CN111833182A - 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 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 incidence relation between the first object and the 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 an apparatus for identifying a risk object.
Background
With the steady and rapid development of market economy in China, the business range is becoming wide, and the incidence relations between enterprises, between enterprises and individuals and between individuals are becoming increasingly complex. In recent years, enterprises, personal financing and loan environments are complex and diverse, so that the enterprise and personal financing risk conduction is difficult to control effectively, and some enterprise groups use associated enterprises and relatives to collect funds of a plurality of financial institutions at the same time, so that potential risks are brought 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 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.
Optionally, the performing risk identification on 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 the corresponding transaction flow indexes have transaction indexes and/or combination of transaction indexes matched with preset abnormal transaction rules.
Optionally, the performing risk identification on the first object based on the association relationship between the first object and the at least one second object includes: acquiring a data map for describing a transaction 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 the corresponding transaction flow indexes have transaction indexes and/or combination of transaction indexes matched with preset abnormal transaction rules.
Optionally, the preset abnormal transaction rule includes: a suspected abnormal transaction model and/or a suspected associated transaction model.
Optionally, the method further comprises: before extracting corresponding transaction flow indexes based on the transaction flow data, filtering specified types of transaction flow data from the transaction flow data; and extracting a corresponding transaction flow index based on the remaining transaction flow data.
Optionally, the transaction relationship includes: a substantive transaction relationship and a derivative transaction relationship, wherein the substantive transaction relationship includes funds outflow information and funds inflow information, and the derivative transaction relationship includes at least one of: transaction frequency, transaction amount, and transaction type.
Optionally, the entity relationship includes: business relations and personal relations.
Optionally, the method further comprises: after the risk identification of the first object, if the first object is determined to be 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 determination 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; and a risk identification module, configured to perform risk identification on the first object based on an association relationship between the first object and the at least one second object.
Another aspect of the present disclosure provides an electronic device including: 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 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, implement the method of embodiments of the present disclosure.
Another aspect of the present disclosure provides a computer program comprising computer executable instructions that when executed perform 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 according to an embodiment of the present disclosure;
FIG. 2 schematically shows 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 building transaction metrics 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 according to an embodiment of the disclosure;
FIG. 5 schematically shows a block diagram of an apparatus for identifying a risk object according to an embodiment of the present disclosure; and
fig. 6 schematically shows 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 illustrative only 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 disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not 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 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 is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.
Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have 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 block diagrams and/or flow diagrams are shown in the figures. It will be understood that some blocks of the block diagrams and/or flowchart illustrations, or combinations thereof, 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, which execute via the processor, create means for implementing the functions/acts specified in the block diagrams and/or flowchart block or blocks. The techniques of this disclosure may be implemented in hardware and/or software (including firmware, microcode, etc.). In addition, the techniques of this disclosure may take the form of a computer program product on a computer-readable storage medium having instructions stored thereon for use by or in connection with an instruction execution system.
The embodiment of the disclosure provides a method for identifying a risk object and a device for identifying the risk object, which can apply 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 incidence relation between the first object and the at least one second object.
The present disclosure will be described in detail below with reference to the drawings and specific embodiments.
Fig. 1 schematically shows 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 the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, and does not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments or scenarios.
As shown in fig. 1, the system architecture 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired and/or wireless communication links, and so forth.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may have installed thereon various communication client applications, such as a web browser application (e.g., a bank APP, etc.), a search-type application, an instant messaging tool, a mailbox client, and/or social platform software, etc. (by way of example only).
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 105 may be a server providing various services, such as a background management server (for 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 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 executed by the server 105. Accordingly, the apparatus for identifying a risk object provided by the embodiments of the present disclosure may be generally disposed in the server 105. The method for identifying risk objects provided by the embodiments of the present disclosure may also be performed by a server or a cluster of servers different from the server 105 and capable of communicating with the terminal devices 101, 102, 103 and/or the server 105. Accordingly, the apparatus for identifying a risk object provided by the embodiment of the present disclosure may also be disposed 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 embodiment of the present disclosure may also be performed by the terminal device 101, 102, or 103, or may also 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 embodiment of the present disclosure may also be disposed 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 shows 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 to which the method for identifying a risk object provided by the embodiment 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, and the 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 (information such as name, ID, and number of the object) input by the user. Wherein the first object may be a business or may be an individual.
