CN111046192A - Identification method and device for bank case-involved account - Google Patents
Identification method and device for bank case-involved account Download PDFInfo
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Abstract
The invention provides a method and a device for identifying a bank involved account, wherein the method comprises the following steps: respectively carrying out entity extraction processing, entity attribute extraction processing and relationship extraction processing among entities on the obtained bank account data to respectively obtain entity data, attribute data and relationship data which respectively correspond to the entity data, the attribute data and the relationship data; performing ontology modeling based on the entity data, the attribute data and the relationship data to generate a knowledge graph; and identifying the bank involved account according to the knowledge graph. The invention can greatly improve the identification efficiency of the case-related account, is convenient, saves the resource waste in all aspects and can meet the supervision requirement.
Description
Technical Field
The invention relates to the technical field of bank account maintenance, in particular to a method and a device for identifying a bank case-involved account.
Background
At present, along with the increase of the attack force on the illegal criminal activities of the telecommunication network and the strict control of the bank on account opening and use of the personal account, the tendency of utilizing the personal account to implement the illegal criminal of the telecommunication network is suppressed. The activities of telecommunication network illegal crimes on public accounts by commercial banks are greatly increased, and in order to attack the illegal crimes implemented on the public accounts, the public accounts and actual controllers thereof need to be identified in advance and controlled in advance by case.
At present, commercial banks identify and predict public accounts involved in case, and the traditional methods include field investigation, manual inquiry, prediction based on experience and the like. However, as the number of enterprises increases, the traditional methods such as field survey have the following problems: firstly, the cost is high and the efficiency is low, and the time and the cost for carrying out traffic investigation on site and consuming on site are higher; secondly, the change is quick, the timeliness is poor, the illegal criminal means of the telecommunication network is continuously renewed, the method tends to be scientific, specialized and large-scale, and is more concealed, and meanwhile, the information of enterprises is continuously changed, and the information of one hand needs to be mastered in time; third, the threshold is high, the actual case handling experience of a manager, enterprise self-related data and relevant data of the enterprise need to be relied on for setting up case involving and public accounts, the data have certain particularity and professionality, and potential risks of the enterprise can be discovered when the data can be processed only by means of professional theory and practice knowledge.
Therefore, there is a need for an efficient and accurate method or apparatus for identifying and predicting public accounts involved in a case.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a method and a device for identifying a bank case-involved account.
In order to solve the technical problems, the invention provides the following technical scheme:
in a first aspect, the present invention provides a method for identifying a bank involved account, including:
respectively carrying out entity extraction processing, entity attribute extraction processing and relationship extraction processing among entities on the obtained bank account data to respectively obtain entity data, attribute data and relationship data which respectively correspond to the entity data, the attribute data and the relationship data;
performing ontology modeling based on the entity data, the attribute data and the relationship data to generate a knowledge graph;
and identifying the bank involved account according to the knowledge graph.
Further, after the ontology modeling is performed based on the entity data, the attribute data and the relationship data to generate the knowledge graph, the method further includes:
and storing the knowledge graph in a graph database mode.
Wherein, the storing the knowledge graph by adopting a graph database mode comprises the following steps:
and storing the knowledge graph by adopting a Neo4j graph database.
Further, after the ontology modeling is performed based on the entity data, the attribute data and the relationship data to generate the knowledge graph, the method further includes:
and visually displaying the knowledge graph in a visual mode.
The ontology modeling based on the entity data, the attribute data and the relationship data to generate the knowledge graph comprises the following steps:
generating a transaction flow graph of the entity based on the entity data, the attribute data and the relationship data;
generating a relationship person association map of the entity based on the entity data, the attribute data and the relationship data;
and generating a customer information association map of the entity based on the entity data, the attribute data and the relationship data.
Identifying the bank involved account according to the knowledge graph comprises the following steps:
and identifying the target account of the knowledge graph according to a pre-trained identification model generated based on a machine learning algorithm.
