CN114820164A - Credit card limit evaluation method, device, equipment and medium - Google Patents
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Abstract
The disclosure provides a credit card limit evaluation method which can be applied to the technical fields of artificial intelligence and finance. The credit card limit evaluation method comprises the following steps: acquiring card using behavior characteristics of a user to be evaluated according to credit card transaction data of the user to be evaluated; inputting the card-using behavior characteristics of a user to be evaluated into a generator in a pre-trained generation countermeasure network, and outputting a node transaction data sequence, wherein the node transaction data sequence comprises a first node transaction data sequence of the user to be evaluated and a second node transaction data sequence of a constructed user; the pre-trained generated countermeasure network is obtained by training based on real sequence information, pseudo sequence information and hidden characteristics; and obtaining the credit card limit evaluation value of the user to be evaluated based on the node transaction data sequence. The disclosure also provides a credit card limit evaluation device, equipment, storage medium and program product.
Description
Technical Field
The present disclosure relates to the field of artificial intelligence and financial technology, and more particularly to a credit card line assessment method, apparatus, device, medium, and program product.
Background
As the credit system becomes more sophisticated with economic development and advancement, credit cards have been accepted and used by more and more people, and a new credit-based consumption model has been developed. The method is characterized in that after the user and the card issuing mechanism reach effective agreement, the user is allowed to overdraft consumption in advance within a certain credit limit, and the overdraft amount is cleared within a specified period. While this model may effectively stimulate consumer consumption and promote a robust credit mechanism, it also presents a financial risk to the card issuer.
Disclosure of Invention
In view of the above, the present disclosure provides a credit card amount evaluation method, apparatus, device, medium, and program product.
According to a first aspect of the present disclosure, there is provided a credit card amount evaluation method including:
acquiring card using behavior characteristics of a user to be evaluated according to credit card transaction data of the user to be evaluated;
inputting the card-using behavior characteristics of a user to be evaluated into a generator in a pre-trained generation countermeasure network, and outputting a node transaction data sequence, wherein the node transaction data sequence comprises a first node transaction data sequence of the user to be evaluated and a second node transaction data sequence of a constructed user; the pre-trained generated countermeasure network is obtained by training based on real sequence information, pseudo sequence information and hidden characteristics; the real sequence information comprises card using behavior characteristics of real users in the transaction network and relationship information between adjacent real users; the pseudo sequence information comprises the card-using behavior characteristics of pseudo users generated by a generator in the initially generated countermeasure network and the relation information between pseudo adjacent users; the hidden features comprise relationship information between non-adjacent real users in the transaction network; and
and obtaining the credit card limit evaluation value of the user to be evaluated based on the node transaction data sequence.
According to the embodiment of the disclosure, obtaining the credit card limit evaluation value of the user to be evaluated based on the node transaction data sequence comprises:
calculating the occurrence frequency of nodes of constructed users adjacent to the nodes of the users to be evaluated in the node transaction data sequence;
taking the constructed user with the frequency exceeding a preset threshold value as a target user;
and obtaining the credit card limit evaluation value of the user to be evaluated based on the credit card limit value corresponding to the target user.
According to the embodiment of the disclosure, the pre-trained generated countermeasure network is trained based on the pseudo sequence information, the real sequence information and the hidden feature, and the training is obtained by:
constructing a transaction network with a service party and a real user as nodes based on credit card transaction data samples of the real user;
carrying out node sampling on a transaction network to obtain real sequence information;
processing the transaction network by using a generator in the initially generated countermeasure network to generate pseudo sequence information;
processing the transaction network by using the graph convolution network to obtain hidden characteristics;
inputting the real sequence information, the pseudo sequence information and the hidden feature into a discriminator in an initial generation countermeasure network, and outputting discrimination scores;
based on the discrimination scores, parameters of the initially generated countermeasure network are adjusted.
According to the embodiment of the disclosure, the node sampling of the transaction network to obtain the real sequence information comprises:
according to the transaction records of the service party and the real user, the relationship information of the service party and the real user in the transaction network is weighted;
and screening real user nodes in the transaction network by combining the weight of the relation information between the service party and the real user and utilizing a random walk method to obtain real sequence information.
According to an embodiment of the present disclosure, processing a transaction network with a generator in an initial generation countermeasure network, generating pseudo sequence information includes:
inputting a preset initial value into a calculation unit of a generator in an initial generation countermeasure network, and outputting a calculation vector;
based on the calculated vector, pseudo sequence information is obtained.
According to an embodiment of the present disclosure, processing a transaction network using a graph and volume network, obtaining hidden features includes:
inputting card-using behavior characteristics of a real user and relationship information between adjacent users into a graph convolution network;
performing aggregation operation on a hidden layer of the graph convolution network to obtain an initial hidden feature;
performing pooling operation on the initial hidden features to obtain pooled initial hidden features;
and carrying out preset rule operation on the pooled initial hidden features, and outputting the hidden features.
