CN115099924A - Financial wind control management method and system, equipment and storage medium - Google Patents
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Abstract
The embodiment of the invention discloses a financial wind control management method, a system, equipment and a storage medium, wherein in the specific embodiment, the method comprises the following steps: s10: establishing a financial wind control knowledge graph, wherein the financial wind control knowledge graph comprises risk merchant nodes, common merchant nodes and edges representing the relationship among the nodes; s20: and responding to query information of at least one dimension input by the wind control personnel through the query control, determining a risk graph formed by nodes corresponding to the query information in the knowledge graph, and displaying the risk graph in a display area. According to the method, historical behavior of a merchant is used as a data source, and basic data required by a risk map is generated by combining a risk map template; and responding to query information of at least one dimension input by the wind control personnel through the query control, determining a risk graph formed by nodes corresponding to the query information in the financial risk knowledge graph and displaying the risk graph in a display area, so that the problems of low flexibility and convenience of financial risk query in the prior art are solved.
Description
Technical Field
The invention relates to the field of wind control management. And more particularly, to a financial wind management method and system, a device, and a storage medium.
Background
In wind control management of internet financial security, a wind control management mode for constructing a risk map based on an existing relationship exists at present, the risk map construction mode is related to an original data source, original data volume, node dimension size and the like, particularly, the difference between the data volume difference and a required calculation mode is greatly different, the total number of nodes is about 6 hundred million +, the original data volume is about more than 10T, common storage and calculation cannot meet requirements, and a basic relationship of the risk map needs to be established by relying on large data related components;
the graph display is queried through the risk graph client side, the query is not flexible and convenient enough, the technical requirements on operators are high, the graph can only display an integral risk graph, the wind control personnel can not check information of a specified merchant according to the target, and the community information cannot be checked directly through the graph.
Disclosure of Invention
The invention aims to provide a financial wind control management method, a financial wind control management system, financial wind control equipment and a storage medium, which are used for solving at least one of the problems in the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a financial wind control management method in a first aspect, which comprises the following steps:
s10: establishing a financial wind control knowledge graph, wherein the financial wind control knowledge graph comprises risk merchant nodes, common merchant nodes and edges representing the relationship among the nodes;
s20: and responding to query information of at least one dimension input by the wind control personnel through the query control, determining a risk graph formed by nodes corresponding to the query information in the financial wind control knowledge graph, and displaying the risk graph in a display area.
Further, the step before S10 further includes:
collecting data by using a big data assembly and converting the data into a node and relation mode for storage; and combining the basic data required by the risk map template to generate the risk map.
Further, the query information is multidimensional query information, including:
the name of the salesman, the serial number of the POS machine, the base station accessed by the POS machine, the mobile phone number, the transaction card number and the settlement card number.
Further, step S20 further includes: and determining a risk graph formed by communities to which the nodes corresponding to the query information in the financial wind control knowledge graph belong and displaying the risk graph in the display area.
Further, step S20 further includes: and calculating the risk level of the common merchant node corresponding to the query information according to other nodes having a relationship with the common merchant node, and displaying the risk level in the display area.
Further, step S20 further includes: and calculating the risk level of the community to which the node corresponding to the query information belongs according to the risk level of the node corresponding to the query information, and displaying in the display area.
Further, the financial wind control knowledge graph also comprises non-merchant nodes which represent attribute characteristics of merchants,
the non-merchant nodes include one or more of: the certificate number, the transaction card number, the network base station, the position, the equal frequency and the like.
Further, when the financial wind control knowledge graph is established, the merchant nodes must point to the non-merchant nodes to form a directed acyclic graph and a unified graph structure.
The invention provides a financial wind control management system in a second aspect, which comprises a data acquisition unit, a knowledge graph construction unit and a risk detection unit; wherein the content of the first and second substances,
the data acquisition unit is configured to: collecting data by using a big data assembly and converting the data into a node and relation mode for storage; generating basic data required by the risk map by combining the risk map template;
the knowledge-graph building unit is configured to: receiving basic data required by the risk map from the data acquisition unit and establishing a financial wind control knowledge map based on the basic data;
the risk detection unit configured to: and responding to query information of at least one dimension input by the wind control personnel through the query control, determining a risk graph formed by nodes corresponding to the query information in the financial wind control knowledge graph, and displaying the risk graph in a display area.