Further, before the identifying the target account of the knowledge graph according to the identification model generated by the pre-trained machine learning algorithm, the method further includes:
respectively adopting an XGBOOST algorithm, a random forest algorithm and a logistic regression algorithm to train the training set samples to generate a first recognition model, a second recognition model and a third recognition model which respectively correspond to the XGBOOST algorithm, the random forest algorithm and the logistic regression algorithm;
and evaluating the recognition results of the first recognition model, the second recognition model and the third recognition model respectively, and determining the recognition models according to the evaluation results.
In a second aspect, the present invention provides an apparatus for identifying a bank involved account, including:
the acquisition unit is used for respectively carrying out entity extraction processing, entity attribute extraction processing and relationship extraction processing among the entities on the acquired bank account data to respectively obtain entity data, attribute data and relationship data corresponding to the entity data, the attribute data and the relationship data;
the map unit is used for carrying out ontology modeling on the basis of the entity data, the attribute data and the relation data to generate a knowledge map;
and the identification unit is used for identifying the bank involved account according to the knowledge graph.
Further, the method also comprises the following steps:
and the storage unit is used for storing the knowledge graph in a graph database mode.
Wherein the storage unit includes:
and the storage subunit is used for storing the knowledge graph in a Neo4j graph database mode.
Further, the method also comprises the following steps:
and the display unit is used for visually displaying the knowledge graph in a visual mode.
Wherein the map unit comprises:
a first graph subunit for generating a transaction pipeline graph of the entity based on the entity data, the attribute data, and the relationship data;
the second map subunit is used for generating a relationship person associated map of the entity based on the entity data, the attribute data and the relationship data;
and the third map subunit is used for generating a customer information association map of the entity based on the entity data, the attribute data and the relationship data.
Wherein the identification unit includes:
and the recognition subunit is used for recognizing the target account of the knowledge graph according to a pre-trained recognition model generated based on a machine learning algorithm.
Further, the identification unit further includes:
the training subunit is used for training samples in the training set by respectively adopting an XGB OST algorithm, a random forest algorithm and a logistic regression algorithm to generate a first recognition model, a second recognition model and a third recognition model which respectively correspond to the XGB OST algorithm, the random forest algorithm and the logistic regression algorithm;
and the optimizing subunit is configured to evaluate recognition results of the first recognition model, the second recognition model and the third recognition model, respectively, and determine the recognition model according to the evaluation results.
In a third aspect, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method for identifying a bank involved account.
In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method for identifying a bank case-involved account.
According to the technical scheme, the invention provides a method and a device for identifying bank case-involved accounts, which respectively obtain corresponding entity data, attribute data and relationship data by respectively carrying out entity extraction processing, entity attribute extraction processing and relationship extraction processing among entities on the obtained bank account data; performing ontology modeling based on the entity data, the attribute data and the relationship data to generate a knowledge graph; the bank case-involved account is identified according to the knowledge map, so that the identification efficiency of the case-involved account can be greatly improved, the resource waste in all aspects is conveniently saved, and the supervision requirement can be met.
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 introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a first flowchart of a method for identifying a bank involved account according to an embodiment of the present invention.
Fig. 2 is a schematic flow chart of a second method for identifying a bank involved account according to an embodiment of the present invention.
Fig. 3 is a third flowchart of the identification method of a bank involved account in the embodiment of the present invention.
Fig. 4 is a schematic structural diagram of an identification device for a bank involved account in an embodiment of the present invention.
Fig. 5 is a schematic structural diagram of an electronic device in an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious 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 invention provides an embodiment of a method for identifying a bank involved account, which specifically comprises the following contents in reference to fig. 1:
s101: respectively carrying out entity extraction processing, entity attribute extraction processing and relationship extraction processing among entities on the obtained bank account data to respectively obtain entity data, attribute data and relationship data which respectively correspond to the entity data, the attribute data and the relationship data;
in this step, the sources of the bank account data are divided into two categories: one is enterprise customer information, account information and transaction information accumulated for many years inside a commercial bank.