A second aspect of the present disclosure provides a credit card amount evaluation device including:
the acquisition module is used for acquiring card using behavior characteristics of the user to be evaluated according to credit card transaction data of the user to be evaluated;
the generation module is used for inputting the card-using behavior characteristics of the user to be evaluated into a generator in a pre-trained generation countermeasure network and outputting a node transaction data sequence, wherein the node transaction data sequence comprises a first node transaction data sequence of the user to be evaluated and a second node transaction data sequence of a constructed user; the pre-trained generated countermeasure network is obtained by training based on real sequence information, pseudo sequence information and hidden characteristics; the real sequence information comprises card using behavior characteristics of real users in the transaction network and relationship information between adjacent real users; the pseudo sequence information comprises the card-using behavior characteristics of pseudo users generated by a generator in the initially generated countermeasure network and the relation information between pseudo adjacent users; the hidden features comprise relationship information between non-adjacent real users in the transaction network; and
and the evaluation module is used for obtaining the credit card limit evaluation value of the user to be evaluated based on the node transaction data sequence.
A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors are caused to perform the credit line assessment method.
The fourth aspect of the present disclosure also provides a computer-readable storage medium having stored thereon executable instructions, which, when executed by a processor, cause the processor to execute the above credit card amount evaluation method.
The fifth aspect of the present disclosure also provides a computer program product comprising a computer program which, when executed by a processor, implements the above-described credit card amount evaluation method.
According to the embodiment of the disclosure, the card using behavior characteristics of the user to be evaluated are input into the generator in the generation countermeasure network which is trained in advance, the node transaction data sequence is generated, the credit card limit evaluation value of the user to be evaluated is obtained according to the node transaction data sequence, the card using limit suitable for the user to be evaluated can be evaluated in time, the user satisfaction is improved, and meanwhile, the financial risk of the card issuing organization can be reduced. The use of pre-trained generators in the countermeasure network can avoid the adverse effect of some abnormal transaction data on the credit evaluation. Meanwhile, the investment of the service personnel in manual evaluation based on expert knowledge is reduced, and the working efficiency of the whole service process is improved.
Drawings
The foregoing and other objects, features and advantages of the disclosure will be apparent from the following description of embodiments of the disclosure, which proceeds with reference to the accompanying drawings, in which:
fig. 1 schematically shows an application scenario diagram of a credit card credit line assessment method, apparatus, device, medium and program product according to an embodiment of the present disclosure;
FIG. 2 schematically shows a flow chart of a credit card line assessment method according to an embodiment of the disclosure;
FIG. 3 is a flow chart schematically illustrating a method for obtaining an evaluation value of credit card amount of a user to be evaluated based on a node transaction data sequence according to an embodiment of the disclosure;
FIG. 4 is a flow chart of a method for generating a countermeasure network trained in advance based on pseudo sequence information, real sequence information, and hidden feature training according to an embodiment of the disclosure;
FIG. 5 schematically illustrates a schematic diagram of generating an antagonistic network training, according to an embodiment of the present disclosure;
FIG. 6 is a block diagram schematically illustrating the structure of a credit card amount evaluation device according to an embodiment of the present disclosure; and
fig. 7 schematically shows a block diagram of an electronic device suitable for implementing a credit card amount evaluation method 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.).
In the technical scheme of the disclosure, the acquisition, storage, application and the like of the personal information of the related user all accord with the regulations of related laws and regulations, necessary security measures are taken, and the customs of the public order is not violated.
In the technical scheme of the embodiment of the disclosure, before the personal information of the user is obtained or collected, the authorization or the consent of the user is obtained.
In the process of implementing the embodiment of the disclosure, the credit card amount evaluation method based on expert knowledge mainly uses a manual mode, needs to invest a large amount of labor and depends on complex business experience, has huge manpower and time cost expenditure, is not beneficial to expansion of subsequent credit card business, and can reduce the satisfaction degree and experience feeling of a customer user on a card. Based on the behavior characteristics of the user and the corresponding credit card quota, the card using mode is learned through a machine learning algorithm, and then the quota which is in accordance with the behavior of the user can be analyzed and predicted through the characteristics and the learned mode. This method assumes that users are independent of each other, and does not conform to the phenomenon that each user is related to each other and affects each other in real life. The trained algorithm model has certain deviation, and certain financial hidden danger is left for using the card limit evaluation. The relationship between the clients is integrated into the learning of the model, so that the prediction accuracy of the model is expected to be increased. However, in an actual transaction network, some transaction relationships generated by abnormal situations (such as cash register, embezzlement, fraud, and the like) are often accompanied, and the introduction of the transaction relationships does not improve the learning effect of the algorithm, but adversely damages the accuracy of the model to a certain extent, thereby causing inaccuracy of the quota evaluation.