A third aspect of the invention provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the financial wind management method of the first aspect of the invention when executing the program.
A fourth aspect of the present invention provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the financial wind management method provided by the first aspect of the present invention.
The invention has the following beneficial effects:
according to the financial wind control management method, historical behaviors of a merchant serve as data sources, and basic data required by a risk map are generated by combining a risk map template; and responding to query information of at least one dimension input by the wind control personnel through the query control, determining a risk graph formed by nodes corresponding to the query information in the financial risk knowledge graph and displaying the risk graph in a display area, so that the problems of low flexibility and convenience of financial risk query in the prior art are solved.
Drawings
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
FIG. 1 illustrates an exemplary system architecture diagram implementing a financial wind management method according to one embodiment of the invention;
FIG. 2 shows a flow diagram of a method according to an embodiment of the invention;
FIG. 3 illustrates a process flow diagram of one embodiment of the invention;
FIG. 4 illustrates a partial screenshot of a query main page of an application according to one embodiment of the invention;
in FIG. 5, 5-a and 5-b show the upper and lower portions, respectively, of a query main page of an application program of another embodiment of the present invention;
FIG. 6 illustrates a page of an application of one embodiment of the invention.
In FIG. 7, 7-a and 7-b show the left half and right half, respectively, of the community risk level statistics page of an application of one embodiment of the present invention;
8-a and 8-b in FIG. 8 show the upper and lower portions, respectively, of a community risk statistics interface for an application of one embodiment of the present invention;
9-11 illustrate application interface screenshots according to another embodiment of the present invention;
FIG. 12 illustrates a system block diagram of one embodiment of the invention;
FIG. 13 illustrates an architecture diagram of a computing device, according to one embodiment of the invention.
Detailed Description
In order to more clearly illustrate the present invention, the present invention will be further described with reference to the following examples and the accompanying drawings. Similar parts in the figures are denoted by the same reference numerals. It is to be understood by persons skilled in the art that the following detailed description is intended to be illustrative and not restrictive, and is not intended to limit the scope of the invention.
Fig. 1 is an exemplary system architecture 100 of a financial wind management method according to an embodiment of the present invention, and as shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and servers 105, 106. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the servers 105, 106. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the servers 105, 106 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may have various communication client applications installed thereon, such as a web browser application, a shopping application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like.
The terminal devices 101, 102, 103 may be hardware or software. When the terminal devices 101, 102, 103 are hardware, they may be various electronic devices having a display screen and supporting text input, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like. When the terminal apparatuses 101, 102, 103 are software, they can be installed in the electronic apparatuses listed above. It may be implemented as multiple pieces of software or software modules, or as a single piece of software or software module. And is not particularly limited herein.
The users of the terminal devices 101, 102, and 103 may be a wind administrator, which is configured to upload query information, and accept and display a community query result and a map query result obtained by a server querying a map library and a community library according to the query information.
The server 105 may be a server that provides various services, such as a store atlas database and a community database. The map library can store risky merchant nodes, common merchant nodes, non-merchant nodes and edges representing the relationship among the nodes. The atlas database is obtained based on the historical behavior of the merchant, and the historical behavior of the merchant can include payment behaviors, such as code scanning payment, card swiping payment, settlement information, terminal sign-in information and the like. The non-merchant node includes: the certificate number, the card number, openid, imei, a network base station and the like can represent information such as position, base station, equal-frequency and equal-amount transaction, transaction card number and the like; the node relation means that the commercial tenant node points to the common non-commercial tenant node to form a connecting edge. Such as: node A-card number 1-node B-card number 2-node C.
Establishing a map, namely establishing merchant nodes and non-merchant nodes, and establishing a relationship between the merchant nodes and the non-merchant nodes to form a topological graph; for the merchant node, the merchant attribute and the risk identifier can be carried, such as whether the bank union early warning is provided, whether the blacklist is provided, whether the consultation is provided, the rule interception times are provided, and the like. For non-merchant nodes, attribute values such as card number transaction amount, affiliated banks and the like can be carried. The merchant nodes are required to be connected through non-merchant nodes to form a directed acyclic graph.
