CN111833182B - Method and device for identifying risk object - Google Patents
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Abstract
The present disclosure provides a method of identifying a risk object, the method comprising: determining a first object; determining at least one second object with an incidence relation with the first object, wherein the incidence relation comprises a transaction relation and an entity relation; and performing risk identification on the first object based on the association relationship between the first object and at least one second object. The present disclosure also provides an apparatus for identifying a risk object, an electronic device, and a computer-readable storage medium.
Description
Technical Field
The present disclosure relates to the field of information processing, and in particular, to a method and apparatus for identifying a risk object.
Background
With the steady and rapid development of market economy in China, the business scope is becoming wider, and the association relationship between enterprises, enterprises and individuals and between individuals is becoming more complex. In recent years, the enterprise and personal financing and loan environments are complex and various, so that the enterprise and personal financing risk conduction is difficult to effectively control, and certain enterprise groups can simultaneously take funds of a plurality of financial institutions by utilizing related enterprises and relatives, thereby bringing potential risks to the financial institutions.
Disclosure of Invention
One aspect of the present disclosure provides a method of identifying a risk object, comprising: determining a first object; determining at least one second object with an incidence relation with the first object, wherein the incidence relation comprises a transaction relation and an entity relation; and performing risk identification on the first object based on the association relationship between the first object and the at least one second object.
Optionally, the risk identification for the first object based on the association relationship between the first object and the at least one second object includes: acquiring transaction flow data generated by the transaction of the first object and the at least one second object; extracting corresponding transaction flow indexes based on the transaction flow data; and verifying whether transaction indexes and/or combination of transaction indexes matched with a preset abnormal transaction rule exist in the corresponding transaction flow indexes.
Optionally, the risk identification for the first object based on the association relationship between the first object and the at least one second object includes: acquiring a data map describing a trade relationship between the first object and the at least one second object; extracting corresponding transaction flow indexes based on the data map; and verifying whether transaction indexes and/or combination of transaction indexes matched with a preset abnormal transaction rule exist in the corresponding transaction flow indexes.
Optionally, the preset abnormal transaction rule includes: a suspected abnormal transaction model and/or a suspected associated transaction model.
Optionally, the method further comprises: filtering the transaction flow data of a specified type from the transaction flow data before extracting the corresponding transaction flow index based on the transaction flow data; and extracting corresponding transaction flow indicators based on the remaining transaction flow data.
Optionally, the transaction relationship includes: a substantial transaction relationship and a derivative transaction relationship, wherein the substantial transaction relationship includes funds outflow information and funds inflow information, and the derivative transaction relationship includes at least one of: frequency of transactions, amount of transactions, and type of transaction.
Optionally, the entity relationship includes: business relationships and personal relationships.
Optionally, the method further comprises: after the risk identification is performed on the first object, if it is determined that the first object is a risk object, a processing countermeasure corresponding to the risk level is presented for the first object based on the risk level of the first object.
Another aspect of the present disclosure provides an apparatus for identifying a risk object, comprising: a first determining module for determining a first object; the second determining module is used for determining at least one second object with an association relationship with the first object, wherein the association relationship comprises a transaction relationship and an entity relationship; and the risk identification module is used for carrying out risk identification on the first object based on the association relation between the first object and the at least one second object.
Another aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the methods of embodiments of the present disclosure.
Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions that, when executed, are configured to implement a method of an embodiment of the present disclosure.
Another aspect of the present disclosure provides a computer program comprising computer executable instructions which, when executed, are for implementing the method of embodiments of the present disclosure.
Drawings
For a more complete understanding of the present disclosure and the advantages thereof, reference is now made to the following descriptions taken in conjunction with the accompanying drawings, in which:
FIG. 1 schematically illustrates a system architecture of a method and apparatus adapted to identify risk objects in accordance with an embodiment of the present disclosure;
FIG. 2 schematically illustrates a flow chart of a method of identifying a risk object according to an embodiment of the present disclosure;
FIG. 3 schematically illustrates a schematic diagram of constructing transaction index data according to an embodiment of the present disclosure;
FIG. 4 schematically illustrates a flow diagram for building an object-relationship network and identifying risk objects in accordance with an embodiment of the present disclosure;
FIG. 5 schematically illustrates a block diagram of an apparatus for identifying risk objects, in accordance with an embodiment of the present disclosure; and
fig. 6 schematically illustrates a block diagram of an electronic device according to an embodiment of the disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is only exemplary and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and/or the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It should be noted that the terms used herein should be construed to have meanings consistent with the context of the present specification and should not be construed in an idealized or overly formal manner.
Where expressions like at least one of "A, B and C, etc. are used, the expressions should generally be interpreted in accordance with the meaning as commonly understood by those skilled in the art (e.g.," a system having at least one of A, B and C "shall include, but not be limited to, a system having a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
Some of the block diagrams and/or flowchart illustrations are shown in the figures. It will be understood that some blocks of the block diagrams and/or flowchart illustrations, or combinations of blocks in the block diagrams and/or flowchart illustrations, 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, or other programmable data processing apparatus, such that the instructions, when executed by the processor, create means for implementing the functions/acts specified in the block diagrams and/or flowchart. The techniques of this disclosure may be implemented in hardware and/or software (including firmware, microcode, etc.). Additionally, the techniques of this disclosure may take the form of a computer program product on a computer-readable storage medium having instructions stored thereon, the computer program product being for use by or in connection with an instruction execution system.