The embodiment of the disclosure provides a credit card limit evaluation method, which comprises the following steps: acquiring card using behavior characteristics of a user to be evaluated according to credit card transaction data of the user to be evaluated; inputting the card-using behavior characteristics of a user to be evaluated into a generator in a pre-trained generation countermeasure network, and outputting a node transaction data sequence, wherein the node transaction data sequence comprises a first node transaction data sequence of the user to be evaluated and a second node transaction data sequence of a constructed user; the pre-trained generated countermeasure network is obtained by training based on real sequence information, pseudo sequence information and hidden characteristics; and obtaining the credit card limit evaluation value of the user to be evaluated based on the node transaction data sequence.
Fig. 1 schematically shows an application scenario diagram of a credit card amount evaluation method, apparatus, device, medium, and program product according to an embodiment of the present disclosure.
As shown in fig. 1, the application scenario 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, wireless communication links, or fiber optic cables, to name a few.
A user may use terminal devices 101, 102, 103 to interact with a server 105 over a 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 shopping-like applications, web browser applications, search-like applications, instant messaging tools, mailbox clients, 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 may analyze and perform other processing on the received data such as the user request, and feed back a processing result (e.g., a webpage, information, or data obtained or generated according to the user request) to the terminal device.
It should be noted that the credit card amount evaluation method provided by the embodiment of the present disclosure may be generally executed by the server 105. Accordingly, the credit card limit evaluation device provided by the embodiment of the present disclosure may be generally disposed in the server 105. The credit card limit evaluation method provided by the embodiment of the disclosure can also be executed by a server or a server cluster which is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and/or the server 105. Correspondingly, the credit card limit evaluation device provided by the embodiment of the 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.
The credit card limit evaluation method provided by the embodiment of the disclosure can also be executed by the terminal devices 101, 102 and 103. Accordingly, the credit card limit evaluation device provided by the embodiment of the present disclosure may also be generally disposed in the terminal devices 101, 102, 103. The credit card limit evaluation method provided by the embodiment of the disclosure can also be executed by other terminals different from the terminal devices 101, 102 and 103. Correspondingly, the credit card limit evaluation device provided by the embodiment of the disclosure can also be arranged in other terminals different from the terminal devices 101, 102 and 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.
The credit card amount evaluation method of the disclosed embodiment will be described in detail below through fig. 2 to 5 based on the scenario described in fig. 1.
FIG. 2 schematically shows a flow chart of a credit card line assessment method according to an embodiment of the disclosure.
As shown in FIG. 2, the credit card amount evaluation method 200 of this embodiment includes operations S201 to S203.
In operation S201, card usage behavior characteristics of the user to be evaluated are acquired according to credit card transaction data of the user to be evaluated.
According to the embodiment of the disclosure, the credit card transaction data of the user to be evaluated can be acquired first through data acquisition software or other data acquisition methods. The card use behavior feature of the user may include: the amount of consumption, the number of times of using the card, the maximum amount of consumption, the minimum amount of consumption and the like.
It should be noted that, before obtaining the credit card transaction data of the user to be evaluated, the authorization of the user to be evaluated on the credit card transaction data is obtained, and after obtaining the authorization, the credit card transaction data of the user to be evaluated is obtained.
In operation S202, the card behavior characteristics of the user to be evaluated are input into the generator in the pre-trained generative countermeasure network, and a node transaction data sequence is output.
The node transaction data sequence comprises a first node transaction data sequence of a user to be evaluated and a second node transaction data sequence of a constructed user; the pre-trained generated countermeasure network is obtained by training based on real sequence information, pseudo sequence information and hidden characteristics; the real sequence information comprises card using behavior characteristics of real users in the transaction network and relationship information between adjacent real users; the pseudo sequence information comprises the card-using behavior characteristics of pseudo users generated by a generator in the initially generated countermeasure network and the relation information between pseudo adjacent users; the hidden features include relationship information between non-adjacent real users in the trading network.
According to the embodiment of the disclosure, after the pre-trained generator in the generation countermeasure network inputs the card-using behavior characteristics of the user to be evaluated, the generator correspondingly generates the first node transaction data sequence of the user to be evaluated and the second node transaction data sequence of the constructed user. The constructed user is a user which is generated by a generator in a pre-trained generation countermeasure network and is associated with a user to be evaluated.
