CN115423492A

CN115423492A - Anti-money laundering operation discrimination analysis system and method and electronic equipment

Info

Publication number: CN115423492A
Application number: CN202211114436.4A
Authority: CN
Inventors: 郏卫士
Original assignee: Ping An Bank Co Ltd
Current assignee: Ping An Bank Co Ltd
Priority date: 2022-09-14
Filing date: 2022-09-14
Publication date: 2022-12-02

Abstract

The invention provides a system and a method for discriminating and analyzing anti-money laundering operation and electronic equipment, wherein the system comprises: the data acquisition module is used for acquiring the money laundering data of the suspicious client; the graph relation network construction module is used for constructing a graph relation network of the suspicious customer according to the anti-money laundering data; and the suspicious characteristic analysis module is used for carrying out suspicious characteristic analysis on the money laundering data of the suspicious client to obtain the suspicious characteristics of the suspicious client and associating the suspicious characteristics with the graph relation network. The money laundering operation screening and analyzing system can show the relationship between the suspicious client and the entity client related to the suspicious client in the form of the graph relationship network, and can give out the suspicious characteristics of the suspicious client.

Description

Anti-money laundering operation discrimination analysis system and method and electronic equipment

Technical Field

The invention relates to the technical field of anti-money laundering discrimination and analysis, in particular to an anti-money laundering operation discrimination and analysis system, method and electronic equipment.

Background

In order to prevent and restrain lawbreakers from cleaning illegal income or subsidizing illegal activities by utilizing a financial system and a specific non-financial industry, the financial industry and the specific non-financial industry have obligations of anti-money laundering and anti-money laundering under the guidance of anti-money laundering financing spirit of people's banks and bank guardians in China according to relevant legal regulations. At present, under the guidance of the supervision authorities such as the pedestrian, each financial institution constructs an anti-money laundering integrated system, which comprises: the anti-money laundering operation analysis system comprises an anti-money laundering model construction module, an anti-money laundering operation analysis system and a supervision reporting pedestrian system, wherein the anti-money laundering model is used for early warning of suspicious customers, information (generally including basic information and transaction information) of the suspicious customers obtained through early warning enters the anti-money laundering operation analysis system, the anti-money laundering operation analysis system displays the information of the suspicious customers in a form on a page, then, bank operators screen the anti-money laundering customers according to the information (viewing the transaction information, risk rating and advance regulation information) of the suspicious customers displayed in the form, and if the suspicious customers are screened, an anti-money laundering report is reported to the pedestrian through the supervision related system. The above systems and links all reflect the deposition of the financial institution on the anti-money laundering construction, but with the upgrading of money laundering modes and illegal activity types (such as ganged mode, money laundering mode, scientific mode and the like), the challenge is brought to the traditional anti-money laundering operation analysis system.

Because the traditional anti-money laundering operation analysis system shows the information of the suspicious client on the page in a table form, bank operators are inconvenient to discriminate and analyze the anti-money laundering client, and the discrimination efficiency of the anti-money laundering client is low.

In summary, the existing anti-money laundering operation analysis system has the technical problems of non-intuitive information display and inconvenient analysis.

Disclosure of Invention

In view of the above, the present invention provides an anti-money laundering operation discrimination analysis system, method and electronic device for use in financial technology or other related fields, so as to solve the technical problems of the existing anti-money laundering operation analysis system that information display is not intuitive and analysis is inconvenient.

In a first aspect, an embodiment of the present invention provides an anti-money laundering operation screening and analyzing system, including: the system comprises a data acquisition module, a graph relation network construction module and a suspicious characteristic analysis module;

the data acquisition module is used for acquiring money laundering data of a suspicious customer, wherein the money laundering data comprises: customer base data, transaction data, account data, device data, and IP data;

the graph relation network construction module is used for constructing the graph relation network of the suspicious customer according to the money laundering prevention data, wherein the graph relation network comprises: the suspicious customer, the entity customers associated with the suspicious customer, the relationships between the suspicious customer and each of the entity customers;

and the suspicious characteristic analysis module is used for performing suspicious characteristic analysis on the money laundering data of the suspicious client to obtain the suspicious characteristic of the suspicious client, and associating the suspicious characteristic with the graph relation network.