Embodiments of the present disclosure provide a method of identifying a risk object and an apparatus for identifying a risk object capable of applying the method. The method includes determining a first object; determining at least one second object having an association relationship with the first object, wherein the association relationship comprises a transaction relationship and an entity relationship; and performing risk identification on the first object based on the association relationship between the first object and the at least one second object.
The disclosure will be described in detail below with reference to the drawings and specific examples.
Fig. 1 schematically illustrates a system architecture of a method and apparatus adapted to identify risk objects according to an embodiment of the present disclosure. It should be noted that fig. 1 is only an example of a system architecture to which embodiments of the present disclosure may be applied to assist those skilled in the art in understanding the technical content of the present disclosure, but does not mean that embodiments of the present disclosure may not be used in other devices, systems, environments, or scenarios.
As shown in fig. 1, a system architecture 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired and/or wireless communication links, and the like.
The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as a web browser application (e.g., a bank APP, etc.), a search class application, an instant messaging tool, a mailbox client and/or social platform software, etc., may be installed on the terminal devices 101, 102, 103, as just examples.
The terminal devices 101, 102, 103 may be a variety of electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like.
The server 105 may be a server providing various services, such as a background management server (by way of example only) providing support for websites browsed by users using the terminal devices 101, 102, 103. The background management server can analyze and process the received data such as the user request and the like, and feed back the processing result to the terminal equipment.
It should be noted that the method for identifying a risk object provided by the embodiments of the present disclosure may be generally performed by the server 105. Accordingly, the apparatus for identifying risk objects provided by embodiments of the present disclosure may be generally disposed in the server 105. The method of identifying risk objects provided by embodiments of the present disclosure may also be performed by a server or cluster of servers other than the server 105 and capable of communicating with the terminal devices 101, 102, 103 and/or the server 105. Accordingly, the means for identifying risk objects provided by the embodiments of the present disclosure may also be provided in a server or a server cluster different from the server 105 and capable of communicating with the terminal devices 101, 102, 103 and/or the server 105. Alternatively, the method for identifying a risk object provided by the embodiments of the present disclosure may be performed by the terminal device 101, 102, or 103, or may be performed by another terminal device different from the terminal device 101, 102, or 103. Accordingly, the apparatus for identifying a risk object provided by the embodiments of the present disclosure may also be provided in the terminal device 101, 102, or 103, or in another terminal device different from the terminal device 101, 102, or 103.
It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Fig. 2 schematically illustrates a flow chart of a method of identifying a risk object according to an embodiment of the present disclosure.
As shown in fig. 2, the method includes operations S201 to S203.
In operation S201, a first object is determined.
It should be noted that, the scenarios in which the method for identifying a risk object provided by the embodiments of the present disclosure may be applied include, but are not limited to: pre-loan risk screening, post-loan risk early warning, regulatory compliance checking, and the like.
Specifically, in operation S201, the first object may be an object targeted for pre-loan risk screening, or may be an object targeted for post-loan risk early warning, or may be an object targeted for regulatory compliance checking, which embodiments of the present disclosure are not limited herein.
More specifically, in operation S201, the first object may be determined according to object basic information (e.g., information such as a name, an ID, and a number of the object) input by the user. Wherein the first object may be an enterprise or may be a person.
Next, at least one second object having an association relationship with the first object is determined in operation S202, wherein the association relationship may include a transaction relationship and an entity relationship.
It should be noted that, as an alternative embodiment, the transaction relationship may include: a substantial trade relationship and a derivative trade relationship. Wherein the substantial transaction relationship further may include funds outflow information and funds inflow information. The derivative transaction relationship further may include at least one of: frequency of transactions, amount of transactions, and type of transaction. Also, as an alternative embodiment, the entity relationship may include: business relationships and personal relationships.
Specifically, in one embodiment of the present disclosure, a transaction relationship network between transaction objects may be created in advance based on banking transaction flow information, and an entity relationship network between the objects may be created in advance based on business registration information, and then all objects (i.e., second objects) having an association relationship with the first object may be searched based on the created transaction relationship network and the entity relationship network.
Or, in particular, in one embodiment of the present disclosure, the transaction relationship network and the entity relationship network may not be created, but transaction flowing information associated with the first object is acquired in real time in the process of performing operation S202, and all transaction objects having a transaction relationship with the first object are determined based on the acquired transaction flowing information. Meanwhile, in the course of performing operation S202, the business registration information associated with the first object is acquired in real time, and all objects having business relationships and personal relationships with the first object are determined based on the acquired business registration information. Finally, all the transaction objects determined by the transaction flow information and all the objects determined by the business registration information are taken as all the objects (namely, second objects) which have association relations with the first objects and are determined by the execution operation S202.