For example, the consumption amount, the card using times, the highest consumption amount and the lowest consumption amount of the card using behavior characteristics of the user waiting to be evaluated can be input into a generator in the pre-trained generation countermeasure network, and a first node transaction data sequence of the user to be evaluated and a second node transaction data sequence of the constructed user can be output. The transaction data may include, among other things, the transaction amount, information about the transaction party and the service party. The second node transaction data sequence of the constructed user can be generated according to the card-using behavior characteristics of the user to be evaluated through a generator in a pre-trained generation countermeasure network.
According to the embodiment of the disclosure, the influence of abnormal transaction data on the credit evaluation is considered, and the pseudo sequence information generated by the generator, the real sequence information in the transaction network and the hidden feature are input into the discriminator to obtain a discrimination score; and adjusting the model parameters of the generated confrontation network based on the discrimination scores, and repeatedly training to obtain the generated confrontation network which is trained in advance.
In operation S203, a credit card amount evaluation value of the user to be evaluated is obtained based on the node transaction data sequence.
According to the embodiment of the disclosure, a second node transaction data sequence of a constructed user adjacent to a user to be evaluated can be selected from the node transaction data sequences; and then obtaining the credit card limit evaluation value of the user to be evaluated according to the credit card limit value corresponding to the second node transaction data sequence of the constructed user. It should be noted that the generator outputs in the pre-trained generated countermeasure network are constructed as users, and all carry the current corresponding credit value.
For example, N constructed users adjacent to the user to be evaluated may be selected, where N is a positive integer; and obtaining an average value of credit card limit values corresponding to the N constructed users, wherein the obtained average value is used as the credit card limit evaluation value of the user to be evaluated.
According to the embodiment of the disclosure, the card using behavior characteristics of the user to be evaluated are input into the generator in the generation countermeasure network which is trained in advance, the node transaction data sequence is generated, the credit card limit evaluation value of the user to be evaluated is obtained according to the node transaction data sequence, the card using limit suitable for the user to be evaluated can be evaluated in time, the user satisfaction is improved, and meanwhile, the financial risk of the card issuing organization can be reduced. The use of pre-trained generators in the countermeasure network can avoid the adverse effect of some abnormal transaction data on the credit evaluation. Meanwhile, the investment of the service personnel in manual evaluation based on expert knowledge is reduced, and the working efficiency of the whole service process is improved.
Fig. 3 schematically shows a flowchart of a method for obtaining an evaluation value of a credit card limit of a user to be evaluated based on a node transaction data sequence according to an embodiment of the disclosure.
As shown in fig. 3, the method 300 for obtaining an evaluation value of a credit card amount of a user to be evaluated based on a node transaction data sequence of this embodiment includes operations S301 to S303.
In operation S301, a frequency of occurrence of a node of a constructed user adjacent to a node of a user to be evaluated in a node transaction data sequence is calculated.
In operation S302, a constructed user having a frequency exceeding a preset threshold is taken as a target user.
According to the embodiment of the disclosure, the preset threshold may be a frequency value determined according to the accuracy of the quota actually required to be evaluated.
In operation S303, a credit card amount evaluation value of the user to be evaluated is obtained based on the credit card amount value corresponding to the target user.
According to the embodiment of the disclosure, the average value of the credit card amount values corresponding to the target user can be calculated, and the average value is used as the credit card amount evaluation value of the user to be evaluated.
For example, the constructed users may be sorted from large to small according to the calculated frequency of occurrence of the nodes of the constructed users adjacent to the node of the user to be evaluated, and then the constructed users corresponding to the frequency exceeding the preset threshold may be used as the target users. And calculating the average value of the credit card quota values of the target users to be used as the credit card quota evaluation value of the user to be evaluated.
According to the embodiment of the disclosure, based on the pre-trained generation countermeasure network, the credit card amount evaluation value of the user to be evaluated is obtained by utilizing a reverse-push idea and according to the credit card amount value of the user associated with the user to be evaluated. The credit card credit line suitable for credit card users can be evaluated in time, and the financial risk of the card issuing institution can be reduced while the customer satisfaction is improved.
FIG. 4 is a flow chart that schematically illustrates a method for generating a countermeasure network trained in advance based on pseudo sequence information, real sequence information, and hidden feature training, in accordance with an embodiment of the present disclosure; fig. 5 schematically illustrates a schematic diagram of generating an antagonistic network training according to an embodiment of the present disclosure.
As shown in fig. 4, the method 400 for generating a countermeasure network trained in advance according to this embodiment based on the dummy sequence information, the real sequence information, and the hidden feature training includes operations S401 to S406.
In operation S401, a transaction network having a service party and a real user as nodes is constructed based on a credit card transaction data sample of the real user.
