CN115345726B - Automatic credit card approval method and device, electronic equipment and medium - Google Patents
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Abstract
The invention discloses an automatic approval method, device, electronic equipment and medium for credit card, the method includes: acquiring a scene picture to be identified of a potential risk user during credit card application; the potential risk users comprise users with good credit reports and no any one or more behavior types in illegal behaviors; acquiring behavior characteristic information of the potential risk user; inputting the behavior characteristic information of the potential risk user and the application scene picture to be identified into a trained risk identification model to output a risk score; and judging whether to issue a credit card to the potential risk user according to the risk score. The invention reduces the probability of risk lending event, reduces the probability of bad account after credit card is issued, and greatly improves the fund security of banks.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to an automatic credit card approval method, an automatic credit card approval device, electronic equipment and a medium.
Background
With the continuous improvement of the living standard of people, the demands for consumption are increasingly increasing. In the future, credit cards will appear to address the consumer needs of most people.
Of course, with the popularity of credit cards, more and more consumers use credit cards for consumption. Currently, banks evaluate whether to issue credit cards to users through credit.
However, this method has a risk that if some users have good credit reports or no illegal actions exist before, the bank cannot identify the users with potential risks, so that the users are easy to be broken after the credit card is issued, and the loss of the bank is definitely great, so that the rights and interests of the bank are difficult to be ensured.
Disclosure of Invention
The embodiment of the invention aims to provide an automatic credit card approval method, device, electronic equipment and computer readable storage medium, which achieve the effects of reducing credit card approval risks and improving credit card automatic approval efficiency.
In a first aspect, to achieve the above object, an embodiment of the present invention provides an automatic credit card approval method, including:
acquiring a scene picture to be identified of a potential risk user during credit card application; the potential risk users comprise users with good credit reports and no any one or more behavior types in illegal behaviors;
acquiring behavior characteristic information of the potential risk user;
Inputting the behavior characteristic information of the potential risk user and the application scene picture to be identified into a trained risk identification model to output a risk score;
and judging whether to issue a credit card to the potential risk user according to the risk score.
In a second aspect, in order to solve the same technical problem, an embodiment of the present invention provides an approval apparatus for a credit card, including:
the image acquisition module is used for acquiring an application scene image to be identified of a potential risk user during the application of the credit card; the potential risk users comprise users with good credit reports and no any one or more behavior types in illegal behaviors;
the information acquisition module is used for acquiring behavior characteristic information of the potential risk user;
the analysis module is used for inputting the behavior characteristic information of the potential risk user and the application scene picture to be identified into the trained risk identification model to output a risk score;
and the processing module is used for judging whether to issue a credit card to the potential risk user according to the risk score.
In a third aspect, to solve the same technical problem, an embodiment of the present invention provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, where the memory is coupled to the processor, and where the processor executes the computer program to implement steps in the method for automatically approving a credit card according to any one of the above.
In a fourth aspect, to solve the same technical problem, an embodiment of the present invention provides a computer readable storage medium, where a computer program is stored, where a device where the computer readable storage medium is controlled to execute the steps in the automatic credit card approval method according to any one of the above-mentioned steps when the computer program runs.
The embodiment of the invention provides an automatic credit card approval method, device, electronic equipment and computer readable storage medium, which are used for carrying out identification analysis on behavior characteristic information of a potential risk user and an application scene picture to be identified through a risk identification model to obtain a risk score, judging whether to issue a credit card to the potential risk user according to the risk score, and compared with manual verification, greatly reducing the probability of missed verification and error rate, and greatly reducing the occurrence of risk borrowing events.
Drawings
The above features, technical features, advantages and implementation of the present invention will be further described in the following description of preferred embodiments with reference to the accompanying drawings in a clear and easily understood manner.
FIG. 1 is a schematic flow chart of an automatic credit card approval method according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of another method for automatically approving a credit card according to an embodiment of the disclosure;
FIG. 3 is a schematic flow chart of another method for automatically approving a credit card according to an embodiment of the disclosure;
FIG. 4 is a schematic flow chart of another method for automatically approving a credit card according to an embodiment of the disclosure;
FIG. 5 is a schematic flow chart of another method for automatically approving a credit card according to an embodiment of the disclosure;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system configurations, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
For the sake of simplicity of the drawing, the parts relevant to the present invention are shown only schematically in the figures, which do not represent the actual structure thereof as a product. Additionally, in order to simplify the drawing for ease of understanding, components having the same structure or function in some of the drawings are shown schematically with only one of them, or only one of them is labeled. Herein, "a" means not only "only this one" but also "more than one" case.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
In addition, in the description of the present application, the terms "first," "second," and the like are used merely to distinguish between descriptions and are not to be construed as indicating or implying relative importance.
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following description will explain the specific embodiments of the present invention with reference to the accompanying drawings. It is evident that the drawings in the following description are only examples of the invention, from which other drawings and other embodiments can be obtained by a person skilled in the art without inventive effort.
Referring to fig. 1, fig. 1 is a flowchart of an automatic credit card approval method according to an embodiment of the invention, including steps S101 to S104.