For example, if the bank agent makes a financing application to the bank agent, the bank agent can screen transaction flow information at the bank agent associated with the bank agent (e.g., transaction flow information for the bank agent to transfer money, pay money, etc., to other business or individuals, transaction flow information for other business or individuals to transfer money, pay money, etc.) over the last 1 year (or over the last half year, over the last quarter, over the last 1 month, etc.), and thereby determine the business agent as the counterparty for the funds outflow party (upstream transaction object) and the funds inflow party (downstream transaction object). The determined transaction counterpart is the transaction object with the transaction relation with the enterprise A.
Further, for example, for the enterprise first in the above example, the bank first may screen the business registration information associated therewith and thereby determine other enterprises for which the enterprise first is a sponsor and a custodian, and other enterprises for which the legal person and responsible person of the enterprise first invest and custody, and other individuals having a relatives relationship with the legal person and responsible person of the enterprise first, and the like. Wherein the other businesses and other individuals identified are objects having an entity relationship with the business a.
Then, in operation S203, risk identification is performed on the first object based on the association relationship between the first object and the at least one second object.
Specifically, in operation S203, it may be determined whether the first object is a suspected risk object based on the transaction object having a transaction relationship with the first object and the transaction situation between the first object determined in operation S202, and whether the first object is a suspected risk object based on the financing, loan situation of the object having an entity relationship with the first object determined in operation S202, respectively.
For example, for enterprise a, if the transaction between enterprise a and enterprise b, and enterprise c is a suspected abnormal transaction, then determining that enterprise a is a suspected risk object.
Or, for example, if the enterprise b, which is the first to pay or hold, appears as a risk object in the past financing or loan, the probability that the enterprise a is a risk object is high, and in this case, the enterprise a may be considered as a suspected risk object.
By using the traditional method for identifying the risk object, the financial institutions such as banks and the like can hardly directly find the relationship among the enterprises and the individuals in terms of the more concealed association relationship established by the forms of cross investment, mutual participation and the like, and the financial institutions such as banks and the like can hardly judge the relationship among the enterprises and the individuals from the surface in terms of the more concealed association established by the forms of controlling multiple enterprises or diluting target enterprise shares of the multi-stage participation enterprises and the like.
Through the embodiment of the disclosure, especially through the transaction relationship among the objects, the hidden association relationship which is difficult to be found among enterprises, enterprises and individuals and among individuals can be deeply mined, and thus, the potential risk objects can be identified, so that the risks of financial institutions in enterprise or individual financing and loan can be reduced as much as possible.
As an alternative embodiment, the operation S203 may include performing risk identification on the first object based on the association relationship between the first object and the at least one second object as follows.
Transaction flow data generated by the transaction of the first object and at least one second object is obtained.
And extracting corresponding transaction flow indexes based on the transaction flow data.
Verifying whether transaction indexes and/or combination of transaction indexes matched with a preset abnormal transaction rule exist in the corresponding transaction flow indexes.
When any two transaction objects conduct transaction, corresponding transaction flow data are generated and recorded. The transaction arrangement data includes information such as a funds flow, a transaction amount, a transaction time, transfer notes, etc., so that a transaction relationship between the transaction objects can be defined based on the information included in the transaction arrangement data.
More specifically, as shown in fig. 3, for transaction flowing data, corresponding transaction flowing indexes may be extracted from different dimensions such as a transaction dimension, a statistics dimension, a time dimension, and the like. For example, the transaction flowing index extracted from the business dimension includes, but is not limited to, a funds outflow amount, a funds inflow amount, a transaction relationship upstream and downstream from each other, and the like. The transaction sequence indicators extracted from the statistical dimension include, but are not limited to, transaction type, transaction frequency, transaction amount, transaction time, and the like. The trade flowing index extracted from the time dimension includes, but is not limited to, approximately 30 days, approximately 90 days, approximately 360 days, 7 days after credit, 15 days after credit, 30 days after credit, etc.
According to the embodiment of the disclosure, when risk transaction objects are identified according to the transaction relation, transaction flow data generated during transaction among the transaction objects can be obtained, and transaction flow indexes in each dimension are extracted from the transaction flow data, so that the extracted indexes are matched with known abnormal transaction rules. If any one or more of the preset abnormal transaction rules can be hit, determining that the first object is a suspected risk object. Otherwise, if any rule in the preset abnormal transaction rules is not hit, determining that the first object is a normal object.
Or, as another alternative embodiment, the operation S203 may further include performing risk identification on the first object based on an association relationship between the first object and at least one second object.
A data map describing a trade relationship between a first object and at least one second object is acquired.
And extracting corresponding transaction flow indexes based on the data map.
Verifying whether transaction indexes and/or combination of transaction indexes matched with a preset abnormal transaction rule exist in the corresponding transaction flow indexes.