Further, the method also comprises the following steps: a presentation module, wherein the presentation module is configured to render a presentation interface, the presentation interface comprising: a graph relation network display area and a feature label display area; and the system is used for displaying the graph relation network of the suspicious customer in the graph relation network display area and displaying the suspicious characteristic of the suspicious customer in the characteristic label display area.

Further, the suspicious characteristics of the suspicious client support drill details; wherein the drill-down detail comprises detail data of the suspicious features.

Further, the graph relation network building module comprises a graph relation network building model, and the graph relation network building module is used for determining a target graph relation network building model according to the money laundering data and building a graph relation network of suspicious customers corresponding to the money laundering data by using the target graph relation network building model, wherein in the graph relation network, nodes represent the suspicious customers and entity customers related to the suspicious customers, and represent the relations between the suspicious customers and the entity customers as well as between the suspicious customers and the entity customers;

the suspicious feature analysis module comprises a feature analysis model, and the feature analysis model comprises: a rule model and a machine learning model; the suspicious characteristic analysis module is used for carrying out suspicious characteristic analysis on the money laundering data of the suspicious client by adopting the rule model and the machine learning model to obtain suspicious characteristics of the suspicious client;

the suspicious clients comprise suspicious individual clients and suspicious group clients, wherein the corresponding suspicious characteristics of the suspicious group clients are group suspicious characteristics.

In a second aspect, an embodiment of the present invention further provides an anti-money laundering operation screening and analyzing method, which is applied to an anti-money laundering operation screening and analyzing system, and the method includes:

obtaining anti-money laundering data of a suspicious customer, wherein the anti-money laundering data comprises: customer basic data, transaction data, account data, equipment data and IP data;

constructing a graph relationship network of the suspicious customer according to the money laundering data, wherein the graph relationship network comprises: the suspicious customer, the entity customers related to the suspicious customer, the relationships between the suspicious customer and each of the entity customers;

carrying out suspicious characteristic analysis on the money laundering data of the suspicious customer to obtain suspicious characteristics of the suspicious customer, and associating the suspicious characteristics with the graph relation network;

and displaying the graph relation network of the suspicious customer in a graph relation network display area of a display interface, and displaying the suspicious characteristic of the suspicious customer in a characteristic label display area of the display interface.

Further, the method further comprises:

and displaying the suspicious characteristics of the target entity client in the characteristic label display area according to the target entity client triggered by the operator in the graph relation network of the suspicious client.

Further, the method further comprises:

and displaying the detail data of the target suspicious characteristics according to a preset format according to the triggering of the operator on the target suspicious characteristics of the suspicious client.

Further, constructing a graph relationship network of the suspicious customer according to the anti-money laundering data includes:

determining in the anti-money laundering data relationships between the suspicious customer, the entity customers associated with the suspicious customer, the suspicious customer and each of the entity customers;

and constructing a graph relation network of the suspicious client by taking the suspicious client, the entity clients related to the suspicious client as nodes and taking the relation between the suspicious client and each entity client as an edge.

In a third aspect, an embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the method according to any one of the above first aspects when executing the computer program.

In a fourth aspect, embodiments of the present invention also provide a computer-readable storage medium storing machine executable instructions, which when invoked and executed by a processor, cause the processor to perform the method of any of the first aspect.