Step S101, acquiring an application scene picture to be identified of a potential risk user during application of a credit card; the potential risk users comprise users with good credit reports and no any one or more behavior types in illegal behaviors;
specifically, after the application scene picture to be identified is used for initiating an application request by using the credit card application terminal, the background server firstly inquires whether a user initiating the application request is a potential risk user, if so, a control instruction is sent to the credit card application terminal, and a camera of the credit card application terminal is triggered to shoot and acquire pictures including the potential risk user and the surrounding environment of the potential risk user to obtain the application scene picture to be identified. Because some black-produced group partners can illegally purchase names, identification card numbers and headshots, and use the software to illegally crack the credit card to forge and steal the identity of other people to apply for the credit card, the credit card approval process of the application also needs to acquire the application scene pictures to be identified.
The user without illegal action refers to a user who does not violate the national current legal regulation and endanger the action of the social relationship protected by the law. Credit reporting well includes two types, a user without credit application records or a user without credit problems, respectively.
The user without credit application records refers to a user without credit records issued by any banking institution, and because the credit is incorporated into the credit report, whether the user has credit application records (such as credit card application records, house credit application records and the like) can be inquired from a personal credit report front page, and certainly, whether the user has credit application records in all banking institutions can be inquired through a background server of cloud flash payment, and if not, the user is determined to be the user without credit application records.
A user without credit problems refers to a user who does not have a record of overdue repayment, although the user has a record of credit (e.g. credit card, bar, etc.). The credit report of the user can be inquired from the home page of the personal credit report, and whether the user is a user without credit problems is judged according to the repayment record in the credit report. From one perspective, the user without credit application records is also a good credit report because the user does not need to pay on time.
The user enters a credit card application page through a credit card application terminal (such as a self-service terminal of a bank self-service area or a mobile phone, a computer and the like used by the user), the credit card application page displays a selection page for the user to select a credit card type, a filling interface for the user to fill in personal information and the like, and after the information is filled in, the credit card application terminal generates a credit card application request and sends the credit card application request to a background server. The personal information includes name, certificate type, certificate number and other information. Credit card types include master, slave, regular, gold, black, etc. The method for acquiring the application scene picture to be identified by the potential risk user during the application of the credit card comprises at least two modes:
the first way is: after receiving a credit card application request sent by a credit card application terminal, the background server immediately establishes an identity verification channel for an audit terminal used by seat personnel of a financial service institution and the credit card application terminal, so that the seat personnel and a user establish video call through a camera of the credit card application terminal, and thus, the seat personnel can manually conduct face sign verification on the identity of the user, and the background server stores video data in the face sign process, so that face identification is conducted on the video data to obtain a scene picture of an application to be identified. Of course, in order to improve the efficiency of obtaining the application scene picture to be identified, the background server directly receives the user side video data sent by the credit card application terminal, and performs face recognition on the user side video data to obtain the application scene picture to be identified.
The second mode is as follows: after receiving a credit card application request sent by a credit card application terminal, the background server initiates a liveness experience request to the credit card application terminal so as to create an identity verification channel between the background server and the credit card application terminal, and therefore, the credit card application terminal starts a liveness experience process to shoot and collect user side video data comprising a user and surrounding environments of the user, and the background server receives the user side video data sent by the credit card application terminal, so that the background server can compare and verify the identity of the user according to a face obtained by identification of an identity card photo in personal information and the user side video data. And meanwhile, the background server carries out face recognition on the video data of the user side to obtain a scene picture of the application to be identified.
Step S102, behavior characteristic information of the potential risk user is obtained;
specifically, the behavior characteristic information includes transaction behavior characteristics, identity characteristics, social relationship characteristics and credit. The transaction performance characteristics may be obtained analytically from personal transaction records including, but not limited to, any one or more of banking, bank account balance, digital wallet balance (e.g., balance of a payment device, change balance of a WeChat). For example, the bank card or credit card bound bank system may be invoked by a common bank card number provided by a potentially risky user, a recently used credit card number, and then data such as bank flows, bank account balances, etc. may be invoked from the bank system. Or the credit center and the system of the loan organization can be called through the bank card number, and then the credit degree and the like can be called from the credit center and the system of the loan organization.
Step S103, the behavior characteristic information of the potential risk user and the application scene picture to be identified are input into a trained risk identification model to output a risk score;
specifically, after the behavior feature information of the potential risk user and the application scene picture to be identified are obtained in the above manner, the behavior feature information of the potential risk user and the application scene picture to be identified are input into a risk identification model trained in advance as input parameters, and risk scores of the potential risk user are identified and output through the risk identification model.
And step S104, judging whether to issue a credit card to the potential risk user according to the risk score.