Next, at operation S202, at least one second object having an association relationship with the first object is determined, 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: substantive transaction relationships and derivative transaction relationships. Wherein the substantive transaction relationship further may include funds outflow information and funds inflow information. The derived transaction relationship further may include at least one of: transaction frequency, transaction amount, and transaction type. As an alternative embodiment, the entity relationship may include: business relations and personal relations.
Specifically, in an embodiment of the present disclosure, a transaction relationship network between transaction objects may be created in advance based on bank transaction flow information, an entity relationship network between 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 found based on the created transaction relationship network and entity relationship network.
Or, specifically, in one embodiment of the present disclosure, the transaction relationship network and the entity relationship network may not be created, but transaction flow information associated with the first object is acquired in real time during the execution of operation S202, and all transaction objects having a transaction relationship with the first object are determined based on the acquired transaction flow information. Meanwhile, in the process of performing operation S202, the business registration information associated with the first object is acquired in real time, and all objects having business 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 used as all the objects (i.e., the second objects) having the association relationship with the first object determined by the execution operation S202.
For example, if the business a submits a financing application to the bank a, the bank a may screen transaction flow information at the bank a associated with the business a within about 1 year (or within about half a year, within about one quarter, within about 1 month, etc.) (e.g., transaction flow information in which the business a transfers, pays, etc. to other businesses or individuals, transaction flow information in which other businesses or individuals transfer, pays, etc. to the business a, etc.), and thereby determine the business a as a transaction partner of the money outflow party (upstream transaction object) and the money inflow party (downstream transaction object). The determined transaction counterpart is the transaction object having a transaction relationship with the enterprise A.
In addition, for the first enterprise in the above example, the first bank may screen the business registration information associated therewith, and thereby determine other enterprises of which the first enterprise is an sponsor and a holder, other enterprises for which a legal person and a responsible person of the first enterprise invest and hold stocks, other individuals related to the legal person and the responsible person of the first enterprise, and the like. Wherein the determined other businesses and other individuals 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 determined in operation S202 and a transaction situation between the first object, and whether the first object is a suspected risk object based on financing and loan situations of the object having a physical relationship with the first object determined in operation S202, respectively.
For example, for the enterprise a, if the transaction between the enterprise a and the enterprise b and between the enterprise a and the enterprise c is a suspected abnormal transaction, it is determined that the enterprise a is a suspected risk object.
Alternatively, for example, if a company b who finances or controls a company a shows a risk target in a past financing or loan, the company a is more likely to be a risk target, and in this case, the company a may be considered to be a suspected risk target.
By using the traditional method for identifying risk objects, the financial institutions such as banks are difficult to directly discover the relationship of certain enterprises and individuals through the relatively hidden relationship established in the forms of cross investment, mutual participation and the like, and the financial institutions such as banks are difficult to judge the relationship between the enterprises and the individuals from the surface by the relatively hidden relationship established in the forms of diluting target enterprise shares and the like of multiple enterprises or multi-stage participation enterprises of certain enterprises and individuals.
Through the embodiment of the disclosure, especially through the transaction relationship among the objects, the implicit and difficultly discovered association relationships between enterprises, between enterprises and individuals and between individuals can be deeply mined, and therefore, the potential risk objects can be identified, so that the risk of the financial institution in enterprise or individual financing and loan is reduced as much as possible.
As an alternative embodiment, the operation S203 of performing risk identification on the first object based on the association relationship between the first object and the at least one second object may include the following operations.
Acquiring transaction flow data generated by the transaction of the first object and at least one second object.
Corresponding transaction flow indicators are extracted based on the transaction flow data.
And verifying whether the corresponding transaction flow indexes have the transaction indexes and/or the combination of the transaction indexes matched with the preset abnormal transaction rules.