According to the embodiment of the disclosure, the whole transaction network can be regarded as a heterogeneous two-part network G ═ { V, U, E, a } by taking the service party and the real user as nodes, and the set V ═ V {, U, E, a }, and the transaction network can be regarded as a heterogeneous two-part network i I is more than or equal to 1 and less than or equal to N represents a node representing a real user; using set U ═ U j I1 ≦ j ≦ M represents a node representing the server. If the real user v i And a service side u j There is a card transaction between them, and there is an edge e between these two nodes ij E. The service party may be, for example, a transaction party, i.e., a merchant, when the user conducts a transaction using a credit card. As shown in fig. 5, a transaction network may be constructed from nodes of merchants and users.
According to the embodiment of the disclosure, the transaction network can represent the relation between each real user node v and each real user node v in addition to the real user and the service party i Characteristic vector a of i E is A; for example, the basic characteristics of each real user may include age, occupation, residence, and ability to consume, etc., vector a i Each element in (a) may represent a quantized value of one of the basic features of each real user.
In operation S402, node sampling is performed on the transaction network to obtain real sequence information.
According to an embodiment of the present disclosure, the node sampling of the transaction network to obtain the real sequence information may include: according to the transaction records of the service party and the real user, the relationship information of the service party and the real user in the transaction network is weighted; and screening real user nodes in the transaction network by combining the weight of the relation information between the service party and the real user and utilizing a random walk method to obtain real sequence information. As shown in fig. 5, the real sequence may be obtained according to a transaction network, and the real sequence carries real sequence information.
For example, edge e can be subtended ij Is given a weight w ij The calculation method can be shown as the following formula (1):
wherein s is ij Equal to real user v i With service side u j The amount of the card transaction is accumulated. I.e. it can indicate that for a real user, the larger the accumulated transaction amount with a service party, the edge e ij The greater the weight of (c).
It should be noted that, because each real user often has transaction behavior with multiple service parties and there are multiple repeated transactions, in order to better show the relationship between the real user and the service party in each transaction, the side e is here ij Is given a weight w ij 。
The random walk method can start from a real user node and alternately jump to a server and a next real user node, and the jump probability is in direct proportion to the weight of an edge between the real user and the server node. After a sampling length T is defined, filtering out a server node, only reserving a starting real user node and the rest T-1 real user nodes to jointly form a real sequence sample seq used by a training model i Repeating the random walk steps for multiple times to generate a real sequence set TSEQ containing a plurality of samples, namely real sequence information. Wherein T is a positive integer greater than 1. T can be set reasonably according to actual needs.
In operation S403, the transaction network is processed using a generator in the initial generation countermeasure network, generating pseudo sequence information.
According to an embodiment of the present disclosure, the generator in the initial generation countermeasure network may be a long-short term memory network for generating the pseudo sequence information. The long-short term memory network includes a plurality of computing units. As shown in fig. 5, the pseudo sequence may be obtained according to a generator in the initially generated countermeasure network, and the pseudo sequence carries pseudo sequence information.
For example, a normal distribution obeying the standard and an all-zero vector in the trading network can be respectively used as initial values to be input into the hidden layer and the input layer of the calculation unit at the first moment of the generator, so that the output layer output vector value of the calculation unit at the current moment can be obtained. The vector is converted into a one-hot code when being output, and the code value is the element serial number corresponding to 1, which can be regarded as a first generated pseudo node. And then, the one-hot code corresponding to the node is used as the input of the input layer of the next time calculation unit, and the pseudo node generated at the next time can be obtained in the same way. Repeating the above steps T times to obtain a pseudo node sequence seq with the length of T t ∈FSEQ。
Wherein, each calculating unit can obtain the result of the output layer of the calculating unit through the following calculation of the steps (2) to (6).
f t ＝σ(W f ·CONCAT(o t-1 ,h t )+b f ) (2)
i t ＝σ(W i ·CONCAT(o t-1 ,h t )+b i ) (3)
o t ＝σ(W o ·CONCAT(o t-1 ,h t )+b o )*tanh(C t ) (6)
Wherein, W f ,W i ,W C ,W o And b f ,b i ,b C ,b o Respectively, a mapping matrix and an offset vector for making linear changes. h is t And o t Representing the input and output of the computing unit at time t, respectively. Sigma (-) and tanh (-) are activation functions, which increase the learning ability of the model. C t The state value of the computing unit connecting two adjacent moments is represented, and the long-short term memory network realizes the selective forgetting and memorizing of the preamble data through the state value.
It should be noted that the activation function may be an exponential function in general, and is used to make a nonlinear transformation on the result.
In operation S404, the transaction network is processed using the graph convolution network to obtain hidden features.