Specifically, once the risk score of the potential risk user is obtained, whether the credit card applied by the potential risk user is issued for the potential risk user is judged according to the risk score. It is known that the concept of behavior of a user at different stages may change, i.e. the user has never been overdue or illicit before, but may be required to apply for credit cards for advanced consumption and no willingness to pay back for external or personal reasons, which may cause a significant loss to the bank. Therefore, the invention carries out recognition analysis on the behavior characteristic information of the potential risk user and the application scene picture to be recognized through the risk recognition model to obtain the risk score, and judges whether to issue the credit card to the potential risk user according to the risk score, so that the probability of bad account after issuing the credit card can be reduced by the bank, and the fund security of the bank is greatly improved.
In addition, as the behavior characteristics of the potential risk user are taken as evaluation factors, the scene pictures and the behavior characteristics of the application to be identified are input into the risk identification model together to identify and analyze whether the credit card is issued to the potential risk user, the probability of fraudulent use of the identity of other people (such as name, identity card number and big head photo) for applying for the credit card by black party can be reduced, the probability of bad account after issuing the credit card is reduced, the credibility of the bank user for the credit card application is improved, disputes between the subsequent user and the bank can be reduced, the management cost and risk of the bank credit are greatly reduced, and the rights and interests of the bank are further ensured.
In an embodiment, please refer to fig. 2, fig. 2 is another flow chart of the method for automatically approving a credit card provided by the embodiment of the present invention, and step S103 includes, before inputting the behavior feature information of the potential risk user and the application scene picture to be identified into a trained risk recognition model to output a risk score:
step S201, acquiring application scene sample pictures and behavior characteristic information of bad account users according to history data of bad account type credit cards;
in particular, the high development of commercial credit is one of important features of market economy, and the development of commercial credit inevitably leads to the occurrence of bad accounts while bringing about an increase in sales revenue for enterprises. Bad account refers to a receivable item that cannot be withdrawn or has little possibility of being withdrawn, and dead account may also be referred to as bad account.
Step S202, performing image recognition on the application scene sample picture through an image recognition algorithm to obtain bad account scene image features;
and step 203, training to obtain a risk identification model according to the bad account scene image characteristics and the behavior characteristic information of the bad account user.
Specifically, a plurality of sample data sets (including a large number of bad account scene image features and bad account user behavior feature information) of a plurality of bad account users clustered together based on service correlation can be obtained by performing preset clustering processing on historical data containing a full amount of service data and the bad account scene image features. And training by utilizing a plurality of sample data sets of a plurality of bad account users to obtain a risk identification model with higher accuracy.
Specifically, the behavior data of the historical bad account user is called, and a machine learning algorithm is used for modeling to obtain a risk identification model. The method specifically comprises the steps of calling behavior data of a history bad account user; carrying out word bag model statistics on the behavior data of the history bad account user to construct a dictionary; obtaining bad account user behavior characteristics according to the constructed dictionary; summing the bad account user behavior characteristics to obtain a bad account user behavior characteristic set; vector representation is carried out on all bad account user behavior characteristics by using a TF-IDF algorithm, so as to obtain corresponding bad account user behavior characteristic vectors; respectively performing feature mapping on the bad account user behavior feature vectors on the bad account user behavior feature sets to obtain TF-IDF values of the bad account user behavior feature vectors; and inputting the bad account user behavior feature vector with the TF-IDF value larger than a preset threshold value and the bad account scene image feature into a machine learning algorithm for modeling to obtain a risk identification model.
The method comprises the steps of carrying out word bag model statistics on behavior data of a history bad account user, wherein the step of carrying out word bag model statistics on the behavior data of the history bad account user comprises the steps of stopping words on the data of the past bad account user, word Net filtering, part-of-speech filtering, word stem extracting and the like.
Among them, TF-IDF (Term Frequency-Inverse Document Frequency) is a common weighting technique for data retrieval and data mining. TF means Term Frequency (Term Frequency), and IDF means inverse text Frequency index (Inverse Document Frequency). TF-IDF is a statistical method for evaluating the importance of a word to one of a set of documents or a corpus. Wherein the importance of a word increases in proportion to the number of times it appears in a file.
Wherein, the calculation formulas of TF, IDF and TF-IDF are as follows:
TF-IDF＝TF×IDF。
according to the risk prediction method and the risk prediction device, after the behavior characteristic information of the potential risk user and the application scene picture to be recognized are obtained, the behavior characteristic information of the potential risk user and the application scene picture to be recognized can be comprehensively processed by calling the preset risk recognition model, so that the risk score of the potential risk user is comprehensively and accurately determined, the risk grade of the potential risk user is further determined, errors in determining the risk of the user can be effectively reduced, and the risk prediction precision is improved.
In an embodiment, referring to fig. 3, fig. 3 is another flow chart of an automatic credit card approval method according to an embodiment of the present invention, the step S104 of determining whether to issue a credit card to the potentially risky user according to the risk score includes:
step S301, comparing the risk score with a first preset risk threshold and a second preset risk threshold; the first preset risk threshold is smaller than the second preset risk threshold;
step S302, refusing to issue a credit card to the potential risk user if the risk score is lower than the first preset risk threshold;
step S303, if the risk score is higher than the second preset risk threshold, allowing a credit card to be issued to the potential risk user;
step S304, if the risk score is higher than the first preset risk threshold and lower than the second preset risk threshold, whether to issue a credit card to the potential risk user is judged again according to the repayment amount of the potential risk user.