It should be noted that most of this kind of data is structured data, and starting from a known case-involved account, the abstract, the description of transaction type, the IP, the MAC, the device, the region information, and the individuals and enterprises having actual control, high management, legal, investment, and guarantee relationship with the related transaction stream are queried, and potential tourists and fund uses and channels of the client are identified, so as to mine a suspected case-involved public account.
The other is provided by external users (authorized institutions), which are already definite legal related public account data, or client data and account data which are related to legal freezing deduction once.
From the above description, in the step, the knowledge graph is constructed by using relatively perfect data inside the commercial bank and combining with explicit case-involved account data provided from the outside, and enterprises or individuals closely related to the case-involved account enterprises can be intuitively and rapidly located through the knowledge graph. And identifying and predicting the account of the public affair based on the knowledge graph, so that the efficiency of identifying or predicting the account of the public affair is improved, and the cost is reduced.
It should be noted that the entities include: for public enterprises, under-name accounts of enterprises and enterprise affiliates. Attributes of an entity include: the system comprises client information, account opening information, transaction detail information, risk information and enterprise business information which are reserved by an enterprise at a bank. For example: customer information: enterprise registration time, registration address, tax payment number and contact information. Account opening information: account opening mechanism and account opening time. Transaction detail information: transaction time, transaction amount, transaction stroke number, transaction counter account number name, transaction counter bank name, transaction ip, transaction Mac and transaction terminal number. Risk information: the information is inquired, frozen and deducted by an authorized authority. Enterprise business information: enterprise registration capital, registration time, and registration location information. Relationships between entities include: the relationship between the client information of the enterprises and the relationship between the account opening information of the enterprises, the relationship between the enterprises and the transaction between the enterprises and the investment and financing relationship between the enterprises.
It can be understood that a Knowledge Graph (Knowledge Graph), which is called Knowledge domain visualization or Knowledge domain mapping map in the book intelligence world, is a series of various graphs displaying the relationship between the Knowledge development process and the structure, describes Knowledge resources and their carriers by using visualization technology, and mines, analyzes, constructs, draws and displays Knowledge and their interrelations. For a case of public account: the method mainly comprises the steps that lawbreakers register and open threshold opportunities for public accounts by using national relaxation companies, empty companies are registered in industrial and commercial institutions by hiring 'horses' or unknown persons, the public accounts of the empty companies are opened in banks, and the new type telecommunication network illegal criminals or money laundering criminals are used for the public accounts.
S102: performing ontology modeling based on the entity data, the attribute data and the relationship data to generate a knowledge graph;
in this step, ontology modeling is performed based on the entity data, the attribute data and the relationship data to generate a knowledge graph, which specifically includes:
generating a transaction flow graph of the entity based on the entity data, the attribute data and the relationship data; wherein the entities are: businesses and individuals; relationships between entities include: transfer between enterprises; the enterprises and the enterprise affiliates comprise transfer among actual control, high management, legal persons, investment and guarantee relations.
It should be noted that, when the frequency and amount of the money between a certain enterprise and the enterprise related to the case account and the enterprise affiliate are larger than the threshold value, the enterprise can be brought into the suspected case related account.
Generating a relationship person association map of the entity based on the entity data, the attribute data and the relationship data; wherein the entities are: businesses and individuals; relationships between entities include: investment, litigation, investment financing, parent-child company relationships among enterprises; the relationship between enterprises and individuals in terms of job, loan and litigation.
It should be noted that, when there is a relationship in the relationship graph between the enterprise and the account related enterprise or the enterprise related to the case, for example, there is the same legal person or there is a parent and child company, the enterprise related person may be included in the suspected case-related molecule.
Generating a customer information association map of the entity based on the entity data, the attribute data and the relationship data; wherein the entities are: an enterprise; relationships between entities include: the relation between the registration time of the enterprise and the enterprise, the customer information of the registration place, the account opening information of the enterprise and the network IP, MAC, terminal and region information used by the transaction of the enterprise and the enterprise.
It should be noted that when the enterprise is related to the account related enterprise in terms of network address, device, region, and enterprise registration information, the enterprise can be taken into the suspected related enterprise.
S103: and identifying the bank involved account according to the knowledge graph.