According to the embodiment of the present disclosure, a three-step operation may be performed in the hidden layer of the graph convolution network as shown in fig. 5, and the first aggregation operation may be performed according to the following equation (7), for example:
wherein,
representing the hidden feature vector of node u in the layer preceding the current hidden layer. In particular, when k is 1 (the first hidden layer),
and is equal to the vector formed by the card behavior characteristics of the corresponding client of the node u. N (v) represents a second-order neighbor customer node of customer v (i.e., other customers who have a transaction behavior with the same merchant as customer v) in the constructed customer-merchant user transaction network. AVG denotes the operation of averaging the elements in the vector.
The second pooling operation may be performed, for example, according to the following equation (8):
wherein σ (-) represents a non-linear activation function forThe ReLu function can be generally selected by increasing the learning ability of the graph convolution network. W k Represents a linear mapping matrix for dimension reduction. CONCAT (a, b) indicates that the two vectors of a and b are cascaded. Wherein a represents
Vector quantity; b represents
And (5) vector quantity.
Thirdly, for each node, regularization operation of the following formula (9) is performed on the pooled initial hidden feature vectors obtained by calculation at the current layer as final output, so that the robustness of the model is ensured:
where | l | · | |, represents the two-norm of the vector.
It should be noted that the card-using behavior feature of each user and the relationship information between adjacent users may be input into the graph convolution network, and finally, the hidden feature vector of each node is obtained.
In operation S405, the real sequence information, the dummy sequence information, and the hidden feature are input to a discriminator in the initially generated countermeasure network, and a discrimination score is output.
According to the embodiment of the disclosure, the arbiter may include a long-short term memory network, and may output an arbitration score for the node sequence at the last computing unit.
In operation S406, parameters of the initially generated countermeasure network are adjusted based on the discrimination scores.
According to the embodiment of the disclosure, the discrimination score can be substituted into the loss function to calculate the loss value, and the parameters of the initially generated confrontation network are continuously adjusted to make the training convergent, so as to obtain the generated confrontation network which is trained in advance.
For example, the loss function that generates the antagonistic network model can be shown as equation (10) below:
wherein f is θ (seq i ) A discrimination score representing a discriminator output of the countermeasure network generated for the real sequence information; f. of θ (seq t ) A discrimination score representing a discriminator output of the countermeasure network generated for the pseudo sequence information;
and
parameters of the generation countermeasure network for the real sequence information and the dummy sequence information are respectively represented.
To train a generative confrontation network for credit line assessment, the score should be as low as possible for the pseudo sequence information; conversely, for true sequence information, it should be as high as possible. The following optimization objective is shown as formula (11), and the optimal parameters can be obtained by learning with a given learning rate and by matching with an Adam optimization method.
min G max D V(D,G) (11)
Wherein D represents the discriminator and G represents the generator.
According to the embodiment of the disclosure, in addition to the card-using behavior characteristic information of the user, the learning of the user relevance data is added in the generation of the countermeasure network by acquiring the hidden characteristic and the relationship information between adjacent users, so that a more accurate and reasonable evaluation result can be obtained when the credit card limit is evaluated. The method can be well applied to a complex credit card transaction network, and adverse effects of some abnormal transaction data on credit line evaluation are avoided, so that the generation countermeasure network for credit card credit line evaluation is more robust and credible.
According to an embodiment of the present disclosure, processing a transaction network with a generator in an initial generation countermeasure network, generating pseudo sequence information may include: inputting a preset initial value into a calculation unit of a generator in an initial generation countermeasure network, and outputting a calculation vector; based on the calculated vector, pseudo sequence information is obtained. Wherein the preset initial value may be a value subject to a preset rule in the transaction network; the preset rule may be, for example, obeying a standard normal distribution or an all-zero vector.
According to the embodiment of the disclosure, the influence of abnormal transaction data on the credit value evaluation is considered, the pseudo sequence information generated by the generator is used for participating in the training of generating the countermeasure network, and the trained countermeasure network is favorable for accurately evaluating the credit card value.
According to an embodiment of the present disclosure, processing a transaction network using a graph and volume network, obtaining hidden features may include: inputting card-using behavior characteristics of a real user and relationship information between adjacent users into a graph convolution network; performing aggregation operation on a hidden layer of the graph convolution network to obtain an initial hidden feature; performing pooling operation on the initial hidden features to obtain pooled initial hidden features; and carrying out preset rule operation on the pooled initial hidden features, and outputting the hidden features.
According to the embodiment of the disclosure, learning of the user relevance data is added in the generation of the countermeasure network by acquiring the hidden features, and a more accurate and reasonable evaluation result can be obtained when the credit card limit is evaluated.
Based on the credit card limit evaluation method, the disclosure also provides a credit card limit evaluation device. The apparatus will be described in detail below with reference to fig. 6.