Specifically, comparing the risk score with a first preset risk threshold and a second preset risk threshold; the first preset risk threshold is smaller than the second preset risk threshold; if the risk score is below the first preset risk threshold, it is indicated that the potentially-risky user is most likely a bad account user who has overdue repayment or even refused repayment in the future, and thus refuses to issue a credit card to the potentially-risky user. If the risk score is above the second preset risk threshold, it is indicated that the potentially risky user has a low probability in the future of being a bad account user for whom overdue repayment or even refusal of repayment, thus allowing credit cards to be issued to the potentially risky user. And if the risk score is higher than the first preset risk threshold and lower than the second preset risk threshold, judging whether to issue a credit card to the potential risk user again according to the repayment amount of the potential risk user.
Since general bad account users are listed in a black list of a bank, it is preferable that the behavior characteristics of the bad account users are sorted according to the black list from large to small according to the TF-IDF values of vectors of the bad account users. And sequentially comparing the numerical values of the behavior characteristics of the potential risk users with the threshold values of the behavior characteristics of the bad account users corresponding to the blacklist according to the ordering, and further predicting the risk scores of the potential risk users belonging to the bad account users in the future. It can be understood that, since the behavior features of the bad account users on the blacklist are ranked from large to small according to the TF-IDF values of the vectors, the values of the behavior features of the potential risk users are compared with the threshold value of the corresponding behavior features of the bad account users on the blacklist, that is, the behavior features of the potential risk users corresponding to the behavior features of the bad account users ranked first in the blacklist are found out from the behavior features of the first preset number, then compared, if the compared result exceeds the threshold value, the comparison of the subsequent ranking features is performed again. And if the comparison result shows that the number of the behavior features of the potential risk users exceeding the threshold value of the behavior features of the corresponding bad account users is larger than the second preset number, determining that the potential risk users are bad account users. Wherein it can be appreciated that the potentially-risky user is determined to be a bad account user, i.e., the potentially-risky user is a blacklisted user. And when the potential risk user is determined to be the bad account user, cutting off the business flow which is operated by the potential risk user. And updating the risk identification model on the blacklist by using the data of the bad account user obtained by comparison in the preset time period and a machine learning algorithm.
For example, assume that bad account user behavior features on a blacklist are ordered from large to small according to their vector's TF-IDF values: the credit degree is greater than bank running water is greater than the balance of the digital wallet. When in comparison, firstly, the credit-related values such as the credit index, the credit increase or decrease of the potential risk user are compared with the credit-related values such as the credit index, the credit increase or decrease of the bad account user on the blacklist; then, comparing the bank running water variable quantity of the potential risk user with the bank running water variable quantity of the bad account user on the blacklist; and comparing the digital wallet balance value of the potential risk user with the digital wallet balance value of the bad account user on the blacklist.
It can be understood that, by updating the risk identification model on the blacklist by using the data of the bad account users and the machine learning algorithm obtained by comparison in the preset time period, the threshold value of the corresponding bad account user behavior feature on the blacklist can be continuously corrected, or the number of the bad account user behavior features on the blacklist can be increased.
It can be seen that, through the technical scheme provided by the embodiment of the application, the bad account user behavior features are ranked from large to small according to the TF-IDF values of the vectors, that is, the bad account user behavior features are ranked from high to low according to importance, when the numerical values of the potential risk user behavior features are compared with the corresponding bad account user behavior features on the blacklist, more important features can be preferentially compared, if the comparison result exceeds the threshold, the comparison of the subsequent ranking features is performed again, so that the comparison times can be reduced, the comparison speed and accuracy are increased, and the blacklist user identification efficiency is improved.
In an embodiment, please refer to fig. 4, fig. 4 is another flow chart of a method for automatically approving a credit card according to an embodiment of the present invention, wherein the re-judging whether to issue the credit card to the potentially risky user according to the credit amount of the potentially risky user includes:
step S401, analyzing and obtaining a repayment amount according to the asset information and the consumption information of the potential risk user;
specifically, the asset information of the present invention refers to mobile funds of a user, and the asset information includes income sources such as payroll income, part-time income, rented income of house or equipment, financial investment income (e.g. profit generated by purchasing financial products), and the like. The consumption information includes consumption items such as daily consumption (e.g., house rentals, water and electricity fees, diet, travel, etc.), shopping consumption (e.g., laundry purchase, electronic product purchase, etc.), repayment on a credit month, etc. Asset information and consumption information may be retrieved from a third party server or a banking server (including debit and/or credit card servers) of a third party payment instrument (e.g., payment balance, weChat change, etc.). And then, calculating the repayment amount (namely the monthly highest repayment amount) of the target user according to the asset information and the consumption information of the target user.
