to obtain a running data set of the target transaction individual; the flow data set at least comprises transaction time, transaction objects, transaction amount and fund outflow direction;
determining a transaction relation network between the target transaction individual and other transaction individuals according to the transaction time and the transaction object in the flow data set;
determining an early warning threshold value of the target transaction individual according to the individual information of the target transaction individual;
and determining the running abnormal grade of the target transaction individual according to the running data set, the early warning threshold value and the transaction relation network.
2. The method according to claim 1, wherein the determining an early warning threshold of the target transaction individual according to the individual information of the target transaction individual specifically comprises:
acquiring individual information of the target trading individual through the individual identification of the target trading individual; the individual information includes at least: the operation property, the operation scale and the asset limit of the target transaction individual;
determining the early warning threshold value according to the individual information and by the following formula:
Q＝k 1 A+k 2 B+C；
wherein Q is the early warning threshold; k is 1 、k 2 Is a preset coefficient, and the size of the preset coefficient is determined by the operation property of the transaction individual; the A is the operation scale of the target trading individual, and the B is the asset limit of the target trading individual; and C is a preset constant.
3. The method according to claim 2, wherein the determining a trading relationship network between the target trading individual and other trading individuals according to the trading time and the trading object in the chronological dataset specifically comprises:
determining the transaction time and the transaction frequency of the other transaction individuals respectively transacting with the target transaction individual according to the transaction time and the transaction object in the flow data set;
generating a frequency time relation network of the target trading individual and the other trading individuals according to the trading time; wherein, in the frequency time relationship network, the distance between any other trading individual and the target trading individual is:
D＝k 3 E-k 4 F+G；
wherein D is the distance between any other trading individual and the target trading individual; k is 3 And k is said 4 Is a preset coefficient, and the size of the preset coefficient is determined by the number of other trading individuals and the size of the running water data set; the E is the transaction time of the first transaction of any other transaction individual and the target transaction individual, and the earlier the transaction time of the first transaction is, the smaller the E is; f is the transaction number between any other transaction individual and the target transaction individual; and G is a preset constant.
4. The method according to claim 3, wherein the determining the level of supervision of the target transaction individual according to the chronological data set, the early warning threshold, and the transaction relationship network specifically comprises:
determining the transaction object and the transaction amount in the chronological dataset of the target transaction individual;
if the transaction amount exceeds the amount early warning threshold value of the target transaction individual or the other transaction individuals, determining the running data as suspicious running data;
and determining outliers of the pipeline data according to the following formula:
wherein H is the outlier of the pipelined data, Q is the outlier of the pipelined data x For the amount of the transaction, said Q 1 And said Q 2 Early warning threshold values of both transaction parties respectively;
and determining the supervision level of the target transaction individual according to a preset running water abnormal level division rule and the abnormal value.
5. The method of claim 1, further comprising:
determining the flow data of a first transaction of the target transaction individual and a second transaction adjacent to the first transaction according to the flow data set;
if the interval time between the first transaction and the second transaction exceeds a silent account judgment threshold value and the transaction amount of the first transaction or the second transaction exceeds the early warning threshold value, the level of the running water abnormity of the target transaction individual is improved.
6. The method of claim 1, wherein after the binding the individual identifier and the account data corresponding to the individual identifier and storing the bound individual identifier and the account data in a regulatory platform, the method further comprises:
determining account opening operation of the target trading individual, and acquiring account opening information of the target trading individual;
acquiring account data of a newly opened account of the target transaction individual according to the account opening information, binding the account data of the newly opened account with the individual identification of the target transaction individual, and storing the bound account data in the supervision platform;
determining the seller operation of the target transaction individual, and acquiring the seller information of the target transaction individual;
and storing the sales information in the supervision platform.
7. The method of claim 1, further comprising:
acquiring transaction times and transaction time between the target transaction individual and the other transaction individuals according to the flow data set;
and determining the transaction frequency between the target transaction individual and the other transaction individuals within preset time according to the transaction times and the transaction time.
8. The method of claim 1, further comprising:
acquiring individual information of the target trading individual and the other trading individuals through the individual identification, and determining overseas trading individuals according to the individual information;
and if the transaction frequency of the target transaction individual and the overseas transaction individual within the preset time is determined to be higher than a preset frequency early warning threshold value and the transaction content does not accord with the operating property of the target transaction individual, the running water abnormity level of the target transaction individual is improved.
9. An assembly line supervision equipment based on identification analysis, comprising:
at least one processor; and a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to cause the at least one processor to perform:
acquiring individual information of a target trading individual, and acquiring account data of the target trading individual according to the individual information;
generating individual identification of the target trading individual by an identification analysis technology according to the individual information of the target trading individual;
binding the individual identification and the account data corresponding to the individual identification, and storing the bound individual identification and the account data in a supervision platform;
acquiring the account data corresponding to the individual identification through the individual identification, and performing format normalization processing on the account data to obtain a running data set of the target transaction individual; the flow data set at least comprises transaction time, transaction objects, transaction amount and fund outflow direction;
determining a transaction relation network between the target transaction individual and other transaction individuals according to the transaction time and the transaction object in the flow data set;
determining an early warning threshold value of the target transaction individual according to the individual information of the target transaction individual;
and determining the running abnormal grade of the target transaction individual according to the running data set, the early warning threshold value and the transaction relation network.
10. A non-transitory computer storage medium storing computer-executable instructions, the computer-executable instructions configured to:
acquiring individual information of a target trading individual, and acquiring account data of the target trading individual according to the individual information;
generating individual identification of the target trading individual by an identification analysis technology according to the individual information of the target trading individual;
binding the individual identification and the account data corresponding to the individual identification, and storing the bound individual identification and the account data in a supervision platform;
acquiring the account data corresponding to the individual identification through the individual identification, and performing format normalization processing on the account data to obtain a running data set of the target transaction individual; the flow data set at least comprises transaction time, transaction objects, transaction amount and fund outflow direction;
determining a transaction relation network between the target transaction individual and other transaction individuals according to the transaction time and the transaction object in the flow data set;
determining an early warning threshold value of the target transaction individual according to the individual information of the target transaction individual;
and determining the running abnormal grade of the target transaction individual according to the running data set, the early warning threshold value and the transaction relation network.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210415902.6A CN114820165A (en) | 2022-04-20 | 2022-04-20 | Flow monitoring method, equipment and medium based on identification analysis |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210415902.6A CN114820165A (en) | 2022-04-20 | 2022-04-20 | Flow monitoring method, equipment and medium based on identification analysis |
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| Publication Number | Publication Date |
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| CN114820165A true CN114820165A (en) | 2022-07-29 |
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ID=82504797
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| CN202210415902.6A Pending CN114820165A (en) | 2022-04-20 | 2022-04-20 | Flow monitoring method, equipment and medium based on identification analysis |
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| Country | Link |
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| CN (1) | CN114820165A (en) |
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