Specifically, in the embodiment of the present disclosure, transaction flow data generated by transactions between each transaction object in a preset period of time may be acquired, and a data map of each transaction object may be constructed based on the acquired transaction flow data. Wherein nodes represent transaction objects (including businesses and individuals) and edges represent transaction relationships between two objects connected in a data graph. Further, the arrow marked on the side indicates the flow of funds. In addition, other transaction flow indicators can be marked on the edges.
According to the embodiment of the disclosure, when the risk object is identified, a pre-constructed data map can be directly utilized, one target object (first object) is firstly determined, each side and each associated object connected with the target object are searched, the corresponding transaction flow index is further read from information marked on the connected side, and the read index is matched with a preset abnormal transaction rule. If any one or more of the preset abnormal transaction rules can be hit, determining that the first object is a suspected risk object. Otherwise, if any rule in the preset abnormal transaction rules is not hit, determining that the first object is a normal object.
Furthermore, in one particular embodiment of the present disclosure, the risk object may also be identified as follows:
and a step a, summarizing multidimensional data, and acquiring enterprise information, group relation information, business relation information, guarantee relation information, fund business information and the like according to an enterprise list to be checked.
And b, constructing a single network, constructing a transaction relation network according to the fund business information in the multi-dimensional data, and constructing an entity relation network according to other information in the multi-dimensional data.
The transaction relation network is built by mainly utilizing fund flow information, and can assist business personnel to verify substantial transaction relations and derivative transaction relations among enterprises, individuals and individuals. The entity relation network is built by mainly utilizing business registration beliefs and personal information.
And c, superposing the multi-source network, namely superposing the transaction relation network and the entity relation network into a comprehensive transaction association network.
And d, risk pattern recognition, namely performing suspected abnormal transaction pattern recognition and associated transaction pattern recognition based on a comprehensive transaction associated network.
And e, business action suggestion, namely applying a risk object identification scheme based on the transaction relationship and the entity relationship to pre-credit risk screening, post-credit risk early warning and supervision compliance inspection, and giving out corresponding action suggestion.
Through the embodiment of the disclosure, whether a certain association relationship such as a transaction relationship, an entity relationship and the like exists between the client list to be inspected and clients (including enterprises and individuals) owned by the financial institutions can be analyzed, a transaction association network is constructed by combining various association relationships, and further, risk identification is carried out on the clients in the client list to be inspected by using the association network, and early warning prompt is issued.
In addition, the disclosed embodiment comprehensively uses technologies such as machine learning, association relation mining and the like, updates the traditional mode of judging the related enterprises by the financial institutions, constructs a transaction related network through transaction flow information, business registration information, loan information, personal information and the like, mines the related closed loop among the enterprises, and monitors abnormal transactions among the enterprises.
As an alternative embodiment, the foregoing preset abnormal transaction rule may include: a suspected abnormal transaction model and/or a suspected associated transaction model. The transaction represented by the suspected abnormal transaction model and the suspected associated transaction model is a transaction with potential risks.
As an alternative embodiment, the method may further comprise: the following operations are performed before the corresponding transaction flowing index is extracted based on the transaction flowing data.
The transaction flow data of the specified type is filtered from the transaction flow data.
And extracting corresponding transaction flow indexes based on the rest transaction flow data.
It should be appreciated that when a financial institution such as a bank is the subject of business and personal transactions, the type of transaction is a banking type transaction. Where banking transactions typically include interest payments, deposits, loans, repayment, and the like. While banking transactions are typically normal transactions. Therefore, before the transaction flow index is extracted, transaction flow data generated by the bank transaction can be filtered from the transaction flow data so as to exclude redundant data which has no practical meaning for identifying the risk object.
It should also be appreciated that when telecommunication businesses such as businesses, mobile businesses, etc. are targeted for transactions by other businesses and individuals, the type of transaction is a telecommunication-type transaction. Where telecommunication-type transactions typically include network fee payments, traffic fee payments, telephone fee charges, and the like. Whereas telecommunication-type transactions are typically normal transactions. Therefore, before the transaction flow index is extracted, transaction flow data generated by telecommunication class transaction can be filtered from the transaction flow data so as to exclude redundant data which has no practical meaning on identifying risk objects.
According to the embodiment of the disclosure, each transaction can be classified according to the fund use so as to remove transaction flow data related to bank transaction parties, telecommunication transaction parties and the like, thereby eliminating redundant data which has no practical meaning on identifying risk objects and improving the risk object identification efficiency.
As an alternative embodiment, the transaction relationship may include: a substantial trade relationship and a derivative trade relationship. The substantial transaction relationship further includes a transaction upstream-downstream relationship between the funds outflow information (e.g., funds outflow amount) and the funds inflow information (e.g., funds inflow amount) (e.g., enterprise a transfers 100 ten thousand to enterprise b, enterprise b transfers 80 ten thousand to enterprise a, and enterprise a and enterprise b are the transaction upstream-downstream relationship). The derivative transaction relationship may further include at least one of: frequency of transactions, amount and type of transactions, time of transactions, etc. Further, the entity relationships may include: business relationships and personal relationships.