The chart library uses a main library and standby library switching mode, and high availability is improved.
The community library divides merchant nodes according to a community discovery algorithm (Louvain), the nodes represent weight values in community formation through weight attributes, iteration is written back to the knowledge graph after community construction is completed, and the nodes in graph display can have communities to which the nodes belong. A rule can be formulated for a community, for example, if more than 30% of nodes in the community are marked blacklist nodes, the community is defined as a risk community, and the importance of the node is identified according to the association number and the degree of the node.
The server 106 may be a server providing various services, for example, a timing scheduler (azkaban) performs data processing to convert data into node, relational storage, and thereby generate basic data required for the risk graph by combining the risk graph template, specifically,
the data processing comprises three parts of data extraction, data conversion and file import; the data extraction means that data are read from each service library to a big data cluster, the read data are inquired according to a map risk template forming inquiry condition through a hive big data assembly, an inquiry result is written back to the hive library, and finally, storage contents comprise dimension data such as merchant dimension, terminal dimension and agent dimension and statistical data required by the risk template; in particular, the method comprises the following steps of,
data are gathered to a big data center from all service lines, and a relational data table is finally formed and stored through condition query; the stored result includes: the system comprises the information of nodes such as merchant data, a network base station, equipment numbers, card numbers, terminal numbers, account settling person identity cards, legal persons certificates, agents, service personnel and the like.
The data conversion is to carry out condition query on the data structure in the last step in a mode of node and relation, write the query result into a csv file required for establishing a knowledge graph, and store the query result into a specified FTP directory according to date; in particular, the method comprises the following steps of,
the basic data processing mode is to form a query condition according to the risk template and store a query result into the corresponding dimension table by querying the original data; the final dimension tables are respectively: merchant basic data, equipment data, card number data, base station data and terminal data, wherein the merchant basic data comprises information such as a merchant number, a merchant name, a state, a service line, a payment state, a legal person certificate number, a legal person mobile phone number, a direct serial number, a direct name, a service person ID, a service person name, a settlement bank card, a settlement person identity card, an allied friend number, an allied friend name, a branch company, a blacklist, early warning, rule intercepting record, creation date and the like; the equipment data comprises an equipment number, a business code, an equipment blacklist state, a Unionpay early warning record, a rule interception record and an equipment early warning state; the card number data comprises an encrypted card number, a quotient code, a card number blacklist state, a Unionpay early warning record, a rule interception record and a card number early warning state; the base station data comprises base station ID, business editing, base station blacklist state, Unionpay early warning record, rule interception record and base station early warning state, for example; the terminal data includes, for example, a terminal ID, a business code, a terminal blacklist state, a union pay early warning record, a rule interception record, and a terminal early warning state.
The file import is to compress and transmit the file in the previous step to a knowledge graph server, and specifically,
the exported content comprises node data and relationship data, the node data is independent data serving as nodes, and the nodes comprise node values and attribute values; the relationship data refers to the association information between nodes, and in this embodiment, all relationship nodes are unidirectional, that is: merchant node-non-merchant node.
The servers 105 and 106 may be hardware or software. When the servers 105 and 106 are hardware, they may be implemented as a distributed server cluster composed of a plurality of servers, or may be implemented as a single server. When the server is software, it may be implemented as multiple pieces of software or software modules (e.g., to provide distributed services), or as a single piece of software or software module. And is not particularly limited herein.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any suitable number of terminal devices, networks, and servers, as desired for implementation.
When a merchant pays through a POS machine card swiping and APP, the payment process needs to be stopped immediately if illegal behaviors such as gambling, fraud, money laundering and the like exist, and aiming at the risks, the scheme mainly identifies the risk of the merchant.
As shown in fig. 2, an embodiment of the present invention provides a financial wind control management method, including:
s10: establishing a financial wind control knowledge graph, wherein the financial wind control knowledge graph comprises risk merchant nodes, common merchant nodes and edges representing the relationship among the nodes;
s20: and responding to query information of at least one dimension input by the wind control personnel through the query control, determining a risk graph formed by nodes corresponding to the query information in the financial wind control knowledge graph, and displaying the risk graph in a display area.
