CN112990919A - Information processing method and device - Google Patents
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Abstract
The invention discloses an information processing method and a device, wherein the method comprises the steps of acquiring transaction data of all cardholders in a preset time period, analyzing the transaction data of all cardholders in the preset time period, determining a plurality of cardholder relationship network communities, determining credit card consumption transaction data and debit card payment transaction data in the cardholder relationship network communities aiming at each cardholder relationship network community, and determining abnormal data in the cardholder relationship network communities according to the credit card consumption transaction data and the debit card payment transaction data. By acquiring the transaction data of all cardholders of each commercial bank in a preset time period, carrying out community division on each transaction data based on graph calculation to obtain the transaction data belonging to the same community, and then carrying out abnormal data detection on the transaction data of the same community, the accuracy and the coverage of abnormal data detection can be improved, the efficiency of system abnormal data detection can be improved, and the system power consumption is reduced.
Description
Technical Field
The embodiment of the invention relates to the technical field of data processing, in particular to an information processing method and device.
Background
The abnormal use of the credit card can lead a cardholder to obtain high funds in a short time, and the abnormal use behavior is forbidden under the temptation of high income, thereby increasing the risk of card issuing banks and increasing the unstable factors of financial order in China. How to start with transaction data of a cardholder, accurately identify abnormal data, deeply mine consumption repayment capacity and credit degree of a customer, better provide card issuing service and obtain more profit points; and the false commercial tenants and mechanisms which are used abnormally in a large scale are attacked, the payment environment is purified, and the method becomes a new concern of all large banks and unions of bank. However, data barriers between commercial banks are built at high levels, each message island is formed, abnormal use behaviors can be identified only according to transaction data characteristics of a local bank, and the problems that fund directions of cross-bank transactions cannot be tracked, and abnormal use scenes of one person with multiple cards and multiple persons with multiple cards cannot be covered exist.
Disclosure of Invention
The embodiment of the invention provides an information processing method and device, which are used for improving the accuracy and the coverage of information processing.
In a first aspect, an embodiment of the present invention provides an information processing method, including:
acquiring transaction data of all cardholders in a preset time period;
analyzing the transaction data of all cardholders in the preset time period to determine a plurality of cardholder relationship network communities;
determining credit card consumption transaction data and debit card payment-instead transaction data in each cardholder relationship network community; and determining abnormal data in the cardholder relationship network community according to the credit card consumption transaction data and the debit card payment-substituting transaction data.
According to the technical scheme, the transaction data of all cardholders of all commercial banks in the preset time period are obtained, community division is carried out on the transaction data based on graph calculation to obtain the transaction data belonging to the same community, then abnormal data detection is carried out on the transaction data of the same community, the accuracy and coverage of the abnormal data detection can be improved, the efficiency of system abnormal data detection can be improved, and the system power consumption is reduced.
Optionally, the analyzing the transaction data of all cardholders in the preset time period to determine a plurality of cardholder relationship network communities includes:
acquiring transaction data of all cardholders in the preset time period; the transaction data comprises a bank card number, a mobile phone number, an identification card number, a device number and an application program user number;
taking the bank card number, the mobile phone number, the identification card number, the equipment number and the application program user number in the transaction data as vertexes, and connecting the mobile phone number, the identification card number, the equipment number and the application program user number in the same transaction data with the bank card number to obtain a cardholder relation network diagram;
and carrying out community division on the cardholder relationship network graph to obtain a plurality of cardholder relationship network communities.
Optionally, the community division is performed on the cardholder relationship network diagram to obtain a plurality of cardholder relationship network communities, including:
traversing each vertex according to the cardholder relationship network graph, determining a plurality of cardholder relationship network graphs with connecting edges and setting a community initial attribute value of each vertex;
iteratively updating the community initial attribute value of each vertex in each cardholder relationship network diagram with a connecting edge until the community attribute values of all the vertices in each cardholder relationship network diagram with a connecting edge are changed into the minimum community attribute value in each cardholder relationship network diagram with a connecting edge, and determining the minimum community attribute value as the community ID of each cardholder relationship network diagram with a connecting edge; updating the community attribute value of the vertex to be the minimum value of the community attribute values of all first-order neighbor vertices and the community attribute value of the vertex per se in each iteration;
and associating the community ID with the transaction data of each vertex in each cardholder relationship network graph with the connecting edges to obtain the plurality of cardholder relationship network communities.