When the transaction is carried out between any two transaction objects, corresponding transaction flow data can be generated and recorded. The transaction flow data comprises information such as fund flow, transaction amount, transaction time, transfer remarks and the like, so that the transaction relationship between transaction objects can be defined based on the information contained in the transaction flow data.
More specifically, as shown in fig. 3, for the transaction flow data, the corresponding transaction flow indexes may be extracted from different dimensions, such as a business dimension, a statistical dimension, and a time dimension. For example, transaction metrics drawn from the business dimension include, but are not limited to, out-of-funds, in-funds, mutual up-and-down-stream transaction relationships, and the like. The transaction pipeline metrics extracted from the statistical dimension include, but are not limited to, transaction type, transaction frequency, transaction amount, transaction time, and the like. Transaction running indicators extracted from the time dimension include, but are not limited to, approximately 30 days, approximately 90 days, approximately 360 days, 7 days post-loan, 15 days post-loan, 30 days post-loan, and the like.
By the embodiment of the disclosure, when risk transaction objects are identified according to transaction relations, transaction flow data generated during transactions among the transaction objects can be acquired, transaction flow indexes on all dimensions are extracted from the transaction flow data, and the extracted indexes are matched with known abnormal transaction rules. And if any one or more of the preset abnormal transaction rules can be hit, determining the first object as a suspected risk object. Otherwise, if any rule in the preset abnormal transaction rules is not hit, the first object is determined to be a normal object.
Or, as another alternative embodiment, the operation S203 may further 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.
A data map is obtained describing a transaction relationship between a first object and at least one second object.
And extracting corresponding transaction flow indexes based on the data map.
And verifying whether the corresponding transaction flow indexes have the transaction indexes and/or the combination of the transaction indexes matched with the preset abnormal transaction rules.
Specifically, in the embodiment of the present disclosure, transaction running data generated due to transactions among the transaction objects within a preset time period may be acquired, and a data map of each transaction object may be constructed based on the acquired transaction running data. Wherein in the data map, the nodes represent transaction objects (including enterprises and individuals), and the edges represent transaction relationships between two connected objects. Further, arrows marked on the sides indicate the flow of funds. Other transaction flow indicators can be marked on the edge.
Through the embodiment of the disclosure, when identifying the risk object, one target object (first object) can be determined first by directly using the pre-constructed data map, each side and each associated object connected with the target object are searched, and then the corresponding transaction running water index is read from the information marked on the connected side, and the read index is matched with the preset abnormal transaction rule. And if any one or more of the preset abnormal transaction rules can be hit, determining the first object as a suspected risk object. Otherwise, if any rule in the preset abnormal transaction rules is not hit, the first object is determined to be a normal object.
Furthermore, in a specific embodiment of the present disclosure, the risk object may be further identified according to the following steps:
step a, collecting multidimensional data, and acquiring enterprise information, group relationship information, business relationship information, guarantee relationship information, fund information and the like according to an enterprise list to be checked.
And b, building a single network, building a transaction relation network according to the fund information in the multidimensional data, and building an entity relation network according to other information in the multidimensional data.
The transaction relationship network is mainly built by using fund flow information, and can assist business personnel to check the actual transaction relationship and the derivative transaction relationship between enterprises, between enterprises and individuals and between individuals. The entity relationship network is mainly built by utilizing the business registration information and personal information.
And c, multi-source network superposition, namely performing multi-source network superposition on the transaction relationship network and the entity relationship network to jointly form a comprehensive transaction association network.
And d, risk pattern recognition, namely recognizing a suspected abnormal transaction pattern and a related transaction pattern based on a comprehensive transaction related network.
And e, recommending business actions, namely applying the risk object identification scheme based on the transaction relationship and the entity relationship to pre-loan risk screening, post-loan risk early warning and supervision compliance check, and giving corresponding action suggestions.
By the aid of the method and the system, whether a certain incidence relation such as a transaction relation, an entity relation and the like exists between the list of the clients to be checked and the clients (including enterprises and individuals) owned by the financial institution, a transaction incidence network is constructed by combining various incidence relations, risk identification is carried out on the clients in the list of the clients to be checked by using the incidence network, and early warning prompts are issued.