In the embodiment of the disclosure, corresponding transaction flow data is generated and recorded when any two transaction objects conduct transactions. The transaction arrangement data includes information such as a funds flow, a transaction amount, a transaction time, transfer notes, etc., so that a transaction relationship between the transaction objects can be defined based on the information included in the transaction arrangement data.
More specifically, for transaction flowing data, corresponding transaction flowing indexes can be extracted from different dimensions such as business dimension, statistical dimension, time dimension and the like. For example, the transaction flowing index extracted from the business dimension includes, but is not limited to, a funds outflow amount, a funds inflow amount, a transaction relationship upstream and downstream from each other, and the like. The transaction sequence indicators extracted from the statistical dimension include, but are not limited to, transaction type, transaction frequency, transaction amount, transaction time, and the like. The trade flowing index extracted from the time dimension includes, but is not limited to, approximately 30 days, approximately 90 days, approximately 360 days, 7 days after credit, 15 days after credit, 30 days after credit, etc.
It should be appreciated that the transaction sequence indicators extracted from the business dimension characterize the substantial transaction relationships between the transaction objects, and the transaction sequence indicators extracted from the statistical dimension and the time dimension characterize the derivative transaction relationships between the transaction objects.
Further, in the embodiments of the present disclosure, the trade flowing water index may be divided into two types of indexes, i.e., an index representing a one-way relationship and an index representing a two-way relationship. Further, the index indicating the one-way relationship includes an index indicating post-loan funds transfer, an index indicating real-time funds transfer, and the like. Further, the index indicating the post-loan funds transfer behavior includes a maximum amount of single funds transferred from the last 7 days, a maximum amount of single funds transferred from the last 15 days, a maximum amount of single funds transferred from the last 30 days, a maximum number of single institution funds transferred from each day, and a ratio of total amount of transferred funds to total borrowing amount. The indexes for representing real-time funds transfer behavior comprise the maximum amount of single funds transferred in the last 30 days, the maximum amount of single funds transferred in the last 360 days, the maximum amount of single funds transferred and the total number of funds transferred. The index representing the real-time funds transfer behavior includes a maximum amount of single funds in the near 30 days and a maximum amount of single funds in the near 360 days. In addition, the index representing the bidirectional relationship includes an index representing the running back and forth behavior of funds, and the index specifically includes the number of enterprises upstream and downstream of funds between the recent 1 year and enterprises applying for financing/loans (abbreviated as application enterprises), the ratio of the number of enterprises upstream and downstream of funds to the number of enterprises downstream (upstream) between them, the ratio of the inflow (outflow) amount and the inflow (outflow) amount of enterprises upstream and downstream of funds between them, and the number of enterprises upstream and downstream of funds between them and forming a flat account with the application enterprises.
In addition, in the embodiment of the disclosure, the trade flowing index may further include an index indicating the stability of the flow direction of funds, where the index specifically includes the stability of the large customer ranked as top 5 in the downstream trade object of funds applied for the enterprise in the last 1 month, the ratio of the single outgoing maximum amount to the single incoming maximum amount in the last 360 days, the ratio of the total outgoing amount to the total incoming amount in the last 360 days, and the like.
In addition, to facilitate data processing, in embodiments of the present disclosure, a transaction flow relationship table may also be created based on transaction flow data of a full-scale business lender. The method is used for combing the one-time transaction relationship among enterprises, enterprises and individuals. (remark: second degree calculated amount is large, relationship constitution is consistent with first degree transaction relationship.) through the transaction flow relationship table, the fund flow information can be extracted, and the fund association relationship is identified.
Specifically, the transaction flow relationship table may be divided into two parts, a substantial transaction relationship part and a derivative transaction relationship part. The substantial trade relationship is to display and classify the objectively existing trade relationship by taking enterprises as granularity. The realization path is to judge the transaction type and the transaction status of the transaction opponent by the name and the annex classification of the transaction opponent. The derived trade relationship is characterized in that enterprises are taken as granularity, and the trade relationship with certain behavior characteristics and change rules is objectively described. In embodiments of the present disclosure, the derived trade relationship may not intersect with the substantial trade relationship, and both trade relationships may be presented as decoupled parallel relationships. It should be appreciated that the two trade relationships described above may also be interleaved if desired by the business. In addition, the realization path of the derived transaction relationship is to refine the transaction rule through the frequency, frequency and amount of the transaction to form the derived transaction relationship.
In the embodiment of the disclosure, the attribute information of the relationship can be expressed on the relationship side of the data map in a coding form in the calculation logic dimension without modification and expansion, so that the effect of extensible and traceable transaction relationship is achieved.
Further, in the disclosed embodiments, the substantial trade relationship may be divided into a one-way forward trade relationship (e.g., for business a, if business a is a funds-outflow party during a trade, business a may be defined as being in a forward trade relationship with respect to the other party of the trade) and a one-way reverse trade relationship (e.g., for business a, if business a is a funds-inflow party during a trade, business a may be defined as being in a reverse trade relationship with respect to the other party of the trade). And the substantial trade relationship can be divided according to trade industry and trade content. The trade industry classification standard can refer to national standard industry classification standard, three major industry classification standard and bank internal division standard. The transaction content classification criteria may refer to B/S subject classification criteria, CF/S subject classification criteria, cost economy usage classification criteria, cost economy content classification criteria, associated transaction category criteria defined by the chinese economic financial database (CCER).