In an embodiment, please refer to fig. 5, fig. 5 is another flow chart of a method for automatically approving a credit card according to an embodiment of the present invention, wherein the analyzing and obtaining the credit amount according to the asset information and the consumption information of the potentially risky user includes:
s501, acquiring the average income amount of the potential risk user according to the asset information;
specifically, after the asset information of the target user is obtained in the above manner, because the asset composition of the user includes at least one revenue source, the total asset information of the target user in a preset period (the first half year or the last year) is obtained, and then the total asset information is divided from the preset period to obtain the corresponding average revenue amount.
S502, obtaining a weight coefficient of each consumption item and average consumption amount according to the consumption information;
s503, carrying out weighted calculation according to the weight coefficient and the average consumption amount to obtain estimated consumption amount;
s504, calculating the difference value between the average income amount and the estimated consumption amount to obtain the repayment amount.
Specifically, after the consumption information of the target user is obtained in the above manner, since the consumption composition of the user includes at least one consumption item, according to the ratio of the average consumption amount of each consumption item to the total consumption amount per month, the weight coefficient of each consumption item can be determined, that is, the larger the ratio is, the larger the weight coefficient of the corresponding consumption item is. Therefore, the estimated consumption amount can be obtained by carrying out weighted calculation according to the weight coefficient and the average consumption amount corresponding to each consumption item. Then, the estimated consumption amount of the month is subtracted from the average amount of the month to calculate the repayment amount.
The invention captures user information data such as credit cards, loan details and the like through the Internet and financial websites, and then performs data processing such as data classification, data deduplication, data filling, data correction, data conversion, data cleaning, data verification and the like on the acquired user information data to obtain processed consumption information. And then, asset information is obtained through calculation from various aspects of enterprise operation, family condition, asset evaluation and the like of the target user, and the repayment amount of the target user can be obtained through calculation in the mode.
Step S402, determining the final credit line of the potential risk user according to the repayment amount and the initial credit line;
step S403, initiating an inquiry request to the potentially risky user to inform the final credit line;
step S404, if a confirmation response message of the potential risk user is received, allowing a credit card to be issued to the potential risk user;
step S405, refusing to issue a credit card to the potentially risk user if a denial response message of the potentially risk user is received.
Specifically, the initial credit limit of the potential risk user is determined according to the preset credit upper limit of the identity type of the potential risk user and the credit coefficient of the potential risk user. In this embodiment, the credit limit of the potential risk user may be calculated according to the preset credit upper limit of the identity type to which the potential risk user belongs, in combination with the credit coefficient of the potential risk user.
For example, the potential risk user is an identity type of an individual business user, the highest credit limit of the preset identity type of the individual business user is 50 ten thousand, the credit coefficient calculated by the potential risk user is 0.5, and then the initial credit limit of the potential risk user is: 50 ten thousand 0.5=25 ten thousand.
If the potential risk user is a general private enterprise employee identity type and the highest credit line of the general private enterprise employee is 10 ten thousand, the credit coefficient calculated by the potential risk user is 0.8, the initial credit line of the potential risk user is: 10 ten thousand 0.8=8ten thousand, the above description only uses two small enterprises as an example, and other identity types, such as a medium-sized identity type or a large-sized identity type, may be stored, which is not particularly limited.
Analyzing and obtaining a repayment amount according to the asset information and the consumption information of the potential risk user, and judging whether the initial credit amount exceeds the repayment amount; if yes, determining the repayment amount as the final credit limit; if not, determining to use the initial line of credit as the final line of credit. In this embodiment, the final credit line of the potentially-risky user may be determined according to the comparison of the initial credit line with the repayment amount of the potentially-risky user, i.e. whether the initial credit line exceeds the repayment amount of the potentially-risky user is determined, and when the initial credit line exceeds the repayment amount, the repayment amount is taken as the final credit line; when the initial line of credit does not exceed the repayment amount, the initial line of credit is taken as the final line of credit.
For example, if the initial credit line of a certain individual subscriber is 6 ten thousand and the repayment amount is 3 ten thousand, comparing the two to obtain the minimum value, and obtaining the final credit line of the individual subscriber to be 3 ten thousand; for example, if the initial credit line of a small enterprise is 80 ten thousand and the repayment amount is 60 ten thousand, the initial credit line of the small enterprise is 60 ten thousand, and the initial credit line of the small enterprise is obtained by comparing the initial credit line with the repayment amount of the small enterprise and the repayment amount of the small enterprise.
Specifically, the invention collects the basic data such as user name, contact information, home address, application type, application amount, application year, user age, marital status and the like provided by the user with active loan will, and captures the user information data such as credit card, loan details and the like through the Internet and financial websites, and then carries out data classification, data deduplication, data filling, data correction, data conversion and other data cleaning and data verification on the collected user information data. And extracting behavior characteristic information and a scene picture of the application to be identified obtained by image processing according to the user information data, and obtaining a risk identification model through data training.
Compared with the traditional credit loan, the invention more comprehensively covers the credit scoring examination points of the user, namely, behavior characteristic information is obtained from a plurality of aspects such as basic information of the user, business operation, family condition, property evaluation and the like, the application scene picture to be identified is obtained through image identification, and is trained and generated into a risk identification model, and meanwhile, the risk score of the user belonging to the bad account user can be more accurately evaluated by utilizing statistics and machine learning to cooperate with metering modeling.