In this step, training sample set samples for training are acquired from the knowledge graph in advance, a machine learning algorithm is adopted to train according to the training sample set samples to generate a recognition model, a target account of the knowledge graph is recognized according to the recognition model, and whether the target account is a bank involved account is determined according to the recognition result of the target account.
In this embodiment, before the target account of the knowledge graph is identified according to the identification model, an XGBOOST algorithm, a random forest algorithm, and a logistic regression algorithm in a machine learning algorithm are respectively trained to generate a first identification model, a second identification model, and a third identification model corresponding to each other; and evaluating the recognition results of the first recognition model, the second recognition model and the third recognition model respectively, and determining the accuracy, the recall rate and the precision rate of the recognition results of the first recognition model, the second recognition model and the third recognition model. And the first recognition model, the second recognition model or the third recognition model with the optimal accuracy, recall rate and precision is used as the final recognition model.
From the above description, the present invention provides a method for identifying a bank involved account, which respectively obtains entity data, attribute data and relationship data corresponding to the obtained bank account data by respectively performing entity extraction processing, entity attribute extraction processing and relationship extraction processing between entities; performing ontology modeling based on the entity data, the attribute data and the relationship data to generate a knowledge graph; the bank case-involved account is identified according to the knowledge map, so that the identification efficiency of the case-involved account can be greatly improved, the resource waste in all aspects is conveniently saved, and the supervision requirement can be met.
In an embodiment of the present invention, referring to fig. 2, the step S102 of the method for identifying a bank account further includes a step S104, which specifically includes the following steps:
s104: and storing the knowledge graph in a graph database mode.
In the step, the knowledge graph is stored by adopting the graph database, so that the nodes, edges and attributes in the structure in the knowledge graph are stored.
In the embodiment, the knowledge graph is stored by adopting a Neo4j graph database, so that the advantage is that the Neo4j graph database provides a perfect graph query language and supports various graph mining algorithms.
In an embodiment of the present invention, referring to fig. 3, the step S102 of the method for identifying a bank account further includes a step S105, which specifically includes the following steps:
s105: and visually displaying the knowledge graph in a visual mode.
In the step, the knowledge graph stored in the Neo4j graph database is displayed through a visual interface, so that a database query statement can be constructed through the visual interface by clicking and inputting in the visual interface, and a returned result is displayed after the database is requested.
To further illustrate the present solution, an embodiment of a method for identifying a model generated based on a machine learning algorithm provided by the present invention specifically includes the following contents:
1. data collection:
and collecting enterprise customer information, account information and transaction information accumulated for many years in the commercial bank. And the public account data which is definitely related to the judicial affairs, or the customer data and the account data which are related to the judicial investigation and the frozen deduction once.
2. And (3) data analysis:
the analytical case detail data are in the following categories:
(1) one is explicit case-related data;
(2) one type is non-case-related data;
the data involved in the case are characterized in that:
case data a: the client information characteristics are as follows:
(1) the involved object has the characteristic of conspiring crime, so the involved object is relatively close to the registered address of the public client;
(2) the contact telephone related to the case has the characteristic of same attribution to the contact telephone left by the public client;
(3) the situation that one involved case molecule simultaneously falsely opens a plurality of pairs of involved case accounts exists, so that the situation that the corporate, the high-level management and the stockholders among the involved case clients are crossed exists;
(4) the situation that one involved case molecule simultaneously falsely opens a plurality of pairs of involved case accounts exists, so that the registration time of the involved case client is relatively close;
(5) for the public affair account opened by the affair concerned molecule, the enterprise registration address usually left is a fictitious address;
(6) the related sub-molecules open the public accounts, so the types and the operation ranges of the enterprise industries are close to each other.
Case data B: the account information characteristics are as follows:
(1) the criminal opens a plurality of cases of involving in cases to public accounts at one website by utilizing the loopholes of certain bank websites, so that the case that the account opening mechanism, the account opening place and the account opening time of the involving cases are the same;
(2) case-related accounts, typically used as accounts for excessive funds, so the balance on the account is typically not too great;
(3) the account involved is usually transacted through electronic channels, thus bringing the quota of the online bank to the maximum.