Also, in the disclosed embodiments, derivative trade relationships may be divided into 8 categories, including scale trade relationships, continuity trade relationships, stability trade relationships, growth trade relationships, centralization trade relationships, periodicity trade relationships, consistency trade relationships, high liveness trade relationships.
As shown in fig. 4, an exemplary, a transaction flow network may be built based on transaction flow data first, and then a transaction relationship network may be built based on the transaction flow network; meanwhile, an industrial and commercial relationship network is built based on industrial and commercial registration information, a personal relationship network is built based on personal information of legal representatives and/or enterprise responsible persons, and an entity relationship network is built based on the industrial and commercial relationship network and the personal relationship network; and further, the transaction relation network and the entity relation network are overlapped and fused to obtain a fund transaction association network (namely, a data map). The fund transaction association network can determine whether the transaction between any object and other objects in the network has the same or similar transaction pattern as the suspected abnormal transaction pattern and the suspected association transaction pattern (such as the transaction pattern between the enterprises A, B and C can be considered to be the suspected association transaction pattern if the enterprise A transfers about 100 ten thousand to the enterprise B and about 100 ten thousand to the enterprise C and about 100 ten thousand to the enterprise A in 7 days after the loan, and the transaction pattern can be considered to be the transaction pattern with higher risk) so as to realize pre-loan risk screening, post-loan risk early warning, supervision compliance checking and the like.
Specifically, in the embodiment of the disclosure, the suspected abnormal transaction mode is a mode which is summarized by integrating internal and external related business experiences of a financial institution, a silver-colored insurance monitoring method, legal provision, transaction relations, a front technology, comprehensive output data rules and the like and can be used for monitoring whether inter-enterprise lending, financial risk transfer and the like are abnormal.
The business experience refers to making some preliminary records and judgment based on the existing business experience inside and outside the financial institution. Searching is carried out according to cases of abnormal risks existing in other scenes, such as some administrative punishment cases monitored by silver insurance, and the case description and implementation behaviors in the cases can be inspired from the side surface to accumulate business experience.
Legal provision refers to the interpretation of issued measures and legal provision, such as "three law and one law" and "financial institution high-volume transaction and suspicious transaction report management method", etc., by which the suspicious abnormal transaction patterns can be further defined and divided.
The trade relation refers to a trade flow statistics index and a trade relation table constructed according to the fund flow information.
The data rule refers to an abnormal transaction rule which is obtained by performing statistical analysis according to the index value expression of the constructed transaction flow statistical index (basic statistical index, including index classification and index name), mining the relevant information of clients such as abnormal values, maximum values and the like, observing whether the abnormal transaction behavior defined before exists, finding out the rule according to the abnormal transaction behavior, and mining.
The front edge technology refers to related technologies such as related network construction, big data computing capacity and the like in the whole process, and finally the purpose of searching out an abnormal transaction mode is achieved through mutual cooperation. And then making a preliminary abnormal transaction rule, and performing cross-validation through experience inside and outside a financial institution to finally output an abnormal transaction mode, wherein the whole process of mining the abnormal transaction mode is the above process.
The suspected association transaction mode is a mode for monitoring risk transactions such as large stakeholder funds transfer, supply and sales reverse cash flow, and platform financing through a transaction association relationship network.
As shown in table 1, the index classification is a basic statistical index of basic indexes (outflow), and the index names may be: the maximum amount of the single funds in 15 days after the application of the enterprise credit, the maximum number of single-institution funds per day in 15 days after the application of the enterprise credit, the ratio of the total amount of the funds in 15 days after the application of the enterprise credit to the total amount of the borrow funds, the maximum amount of the single-institution funds in 30 days after the application of the enterprise credit, and the maximum number of single-institution funds per day in 30 days after the application of the enterprise credit.
TABLE 1
| Index classification | Index name |
| Basic index (outflow) | The maximum amount of a single fund is discharged within 15 days after the application of the enterprise to credit |
| Basic index (outflow) | The highest number of single daily institution funds issued within 15 days after the application of the business credit |
| Basic index (outflow) | The ratio of total amount of fluid funds to total borrowing amount 15 days after the application of the enterprise credit |
| Basic index (outflow) | The maximum amount of a single fund is discharged within 30 days after the application of the enterprise to credit |
| Basic index (outflow) | The highest number of daily single institution funds flows out within 30 days after applying for the enterprise to credit |
As shown in table 2, the first to fourth rules in the suspected abnormal transaction rules, and the corresponding rule contents and business meanings are exemplarily shown in table 2.