The credit card application qualification of the claimant (namely the potential risk user) can be determined according to the scene picture of the application to be identified of the potential risk user, the behavior characteristic information and the risk identification model. The risk identification model may be set according to a specific bank rule, which is not specifically limited in this case. For example, the applicant's business status and the estimated loan level may be weighted, and the risk score may be determined based on different weights. For example, if the operation status is not abnormal, the weight is set to 1.1, and if the estimated loan level is high-quality, the weight is set to 1.1, and the loan amount is a 1.1.1. Wherein, A is set according to income amount, expense amount, credit report, national regulation and the like of claimant, and the method is not particularly limited.
Compared with manual verification, the invention can greatly reduce the error detection probability, greatly reduce the occurrence of risk loan incidents, and can reduce the labor cost and the error rate of the loan request verification process, thereby improving the verification efficiency and the accuracy rate and effectively controlling and checking the loan risks.
The credit card auditing method provided by the invention enables a user to apply for the credit card on line or off line through the user terminal, does not need to apply for the credit card with auditing personnel of a financial service institution in a face-to-face manner, saves manpower and material resources, improves auditing efficiency of loan business, greatly reduces manpower and material resources, does not need to go to the site for applying for the credit card, and improves use experience of the user.
According to the method described in the above embodiments, the present embodiment will be further described from the perspective of a credit card approval apparatus, which may be implemented as a separate entity or integrated into an electronic device, such as a terminal, which may include a mobile phone, a tablet computer, or the like.
The application also provides an embodiment, and the credit card approval device provided by the embodiment includes:
the image acquisition module is used for acquiring an application scene image to be identified of a potential risk user during the application of the credit card; the potential risk users comprise users with good credit reports and no any one or more behavior types in illegal behaviors;
the information acquisition module is used for acquiring behavior characteristic information of the potential risk user;
the analysis module is used for inputting the behavior characteristic information of the potential risk user and the application scene picture to be identified into the trained risk identification model to output a risk score;
and the processing module is used for judging whether to issue a credit card to the potential risk user according to the risk score.
In the implementation, each module may be implemented as an independent entity, or may be combined arbitrarily, and implemented as the same entity or several entities, and the implementation of each module may be referred to the foregoing method embodiment, which is not described herein again.
Fig. 6 is a schematic structural diagram of the electronic device provided in the embodiment of the present application, and fig. 6 shows a specific structural block diagram of the electronic device provided in the embodiment of the present application, where the electronic device may be used to implement the method for automatically approving the credit card provided in the embodiment described above. The electronic device 900 may be a mobile terminal such as a smart phone or a notebook computer.
The RF circuit 910 is configured to receive and transmit electromagnetic waves, and to perform mutual conversion between the electromagnetic waves and the electrical signals, so as to communicate with a communication network or other devices. The RF circuitry 910 may include various existing circuit elements for performing these functions, such as an antenna, a radio frequency transceiver, a digital signal processor, an encryption/decryption chip, a Subscriber Identity Module (SIM) card, memory, and the like. The RF circuitry 910 may communicate with various networks such as the internet, intranets, wireless networks, or with other devices via wireless networks. The wireless network may include a cellular telephone network, a wireless local area network, or a metropolitan area network. The wireless network may use various communication standards, protocols, and technologies including, but not limited to, global system for mobile communications (Global System for Mobile Communication, GSM), enhanced mobile communications technology (Enhanced Data GSM Environment, EDGE), wideband code division multiple access technology (Wideband Code Division Multiple Access, WCDMA), code division multiple access technology (Code Division Access, CDMA), time division multiple access technology (Time Division Multiple Access, TDMA), wireless fidelity technology (Wireless Fidelity, wi-Fi) (e.g., institute of electrical and electronics engineers standards IEEE 802.11a,IEEE 802.11b,IEEE802.11g and/or IEEE802.11 n), internet telephony (Voice over Internet Protocol, voIP), worldwide interoperability for microwave access (Worldwide Interoperability for Microwave Access, wi-Max), other protocols for mail, instant messaging, and short messaging, as well as any other suitable communication protocols, even including those not currently developed.
The memory 920 may be used to store software programs and modules, such as program instructions/modules corresponding to the automatic approval method of the credit card in the above embodiments, and the processor 980 executes the automatic approval process of the credit card by running the software programs and modules stored in the memory 920 as follows:
acquiring a scene picture to be identified of a potential risk user during credit card application; the potential risk users comprise users with good credit reports and no any one or more behavior types in illegal behaviors;
acquiring behavior characteristic information of the potential risk user;
inputting the behavior characteristic information of the potential risk user and the application scene picture to be identified into a trained risk identification model to output a risk score;
and judging whether to issue a credit card to the potential risk user according to the risk score.