Fig. 6 is a block diagram schematically showing the structure of a credit card amount evaluation device according to an embodiment of the present disclosure.
As shown in fig. 6, the credit card amount evaluation device 600 of this embodiment includes an acquisition module 610, a generation module 620, and an evaluation module 630.
The obtaining module 610 is configured to obtain card usage behavior characteristics of the user to be evaluated according to credit card transaction data of the user to be evaluated. In an embodiment, the obtaining module 610 may be configured to perform the operation S201 described above, which is not described herein again.
The generating module 620 is configured to input card-using behavior characteristics of a user to be evaluated into a generator in a pre-trained generated countermeasure network, and output a node transaction data sequence, where the node transaction data sequence includes a first node transaction data sequence of the user to be evaluated and a second node transaction data sequence of a constructed user; the pre-trained generated countermeasure network is obtained by training based on real sequence information, pseudo sequence information and hidden characteristics; the real sequence information comprises card using behavior characteristics of real users in the transaction network and relationship information between adjacent real users; the pseudo sequence information comprises the card-using behavior characteristics of pseudo users generated by a generator in the initially generated countermeasure network and the relation information between pseudo adjacent users; the hidden features include relationship information between non-adjacent real users in the trading network. In an embodiment, the generating module 620 may be configured to perform the operation S202 described above, which is not described herein again.
The evaluation module 630 is configured to obtain an evaluation value of the credit card limit of the user to be evaluated based on the node transaction data sequence. In an embodiment, the evaluation module 630 may be configured to perform the operation S203 described above, which is not described herein again.
According to an embodiment of the present disclosure, any plurality of the obtaining module 610, the generating module 620, and the evaluating module 630 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 obtaining module 610, the generating module 620, and the evaluating module 630 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 by any other reasonable manner of integrating or packaging a circuit, or may be implemented in any one of or a suitable combination of software, hardware, and firmware. Alternatively, at least one of the obtaining module 610, the generating module 620 and the evaluating module 630 may be at least partially implemented as a computer program module, which when executed may perform the respective functions.
Fig. 7 schematically shows a block diagram of an electronic device suitable for implementing a credit card amount evaluation method according to an embodiment of the disclosure.
As shown in fig. 7, an electronic device 700 according to an embodiment of the present disclosure includes a processor 701, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)702 or a program loaded from a storage section 708 into a Random Access Memory (RAM) 703. The processor 701 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), among others. The processor 701 may also include on-board memory for caching purposes. The processor 701 may comprise 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.
In the RAM 703, various programs and data necessary for the operation of the electronic apparatus 700 are stored. The processor 701, the ROM 702, and the RAM 703 are connected to each other by a bus 704. The processor 701 performs various operations of the method flows according to the embodiments of the present disclosure by executing programs in the ROM 702 and/or the RAM 703. Note that the programs may also be stored in one or more memories other than the ROM 702 and the RAM 703. The processor 701 may also perform various operations of method flows according to embodiments of the present disclosure by executing programs stored in the one or more memories.
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.
According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. For example, according to embodiments of the present disclosure, a computer-readable storage medium may include the ROM 702 and/or the RAM 703 and/or one or more memories other than the ROM 702 and the RAM 703 described above.
Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the method illustrated in the flow chart. When the computer program product runs in a computer system, the program code is used for causing the computer system to realize the method provided by the embodiment of the disclosure.
The computer program performs the above-described functions defined in the system/apparatus of the embodiments of the present disclosure when executed by the processor 701. The systems, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.
In one embodiment, the computer program may be hosted on a tangible storage medium such as an optical storage device, a magnetic storage device, or the like. In another embodiment, the computer program may also be transmitted in the form of a signal on a network medium, distributed, downloaded and installed via the communication section 709, and/or installed from the removable medium 711. The computer program containing program code may be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 709, and/or installed from the removable medium 711. The computer program, when executed by the processor 701, performs the above-described functions defined in the system of the embodiment of the present disclosure. The systems, devices, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.
In accordance with embodiments of the present disclosure, program code for executing computer programs provided by embodiments of the present disclosure may be written in any combination of one or more programming languages, and in particular, these computer programs may be implemented using high level procedural and/or object oriented programming languages, and/or assembly/machine languages. The programming language includes, but is not limited to, programming languages such as Java, C + +, python, the "C" language, or the like. The program code may execute entirely on the user computing device, partly on the user device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
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.
Those skilled in the art will appreciate that various combinations and/or combinations of features recited in the various embodiments and/or claims of the present disclosure can be made, even if such combinations or combinations are not expressly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments and/or claims of the present disclosure may be made without departing from the spirit or teaching of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure.