The step before S10 further includes:
collecting data by using a big data assembly and converting the data into a node and relation mode for storage; and combining the basic data required by the risk map template to generate the risk map.
In a particular embodiment, the financial wind-control knowledge-graph further includes non-merchant nodes, the non-merchant nodes representing attribute characteristics of merchants,
the non-merchant nodes include one or more of: the certificate number, the transaction card number, the network base station, the position, the equal frequency and the like.
In a specific embodiment, the problem of bidirectional association generated by establishing direct connection between merchant nodes causes complex association between map nodes, unclear map display and inconvenience for business personnel to visually analyze and check the map relationship.
Specifically, the risk template includes, for example, the following three types of relationships:
a risk commercial tenant A-non commercial tenant B-commercial tenant C
b. Risk merchant A-non-merchant B-merchant C-non-merchant D-risk merchant E
c. Merchant a-statistical node B-merchant C.
If A transfers the account to B, B also transfers the account to A, and the template relationship is as follows: a < - > B thus represents that the map is complex. A- > card < - - -B should be established, namely the relationship of A and B is established through the same equipment, and the structure is expressed by the same relationship.
In a specific embodiment, as shown in fig. 3, for a processing flow chart for establishing a financial risk knowledge graph, the system sets a timing time and configures a task dependency relationship to realize establishment of a financial wind control knowledge graph, where the task dependency includes four modules: a data processing module, a knowledge map establishing module, a community dividing module and a rule executing module,
the data processing comprises three parts of data extraction, data conversion and file import; the data extraction refers to reading data from each service library to a big data cluster, and forming query conditions for querying the read data according to the hive big data assembly and the atlas risk template. Writing the query result back to the hive library, and finally storing the content, such as dimension data of merchant dimension, terminal dimension, agent dimension and the like and statistical data required by a risk template; the data conversion is to carry out condition query on the data structure in the last step in a mode of node and relation, write the query result into a csv file required for establishing a knowledge graph, and store the query result into a specified FTP directory according to date; the file import is to compress the file in the last step and transmit the compressed file to a knowledge graph server, wherein,
the nodes comprise risk commercial tenant nodes, common commercial tenant nodes and non-commercial tenant nodes;
the establishing of the knowledge graph comprises establishing a graph library, assigning a risk template, importing node data and importing relationship data, wherein,
and the node data and the relation data are output by the data processing module.
In this embodiment, the non-merchant node may also have a risk attribute and a weight, and the non-merchant node may be a non-attribute node in which behavior data is collected and is not only used as a merchant attribute node, for example, a transaction with an equal frequency and the like exists as a non-merchant node, so as to increase a risk identification rate.
In a specific embodiment, the community partitioning module is used for implementing community classification and community result write-back, wherein,
the community classification is to classify the merchant nodes by operating a louvain algorithm and store the classification result; the community result write-back is to update the community classification result to the atlas database in an iterative manner, and the merchant node of the query knowledge atlas can carry the affiliated community information, specifically,
the community classification divides the merchant nodes through a louvain algorithm, the nodes represent weight values in community formation through weight attributes, after the community construction is completed, iteration is written back to the knowledge graph, and the nodes in the graph display can have communities to which the nodes belong. A rule can be established for the community, if more than 30% of nodes in the community are marked blacklist nodes, the community is defined as a risk community, and the importance degree of the node is identified according to the association number and the degree of the node.
In a specific embodiment, the rule execution means that the merchant relationships generated by the community classification are subjected to rule matching, and the rule execution result is stored, so that the condition of the community matching rule can be queried according to the rule.
In a specific embodiment, the front-end display, that is, the front-end display obtains a query result in response to query information input by a wind control worker through the terminal device and returns the query result to the terminal device for display, which is divided into map query, community query, and rule query,
the graph query can query an associated topological graph according to the selected dimension, and the risk type or the node type is represented by different colors;
the community query can check information such as community summarized data, trend data, community scale distribution and the like;
the rule inquiry can filter and inquire information such as the associated quotient node number, the black name singular number, the risk quotient node number and the like according to three conditions of a rule name, a service line and a penalty time. And can view the information in a graph mode
In a specific embodiment, step S20 further includes: and determining a risk graph formed by communities to which the nodes corresponding to the query information in the financial wind control knowledge graph belong and displaying the risk graph in the display area.