In an embodiment of the present invention, there is provided an anti-money laundering operation discriminating analysis system including: the system comprises a data acquisition module, a graph relation network construction module and a suspicious characteristic analysis module; a data acquisition module for acquiring money laundering data of a suspicious customer, wherein the money laundering data comprises: customer base data, transaction data, account data, device data, and IP data; the graph relation network construction module is used for constructing a graph relation network of suspicious customers according to the anti-money laundering data, wherein the graph relation network comprises: suspicious clients, entity clients related to suspicious clients, relationships between suspicious clients and each entity client and among entity clients; and the suspicious characteristic analysis module is used for carrying out suspicious characteristic analysis on the money laundering data of the suspicious client to obtain the suspicious characteristics of the suspicious client and associating the suspicious characteristics with the graph relation network. According to the description, the anti-money laundering operation screening and analyzing system can show the relationship between the suspicious client and the entity client related to the suspicious client in the form of the graph relationship network, and can give out the suspicious characteristics of the suspicious client.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.

Fig. 1 is a schematic diagram of an anti-money laundering operation discriminating analysis system according to an embodiment of the present invention;

FIG. 2 is a schematic diagram of a graph relationship network of suspicious clients with suspicious features according to an embodiment of the present invention;

FIG. 3 is a flowchart of a method for discriminating and analyzing money laundering operations according to an embodiment of the present invention;

fig. 4 is a schematic diagram of an electronic device according to an embodiment of the present invention.

Detailed Description

The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are some, but not all, embodiments of the present invention. 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 invention.

The traditional anti-money laundering operation analysis system shows the information of suspicious customers on a page in a form of a table, is inconvenient for bank operators to discriminate and analyze the anti-money laundering customers, and has low discrimination efficiency of the anti-money laundering customers.

Based on the above, the money laundering operation screening and analyzing system can show the relationship between the suspicious client and the related entity client in the form of the graph relationship network, and can give out the suspicious characteristics of the suspicious client.

To facilitate understanding of the present embodiment, a detailed description will be given of an anti-money laundering operation screening and analyzing system disclosed in the present embodiment.

The first embodiment is as follows:

according to an embodiment of the present invention, there is provided an embodiment of an anti-money laundering screening analysis system, and fig. 1 is a schematic diagram of an anti-money laundering screening analysis system according to an embodiment of the present invention, as shown in fig. 1, including: the system comprises a data acquisition module, a graph relation network construction module and a suspicious characteristic analysis module;

a data acquisition module for acquiring money laundering data of a suspicious customer, wherein the money laundering data comprises: customer base data, transaction data, account data, device data, and IP data;

the graph relation network construction module is used for constructing a graph relation network of suspicious customers according to the anti-money laundering data, wherein the graph relation network comprises: suspicious clients, entity clients related to suspicious clients, relationships between suspicious clients and each entity client and among entity clients;

and the suspicious characteristic analysis module is used for carrying out suspicious characteristic analysis on the money laundering data of the suspicious client to obtain the suspicious characteristics of the suspicious client and associating the suspicious characteristics with the graph relation network.

In an embodiment of the present invention, the money laundering data of the suspicious client may be obtained by an anti-money laundering model early warning, and the money laundering data may include: the basic information of the customer may refer to name information, age information, account opening area information, and the like of the customer, and the basic information of the customer is not specifically limited in the embodiment of the present invention.

Specifically, in the graph relationship network of the suspicious client, the suspicious client and the entity clients related to the suspicious client are nodes in the graph relationship network, and the relationships between the suspicious client and each entity client and between each entity client are edges in the graph relationship network, as shown in fig. 2, that is, the graph relationship network of the suspicious client is obtained by construction; meanwhile, the suspicious characteristic analysis module performs suspicious characteristic analysis on the money laundering data of the suspicious client to obtain suspicious characteristics of the suspicious client, and associates the suspicious characteristics with the graph relation network, as shown in fig. 2. In fig. 2, the left side shows a visual display of the customer on some relations such as a capital exchange, a shared device, an associated IP, and the like, and the right side shows suspicious features of the suspicious customer, where the suspicious features on the right side are feature labels printed by the suspicious feature analysis module on the suspicious customer, and the money laundering worker (i.e., a bank worker) performs a job through the visual suspicious features to screen the suspicious features of the customer.