The embodiments of the present disclosure have been described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described separately above, this does not mean that the measures in the embodiments cannot be used in advantageous combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the present disclosure, and such alternatives and modifications are intended to be within the scope of the present disclosure.
Claims (10)
1. A credit card limit evaluation method comprises the following steps:
acquiring card using behavior characteristics of a user to be evaluated according to credit card transaction data of the user to be evaluated;
inputting the card-using behavior characteristics of the user to be evaluated into a generator in a pre-trained generation countermeasure network, and outputting a node transaction data sequence, wherein the node transaction data sequence comprises a first node transaction data sequence of the user to be evaluated and a second node transaction data sequence of a constructed user; the pre-trained generated confrontation network is obtained based on real sequence information, pseudo sequence information and hidden feature training; the real sequence information comprises card using behavior characteristics of real users in the transaction network and relationship information between adjacent real users; the pseudo sequence information comprises card-using behavior characteristics of pseudo users generated by a generator in an initially generated countermeasure network and relationship information between pseudo adjacent users; the hidden features comprise relationship information between non-adjacent real users in the transaction network; and
and obtaining the credit card limit evaluation value of the user to be evaluated based on the node transaction data sequence.
2. The method as claimed in claim 1, wherein the obtaining an evaluation value of credit card amount of the user to be evaluated based on the node transaction data sequence comprises:
calculating the frequency of appearance of the nodes of the constructed user adjacent to the nodes of the user to be evaluated in the node transaction data sequence;
taking the constructed users with the frequency exceeding a preset threshold value as target users;
and obtaining the credit card limit evaluation value of the user to be evaluated based on the credit card limit value corresponding to the target user.
3. The method of claim 1, wherein the pre-trained generative confrontation network is trained based on pseudo sequence information, real sequence information, hidden features comprising:
constructing the transaction network with a service party and the real user as nodes based on credit card transaction data samples of the real user;
carrying out node sampling on the transaction network to obtain the real sequence information;
processing the transaction network with a generator in the initially generated counterpoise network, generating the pseudo sequence information;
processing the transaction network by using a graph convolution network to obtain the hidden features;
inputting the real sequence information, the pseudo sequence information and the hidden feature into a discriminator in the initially generated countermeasure network, and outputting a discrimination score;
adjusting parameters of the initially generated countermeasure network based on a discriminant score.
4. The method of claim 3, wherein the sampling nodes of the transaction network to obtain the true sequence information comprises:
according to the transaction records of the service party and the real user, assigning a weight value to the relationship information of the service party and the real user in the transaction network;
and screening real user nodes in the transaction network by using a random walk method in combination with the weight of the relation information between the service party and the real user to obtain the real sequence information.
5. The method of claim 3, wherein said processing the trading network with a generator in the initial generation countermeasure network, generating the pseudo sequence information comprises:
inputting a preset initial value into a calculation unit of a generator in the initial generation countermeasure network, and outputting a calculation vector;
and acquiring the pseudo sequence information based on the calculation vector.
6. The method of claim 3, wherein said processing said transaction network using a graph convolution network to obtain said hidden features comprises:
inputting the card using behavior characteristics of the real user and the relationship information between the adjacent users into the graph convolution network;
performing aggregation operation on a hidden layer of the graph convolution network to obtain an initial hidden feature;
performing pooling operation on the initial hidden features to obtain pooled initial hidden features;
and carrying out preset rule operation on the pooled initial hidden features, and outputting the hidden features.
7. A credit card limit evaluation device includes:
the acquisition module is used for acquiring card using behavior characteristics of the user to be evaluated according to credit card transaction data of the user to be evaluated;
the generation module is used for inputting the card-using behavior characteristics of the user to be evaluated into a generator in a pre-trained generation countermeasure network and outputting a node transaction data sequence, wherein the node transaction data sequence comprises a first node transaction data sequence of the user to be evaluated and a second node transaction data sequence of a constructed user; the pre-trained generated confrontation network is obtained based on real sequence information, pseudo sequence information and hidden feature training; the real sequence information comprises card using behavior characteristics of real users in the transaction network and relationship information between adjacent real users; the pseudo sequence information comprises card-using behavior characteristics of pseudo users generated by a generator in an initially generated countermeasure network and relationship information between pseudo adjacent users; the hidden features comprise relationship information between non-adjacent real users in the transaction network; and
and the evaluation module is used for obtaining the credit card limit evaluation value of the user to be evaluated based on the node transaction data sequence.
8. An electronic device, comprising:
one or more processors;
a storage device 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 perform the method of any of claims 1-6.
9. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to perform the method of any one of claims 1 to 6.
10. A computer program product comprising a computer program which, when executed by a processor, implements a method according to any one of claims 1 to 6.
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