Case data C: transaction characteristics:
(1) the transaction is usually completed between the partners of the case-involved account, so that the client or account number of the counterparty has been definitely involved with the case with a large probability;
(2) the characteristics of conspiring crime exist in case, so that the ip address and Mac information of the transaction have similarity;
(3) the channel of transaction is mainly completed through an internet banking channel;
(4) the time of occurrence of the transaction, centered on a certain time period, such as night;
(5) different from normal enterprises, the transaction related to case accounts is less in tax, wage, water charge and social security transaction;
(6) account funds are generally transferred in a scattered manner, so that the transaction occurrence amount and the transaction occurrence number have the characteristic of being concentrated in a certain interval;
(7) the upstream and downstream funds are usually unilateral transactions, so the characteristic that the intersection of the transferred-in opponent account and the transferred-out opponent account is small exists.
3. Preparation of data:
the labels corresponding to the data are defined as two types, 0-non-involved account and 1-definite involved account, and 10 thousands of involved data provided by authorized authorities and 90 thousands of normal customer data in a bank are taken as sample data according to the data distribution condition of 1:9 of involved data and non-involved data. And splitting the sample data into a training set, a verification set and a test set, and using the training set, the verification set and the test set to train the model and generate the recognition model.
According to the description, the efficiency of identifying the risk of the public affair account is greatly improved in the public affair account identification model established on the basis of the knowledge graph, the risk is timely and accurately predicted while the manual workload is reduced, and the expected target is achieved. By constructing the knowledge map of the public involved case account, the commercial bank is helped to realize the comprehensive monitoring of the public enterprise customers, the associated enterprises and the affiliates, the risk is predicted in advance, the loss is reduced, and the method is an effective way.
The embodiment of the invention provides a specific implementation mode of a bank involved account recognition device capable of realizing all contents in the bank involved account recognition method, and referring to fig. 4, the bank involved account recognition device specifically comprises the following contents:
the acquisition unit 10 is configured to perform entity extraction processing, entity attribute extraction processing, and relationship extraction processing between entities on the acquired bank account data, so as to obtain entity data, attribute data, and relationship data corresponding to each entity;
the map unit 20 is used for performing ontology modeling on the basis of the entity data, the attribute data and the relationship data to generate a knowledge map;
and the identification unit 30 is used for identifying the bank involved account according to the knowledge graph.
Further, the method also comprises the following steps:
and the storage unit 40 is used for storing the knowledge graph in a mode of adopting a graph database.
Wherein the storage unit includes:
and the storage subunit is used for storing the knowledge graph in a Neo4j graph database mode.
Further, the method also comprises the following steps:
and the display unit 50 is used for visually displaying the knowledge graph in a visual mode.
Wherein the map unit comprises:
a first graph subunit for generating a transaction pipeline graph of the entity based on the entity data, the attribute data, and the relationship data;
the second map subunit is used for generating a relationship person associated map of the entity based on the entity data, the attribute data and the relationship data;
and the third map subunit is used for generating a customer information association map of the entity based on the entity data, the attribute data and the relationship data.
Wherein the identification unit includes:
and the recognition subunit is used for recognizing the target account of the knowledge graph according to a pre-trained recognition model generated based on a machine learning algorithm.
Further, the identification unit further includes:
the training subunit is used for training samples in the training set by respectively adopting an XGB OST algorithm, a random forest algorithm and a logistic regression algorithm to generate a first recognition model, a second recognition model and a third recognition model which respectively correspond to the XGB OST algorithm, the random forest algorithm and the logistic regression algorithm;
and the optimizing subunit is configured to evaluate recognition results of the first recognition model, the second recognition model and the third recognition model, respectively, and determine the recognition model according to the evaluation results.
The embodiment of the identification device of the bank involved account provided by the invention can be specifically used for executing the processing flow of the embodiment of the identification method of the bank involved account in the embodiment, the functions of the embodiment are not repeated herein, and the detailed description of the embodiment of the method can be referred.