In addition, the embodiment of the disclosure comprehensively uses the technologies of machine learning, association relationship mining and the like, updates the traditional mode of judging the association between enterprises by the financial institution, constructs a transaction association network through transaction flow information, business and business registration information, loan information, personal information and the like, mines the association closed loop between the enterprises, and monitors abnormal transactions between the enterprises.
As an alternative embodiment, the preset abnormal transaction rule may include: a suspected abnormal transaction model and/or a suspected associated transaction model. And the transactions represented by the suspected abnormal transaction model and the suspected related transaction model are all transactions with potential risks.
As an optional embodiment, the method may further include: the following operations are performed before extracting the corresponding transaction flow indicators based on the transaction flow data.
Specified types of transaction pipeline data are filtered from the transaction pipeline data.
Corresponding transaction flow indicators are extracted based on the remaining transaction flow data.
It should be understood that when a financial institution such as a bank is a transaction object of an enterprise and an individual, the transaction type is a bank-type transaction. Where banking transactions typically include interest payments, deposits, loans, repayment, etc. While banking transactions are generally normal transactions. Therefore, before extracting the transaction flow index, the transaction flow data generated by bank type transaction can be filtered from the transaction flow data so as to eliminate redundant data which has no practical significance for identifying the risk object.
In addition, it should be understood that when the communication, mobile and other telecommunication enterprises are the transaction objects of other enterprises and individuals, the transaction type is telecommunication transaction. Wherein the telecommunication transaction generally comprises network fee payment, flow fee payment, telephone fee charging and the like. Whereas telecommunications type transactions are generally normal transactions. Therefore, before extracting the transaction flow index, the transaction flow data generated by the telecommunication transaction can be filtered from the transaction flow data so as to eliminate redundant data which has no practical significance for identifying the risk object.
By the embodiment of the disclosure, each transaction can be classified according to the fund use, so that transaction flow data related to bank transaction parties, telecommunication transaction parties and the like can be eliminated, redundant data which have no practical significance for identifying the risk object can be eliminated, and the risk object identification efficiency is improved.
As an alternative embodiment, the transaction relationship may include: substantive transaction relationships and derivative transaction relationships. The substantial transaction relationship may further include the information of the outflow of funds (such as the amount of the outflow of funds) and the information of the inflow of funds (such as the amount of the inflow of funds), and the relationship is the transaction upstream and downstream relationship (such as the account transfer from the enterprise A to the enterprise B is 100 ten thousand, the account transfer from the enterprise B to the enterprise A is 80 ten thousand, and the transaction relationship between the enterprise A and the enterprise B is the transaction upstream and downstream relationship). The derived transaction relationships further may include at least one of: transaction frequency, transaction amount and type, transaction time, etc. Further, the entity relationships may include: business relations and personal relations.
In the embodiment of the disclosure, corresponding transaction flow data is generated and recorded when a transaction is performed between any two transaction objects. The transaction flow data comprises information such as fund flow, transaction amount, transaction time, transfer remarks and the like, so that the transaction relationship between transaction objects can be defined based on the information contained in the transaction flow data.
More specifically, for the transaction flow data, the corresponding transaction flow indexes may be extracted from different dimensions, such as a business dimension, a statistical dimension, and a time dimension. For example, transaction metrics drawn from the business dimension include, but are not limited to, out-of-funds, in-funds, mutual up-and-down-stream transaction relationships, and the like. The transaction pipeline metrics extracted from the statistical dimension include, but are not limited to, transaction type, transaction frequency, transaction amount, transaction time, and the like. Transaction running indicators extracted from the time dimension include, but are not limited to, approximately 30 days, approximately 90 days, approximately 360 days, 7 days post-loan, 15 days post-loan, 30 days post-loan, and the like.
It should be understood that the transaction flow indicators extracted from the business dimension characterize substantial transaction relationships between transaction objects, and the transaction flow indicators extracted from the statistical and temporal dimensions characterize derivative transaction relationships between transaction objects.