Optionally, the iteratively updating the community initial attribute value of each vertex in each cardholder relationship network diagram having a connection edge includes:
counting the number of first-order neighbor vertexes of each vertex;
if the number of the first-order neighbor vertexes is smaller than a preset data processing threshold value, recording the first-order neighbor vertexes of each vertex in a first set to perform iterative updating of community initial attribute values; otherwise, recording the first-order neighbor vertex of each vertex in a second set, and initializing to a driving end (driver); and broadcasting the vertex in the second set to other thread pools (executors) so that the other executors obtain the vertex from the second set to perform iterative update of the community initial attribute value.
Optionally, the determining abnormal data in the cardholder relationship network community according to the credit card consumption transaction data and the debit card payment-replacement transaction data includes:
determining credit card consumption transaction data and debit card payment transaction data of which the percentage of the amount difference is smaller than a first threshold and the absolute value of the transaction time difference is smaller than a second threshold from the credit card consumption transaction data and the debit card payment transaction data in the cardholder relationship network community, and determining the credit card consumption transaction data and the debit card payment transaction data of which the percentage of the amount difference is smaller than the first threshold and the absolute value of the transaction time difference is smaller than the second threshold as abnormal data in the cardholder relationship network community;
after credit card consumption transaction data and debit card payment-replacement transaction data of which the percentage of the amount difference is smaller than a first threshold and the absolute value of the transaction time difference is smaller than a second threshold are removed from the credit card consumption transaction data and the debit card payment-replacement transaction data in the cardholder relationship network community, traversing each payment-replacement transaction data to obtain a community ID, a payment-replacement amount and payment-replacement time corresponding to each payment-replacement transaction data;
traversing consumption transaction data of each credit card under the community ID to obtain a transaction set;
determining a transaction set subset with the smallest absolute value of the difference between the sum of the transaction amounts and the payment amount from the transaction set;
and if the percentage of the absolute value of the difference between the sum of the transaction amounts of the transaction set subset and the payment amount is smaller than the first threshold and the difference between the transaction time and the payment time in the transaction set subset is smaller than the second threshold, determining the consumption transaction data of the credit card in the transaction set subset and the traversed payment transaction data as abnormal data in the cardholder relationship network community.
In a second aspect, an embodiment of the present invention provides an information processing apparatus, including:
the acquisition unit is used for acquiring the transaction data of all cardholders in a preset time period;
the processing unit is used for analyzing the transaction data of all cardholders in the preset time period and determining a plurality of cardholder relationship network communities; determining credit card consumption transaction data and debit card payment-instead transaction data in each cardholder relationship network community; and determining abnormal data in the cardholder relationship network community according to the credit card consumption transaction data and the debit card payment-substituting transaction data.
Optionally, the processing unit is specifically configured to:
acquiring transaction data of all cardholders in the preset time period; the transaction data comprises a bank card number, a mobile phone number, an identification card number, a device number and an application program user number;
taking the bank card number, the mobile phone number, the identification card number, the equipment number and the application program user number in the transaction data as vertexes, and connecting the mobile phone number, the identification card number, the equipment number and the application program user number in the same transaction data with the bank card number to obtain a cardholder relation network diagram;
and carrying out community division on the cardholder relationship network graph to obtain a plurality of cardholder relationship network communities.
Optionally, the processing unit is specifically configured to:
traversing each vertex according to the cardholder relationship network graph, determining a plurality of cardholder relationship network graphs with connecting edges and setting a community initial attribute value of each vertex;
iteratively updating the community initial attribute value of each vertex in each cardholder relationship network diagram with a connecting edge until the community attribute values of all the vertices in each cardholder relationship network diagram with a connecting edge are changed into the minimum community attribute value in each cardholder relationship network diagram with a connecting edge, and determining the minimum community attribute value as the community ID of each cardholder relationship network diagram with a connecting edge; updating the community attribute value of the vertex to be the minimum value of the community attribute values of all first-order neighbor vertices and the community attribute value of the vertex per se in each iteration;
and associating the community ID with the transaction data of each vertex in each cardholder relationship network graph with the connecting edges to obtain the plurality of cardholder relationship network communities.
Optionally, the processing unit is specifically configured to:
counting the number of first-order neighbor vertexes of each vertex;
if the number of the first-order neighbor vertexes is smaller than a preset data processing threshold value, recording the first-order neighbor vertexes of each vertex in a first set to perform iterative updating of community initial attribute values; otherwise, recording the first-order neighbor vertex of each vertex in a second set, and initializing to a driver; and broadcasting the vertex in the second set to other executors so that the other executors obtain the vertex from the second set to perform iterative update of the community initial attribute value.