As can be seen from the above description, the identification apparatus for bank-related accounts according to the embodiments of the present invention respectively obtains entity data, attribute data, and relationship data corresponding to the obtained bank account data by respectively performing entity extraction processing, attribute extraction processing of entities, and relationship extraction processing between entities; performing ontology modeling based on the entity data, the attribute data and the relationship data to generate a knowledge graph; the bank case-involved account is identified according to the knowledge map, so that the identification efficiency of the case-involved account can be greatly improved, the resource waste in all aspects is conveniently saved, and the supervision requirement can be met.
The application provides an embodiment of an electronic device for realizing all or part of contents in the identification method of the bank involved accounts, and the electronic device specifically comprises the following contents:
a processor (processor), a memory (memory), a communication Interface (Communications Interface), and a bus; the processor, the memory and the communication interface complete mutual communication through the bus; the communication interface is used for realizing information transmission between related devices; the electronic device may be a desktop computer, a tablet computer, a mobile terminal, and the like, but the embodiment is not limited thereto. In this embodiment, the electronic device may be implemented by referring to the embodiment of the method for implementing the identification of the bank related-to account and the embodiment of the device for implementing the identification of the bank related-to account in the embodiments, and the contents thereof are incorporated herein, and repeated details are not repeated herein.
Fig. 5 is a schematic block diagram of a system configuration of an electronic device 9600 according to an embodiment of the present application. As shown in fig. 5, the electronic device 9600 can include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. Notably, this FIG. 5 is exemplary; other types of structures may also be used in addition to or in place of the structure to implement telecommunications or other functions.
In one embodiment, the identification of the bank account involved may be integrated into the central processor 9100. The central processor 9100 may be configured to control as follows:
respectively carrying out entity extraction processing, entity attribute extraction processing and relationship extraction processing among entities on the obtained bank account data to respectively obtain entity data, attribute data and relationship data which respectively correspond to the entity data, the attribute data and the relationship data;
performing ontology modeling based on the entity data, the attribute data and the relationship data to generate a knowledge graph;
and identifying the bank involved account according to the knowledge graph.
As can be seen from the above description, in the electronic device provided in the embodiment of the present application, entity data, attribute data, and relationship data corresponding to the obtained bank account data are obtained by performing entity extraction processing, entity attribute extraction processing, and relationship extraction processing between entities, respectively; performing ontology modeling based on the entity data, the attribute data and the relationship data to generate a knowledge graph; the bank case-involved account is identified according to the knowledge map, so that the identification efficiency of the case-involved account can be greatly improved, the resource waste in all aspects is conveniently saved, and the supervision requirement can be met.
In another embodiment, the identification device of the bank related account may be configured separately from the central processor 9100, for example, the identification device of the bank related account may be configured as a chip connected to the central processor 9100, and the identification function of the bank related account may be realized by the control of the central processor.
As shown in fig. 5, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is noted that the electronic device 9600 also does not necessarily include all of the components shown in fig. 5; further, the electronic device 9600 may further include components not shown in fig. 5, which may be referred to in the art.
As shown in fig. 5, a central processor 9100, sometimes referred to as a controller or operational control, can include a microprocessor or other processor device and/or logic device, which central processor 9100 receives input and controls the operation of the various components of the electronic device 9600.
The memory 9140 can be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information relating to the failure may be stored, and a program for executing the information may be stored. And the central processing unit 9100 can execute the program stored in the memory 9140 to realize information storage or processing, or the like.
The input unit 9120 provides input to the central processor 9100. The input unit 9120 is, for example, a key or a touch input device. Power supply 9170 is used to provide power to electronic device 9600. The display 9160 is used for displaying display objects such as images and characters. The display may be, for example, an LCD display, but is not limited thereto.
The memory 9140 can be a solid state memory, e.g., Read Only Memory (ROM), Random Access Memory (RAM), a SIM card, or the like. There may also be a memory that holds information even when power is off, can be selectively erased, and is provided with more data, an example of which is sometimes called an EPROM or the like. The memory 9140 could also be some other type of device. Memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application/function storage portion 9142, the application/function storage portion 9142 being used for storing application programs and function programs or for executing a flow of operations of the electronic device 9600 by the central processor 9100.