Further, in the embodiments of the present disclosure, the transaction flow index may be divided into two types of indexes, that is, an index representing a one-way relationship and an index representing a two-way relationship. Furthermore, the indexes representing the one-way relationship comprise indexes representing actions of fund transfer after loan, real-time fund transfer and the like. Further, the indexes indicating the post-loan fund transfer behavior include the maximum amount of money discharged from a single fund in about 7 days after the loan, the maximum amount of money discharged from a single fund in about 15 days after the loan, the maximum amount of money discharged from a single fund in about 30 days after the loan, the maximum number of times of money discharged from a single institution per day, and the ratio of the total amount of the discharged fund to the total amount of the borrowed fund. The indexes representing the real-time fund transfer behavior comprise the maximum amount of single fund flow in nearly 30 days, the maximum amount of single fund flow in nearly 360 days, the maximum amount of single fund flow and the total number of fund flow. The indexes representing real-time fund transfer behaviors comprise maximum amount of single fund in 30 days and maximum amount of single fund in 360 days. In addition, the indexes representing the bidirectional relationship include indexes representing the behavior of back-and-forth fund payment, and specifically include the number of enterprises which mutually fund upstream and downstream with enterprises applying financing/loan (referred to as applying enterprises for short) in about 1 year, the ratio of the number of enterprises which mutually fund upstream and downstream with the number of enterprises which mutually fund downstream with the downstream, the ratio of the amount of inflow (outflow) of the enterprises which mutually fund upstream and downstream with the total amount of inflow (outflow), and the number of enterprises which mutually fund upstream and downstream and form a tie account with the applying enterprises.
In addition, in the embodiment of the present disclosure, the transaction flow indexes may further include an index indicating stability of the fund flow direction, where the index specifically includes stability of a big client ranked as top 5 in the fund downstream transaction object of the application enterprise in the last 1 month, a ratio of a maximum amount of a single outflow to a maximum amount of a single inflow in the last 360 days, a ratio of a total amount of the outflow to a total amount of the inflow in the last 360 days, and the like.
In addition, to facilitate data processing, in embodiments of the present disclosure, a transaction flow chart may also be created based on transaction flow data of a wholesale operation fast-crediting customer. The method is used for combing one-degree transaction relationships between enterprises, enterprises and individuals. (remark: the second-degree calculated amount is large, and the relationship formation is consistent with the first-degree transaction relationship.) the fund flow information can be extracted through the transaction flow relationship table, 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 actual transaction relation is the granularity of enterprises, and the objectively existing transaction relation is displayed and classified. The realization path is that the transaction type and the transaction status are judged by the name and the epilogue classification of the transaction opponent. The derived transaction relationship is a transaction relationship which takes enterprises as granularity and objectively describes certain behavior characteristics and change rules. In the embodiments of the present disclosure, the derived transaction relationship may not intersect with the actual transaction relationship, and the two transaction relationships may be displayed as a decoupled parallel relationship. It should be understood that the two transaction relationships described above may also be interleaved if desired by the business. In addition, the realization path of the derivative transaction relationship is to refine the transaction rule through the frequency, frequency and amount of the transaction to form the derivative transaction relationship.
In the embodiment of the disclosure, the relationship dimension may not be modified or expanded, but the attribute information of the relationship is expressed on the relationship edge of the data map in a coding form on the computational logic dimension, so as to achieve the effect that the transaction relationship is extensible and traceable.
Further, in embodiments of the present disclosure, the substantive transaction relationship may be divided into a one-way forward transaction relationship (e.g., for business a, the business a may be defined as being in a forward transaction relationship with respect to the other party to the transaction if the business a is an out-of-funds party during the transaction) and a one-way reverse transaction relationship (e.g., for business a, the business a may be defined as being in a reverse transaction relationship with respect to the other party to the transaction if the business a is an in-funds party during the transaction) in terms of the flow of funds. And the actual transaction relationship can be divided according to transaction industries and transaction contents. The trade industry classification standard can refer to national standard industry classification standard, three major industry classification standards and bank internal division standard. The transaction content division standard can refer to B/S subject classification standard, CF/S subject classification standard, cost-economic use classification standard, cost-economic content classification standard and associated transaction category standard defined by Chinese economic financial database (CCER).