In a specific embodiment, step S20 further includes: and calculating the risk level of the common merchant node corresponding to the query information according to other nodes having a relationship with the common merchant node, and displaying the risk level in the display area.
In one possible implementation, step S20 further includes: and calculating the risk level of the community to which the node corresponding to the query information belongs according to the risk level of the node corresponding to the query information, and displaying the risk level in the display area.
In a specific embodiment, the wind control personnel first opens the financial wind control system through the terminal equipment and logs in, and in response to the login operation of the wind control personnel, the financial wind control system displays a query main interface as shown in figure 4,
the query is made in response to the dimension to be queried selected by the wind operator in the query drop-down box marked in the query main interface shown in fig. 4, wherein,
examples of dimensions that can be queried are: the system comprises a merchant, a settling account person identification card, a legal person identification card, a transaction card, a settlement card, openid, imei, a merchant mobile phone number, a primary agent, a direct agent, a salesman, a base station, a direct ally allied friend, a settlement transaction card, a settlement legal person identification number and community options.
In addition, after the dimension to be queried is selected, a specific value of the dimension to be queried, such as a merchant to be identified and other related information (a salesman name, a POS machine number, a base station to which the POS machine is accessed, a mobile phone number, a transaction card (consumer payment) number or a settlement card (merchant payment) number) can be input according to an actual situation, and after the completion, a query button shown in fig. 4 is clicked to perform map query.
The system establishes the relationship between merchants through a settlement card, a mobile phone number, a salesman, a unique identification number of a mobile phone and the like; when the risk of the merchant is identified, the number of the merchant is input, the nodes are displayed as the merchant, a settlement card and the like, the merchant with problems in the merchant nodes (such as unionpay early warning, investigation under investigation, blacklist and suspended transaction) is identified by marks such as black circles and red circles, and thus, the relationship between the merchant without the marks and the merchant with the marks can be visually displayed, and the risk of the merchant without the marks is obtained. According to the risk level of the commercial tenant (obtained by setting weight and calculating a community discovery algorithm) of the commercial tenant, the community risk level can be obtained, and corresponding rules (monitoring, quota and the like) of the community and the commercial tenant are further given.
In addition, as shown in fig. 5, a query main page provided by the present embodiment is shown, where 5-a shows an upper part of the query main page, 5-b shows a lower part of the query main page, and the wind controller may further select a relationship node;
as shown in FIG. 5-a, the default relationship node is: with the settlement card, with IMEI, with the number of the certificate of the settlement legal person, with the settlement transaction card, with the number of the certificate of the legal person, with the number of the certificate of the settlement person, with the mobile phone number. Or adding the same-genus agent, the same service personnel, the same-genus allied, the same openID, the same-level agent and the same base station; and the risk personnel can cancel the checking of the selected nodes, and the system responds to the operation of the wind control personnel to determine a risk graph formed by the nodes corresponding to the query information in the financial wind control knowledge graph and a risk graph of a community to which the nodes corresponding to the query information belong to reform the knowledge graph and display the knowledge graph in a display area.
In a specific embodiment, the system is responsive to the operation of the wind control personnel and can also determine a risk graph formed by communities to which the nodes corresponding to the query information in the financial wind control knowledge graph belong and display the risk graph in the display area.
In a specific embodiment, the wind control personnel responds to the operation of the wind control personnel by inquiring query information of at least one dimension input by a control, the system determines a risk graph formed by nodes corresponding to the query information in the financial wind control knowledge graph and a risk graph of a community to which the nodes corresponding to the query information belong, and displays the risk graphs in a display area, the default display effect is shown as 5-b, and different types of nodes are distinguished by different color identifiers.
In addition, the displayed map is more conveniently and visually presented in response to dragging and zooming of the wind control personnel on the displayed map so as to adapt to habits of operators, the wind control personnel can click the associated node on the left side of the 5-b to open or close the associated relation of certain nodes, and the system automatically regenerates and refreshes the map.