Optionally, the processing unit is specifically configured to:
determining credit card consumption transaction data and debit card payment transaction data of which the percentage of the amount difference is smaller than a first threshold and the absolute value of the transaction time difference is smaller than a second threshold from the credit card consumption transaction data and the debit card payment transaction data in the cardholder relationship network community, and determining the credit card consumption transaction data and the debit card payment transaction data of which the percentage of the amount difference is smaller than the first threshold and the absolute value of the transaction time difference is smaller than the second threshold as abnormal data in the cardholder relationship network community;
after credit card consumption transaction data and debit card payment-replacement transaction data of which the percentage of the amount difference is smaller than a first threshold and the absolute value of the transaction time difference is smaller than a second threshold are removed from the credit card consumption transaction data and the debit card payment-replacement transaction data in the cardholder relationship network community, traversing each payment-replacement transaction data to obtain a community ID, a payment-replacement amount and payment-replacement time corresponding to each payment-replacement transaction data;
traversing consumption transaction data of each credit card under the community ID to obtain a transaction set;
determining a transaction set subset with the smallest absolute value of the difference between the sum of the transaction amounts and the payment amount from the transaction set;
and if the percentage of the absolute value of the difference between the sum of the transaction amounts of the transaction set subset and the payment amount is smaller than the first threshold and the difference between the transaction time and the payment time in the transaction set subset is smaller than the second threshold, determining the consumption transaction data of the credit card in the transaction set subset and the traversed payment transaction data as abnormal data in the cardholder relationship network community.
In a third aspect, an embodiment of the present invention further provides a computing device, including:
a memory for storing program instructions;
and the processor is used for calling the program instructions stored in the memory and executing the information processing method according to the obtained program.
In a fourth aspect, the embodiment of the present invention further provides a computer-readable non-volatile storage medium, which includes computer-readable instructions, and when the computer-readable instructions are read and executed by a computer, the computer is caused to execute the above-mentioned information processing method.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic diagram of a system architecture according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating an information processing method according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a cardholder relationship network according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating an exemplary iterative update of community attribute values according to an embodiment of the present invention;
FIG. 5 is a diagram illustrating an exemplary iterative update of community attribute values according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of an information processing apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the present invention will be described in further detail with reference to the accompanying drawings, and it is apparent that the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 illustrates an exemplary system architecture to which embodiments of the present invention may be applied, which may be a server 100, and the server 100 may include a processor 110, a communication interface 120, and a memory 130.
The communication interface 120 is used for communicating with the servers of the large commercial banks, and transceiving data transmitted by the servers of the large commercial banks to realize communication.
The processor 110 is a control center of the server 100, connects various parts of the entire server 100 using various interfaces and routes, performs various functions of the server 100 and processes data by operating or executing software programs and/or modules stored in the memory 130 and calling data stored in the memory 130. Alternatively, processor 110 may include one or more processing units.
The memory 130 may be used to store software programs and modules, and the processor 110 executes various functional applications and data processing by operating the software programs and modules stored in the memory 130. The memory 130 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to a business process, and the like. Further, the memory 130 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
It should be noted that the structure shown in fig. 1 is only an example, and the embodiment of the present invention is not limited thereto.
Based on the above description, fig. 2 shows in detail a flow of a method for information processing according to an embodiment of the present invention, where the flow may be executed by an information processing apparatus, which may be located in the server 100 or the server 100.
As shown in fig. 2, the process specifically includes:
in step 201, transaction data of all cardholders in a preset time period is acquired.
In the embodiment of the invention, the transaction data of all cardholders of each commercial bank can be acquired, wherein the transaction data can comprise a bank card number, a mobile phone number, an identity card number, a device number and an application program user number, and the application program user number can be a cloud flash payment user number.
The Chinese Union of Pay is a union organization of Chinese bank cards, and interconnection and intercommunication and resource sharing among commercial bank systems are realized through a Union of Pay and Cross-bank transaction clearing system, so that cross-bank, cross-regional and cross-border use of the bank cards is ensured. China Unionpay has a large amount of transaction consumption data of cardholders, particularly has a large amount of accumulation on cross-bank transaction data, and the large amount of data can break the current situation of information isolated islands between commercial banks.
The card binding of all channels, the card binding of all channels of all channel commercial tenants, the card binding of cloud flash payment APP registration and binding, the card binding of mobile phone flash payment registration, the card binding of unified users and the relevant information of the card holders in the universal user data can be integrated into a card holder relationship table after duplication removal through various communication interfaces, and fields comprise: the system comprises a bank card number, a mobile phone number, an identity card, an equipment number and a cloud flash payment user number.
The preset time period may be set empirically, and may be, for example, a week, a month, a quarter, and the like.
Step 202, analyzing the transaction data of all cardholders in the preset time period, and determining a plurality of cardholder relationship network communities.