TABLE 2
As an alternative embodiment, the method may further comprise: after risk identification of the first object, the following operations are performed: and if the first object is determined to be a risk object, displaying a processing countermeasure corresponding to the risk level based on the risk level of the first object for the first object. For example, credit may be prohibited for primary risk objects in pre-loan risk screening, and business personnel may be prompted to call back for primary risk objects in post-loan risk early warning to further understand the repayment capabilities of the risk objects.
Fig. 5 schematically illustrates a block diagram of an apparatus for identifying risk objects according to an embodiment of the disclosure.
As shown in fig. 5, an apparatus 500 for identifying a risk object includes a first determination module 501, a second determination module 502, and a risk identification module 503. The apparatus for identifying risk objects may perform the method described above with reference to the method embodiment section, and will not be described here again.
Specifically, the first determining module 501 is configured to determine a first object.
A second determining module 502, configured to determine at least one second object having an association relationship with the first object, where the association relationship includes a transaction relationship and an entity relationship.
The risk identification module 503 is configured to perform risk identification on the first object based on an association relationship between the first object and at least one second object.
As an alternative embodiment, the risk identification module comprises: an acquisition unit, an extraction unit, and a verification unit.
Specifically, the first acquisition unit is used for acquiring transaction flow data generated by the transaction of the first object and at least one second object.
The first extraction unit is used for extracting corresponding transaction flow indexes based on the transaction flow data.
The first verification unit is used for verifying whether the corresponding transaction flow indexes have transaction indexes and/or combination of the transaction indexes matched with the preset abnormal transaction rules or not.
Or, as another alternative embodiment, the risk identification module includes:
specifically, the second acquisition unit is used for acquiring a data map for describing the transaction relationship between the first object and at least one second object.
And the second extraction unit is used for extracting the corresponding transaction flow index based on the data map.
And the second verification unit is used for verifying whether the corresponding transaction flow indexes have transaction indexes and/or combination of the transaction indexes matched with the preset abnormal transaction rules or not.
As an alternative embodiment, the preset abnormal transaction rules may include: a suspected abnormal transaction model and/or a suspected associated transaction model.
As an alternative embodiment, the apparatus may further include: a filtering module and an extracting module.
Specifically, the filtering module is configured to filter the transaction flow data of the specified type from the transaction flow data before extracting the corresponding transaction flow index based on the transaction flow data.
And the extraction module is used for extracting the corresponding transaction flow index based on the rest transaction flow data.
As an alternative embodiment, the transaction relationship may include: a substantial trade relationship and a derivative trade relationship. Wherein the substantial transaction relationship further may include funds outflow information and funds inflow information. The derivative transaction relationship may further include at least one of: frequency of transactions, amount of transactions, and type of transaction.
As an alternative embodiment, the entity relationships may include: business relationships and personal relationships.
As an alternative embodiment, the apparatus may further include: and a display module. Specifically, the display module is configured to display, for the first object, a processing countermeasure corresponding to the risk level based on the risk level of the first object if it is determined that the first object is a risk object after risk identification is performed on the first object.
It should be noted that, the embodiments of the apparatus portion of the present disclosure correspond to the embodiments of the method portion, and the achieved technical effects also correspond to the same or similar, which are not described herein again.
Any number of the modules, units, or at least some of the functionality of any number of the modules, units, or units according to embodiments of the present disclosure may be implemented in one module. Any one or more of the modules, units according to embodiments of the present disclosure may be implemented as split into multiple modules. Any one or more of the modules, units according to embodiments of the present disclosure may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or in hardware or firmware in any other reasonable manner of integrating or packaging the circuits, or in any one of or in any suitable combination of three of software, hardware, and firmware. Alternatively, one or more of the modules, units according to embodiments of the disclosure may be at least partially implemented as computer program modules, which when executed, may perform the corresponding functions.
For example, any of the first determination module 501, the second determination module 502, and the risk identification module 503 may be combined and implemented in one module, or any of the modules may be split into a plurality of modules. Alternatively, at least some of the functionality of one or more of the modules may be combined with at least some of the functionality of other modules and implemented in one module. According to embodiments of the present disclosure, at least one of the first determination module 501, the second determination module 502, and the risk identification module 503 may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in hardware or firmware in any other reasonable way of integrating or packaging the circuits, or in any one of or a suitable combination of three of software, hardware, and firmware. Alternatively, at least one of the first determination module 501, the second determination module 502 and the risk identification module 503 may be at least partially implemented as computer program modules, which when executed may perform the respective functions.
Fig. 6 schematically illustrates a block diagram of an electronic device according to an embodiment of the disclosure. The electronic device shown in fig. 6 is merely an example and should not be construed to limit the functionality and scope of use of the disclosed embodiments.
As shown in fig. 6, the electronic device 600 includes a processor 610, a computer-readable storage medium 620. The electronic device 600 may perform methods according to embodiments of the present disclosure.
In particular, the processor 610 may include, for example, a general purpose microprocessor, an instruction set processor and/or an associated chipset and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), or the like. The processor 610 may also include on-board memory for caching purposes. The processor 610 may be a single processing unit or multiple processing units for performing the different actions of the method flows according to embodiments of the disclosure.