In an embodiment of the present invention, there is provided an anti-money laundering operation discriminating analysis system including: the system comprises a data acquisition module, a graph relation network construction module and a suspicious characteristic analysis module; a data acquisition module for acquiring money laundering data of a suspicious customer, wherein the money laundering data comprises: customer base data, transaction data, account data, device data, and IP data; the graph relation network construction module is used for constructing a graph relation network of suspicious customers according to the anti-money laundering data, wherein the graph relation network comprises: suspicious clients, entity clients related to suspicious clients, relationships between suspicious clients and each entity client and among entity clients; and the suspicious characteristic analysis module is used for carrying out suspicious characteristic analysis on the money laundering data of the suspicious client to obtain the suspicious characteristic of the suspicious client and associating the suspicious characteristic with the graph relation network. According to the description, the anti-money laundering operation screening and analyzing system can show the relationship between the suspicious client and the entity client related to the suspicious client in the form of the graph relationship network, and can give out the suspicious characteristics of the suspicious client.

The foregoing briefly describes the anti-money laundering operation screening analysis system of the present invention, and the details of which are described in detail below.

In an optional embodiment of the invention, the system further comprises: a display module;

the display module is used for rendering a display interface, and the display interface comprises: a graph relation network display area and a feature label display area; the display module is used for displaying the graph relation network of the suspicious customer in the graph relation network display area and displaying the suspicious characteristic of the suspicious customer in the characteristic label display area.

In an optional embodiment of the invention, the suspicious characteristics of the suspicious client support drill-down; wherein the drill-down detail contains detail data of suspicious features.

Specifically, the detail data of the suspicious features may be displayed in a form of a graph or a list, and the specific form is not limited in the embodiment of the present invention as long as the detail data of the suspicious features can be visually displayed.

As shown in fig. 2, if the operator selects the transaction information category, i.e., fast-in and fast-out, a transaction graph of the suspicious customer can be displayed, and the transaction graph can clearly show the fast-in and fast-out transaction characteristics of the suspicious customer; if the operator selects the transaction information category, i.e., abnormal time, the transaction details of the abnormal time (e.g., 0-5 am) of the customer may be displayed in the form of a list.

In an optional embodiment of the present invention, the graph relation network construction module includes a graph relation network construction model, and the graph relation network construction module is configured to determine a target graph relation network construction model according to the money laundering data, and construct a graph relation network of suspicious clients corresponding to the money laundering data using the target graph relation network construction model, where in the graph relation network, nodes represent the suspicious clients and entity clients related to the suspicious clients, and edges represent relationships between the suspicious clients and the entity clients and between the suspicious clients and the entity clients;

the suspicious characteristic analysis module comprises a characteristic analysis model, and the characteristic analysis model comprises: a rule model and a machine learning model; the suspicious characteristic analysis module is used for carrying out suspicious characteristic analysis on the money laundering data of the suspicious client by adopting a rule model and a machine learning model to obtain the suspicious characteristics of the suspicious client;

Specifically, the rule model may specifically be some rules, for example, if the anti-money laundering data shows that the suspected customer is under 18 years old and the fund is more than 10 ten thousand, it may be determined that the suspected characteristic is under age; for example, the money laundering data shows that the suspicious client transfers to the account with a certain fund and then transfers out immediately, and the suspicious characteristics of the money laundering data can be judged to be fast forward and fast out;

the machine learning model may specifically be a pre-trained feature model for certain feature recognition.

It should be noted that the above suspicious features may specifically include: some suspicious features given by the bank also include suspicious features summarized by experience of the bank in actual operation, and in addition, after the operator observes the graph relation network, if the operator finds that the suspicious client has a certain suspicious feature, but the suspicious features given by the corresponding right suspicious feature analysis module do not include the suspicious features, the operator can check the suspicious features which are not included in the right suspicious feature area.