The memory 9140 can also include a data store 9143, the data store 9143 being used to store data, such as contacts, digital data, pictures, sounds, and/or any other data used by an electronic device. The driver storage portion 9144 of the memory 9140 may include various drivers for the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging applications, contact book applications, etc.).
The communication module 9110 is a transmitter/receiver 9110 that transmits and receives signals via an antenna 9111. The communication module (transmitter/receiver) 9110 is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as in the case of a conventional mobile communication terminal.
Based on different communication technologies, a plurality of communication modules 9110, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, may be provided in the same electronic device. The communication module (transmitter/receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and receive audio input from the microphone 9132, thereby implementing ordinary telecommunications functions. The audio processor 9130 may include any suitable buffers, decoders, amplifiers and so forth. In addition, the audio processor 9130 is also coupled to the central processor 9100, thereby enabling recording locally through the microphone 9132 and enabling locally stored sounds to be played through the speaker 9131.
An embodiment of the present invention further provides a computer-readable storage medium capable of implementing all the steps in the method for identifying a bank case-involved account in the above embodiments, where the computer-readable storage medium stores a computer program, and the computer program, when executed by a processor, implements all the steps in the method for identifying a bank case-involved account in the above embodiments, for example, when the processor executes the computer program, implements the following steps:
respectively carrying out entity extraction processing, entity attribute extraction processing and relationship extraction processing among entities on the obtained bank account data to respectively obtain entity data, attribute data and relationship data which respectively correspond to the entity data, the attribute data and the relationship data;
performing ontology modeling based on the entity data, the attribute data and the relationship data to generate a knowledge graph;
and identifying the bank involved account according to the knowledge graph.
As can be seen from the above description, in the computer-readable storage medium provided in the embodiment of the present invention, entity data, attribute data, and relationship data corresponding to the obtained bank account data are obtained by performing entity extraction processing, entity attribute extraction processing, and relationship extraction processing between entities, respectively; performing ontology modeling based on the entity data, the attribute data and the relationship data to generate a knowledge graph; the bank case-involved account is identified according to the knowledge map, so that the identification efficiency of the case-involved account can be greatly improved, the resource waste in all aspects is conveniently saved, and the supervision requirement can be met.
Although the present invention provides method steps as described in the examples or flowcharts, more or fewer steps may be included based on routine or non-inventive labor. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When an actual apparatus or client product executes, it may execute sequentially or in parallel (e.g., in the context of parallel processors or multi-threaded processing) according to the embodiments or methods shown in the figures.
As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, apparatus (system) or computer program product. Accordingly, embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the 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.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment. In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, 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. The terms "upper", "lower", and the like, indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience in describing the present invention and simplifying the description, but do not indicate or imply that the referred devices or elements must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention. Unless expressly stated or limited otherwise, the terms "mounted," "connected," and "connected" are intended to be inclusive and mean, for example, that they may be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations. It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict. The present invention is not limited to any single aspect, nor is it limited to any single embodiment, nor is it limited to any combination and/or permutation of these aspects and/or embodiments. Moreover, each aspect and/or embodiment of the present invention may be utilized alone or in combination with one or more other aspects and/or embodiments thereof.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (16)
1. A method for identifying a bank involved account is characterized by comprising the following steps:
respectively carrying out entity extraction processing, entity attribute extraction processing and relationship extraction processing among entities on the obtained bank account data to respectively obtain entity data, attribute data and relationship data which respectively correspond to the entity data, the attribute data and the relationship data;
performing ontology modeling based on the entity data, the attribute data and the relationship data to generate a knowledge graph;
and identifying the bank involved account according to the knowledge graph.
2. The method for identifying a bank involved account as claimed in claim 1, wherein after the generating of the knowledge graph by ontology modeling based on the entity data, the attribute data and the relationship data, the method further comprises:
and storing the knowledge graph in a graph database mode.