In addition, in the embodiment of the present disclosure, the derived transaction relationships may be divided into 8 types, including a large-scale transaction relationship, a continuous transaction relationship, a stable transaction relationship, an increasing transaction relationship, a centralized transaction relationship, a periodic transaction relationship, a consistent transaction relationship, and a high-liveness transaction relationship.
As shown in fig. 4, for example, a transaction pipeline network may be built based on transaction pipeline data, and then a transaction relationship network may be built based on the transaction pipeline network; meanwhile, a business-to-business relationship network is built based on the business registration information, a personal relationship network is built based on the personal information of legal representatives and/or enterprise principals, and an entity relationship network is built based on the business-to-business relationship network and the personal relationship network; and further overlapping and fusing the transaction relationship network and the entity relationship network to obtain a fund transaction association network (namely a data map). Whether the transactions between any object and other objects in the network have the same or similar transaction modes with a suspected abnormal transaction mode and a suspected associated transaction mode (for example, within 7 days after the loan, if an enterprise A transfers about 100 thousands of money to an enterprise B, an enterprise B transfers about 100 thousands of money to an enterprise C, and an enterprise C transfers about 100 thousands of money to the enterprise A, the transaction mode between the enterprise A, the enterprise B and the enterprise C can be considered as the suspected associated transaction mode, and the transaction mode can be considered as a transaction mode with higher risk) can be judged through the fund transaction associated network, so that pre-loan risk screening, post-loan risk early warning, supervision and compliance check and the like are realized.
Specifically, in the embodiment of the present disclosure, the suspected abnormal transaction mode is a mode that is summarized and summarized by the internal and external related business experiences, the bank protection monitoring method, the legal provision and the transaction relationship of the financial institution, and the leading edge technology, the comprehensive output data rule, and the like, and can monitor whether the inter-enterprise loan, the financial risk type transfer, and the like are abnormal or not.
The business experience refers to making some preliminary records and judgments based on the existing business experience inside and outside the financial institution. The search is carried out according to the existing cases with abnormal risks in other scenes, such as some administrative penalty cases monitored by the bank protection, case description and implementation behaviors in the cases can be elicited from the side so as to accumulate business experience.
Legal provisions are interpreted from issued laws and regulations, such as "three law references", "financial institution large amount transaction and suspicious transaction report management" and the like, and through these provisions, the suspected abnormal transaction pattern can be further defined and divided.
The transaction relationship refers to a transaction flow statistic index and a transaction relationship 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 running statistical index (basic statistical index including index classification and index name), mining relevant information of customers such as abnormal values, maximum values and the like, observing whether abnormal transaction behavior defined before occurs or not, and discovering rules and mining according to the abnormal transaction behavior.
The front-edge technology refers to related technologies such as related network building and big data computing capacity in the whole process, and the purpose of finding out an abnormal transaction mode is finally achieved through mutual matching. And then, formulating a preliminary abnormal transaction rule, performing cross validation through experience inside and outside the financial institution, and finally outputting an abnormal transaction mode, namely the whole process of mining the abnormal transaction mode.
The suspected associated transaction mode refers to a mode for monitoring risk transactions such as capital transfer of stockholders, cash flow for supply and sale, platform financing and the like through a transaction association relationship network.
As shown in table 1, the index is classified as a basic statistical index of a basic index (outflow), and the index name thereof may be: the maximum amount of money is discharged by a single fund within 15 days after the enterprise application is credited, the maximum number of times of money discharge of a single institution per day within 15 days after the enterprise application is credited, the proportion of the total amount of money discharged within 15 days after the enterprise application is credited to the total debit amount, the maximum amount of money is discharged by a single fund within 30 days after the enterprise application is credited, and the maximum number of times of money discharge of a single institution per day within 30 days after the enterprise application is credited.