Specifically, when a plurality of cardholder relationship network communities are obtained, the bank card number, the mobile phone number, the identification number, the device number and the application user number in the transaction data may be used as vertexes, and the mobile phone number, the identification number, the device number and the application user number in the same transaction data may be connected with the bank card number to obtain the cardholder relationship network map. And then carrying out community division on the cardholder relationship network graph to obtain a plurality of cardholder relationship network communities. When community division is carried out, traversing each vertex according to the cardholder relationship network graph, determining a plurality of cardholder relationship network graphs with connecting edges and setting a community initial attribute value of each vertex, then carrying out iterative updating on the community initial attribute value of each vertex in the cardholder relationship network graph with each connecting edge until the community attribute values of all the vertices in the cardholder relationship network graph with each connecting edge are changed into the minimum community attribute value in the cardholder relationship network graph with each connecting edge, and determining the minimum community attribute value as the community ID of the cardholder relationship network graph with each connecting edge. And finally, associating the community ID with the transaction data of each vertex in each cardholder relationship network graph with the connecting edges to obtain a plurality of cardholder relationship network communities.
For example, by using all fields in the above cardholder relation table as vertices, the fields appearing in the same record are connected to the card number, and using spark graph x technology, a network diagram of cardholder relations (such as shown in fig. 3) containing hundreds of millions of vertices and hundreds of millions of edges is constructed, and can be updated all at once every month.
Then, based on the cardholder relationship network diagram shown in fig. 3, a connected component algorithm and a Louvain algorithm can be adopted to perform community division, and the specific steps are as follows:
the method is characterized in that a spark graph x technology is adopted to calculate a connected body of a whole graph, initially, the community initial attribute value of each vertex has a unique integer id value, each point only considers the value of the point and all neighbors with connecting edges, the value of the point is updated to be the minimum value of the id value of the point and the id values of all first-order neighbors in each iteration, and the calculation process can be as shown in fig. 4. After iterative convergence, the community attribute values of all the vertexes of the same community become the minimum ID value of the vertexes in the community, and the minimum ID value is used as the community ID of the cardholder relationship network community.
In order to solve the technical problem, the number of first-order neighbor vertices of each vertex may be specifically counted first when the initial attribute value of the community is updated iteratively. Then judging whether the number of the first-order neighbor vertexes is smaller than a preset data processing threshold value, if so, recording the first-order neighbor vertexes of each vertex in a first set to perform iterative updating of the community initial attribute value; otherwise, recording the first-order neighbor vertices of each vertex in a second set and initializing to a drive end (driver); and broadcasting the vertex in the second set to other thread pools (executors) so that the other executors can acquire the vertex from the second set to carry out iterative updating of the community initial attribute value. The preset data processing threshold may be determined according to the memory of the thread pool.
For example, as shown in fig. 5, the following steps may be performed when performing the iterative update:
1. the first order neighbor vertex number cnt [ D ] of each vertex D (data) is counted.
2. The data processing threshold N is estimated according to the memory size of the executor (thread pool).
3. If cnt [ D ] < N, record the point in set A; if cnt [ D ] > < N, it is recorded in set B.
4. The vertex in the set A transmits an update value through an edge; and initializing the vertex and the value in the set B to a driver (driving) end through sc.broadcast, and broadcasting to each execution, so that each execution only needs to acquire the value of the vertex in the set B from a memory to update.
5. Checking the updated vertex number and recording as check;
6. if check is greater than 0, repeating the steps 1-5; otherwise, the iteration is finished and the result is output.
If the spark encounters memory resource problem in the calculation process, the normal calculation process can be ensured by clearing the older memory data which is not persisted. In the process of calculating large data volume, due to the limitation of memory resources, part of useful temporary data is cleared by errors and needs to be recalculated, so that checkpoint is performed on the whole image data once in each iteration, the data quality is ensured, and the calculation speed is accelerated.
Through the strategy, the time of the connected component algorithm under the large data volume is reduced by 50 to 70 percent compared with the self-contained algorithm in the graph X, and meanwhile, the network transmission and the memory consumption are reduced to a certain extent.
And importing the community ID obtained in the steps into a cardholder relationship network gallery to generate a cardholder community table (namely the cardholder relationship network community) which comprises three fields of an attribute value, an attribute type and a community ID and supports community ID inquiry of any bank card, mobile phone number, identity card, equipment and cloud flash payment APP user number. The attribute value is a value corresponding to the attribute type, for example, if the attribute type is a bank card, the attribute value is a bank card number, and if the attribute type is an identity card, the attribute value is an identity card number.
The community division method can divide a 25 hundred million vertex and 60 hundred million edge relationship network into 3.9 hundred million communities within 3 hours. All elements that exist in the same community are considered a community of interest, containing information about individuals, relatives and friends.