3. The method for identifying a bank involved account as claimed in claim 2, wherein said storing said knowledge-graph using a graph database comprises:
and storing the knowledge graph by adopting a Neo4j graph database.
4. The method for identifying a bank involved account as claimed in claim 1, wherein after the generating of the knowledge graph by ontology modeling based on the entity data, the attribute data and the relationship data, the method further comprises:
and visually displaying the knowledge graph in a visual mode.
5. The method for identifying a bank involved account as claimed in claim 1, wherein the generating of the knowledge graph by ontology modeling based on the entity data, the attribute data and the relationship data comprises:
generating a transaction flow graph of the entity based on the entity data, the attribute data and the relationship data;
generating a relationship person association map of the entity based on the entity data, the attribute data and the relationship data;
and generating a customer information association map of the entity based on the entity data, the attribute data and the relationship data.
6. The method for identifying bank involved account according to claim 1, wherein the identifying bank involved account according to the knowledge graph comprises:
and identifying the target account of the knowledge graph according to a pre-trained identification model generated based on a machine learning algorithm.
7. The method for identifying a bank involved account according to claim 6, wherein before the identifying the target account of the knowledge graph according to the pre-trained identification model generated based on the machine learning algorithm, the method further comprises:
respectively adopting an XGBOOST algorithm, a random forest algorithm and a logistic regression algorithm to train the training set samples to generate a first recognition model, a second recognition model and a third recognition model which respectively correspond to the XGBOOST algorithm, the random forest algorithm and the logistic regression algorithm;
and evaluating the recognition results of the first recognition model, the second recognition model and the third recognition model respectively, and determining the recognition models according to the evaluation results.
8. An identification device for bank involved account, characterized by comprising:
the acquisition unit is used for respectively carrying out entity extraction processing, entity attribute extraction processing and relationship extraction processing among the entities on the acquired bank account data to respectively obtain entity data, attribute data and relationship data corresponding to the entity data, the attribute data and the relationship data;
the map unit is used for carrying out ontology modeling on the basis of the entity data, the attribute data and the relation data to generate a knowledge map;
and the identification unit is used for identifying the bank involved account according to the knowledge graph.
9. The apparatus for identifying a bank involved account as claimed in claim 8, further comprising:
and the storage unit is used for storing the knowledge graph in a graph database mode.
10. The apparatus for identifying a bank involved account as claimed in claim 9, wherein the storage unit comprises:
and the storage subunit is used for storing the knowledge graph in a Neo4j graph database mode.
11. The apparatus for identifying a bank involved account as claimed in claim 8, further comprising:
and the display unit is used for visually displaying the knowledge graph in a visual mode.
12. The apparatus for identifying a bank involved account as claimed in claim 8, wherein the map unit comprises:
a first graph subunit for generating a transaction pipeline graph of the entity based on the entity data, the attribute data, and the relationship data;
the second map subunit is used for generating a relationship person associated map of the entity based on the entity data, the attribute data and the relationship data;
and the third map subunit is used for generating a customer information association map of the entity based on the entity data, the attribute data and the relationship data.
13. The apparatus for identifying a bank involved account as claimed in claim 8, wherein the identification unit comprises:
and the recognition subunit is used for recognizing the target account of the knowledge graph according to a pre-trained recognition model generated based on a machine learning algorithm.
14. The apparatus for identifying a bank involved account as claimed in claim 13, wherein the identification unit further comprises:
the training subunit is used for training samples in the training set by respectively adopting an XGB OST algorithm, a random forest algorithm and a logistic regression algorithm to generate a first recognition model, a second recognition model and a third recognition model which respectively correspond to the XGB OST algorithm, the random forest algorithm and the logistic regression algorithm;
and the optimizing subunit is configured to evaluate recognition results of the first recognition model, the second recognition model and the third recognition model, respectively, and determine the recognition model according to the evaluation results.
15. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method for identifying a bank involved account as claimed in any one of claims 1 to 7 when executing the program.
16. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method for identifying a bank involved account according to any one of claims 1 to 7.
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