TABLE 1
| Index classification | Index name |
| Basic index (outflow) | The maximum amount of money flows out by a single fund within 15 days after the enterprise loan |
| Basic index (outflow) | Maximum daily single institution fund flow within 15 days after enterprise loan application |
| Basic index (outflow) | The ratio of the total amount of the capital output to the total amount of the borrowed data within 15 days after the loan of the applied enterprise |
| Basic index (outflow) | The maximum amount of money flows out from a single fund within 30 days after the enterprise is applied for loan |
| Basic index (outflow) | Daily single institution funds outflow maximum number within 30 days after enterprise loan application |
As shown in table 2, rules one to four in the suspected abnormal transaction rules, and corresponding rule contents and business meanings are exemplarily shown.
TABLE 2
As an optional embodiment, the method may further include: after risk identification of the first object, the following operations are performed: and if the first object is determined to be the risk object, displaying a processing strategy corresponding to the risk level based on the risk level of the first object aiming at the first object. For example, in the pre-loan risk screening, the credit granting of the first-class risk object may be prohibited, and in the post-loan risk early warning, the business personnel may be prompted to call back to the first-class risk object to further know the repayment capability of the risk object.
Fig. 5 schematically shows a block diagram of an apparatus for identifying a risk object according to an embodiment of the present disclosure.
As shown in fig. 5, the apparatus 500 for identifying a risk object includes a first determining module 501, a second determining module 502 and a risk identifying module 503. The apparatus for identifying a risk object may perform the method described above with reference to the method embodiment, and is not described herein again.
In particular, 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.
A risk identification module 503, configured to perform risk identification on the first object based on an association relationship between the first object and the at least one second object.
As an alternative embodiment, the risk identification module comprises: the device 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 a first object and at least one second object.
And the first extraction unit is used for extracting the corresponding transaction flow indexes based on the transaction flow data.
And the first verification unit is used for verifying whether the corresponding transaction flow indexes have the transaction indexes and/or the combination of the transaction indexes matched with the preset abnormal transaction rules.
Or, as another alternative embodiment, the risk identification module includes:
specifically, the second obtaining unit is used for obtaining a data map for describing a 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 indexes based on the data map.
And the second verification unit is used for verifying whether the corresponding transaction flow indexes have the transaction indexes and/or the combination of the transaction indexes matched with the preset abnormal transaction rules.
As an alternative embodiment, the preset abnormal transaction rule may include: a suspected abnormal transaction model and/or a suspected associated transaction model.
As an optional embodiment, the apparatus may further include: the device comprises a filtering module and an extracting module.
Specifically, the filtering module is configured to filter out transaction flow data of a specified type from the transaction flow data before extracting a corresponding transaction flow index based on the transaction flow data.
And the extraction module is used for extracting the corresponding transaction flow indexes based on the residual transaction flow data.
As an alternative embodiment, the transaction relationship may include: substantive transaction relationships and derivative transaction relationships. Wherein the substantive transaction relationship further may include funds outflow information and funds inflow information. The derived transaction relationships further may include at least one of: transaction frequency, transaction amount, and transaction type.
As an alternative embodiment, the entity relationship may include: business relations and personal relations.
As an optional embodiment, the apparatus may further include: and a display module. Specifically, the presentation module is configured to, after performing risk identification on the first object, present, 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.
It should be noted that the embodiments of the apparatus portion and the method portion of the present disclosure are the same or similar, and the achieved technical effects are also the same or similar, which are not described herein again.
Any of the modules, units, or at least part of the functionality of any of them according to embodiments of the present disclosure may be implemented in one module. Any one or more of the modules and units according to the embodiments of the present disclosure may be implemented by being split into a plurality of modules. Any one or more of the modules, units according to the embodiments of the present disclosure may be implemented at least partially 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 by any other reasonable means of hardware or firmware by integrating or packaging the circuits, or in any one of three implementations of software, hardware and firmware, or in any suitable combination of any of them. Alternatively, one or more of the modules, units according to embodiments of the present disclosure may be implemented at least partly as computer program modules, which, when executed, may perform the respective functions.
For example, any plurality of the first determining module 501, the second determining module 502, and the risk identifying module 503 may be combined and implemented in one module, or any one of them may be split into a plurality of modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the first determining module 501, the second determining module 502, and the risk identifying module 503 may be implemented at least partially 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 manner of integrating or packaging a circuit, or in any one of three implementations of software, hardware, and firmware, or in any suitable combination of any of them. Alternatively, at least one of the first determining module 501, the second determining module 502 and the risk identification module 503 may be at least partly implemented as a computer program module, which when executed may perform a corresponding function.