Memory 920 may include high-speed random access memory, but may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, memory 920 may further include memory located remotely from processor 980, which may be connected to electronic device 900 by a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input unit 930 may be used to receive input numeric or character information and to generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control. In particular, the input unit 930 may comprise a touch-sensitive surface 931 and other input devices 932. The touch-sensitive surface 931, also referred to as a touch display screen or touch pad, may collect touch operations thereon or thereabout by a user (e.g., operations of the user on or thereabout the touch-sensitive surface 931 using a finger, stylus, or any other suitable object or accessory) and actuate the corresponding connection device according to a predetermined program. Alternatively, the touch sensitive surface 931 may include two portions, a touch detection device and a touch controller. The touch detection device detects the touch azimuth of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch detection device and converts it into touch point coordinates, which are then sent to the processor 980, and can receive commands from the processor 980 and execute them. In addition, the touch-sensitive surface 931 may be implemented in various types of resistive, capacitive, infrared, surface acoustic wave, and the like. In addition to the touch-sensitive surface 931, the input unit 930 may also include other input devices 932. In particular, other input devices 932 may include, but are not limited to, one or more of a physical keyboard, function keys (e.g., volume control keys, switch keys, etc.), a trackball, mouse, joystick, etc.
The display unit 940 may be used to display information entered by a user or provided to a user as well as various graphical user interfaces of the electronic device 900, which may be composed of graphics, text, icons, video, and any combination thereof. The display unit 940 may include a display panel 941, and alternatively, the display panel 941 may be configured in the form of an LCD (Liquid Crystal Display ), an OLED (Organic Light-Emitting Diode), or the like. Further, the touch-sensitive surface 931 may overlay the display panel 941, and upon detection of a touch operation thereon or thereabout, the touch-sensitive surface 931 is passed to the processor 980 to determine the type of touch event, and the processor 980 then provides a corresponding visual output on the display panel 941 depending on the type of touch event. Although in the figures the touch-sensitive surface 931 and the display panel 941 are implemented as two separate components, in some embodiments the touch-sensitive surface 931 may be integrated with the display panel 941 to implement the input and output functions.
The electronic device 900 may also include at least one sensor 950, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor, where the ambient light sensor may adjust the brightness of the display panel 941 according to the brightness of ambient light, and the proximity sensor may generate an interruption when the flip cover is closed or closed. As one of the motion sensors, the gravity acceleration sensor can detect the acceleration in all directions (generally three axes), and can detect the gravity and the direction when the mobile phone is stationary, and can be used for applications of recognizing the gesture of the mobile phone (such as horizontal and vertical screen switching, related games, magnetometer gesture calibration), vibration recognition related functions (such as pedometer and knocking), and the like; other sensors such as gyroscopes, barometers, hygrometers, thermometers, infrared sensors, etc. that may also be configured with the electronic device 900 are not described in detail herein.
Audio circuitry 960, speaker 961, microphone 962 may provide an audio interface between a user and electronic device 900. Audio circuit 960 may transmit the received electrical signal converted from audio data to speaker 961, where it is converted to a sound signal by speaker 961 for output; on the other hand, microphone 962 converts the collected sound signals into electrical signals, which are received by audio circuit 960 and converted into audio data, which are processed by audio data output processor 980 for transmission to, for example, another terminal via RF circuit 910 or for output to memory 920 for further processing. Audio circuitry 960 may also include an ear bud jack to provide communication of a peripheral ear bud with electronic device 900.
The electronic device 900 may facilitate user reception of requests, transmission of information, etc. via the transmission module 970 (e.g., wi-Fi module), which provides wireless broadband internet access to the user. Although the transmission module 970 is shown in the drawings, it is understood that it does not belong to the essential constitution of the electronic device 900, and can be omitted entirely as required within the scope of not changing the essence of the invention.
Processor 980 is a control center for electronic device 900, and utilizes various interfaces and lines to connect the various parts of the overall handset, performing various functions and processing data for electronic device 900 by running or executing software programs and/or modules stored in memory 920, and invoking data stored in memory 920, thereby performing overall monitoring of the electronic device. Optionally, processor 980 may include one or more processing cores; in some embodiments, processor 980 may integrate an application processor with a modem processor, where the application processor primarily handles operating systems, user interfaces, applications programs, and the like, and the modem processor primarily handles wireless communications. It is to be appreciated that the modem processor described above may not be integrated into the processor 980.
The electronic device 900 also includes a power supply 990 (e.g., a battery) that provides power to the various components, and in some embodiments, may be logically coupled to the processor 980 through a power management system to perform functions such as managing charging, discharging, and power consumption by the power management system. The power source 990 may also include one or more of any components, such as a direct current or alternating current power source, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and the like.
Although not shown, the electronic device 900 further includes a camera (e.g., front camera, rear camera), a bluetooth module, etc., which are not described herein. In particular, in this embodiment, the display unit of the electronic device is a touch screen display, the mobile terminal further includes a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for:
acquiring a scene picture to be identified of a potential risk user during credit card application; the potential risk users comprise users with good credit reports and no any one or more behavior types in illegal behaviors;
Acquiring behavior characteristic information of the potential risk user;
inputting the behavior characteristic information of the potential risk user and the application scene picture to be identified into a trained risk identification model to output a risk score;
and judging whether to issue a credit card to the potential risk user according to the risk score.