Step 203, determining credit card consumption transaction data and debit card payment-as-a-place transaction data in each cardholder relationship network community; and determining abnormal data in the cardholder relationship network community according to the credit card consumption transaction data and the debit card payment-substituting transaction data.
The abnormal data is the data generated by the abnormal use of the credit card.
For each cardholder relationship network community, one-to-one transaction matching and many-to-one transaction matching can be performed, specifically as follows:
one-to-one transaction matching:
and determining credit card consumption transaction data and debit card payment transaction data of which the percentage of the amount difference is smaller than a first threshold value and the absolute value of the transaction time difference is smaller than a second threshold value from the credit card consumption transaction data and the debit card payment transaction data in the cardholder relationship network community, and determining the credit card consumption transaction data and the debit card payment transaction data of which the percentage of the amount difference is smaller than the first threshold value and the absolute value of the transaction time difference is smaller than the second threshold value as abnormal data in the cardholder relationship network community.
Many-to-one transaction matching:
after credit card consumption transaction data and debit card payment-replacement transaction data with the percentage of the amount difference smaller than a first threshold and the absolute value of the transaction time difference smaller than a second threshold are removed from credit card consumption transaction data and debit card payment-replacement transaction data in a cardholder relationship network community, traversing each payment-replacement transaction data to obtain a community ID, a payment-replacement amount and payment-replacement time corresponding to the payment-replacement transaction data.
Traversing consumption transaction data of each credit card under the community ID to obtain a transaction set; determining a transaction set subset with the smallest absolute value of the difference between the sum of the transaction amounts and the payment amount from the transaction set; and if the percentage of the absolute value of the difference between the sum of the transaction amounts of the subset of the transaction set and the payment amount is smaller than a first threshold value and the difference between the transaction time and the payment time in the subset of the transaction set is smaller than a second threshold value, determining the consumption transaction data of the credit card in the subset of the transaction set and the traversed payment transaction data as abnormal data in the cardholder relationship network community.
The first threshold and the second threshold may be set empirically.
Specifically, based on the business characteristics of "different cards in the same community, different merchants perform the transaction of first consumption and then payment, and the transaction amount is close" in a short time, information processing can be performed, specifically:
credit card consumption transaction data and debit card payment transaction data are determined from the cardholder relationship network community.
One-to-one matching: traversing credit card consumption transactions and debit card payment replacement transactions in the same community, if the amounts of the two transactions differ by a percentage less than a threshold r (a first threshold) and the absolute value of the transaction time difference is less than a threshold T (a second threshold), marking the two transactions as one-to-one abnormal use pairing transactions.
Many-to-one matching:
removing the consumption transaction and the payment-for-replacement transaction detected in the one-to-one matching from the cardholder relationship network community, then traversing each payment-for-replacement transaction trans1 to obtain the community ID, the payment amount trans _ at1 and the payment-for-replacement time 1; traversing each credit card under the community ID, recording a set of all consumption transactions generated by the credit card as trans _ set2, screening a subset trans _ set2_ sub in trans _ set2 by adopting a backtracking + bound method so that the absolute value of the difference between the sum of the transaction amount in trans _ set2_ sub and trans _ at1 is minimum, and recording trans _ at _ set2_ sub as a set of all transaction amounts in trans _ set2_ sub.
If the following two conditions are satisfied:
①、|sum(trans_at_set2_sub)-trans_at1|/sum(trans_at_set2_sub)<r；
② the maximum value of the time difference between two transactions in trans _ at _ set2_ sub and trans1 < T;
then match is a many-to-one exception usage pairing transaction (trans _ at _ set2_ sub, trans 1).
Because a polynomial time algorithm does not exist, the recursion times are controlled to be 10 hundred million times by adopting a backtracking and limiting method in the embodiment of the invention. The whole algorithm is realized by adopting MR, and abnormal data detection of about 30 hundred million transaction data in one week can be completed within 2 hours. Taking XX days 6 months to XX days 6 months in 2019 as an example, the detection results are as follows: 376 bill collecting headquarters, 4959103 merchants and 7028480 communities are used for suspected abnormality; 23685132 consumption transactions of suspected abnormal data, wherein the amount reaches 1000 hundred million yuan; 25736050 transactions of payment for suspected abnormal use, the amount of the transactions reaches 1100 hundred million yuan, and the result of the model is approved by the service operation center.