Computer-readable storage medium 620, which may be, for example, a non-volatile computer-readable storage medium, specific examples include, but are not limited to: magnetic storage devices such as magnetic tape or hard disk (HDD); optical storage devices such as compact discs (CD-ROMs); a memory, such as a Random Access Memory (RAM) or a flash memory; etc.
The computer-readable storage medium 620 may include a computer program 621, which computer program 621 may include code/computer-executable instructions that, when executed by the processor 610, cause the processor 610 to perform a method according to an embodiment of the present disclosure or any variation thereof.
The computer program 621 may be configured with computer program code comprising, for example, computer program modules. For example, in an example embodiment, code in computer program 621 may include one or more program modules, including 621A, modules 621B, … …, for example. It should be noted that the division and number of modules is not fixed, and that a person skilled in the art may use suitable program modules or combinations of program modules depending on the actual situation, which when executed by the processor 610, enable the processor 610 to perform the methods according to embodiments of the present disclosure or any variations thereof.
At least one of the first determination module 501, the second determination module 502, and the risk identification module 503 may be implemented as computer program modules described with reference to fig. 6, which when executed by the processor 610, may implement the respective operations described above, in accordance with embodiments of the present disclosure.
The present disclosure also provides a computer-readable storage medium that may be embodied in the apparatus/device/system described in the above embodiments; or may exist alone without being assembled into the apparatus/device/system. The computer-readable storage medium carries one or more programs which, when executed, implement methods in accordance with embodiments of the present disclosure.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams 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.
It will be understood by those skilled in the art that while the present disclosure has been shown and described with reference to particular exemplary embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present disclosure as defined by the appended claims and their equivalents. The scope of the disclosure should, therefore, not be limited to the above-described embodiments, but should be determined not only by the following claims, but also by the equivalents of the following claims.
Claims (8)
1. A method of identifying a risk object, comprising:
determining a first object;
determining at least one second object with an incidence relation with the first object, wherein the incidence relation comprises a transaction relation and an entity relation;
wherein the trade relationship comprises: a substantial transaction relationship and a derivative transaction relationship, wherein the substantial transaction relationship includes funds outflow information and funds inflow information, the derivative transaction relationship including at least one of: frequency of transactions, amount of transactions, and type of transaction; the entity relationship includes: business relationships and personal relationships;
performing risk identification on the first object based on the association relationship between the first object and the at least one second object;
respectively building a transaction relation network and an entity relation network by combining each association relation, and carrying out multi-source network superposition on the transaction relation network and the entity relation network to form a data map;
obtaining the data map describing a trade relationship between the first object and the at least one second object;
extracting corresponding transaction flow indexes based on the data map;
verifying whether transaction indexes and/or combination of transaction indexes matched with a preset abnormal transaction rule exist in the corresponding transaction flow indexes.
2. The method of claim 1, wherein the risk identification of the first object based on the association between the first object and the at least one second object comprises:
acquiring transaction flow data generated by the transaction of the first object and the at least one second object;
extracting corresponding transaction flow indexes based on the transaction flow data; and
verifying whether transaction indexes and/or combination of transaction indexes matched with a preset abnormal transaction rule exist in the corresponding transaction flow indexes.
3. The method of claim 2, wherein the preset abnormal transaction rules comprise: a suspected abnormal transaction model and/or a suspected associated transaction model.
4. The method of claim 2, further comprising: before extracting the corresponding transaction flowing index based on the transaction flowing data,
filtering the appointed type of transaction flow data from the transaction flow data; and
and extracting corresponding transaction flow indexes based on the rest transaction flow data.
5. The method of claim 1, further comprising: after risk identification of the first object,
And if the first object is determined to be a risk object, displaying a processing countermeasure corresponding to the risk level based on the risk level of the first object for the first object.
6. An apparatus for identifying a risk object, comprising:
a first determining module for determining a first object;
a second determining module, configured to determine at least one second object having an association relationship with the first object, where the association relationship includes a transaction relationship and an entity relationship; wherein the trade relationship comprises: a substantial transaction relationship and a derivative transaction relationship, wherein the substantial transaction relationship includes funds outflow information and funds inflow information, the derivative transaction relationship including at least one of: frequency of transactions, amount of transactions, and type of transaction; the entity relationship includes: business relationships and personal relationships;
the risk identification module is used for carrying out risk identification on the first object based on the association relation between the first object and the at least one second object; respectively building a transaction relation network and an entity relation network by combining each association relation, and carrying out multi-source network superposition on the transaction relation network and the entity relation network to form a data map; obtaining the data map describing a trade relationship between the first object and the at least one second object; extracting corresponding transaction flow indexes based on the data map; verifying whether transaction indexes and/or combination of transaction indexes matched with a preset abnormal transaction rule exist in the corresponding transaction flow indexes.
7. An electronic device, comprising:
one or more processors;
a memory for storing one or more programs,
wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1 to 5.
8. A computer readable storage medium storing computer executable instructions which, when executed, are adapted to carry out the method of any one of claims 1 to 5.
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