The anti-money laundering operation screening and analyzing system can further show suspicious characteristics of suspicious customers in the directions of fund flow direction, transaction relationship and the like in a graph relationship network mode so as to provide more support for anti-money laundering operators to screen suspicious customers and provide more accurate suspicious point conclusions.

The graph relation network with the characteristic labels (namely suspicious characteristics) can greatly improve the quality of discrimination analysis of operators, is greatly beneficial to writing anti-money laundering reports in the later period, has certain support for concluding and summarizing the types of crimes of suspicious clients, and can fully utilize the characteristics of the relation network to visualize the anti-money laundering operators in the process of discriminating and analyzing the suspicious clients/suspicious groups, emphatically analyze the capital flow direction, the equipment sharing relation, the distribution of transaction opponents and the like of the clients, drill down the suspicious points of the clients through the suspicious characteristics to obtain the specific description of the suspicious characteristic points, and comprehensively improve the efficiency and the comprehensiveness of the anti-money laundering operation analysis; in addition, the graph relation network with the feature labels (namely the suspicious features) can help the operators to deeply dig the thread outlines, analyze the support points of the thread outlines, form main conclusions by taking the support points as the basis, and further improve the quality and the efficiency of the anti-money laundering operation analysis; in addition, in a graph relation network with feature labels (namely suspicious features), the suspicious features support drill-down display, so that the thinking of operators for screening and analyzing can be met, the suspicious features of suspicious customers can be summarized, and supporting illegal activity points can be given.

The anti-money laundering operation discrimination analysis system has the following characteristics:

(1) The graph relation network of the suspicious client on the aspects of transaction relation, fund flow direction and the like can be displayed friendly, intuitively and beautifully;

(2) The method has the advantages that the relations of the suspicious customers about transactions, shared IP/MAC and the like on the suspicious transaction layer are visually displayed, the suspicious characteristics of the suspicious customers can be clearly screened and analyzed, and the method accords with the thinking of screening and analyzing while carrying out anti-money laundering operation;

(3) For ganged money laundering numerators, the traditional anti-money laundering operation analysis system cannot show the suspicious characteristics of gangs, such as ganged fund flow, equipment flow and other characteristics, but the graph relation network can well show the relevant suspicious characteristics of gangs, such as transaction flow characteristics, shared equipment characteristics and the like.

Example two:

in accordance with an embodiment of the present invention, there is provided an embodiment of an anti-money laundering job screening method, it should be noted that the steps illustrated in the flowchart of the figure may be performed in a computer system, such as a set of computer-executable instructions, and that while a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be performed in an order different than here.

Fig. 3 is a flowchart of an anti-money laundering screening method according to an embodiment of the present invention, as shown in fig. 3, the method including the steps of:

step S302, obtaining the money laundering data of the suspicious client, wherein the money laundering data comprises: customer basic data, transaction data, account data, equipment data and IP data;

the anti-money laundering operation discrimination analysis method can be applied to an anti-money laundering operation discrimination analysis system, and when the anti-money laundering operation discrimination analysis is carried out, anti-money laundering data of a suspicious client are obtained firstly, and the anti-money laundering data of the suspicious client can be obtained by early warning of an anti-money laundering model.

Step S304, constructing a graph relation network of suspicious customers according to the anti-money laundering data, wherein the graph relation network comprises: suspicious clients, entity clients related to suspicious clients, relationships between suspicious clients and each entity client and among entity clients;

the anti-money laundering operation screening analysis system comprises a graph relation network construction model, and the graph relation network construction model can construct a graph relation network of suspicious clients corresponding to anti-money laundering data. In the graph relation network, nodes represent suspicious clients and entity clients related to the suspicious clients, and edges represent the relations between the suspicious clients and the entity clients and between the suspicious clients and the entity clients;

step S306, carrying out suspicious characteristic analysis on the money laundering data of the suspicious client to obtain suspicious characteristics of the suspicious client, and associating the suspicious characteristics with a graph relation network;

specifically, the money laundering operation screening and analyzing system comprises a regular model and a machine learning model, and can perform suspicious characteristic analysis on money laundering data of suspicious customers to obtain suspicious characteristics of the suspicious customers.