The above embodiment shows that the transaction data of all cardholders in a preset time period is acquired, the transaction data of all cardholders in the preset time period is analyzed, a plurality of cardholder relationship network communities are determined, credit card consumption transaction data and debit card payment replacement transaction data in the cardholder relationship network communities are determined for each cardholder relationship network community, and abnormal data in the cardholder relationship network communities are determined according to the credit card consumption transaction data and the debit card payment replacement transaction data. By acquiring the transaction data of all cardholders of each commercial bank in a preset time period, carrying out community division on each transaction data based on graph calculation to obtain the transaction data belonging to the same community, and then carrying out abnormal data detection on the transaction data of the same community, the accuracy and the coverage of abnormal data detection can be improved, the efficiency of system abnormal data detection can be improved, and the system power consumption is reduced.
Based on the same technical concept, fig. 6 exemplarily shows a structure of an information processing apparatus, which may execute a flow of information processing, according to an embodiment of the present invention, and the apparatus may be the server 100 shown in fig. 1 or the server 100.
As shown in fig. 6, the apparatus specifically includes:
an acquisition unit 601, configured to acquire transaction data of all cardholders in a preset time period;
the processing unit 602 is configured to analyze transaction data of all cardholders in the preset time period, and determine a plurality of cardholder relationship network communities; determining credit card consumption transaction data and debit card payment-instead transaction data in each cardholder relationship network community; and determining abnormal data in the cardholder relationship network community according to the credit card consumption transaction data and the debit card payment-substituting transaction data.
Optionally, the processing unit 602 is specifically configured to:
acquiring transaction data of all cardholders in the preset time period; the transaction data comprises a bank card number, a mobile phone number, an identification card number, a device number and an application program user number;
taking the bank card number, the mobile phone number, the identification card number, the equipment number and the application program user number in the transaction data as vertexes, and connecting the mobile phone number, the identification card number, the equipment number and the application program user number in the same transaction data with the bank card number to obtain a cardholder relation network diagram;
and carrying out community division on the cardholder relationship network graph to obtain a plurality of cardholder relationship network communities.
Optionally, the processing unit 602 is specifically configured to:
traversing each vertex according to the cardholder relationship network graph, determining a plurality of cardholder relationship network graphs with connecting edges and setting a community initial attribute value of each vertex;
iteratively updating the community initial attribute value of each vertex in each cardholder relationship network diagram with a connecting edge until the community attribute values of all the vertices in each cardholder relationship network diagram with a connecting edge are changed into the minimum community attribute value in each cardholder relationship network diagram with a connecting edge, and determining the minimum community attribute value as the community ID of each cardholder relationship network diagram with a connecting edge; updating the community attribute value of the vertex to be the minimum value of the community attribute values of all first-order neighbor vertices and the community attribute value of the vertex per se in each iteration;
and associating the community ID with the transaction data of each vertex in each cardholder relationship network graph with the connecting edges to obtain the plurality of cardholder relationship network communities.
Optionally, the processing unit 602 is specifically configured to:
counting the number of first-order neighbor vertexes of each vertex;
if the number of the first-order neighbor vertexes is smaller than a preset data processing threshold value, recording the first-order neighbor vertexes of each vertex in a first set to perform iterative updating of community initial attribute values; otherwise, recording the first-order neighbor vertex of each vertex in a second set, and initializing to a drive end driver; and broadcasting the vertex in the second set to other thread pools executors so that the other executors obtain the vertex from the second set to perform iterative updating of the community initial attribute value.
Optionally, the processing unit 602 is specifically configured to:
determining credit card consumption transaction data and debit card payment transaction data of which the percentage of the amount difference is smaller than a first threshold and the absolute value of the transaction time difference is smaller than a second threshold from the credit card consumption transaction data and the debit card payment transaction data in the cardholder relationship network community, and determining the credit card consumption transaction data and the debit card payment transaction data of which the percentage of the amount difference is smaller than the first threshold and the absolute value of the transaction time difference is smaller than the second threshold as abnormal data in the cardholder relationship network community;
after credit card consumption transaction data and debit card payment-replacement transaction data of which the percentage of the amount difference is smaller than a first threshold and the absolute value of the transaction time difference is smaller than a second threshold are removed from the credit card consumption transaction data and the debit card payment-replacement transaction data in the cardholder relationship network community, traversing each payment-replacement transaction data to obtain a community ID, a payment-replacement amount and payment-replacement time corresponding to each payment-replacement transaction data;
traversing consumption transaction data of each credit card under the community ID to obtain a transaction set;
determining a transaction set subset with the smallest absolute value of the difference between the sum of the transaction amounts and the payment amount from the transaction set;
and if the percentage of the absolute value of the difference between the sum of the transaction amounts of the transaction set subset and the payment amount is smaller than the first threshold and the difference between the transaction time and the payment time in the transaction set subset is smaller than the second threshold, determining the consumption transaction data of the credit card in the transaction set subset and the traversed payment transaction data as abnormal data in the cardholder relationship network community.