Fig. 6 schematically shows a block diagram of an electronic device according to an embodiment of the disclosure. The electronic device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
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 a method according to an embodiment of the present disclosure.
In particular, the processor 610 may comprise, for example, a general purpose microprocessor, an instruction set processor and/or related chip set and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), or the like. The processor 610 may also include onboard memory for caching purposes. The processor 610 may be a single processing unit or a plurality of processing units for performing the different actions of the method flows according to embodiments of the present disclosure.
Computer-readable storage medium 620, for example, may be a non-volatile computer-readable storage medium, specific examples including, but not limited to: magnetic storage devices, such as magnetic tape or Hard Disk Drives (HDDs); optical storage devices, such as compact disks (CD-ROMs); a memory, such as a Random Access Memory (RAM) or a flash memory; and so on.
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 disclosure, or any variation thereof.
The computer program 621 may be configured with, for example, computer program code comprising computer program modules. For example, in an example embodiment, code in computer program 621 may include one or more program modules, including 621A, 621B, … …, for example. It should be noted that the division and number of the modules are not fixed, and those skilled in the art may use suitable program modules or program module combinations according to actual situations, so that the processor 610 may execute the method according to the embodiment of the present disclosure or any variation thereof when the program modules are executed by the processor 610.
According to an embodiment of the present disclosure, at least one of the first determining module 501, the second determining module 502 and the risk identification module 503 may be implemented as a computer program module described with reference to fig. 6, which, when executed by the processor 610, may implement the respective operations described above.
The present disclosure also provides a computer-readable storage medium, which may be contained in the apparatus/device/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer-readable storage medium carries one or more programs which, when executed, implement the method according to an embodiment of the disclosure.
The flowchart 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 certain 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. Accordingly, the scope of the present disclosure should not be limited to the above-described embodiments, but should be defined not only by the appended claims, but also by equivalents thereof.
Claims (11)
1. A method of identifying a risk object, comprising:
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
and performing risk identification on the first object based on the incidence relation between the first object and the at least one second object.
2. The method of claim 1, wherein the risk identification of the first object based on the associative relationship 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 a corresponding transaction flow index based on the transaction flow data; and
and verifying whether the corresponding transaction flow indexes have transaction indexes and/or combination of transaction indexes matched with preset abnormal transaction rules.
3. The method of claim 1, wherein the risk identification of the first object based on the associative relationship between the first object and the at least one second object comprises:
obtaining a data map describing a transaction relationship between the first object and the at least one second object;
extracting corresponding transaction flow indexes based on the data map; and
and verifying whether the corresponding transaction flow indexes have transaction indexes and/or combination of transaction indexes matched with preset abnormal transaction rules.
4. The method of claim 2 or 3, wherein the preset exception transaction rule comprises: a suspected abnormal transaction model and/or a suspected associated transaction model.
5. The method of claim 2, further comprising: prior to extracting a corresponding transaction pipeline metric based on the transaction pipeline data,
filtering out transaction flow data of a specified type from the transaction flow data; and
corresponding transaction flow indicators are extracted based on the remaining transaction flow data.
6. The method of claim 1, wherein the transaction relationship comprises: a substantive transaction relationship and a derivative transaction relationship, wherein the substantive transaction relationship includes funds outflow information and funds inflow information, the derivative transaction relationship including at least one of: transaction frequency, transaction amount, and transaction type.
7. The method of claim 1, wherein the entity relationship comprises: business relations and personal relations.
8. 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 strategy corresponding to the risk level based on the risk level of the first object aiming at the first object.
9. An apparatus for identifying a risk object, comprising:
a first determination module for determining a first object;
a second determination 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; and
and the risk identification module is used for carrying out risk identification on the first object based on the incidence relation between the first object and the at least one second object.
10. 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 1-8.
11. A computer-readable storage medium storing computer-executable instructions for implementing the method of any one of claims 1 to 8 when executed.
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