And S308, displaying the graph relation network of the suspicious customer in the graph relation network display area of the display interface, and displaying the suspicious characteristic of the suspicious customer in the characteristic label display area of the display interface.

In an embodiment of the present invention, a money laundering operation discriminating analysis method is provided, including: obtaining anti-money laundering data of a suspicious customer, wherein the anti-money laundering data comprises: customer basic data, transaction data, account data, equipment data and IP data; constructing a graph relationship network of suspicious customers according to the anti-money laundering data, wherein the graph relationship network comprises: suspicious clients, entity clients related to suspicious clients, relationships between suspicious clients and each entity client and among entity clients; carrying out suspicious characteristic analysis on the money laundering data of the suspicious client to obtain suspicious characteristics of the suspicious client, and associating the suspicious characteristics with a graph relation network; and displaying the graph relation network of the suspicious customer in a graph relation network display area of the display interface, and displaying the suspicious characteristic of the suspicious customer in a characteristic label display area of the display interface. According to the description, the anti-money laundering operation screening and analyzing method can display the relationship between the suspicious client and the entity client related to the suspicious client in the form of the graph relationship network, and can give the suspicious characteristics of the suspicious client.

The foregoing briefly introduces the anti-money laundering operation screening method of the present invention, and the details thereof are described in detail below.

In an optional embodiment of the invention, the method further comprises:

Specifically, as shown in fig. 2, if the operator clicks the person 2 in the graph relationship network of the suspicious client, the suspicious feature of the person 2 is displayed in the feature tag display area on the right side; if the operator clicks on person 4 in the graph relationship network of the suspicious customer, the suspicious features of person 4 will be displayed in the feature tag display area on the right side.

In an optional embodiment of the invention, the method further comprises:

and displaying detailed data of the target suspicious features according to a preset format according to the trigger of the operator on the target suspicious features of the suspicious client.

In an optional embodiment of the present invention, the constructing a graph relationship network of suspicious customers according to the money laundering data specifically includes the following steps:

(1) Determining relationships among the suspicious customer, the entity customers related to the suspicious customer, the suspicious customer and each entity customer in the money laundering data;

(2) And constructing a graph relation network of the suspicious client by taking the suspicious client, the entity clients related to the suspicious client as nodes and taking the relations between the suspicious client and each entity client and among the entity clients as edges.

The method for discriminating and analyzing the money laundering operation can further show the suspicious characteristics of the suspicious customer in the directions of fund flow direction, transaction relationship and the like in a graph relation network mode so as to provide more support for money laundering operation personnel to discriminate the suspicious customer and provide more accurate suspicious point conclusions.

The method provided by the embodiment of the present invention has the same implementation principle and technical effect as the system embodiment, and for the sake of brief description, reference may be made to the corresponding contents in the system embodiment for the parts that are not mentioned in the method embodiment.

As shown in fig. 4, an electronic device 600 provided in an embodiment of the present application includes: a processor 601, a memory 602 and a bus, the memory 602 storing machine-readable instructions executable by the processor 601, the processor 601 and the memory 602 communicating via the bus when the electronic device is operating, the processor 601 executing the machine-readable instructions to perform the steps of the anti-money laundering job screening method as described above.

Specifically, the memory 602 and the processor 601 can be general-purpose memories and processors, and are not particularly limited thereto, and the anti-money laundering job discriminating analysis method can be performed when the processor 601 runs a computer program stored in the memory 602.

The processor 601 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 601. The Processor 601 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the device can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in the memory 602, and the processor 601 reads the information in the memory 602 and completes the steps of the method in combination with the hardware thereof.