Based on the same technical concept, an embodiment of the present invention further provides a computing device, including:
a memory for storing program instructions;
and the processor is used for calling the program instructions stored in the memory and executing the information processing method according to the obtained program.
Based on the same technical concept, embodiments of the present invention also provide a computer-readable non-volatile storage medium, which includes computer-readable instructions, and when the computer-readable instructions are read and executed by a computer, the computer is caused to execute the above-mentioned information processing method.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (12)
1. A method of information processing, comprising:
acquiring transaction data of all cardholders in a preset time period;
analyzing the transaction data of all cardholders in the preset time period to determine a plurality of cardholder relationship network communities;
determining credit card consumption transaction data and debit card payment-instead transaction data in each cardholder relationship network community; and determining abnormal data in the cardholder relationship network community according to the credit card consumption transaction data and the debit card payment-substituting transaction data.
2. The method of claim 1, wherein analyzing the transaction data of all cardholders within the preset time period to determine a plurality of cardholder relationship network communities comprises:
acquiring transaction data of all cardholders in the preset time period; the transaction data comprises a bank card number, a mobile phone number, an identification card number, a device number and an application program user number;
taking the bank card number, the mobile phone number, the identification card number, the equipment number and the application program user number in the transaction data as vertexes, and connecting the mobile phone number, the identification card number, the equipment number and the application program user number in the same transaction data with the bank card number to obtain a cardholder relation network diagram;
and carrying out community division on the cardholder relationship network graph to obtain a plurality of cardholder relationship network communities.
3. The method of claim 2, wherein the community partitioning of the cardholder relationship network graph into a plurality of cardholder relationship network communities comprises:
traversing each vertex according to the cardholder relationship network graph, determining a plurality of cardholder relationship network graphs with connecting edges and setting a community initial attribute value of each vertex;
iteratively updating the community initial attribute value of each vertex in each cardholder relationship network diagram with a connecting edge until the community attribute values of all the vertices in each cardholder relationship network diagram with a connecting edge are changed into the minimum community attribute value in each cardholder relationship network diagram with a connecting edge, and determining the minimum community attribute value as the community ID of each cardholder relationship network diagram with a connecting edge; updating the community attribute value of the vertex to be the minimum value of the community attribute values of all first-order neighbor vertices and the community attribute value of the vertex per se in each iteration;
and associating the community ID with the transaction data of each vertex in each cardholder relationship network graph with the connecting edges to obtain the plurality of cardholder relationship network communities.
4. The method of claim 3, wherein iteratively updating the community initial attribute value for each vertex in each cardholder relationship network graph for which a connecting edge exists comprises:
counting the number of first-order neighbor vertexes of each vertex;
if the number of the first-order neighbor vertexes is smaller than a preset data processing threshold value, recording the first-order neighbor vertexes of each vertex in a first set to perform iterative updating of community initial attribute values; otherwise, recording the first-order neighbor vertex of each vertex in a second set, and initializing to a drive end driver; and broadcasting the vertex in the second set to other thread pools executors so that the other executors obtain the vertex from the second set to perform iterative updating of the community initial attribute value.
5. The method of claim 1, wherein said determining anomalous data in said cardholder relationship network community based on said credit card consumption transaction data and said debit card payment transaction data comprises:
determining credit card consumption transaction data and debit card payment transaction data of which the percentage of the amount difference is smaller than a first threshold and the absolute value of the transaction time difference is smaller than a second threshold from the credit card consumption transaction data and the debit card payment transaction data in the cardholder relationship network community, and determining the credit card consumption transaction data and the debit card payment transaction data of which the percentage of the amount difference is smaller than the first threshold and the absolute value of the transaction time difference is smaller than the second threshold as abnormal data in the cardholder relationship network community;
after credit card consumption transaction data and debit card payment-replacement transaction data of which the percentage of the amount difference is smaller than a first threshold and the absolute value of the transaction time difference is smaller than a second threshold are removed from the credit card consumption transaction data and the debit card payment-replacement transaction data in the cardholder relationship network community, traversing each payment-replacement transaction data to obtain a community ID, a payment-replacement amount and payment-replacement time corresponding to each payment-replacement transaction data;
traversing consumption transaction data of each credit card under the community ID to obtain a transaction set;
determining a transaction set subset with the smallest absolute value of the difference between the sum of the transaction amounts and the payment amount from the transaction set;
and if the percentage of the absolute value of the difference between the sum of the transaction amounts of the transaction set subset and the payment amount is smaller than the first threshold and the difference between the transaction time and the payment time in the transaction set subset is smaller than the second threshold, determining the consumption transaction data of the credit card in the transaction set subset and the traversed payment transaction data as abnormal data in the cardholder relationship network community.