It should be noted that, if the selected query dimension is a certificate type query, the specific value part can be input with a plaintext or a ciphertext, and the background performs encryption and decryption conversion.
In a specific embodiment, the wind control personnel can export the knowledge graph to Excel in a tabular data mode, so that the wind control personnel can conveniently make statistical query,
specifically, the derived content includes a graph node, a node attribute, and a node relationship.
In addition, the wind control personnel can perform batch query, that is, perform batch query by uploading data of a plurality of main body nodes, as shown in fig. 6, the wind control personnel can download a template, fill in query conditions according to the import description in the template, and operate batch query after uploading is completed.
In a specific embodiment, in response to the fact that the wind-controlled personnel clicks the community to find and view the community risk statistical information, the system calculates the risk level of the community to which the node corresponding to the query information belongs according to the risk level of the node corresponding to the query information, and displays the risk level on the interface shown in fig. 7, wherein 7-a shows the left half of the interface, and 7-b shows the right half of the interface.
In a specific embodiment, the community risk statistics are presented as community statistics by default. And the wind control personnel can select the query time and count the community detailed information on the specified date. The community discovery information includes: the method comprises the following steps of community total number, suspected risk community number (including black sample proportion of a specified number), node total number (data amount of all nodes), risk node number (number of nodes containing risk marks), community scale distribution statistics, a community scale distribution diagram, a suspected risk community trend diagram, a risk node trend diagram, and event ranges which can be selected or customized by business personnel of the trend diagram. Including the number of newly added suspected risk communities and the addition and proportion. The risk node trend graph may also define a time range for interactive queries. The business personnel can also download the graph to a local viewing area, and the system responds to the operation of the wind control personnel on the download control and displays the community risk statistical graph shown in fig. 8, wherein 8-a shows the upper half part of the community risk statistical graph interface, and 8-b shows the lower half part of the community risk statistical graph interface.
In addition, the system is also responsive to the operation of the wind control personnel for viewing the community risk event, the business personnel can pull down and select the rule name and the product line, the rule penalty time is used as a query condition, the data matched to the rule can display the risk event ID, the rule name, the product line, the penalty time, the association quotient node number, the blacklist quotient node number, the risk quotient node number and view the community risk event details, the community risk event details display page is shown in FIG. 9, and the risk business personnel can export the community risk event details to EXCEL or view the risk event details through a map, as shown in FIG. 10.
In addition, the system also responds to the wind control personnel to inquire the detailed community information according to the community number, the business personnel can click any node, and the system responds to the displayed node details and the community and can copy the community number for inquiry, as shown in fig. 11.
As shown in fig. 12, another embodiment of the present invention provides a financial wind control management system, where the financial wind control management system 20 includes a data acquisition unit 201, a knowledge graph construction unit 202, and a risk detection unit 203; wherein the content of the first and second substances,
the data acquisition unit 201 is configured to: collecting data by using a big data assembly and converting the data into a node and relation mode for storage; generating basic data required by the risk map by combining the risk map template;
the knowledge-graph building unit 202 is configured to: receiving basic data required by the risk map from the data acquisition unit 201 and establishing a financial wind control knowledge map based on the basic data;
the risk detection unit 203 configured to: and responding to query information of at least one dimension input by the wind control personnel through the query control, determining a risk graph formed by nodes corresponding to the query information in the financial wind control knowledge graph, and displaying the risk graph in a display area.
The above units correspond to corresponding function menus in the above interface screenshot, however, those skilled in the art can understand that the units of the system of the present invention should not be understood as merely menus or controls on the interactive interface, but as modules for implementing corresponding functions.
It should be noted that the principle and the work flow of the financial wind control management system provided in this embodiment are similar to those of the financial wind control management method, and reference may be made to the above description for relevant points, which are not described herein again.
As shown in fig. 13, a computing device suitable for use in performing the methods provided by the above embodiments or in loading the above systems. The computing device includes a central processing module (CPU) that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) or a program loaded from a storage section into a Random Access Memory (RAM). In the RAM, various programs and data necessary for the operation of the computer system are also stored. The CPU, ROM, and RAM are connected thereto via a bus. An input/output (I/O) interface is also connected to the bus.