In response to the anti-money laundering job screening method, the embodiment of the present application further provides a computer-readable storage medium, in which machine executable instructions are stored, and when the computer executable instructions are called and executed by a processor, the computer executable instructions cause the processor to execute the steps of the anti-money laundering job screening method.

The anti-money laundering operation discrimination and analysis device provided by the embodiment of the application can be specific hardware on equipment, or software or firmware installed on the equipment, and the like. The device provided in the embodiment of the present application has the same implementation principle and the same technical effects as those of the foregoing method embodiments, and for the sake of brief description, reference may be made to corresponding contents in the foregoing method embodiments for the absence of any mention in the device embodiment. It can be clearly understood by those skilled in the art that, for convenience and simplicity of description, the specific working processes of the system, the apparatus and the unit described above may all refer to the corresponding processes in the method embodiments, and are not described herein again.

In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed coupling or direct coupling or communication connection between each other may be through some communication interfaces, indirect coupling or communication connection between devices or units, and may be in an electrical, mechanical or other form.

For another example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments provided in the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing an electronic device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the vehicle marking method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.

It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus once an item is defined in one figure, it need not be further defined and explained in subsequent figures, and moreover, the terms "first", "second", "third", etc. are used merely to distinguish one description from another and are not to be construed as indicating or implying relative importance.

Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present application, and are used for illustrating the technical solutions of the present application, but not limiting the same, and the scope of the present application is not limited thereto, and although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope disclosed in the present application; such modifications, changes or substitutions do not depart from the scope of the embodiments of the present application. Are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. An anti-money laundering operation discrimination analysis system, comprising: the system comprises a data acquisition module, a graph relation network construction module and a suspicious characteristic analysis module;

the graph relation network construction module is used for constructing the graph relation network of the suspicious customer according to the money laundering prevention data, wherein the graph relation network comprises: the suspicious customer, the entity customers related to the suspicious customer, the relationships between the suspicious customer and each of the entity customers;

2. The anti-money laundering operation screening analysis system according to claim 1, further comprising: a presentation module, wherein the presentation module is configured to render a presentation interface, the presentation interface comprising: a graph relation network display area and a feature label display area; and the system is used for displaying the graph relation network of the suspicious customer in the graph relation network display area and displaying the suspicious characteristic of the suspicious customer in the characteristic label display area.

3. The anti-money laundering job screening analysis system according to claim 1, wherein the suspicious features of the suspicious customer support drill-down; wherein the drill-down detail includes detail data of the suspicious feature.

4. The anti-money laundering operation screening analysis system according to claim 1,

the graph relation network building module comprises a graph relation network building model, and is used for determining a target graph relation network building model according to the money laundering data and building a graph relation network of suspicious customers corresponding to the money laundering data by using the target graph relation network building model, wherein in the graph relation network, nodes represent the suspicious customers and entity customers related to the suspicious customers, and represent the relations among the suspicious customers, the entity customers and the entity customers;

the suspicious clients comprise suspicious individual clients and suspicious group clients, wherein the corresponding suspicious characteristics of the suspicious group clients are group-partner suspicious characteristics.

5. An anti-money laundering operation discrimination analysis method is applied to an anti-money laundering operation discrimination analysis system, and the method comprises the following steps:

performing suspicious characteristic analysis on the money laundering data of the suspicious customers to obtain suspicious characteristics of the suspicious customers, and associating the suspicious characteristics with the graph relation network;

6. The method of claim 5, further comprising:

7. The method of claim 5, further comprising:

8. The method of claim 5, wherein constructing a graph relationship network of the suspicious customer based on the anti-money laundering data comprises:

determining in the anti-money laundering data relationships among the suspicious customer, the entity customers related to the suspicious customer, the suspicious customer and each of the entity customers;

9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the method of any of the preceding claims 5 to 8 are implemented when the computer program is executed by the processor.

10. A computer readable storage medium having stored thereon machine executable instructions which, when invoked and executed by a processor, cause the processor to perform the method of any of claims 5 to 8.