6. An information processing apparatus, comprising:
the acquisition unit is used for acquiring the transaction data of all cardholders in a preset time period;
the processing unit is used for analyzing the transaction data of all cardholders in the preset time period and determining a plurality of cardholder relationship network communities; determining credit card consumption transaction data and debit card payment-instead transaction data in each cardholder relationship network community; and determining abnormal data in the cardholder relationship network community according to the credit card consumption transaction data and the debit card payment-substituting transaction data.
7. The apparatus as claimed in claim 6, wherein said processing unit is specifically configured to:
acquiring transaction data of all cardholders in the preset time period; the transaction data comprises a bank card number, a mobile phone number, an identification card number, a device number and an application program user number;
taking the bank card number, the mobile phone number, the identification card number, the equipment number and the application program user number in the transaction data as vertexes, and connecting the mobile phone number, the identification card number, the equipment number and the application program user number in the same transaction data with the bank card number to obtain a cardholder relation network diagram;
and carrying out community division on the cardholder relationship network graph to obtain a plurality of cardholder relationship network communities.
8. The apparatus as claimed in claim 7, wherein said processing unit is specifically configured to:
traversing each vertex according to the cardholder relationship network graph, determining a plurality of cardholder relationship network graphs with connecting edges and setting a community initial attribute value of each vertex;
iteratively updating the community initial attribute value of each vertex in each cardholder relationship network diagram with a connecting edge until the community attribute values of all the vertices in each cardholder relationship network diagram with a connecting edge are changed into the minimum community attribute value in each cardholder relationship network diagram with a connecting edge, and determining the minimum community attribute value as the community ID of each cardholder relationship network diagram with a connecting edge; updating the community attribute value of the vertex to be the minimum value of the community attribute values of all first-order neighbor vertices and the community attribute value of the vertex per se in each iteration;
and associating the community ID with the transaction data of each vertex in each cardholder relationship network graph with the connecting edges to obtain the plurality of cardholder relationship network communities.
9. The apparatus as claimed in claim 8, wherein said processing unit is specifically configured to:
counting the number of first-order neighbor vertexes of each vertex;
if the number of the first-order neighbor vertexes is smaller than a preset data processing threshold value, recording the first-order neighbor vertexes of each vertex in a first set to perform iterative updating of community initial attribute values; otherwise, recording the first-order neighbor vertex of each vertex in a second set, and initializing to a drive end driver; and broadcasting the vertex in the second set to other thread pools executors so that the other executors obtain the vertex from the second set to perform iterative updating of the community initial attribute value.
10. The apparatus according to any one of claims 6 to 9, wherein the processing unit is specifically configured to:
determining credit card consumption transaction data and debit card payment transaction data of which the percentage of the amount difference is smaller than a first threshold and the absolute value of the transaction time difference is smaller than a second threshold from the credit card consumption transaction data and the debit card payment transaction data in the cardholder relationship network community, and determining the credit card consumption transaction data and the debit card payment transaction data of which the percentage of the amount difference is smaller than the first threshold and the absolute value of the transaction time difference is smaller than the second threshold as abnormal data in the cardholder relationship network community;
after credit card consumption transaction data and debit card payment-replacement transaction data of which the percentage of the amount difference is smaller than a first threshold and the absolute value of the transaction time difference is smaller than a second threshold are removed from the credit card consumption transaction data and the debit card payment-replacement transaction data in the cardholder relationship network community, traversing each payment-replacement transaction data to obtain a community ID, a payment-replacement amount and payment-replacement time corresponding to each payment-replacement transaction data;
traversing consumption transaction data of each credit card under the community ID to obtain a transaction set;
determining a transaction set subset with the smallest absolute value of the difference between the sum of the transaction amounts and the payment amount from the transaction set;
and if the percentage of the absolute value of the difference between the sum of the transaction amounts of the transaction set subset and the payment amount is smaller than the first threshold and the difference between the transaction time and the payment time in the transaction set subset is smaller than the second threshold, determining the consumption transaction data of the credit card in the transaction set subset and the traversed payment transaction data as abnormal data in the cardholder relationship network community.
11. A computing device, comprising:
a memory for storing program instructions;
a processor for calling program instructions stored in said memory to execute the method of any one of claims 1 to 5 in accordance with the obtained program.
12. A computer-readable non-transitory storage medium including computer-readable instructions which, when read and executed by a computer, cause the computer to perform the method of any one of claims 1 to 5.
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