An input section including a keyboard, a mouse, and the like; an output section including a speaker and the like such as a Liquid Crystal Display (LCD); a storage section including a hard disk and the like; and a communication section including a network interface card such as a LAN card, a modem, or the like. The communication section performs communication processing via a network such as the internet. The drive is also connected to the I/O interface as needed. A removable medium such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive as necessary, so that a computer program read out therefrom is mounted into the storage section as necessary.
In particular, the processes described in the above flowcharts may be implemented as computer software programs according to the present embodiment. For example, the present embodiments include a computer program product comprising a computer program tangibly embodied on a computer-readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication section, and/or installed from a removable medium.
The flowchart and schematic diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to the present embodiments. In this regard, each block in the flowchart or schematic 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 schematic and/or flowchart illustration, and combinations of blocks in the schematic 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 modules described in the present embodiment may be implemented by software or hardware.
As another aspect, an embodiment of the present invention further provides a nonvolatile computer storage medium, where the nonvolatile computer storage medium may be a nonvolatile computer storage medium included in the apparatus in the foregoing embodiment, or a nonvolatile computer storage medium that exists separately and is not assembled in a terminal.
The non-volatile computer storage medium stores one or more programs that, when executed by an apparatus, cause the apparatus to implement the financial wind management method according to the above-described embodiment of the present application.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the invention and are not intended to limit the embodiments of the present invention, and that various other modifications and variations can be made by one skilled in the art in light of the above description.
Claims (10)
1. A financial wind control management method is characterized by comprising the following steps:
s10: establishing a financial wind control knowledge graph, wherein the financial wind control knowledge graph comprises risk merchant nodes, common merchant nodes and edges representing the relationship among the nodes;
s20: and responding to query information of at least one dimension input by the wind control personnel through the query control, determining a risk graph formed by nodes corresponding to the query information in the financial wind control knowledge graph, and displaying the risk graph in a display area.
2. The method of claim 1, wherein the step before the S10 further comprises:
collecting data by using a big data assembly and converting the data into a node and relation mode for storage; and combining the basic data required by the risk map template to generate the risk map.
3. The method of claim 1,
the query information is multidimensional query information, and comprises the following steps:
the name of the salesman, the serial number of the POS machine, the base station accessed by the POS machine, the mobile phone number, the transaction card number and the settlement card number.
4. The method of claim 1,
step S20 further includes: and determining a risk graph formed by communities to which the nodes corresponding to the query information in the financial wind control knowledge graph belong, and displaying the risk graph in the display area.
5. The method of claim 1,
step S20 further includes: and calculating the risk level of the common merchant node corresponding to the query information according to other nodes having a relationship with the common merchant node, and displaying the risk level in the display area.
6. The method of claim 1,
step S20 further includes: and calculating the risk level of the community to which the node corresponding to the query information belongs according to the risk level of the node corresponding to the query information, and displaying in the display area.
7. The method of claim 1,
the financial wind-control knowledge-graph further comprises non-merchant nodes representing attribute characteristics of merchants,
the non-merchant nodes include one or more of: the certificate number, the transaction card number, the network base station, the position, the equal frequency and the like are traded;
when the financial wind control knowledge graph is established, the merchant nodes must point to the non-merchant nodes so as to form a directed acyclic graph and a unified graph structure.
8. A financial wind management system, comprising:
the system comprises a data acquisition unit, a knowledge graph construction unit and a risk detection unit; wherein the content of the first and second substances,
the data acquisition unit is configured to: collecting data by using a big data assembly and converting the data into a node and relation mode for storage; generating basic data required by the risk map by combining the risk map template;
the knowledge-graph building unit is configured to: receiving basic data required by the risk map from the data acquisition unit and establishing a financial wind control knowledge map based on the basic data;
the risk detection unit configured to: and responding to query information of at least one dimension input by the wind control personnel through the query control, determining a risk graph formed by nodes corresponding to the query information in the financial wind control knowledge graph, and displaying the risk graph in a display area.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1-7 when executing the program.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1 to 7.
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