In the implementation, each module may be implemented as an independent entity, or may be combined arbitrarily, and implemented as the same entity or several entities, and the implementation of each module may be referred to the foregoing method embodiment, which is not described herein again.
Those of ordinary skill in the art will appreciate that all or a portion of the steps of the various methods of the above embodiments may be performed by instructions, or by instructions controlling associated hardware, which may be stored in a computer-readable storage medium and loaded and executed by a processor. To this end, embodiments of the present application provide a storage medium having stored therein a plurality of instructions capable of being loaded by a processor to perform the steps of any of the embodiments of the method for automated approval of credit cards provided by embodiments of the present application.
Wherein the storage medium may include: read Only Memory (ROM), random access Memory (RAM, random Access Memory), magnetic or optical disk, and the like.
The instructions stored in the storage medium may perform the steps in any embodiment of the method for automatically approving a credit card provided in the embodiments of the present application, so that the beneficial effects that any method for automatically approving a credit card provided in the embodiments of the present application can be achieved, which are detailed in the previous embodiments and are not described herein.
The above detailed description of the method, the device, the electronic device and the storage medium for automatic credit card approval provided in the embodiments of the present application applies specific examples to illustrate the principles and the embodiments of the present application, and the above description of the embodiments is only used to help understand the method and the core idea of the present application; meanwhile, those skilled in the art will have variations in the specific embodiments and application scope in light of the ideas of the present application, and the present description should not be construed as limiting the present application in view of the above. Moreover, it will be apparent to those skilled in the art that various modifications and variations can be made without departing from the principles of the present application, and such modifications and variations are considered to be within the scope of the present application.
Claims (7)
1. An automatic approval method for a credit card, comprising:
acquiring a scene picture to be identified of a potential risk user during credit card application; the potential risk users comprise users with good credit reports and no any one or more behavior types in illegal behaviors;
acquiring behavior characteristic information of the potential risk user;
the step of obtaining the behavior characteristic information of the potential risk user comprises the following steps:
acquiring identity characteristics according to the personal identity information of the potential risk user;
acquiring social relationship characteristics according to personal social information of the potential risk user;
acquiring transaction behavior characteristics according to the personal transaction records of the potential risk users;
acquiring credit according to the personal credit report of the potential risk user;
the behavior characteristic information comprises any one or more of identity characteristics, social relation characteristics, transaction behavior characteristics and credibility;
inputting the behavior characteristic information of the potential risk user and the application scene picture to be identified into a trained risk identification model to output a risk score;
before inputting the behavior characteristic information of the potential risk user and the application scene picture to be identified into the trained risk identification model to output a risk score, the method comprises the following steps:
Acquiring application scene sample pictures and behavior characteristic information of bad account users according to historical data of bad account type credit cards;
carrying out image recognition on the application scene sample picture through an image recognition algorithm to obtain bad account scene image features;
training to obtain a risk identification model according to the bad account scene image characteristics and the behavior characteristic information of the bad account user;
judging whether to issue a credit card to the potential risk user according to the risk score;
the determining whether to issue a credit card to the potentially risky user based on the risk score includes:
comparing the risk score with a first preset risk threshold and a second preset risk threshold; the first preset risk threshold is smaller than the second preset risk threshold;
if the risk score is lower than the first preset risk threshold, rejecting to issue a credit card to the potential risk user;
if the risk score is higher than the second preset risk threshold, allowing a credit card to be issued to the potential risk user;
and if the risk score is higher than the first preset risk threshold and lower than the second preset risk threshold, judging whether to issue a credit card to the potential risk user again according to the repayment amount of the potential risk user.
2. The method of claim 1, wherein the re-evaluating whether to issue a credit card to the potentially risky user based on the amount of credit paid by the potentially risky user comprises:
analyzing and obtaining a repayment amount according to the asset information and the consumption information of the potential risk user;
determining a final credit line for the potentially risky user based on the repayment amount and the initial credit line;
initiating a query request to the potentially risky user to inform the final credit line;
if a confirmation response message of the potential risk user is received, allowing a credit card to be issued to the potential risk user;
and if the denial response message of the potential risk user is received, refusing to issue the credit card to the potential risk user.
3. The method of claim 2, wherein analyzing the credit returns based on the asset information and the consumption information of the potentially risky user comprises:
acquiring the average income amount of the potential risk user according to the asset information;
acquiring a weight coefficient of each consumption item and average consumption amount according to the consumption information;
Weighting calculation is carried out according to the weight coefficient and the average consumption amount to obtain estimated consumption amount;
and calculating the difference value between the average income amount and the estimated consumption amount to obtain the repayment amount.
4. A method of automated credit card approval as in claim 3, wherein said determining a final line of credit for said potentially risky user based on said amount of repayment and an initial line of credit comprises:
determining whether the initial credit line exceeds the repayment amount;
if yes, determining the repayment amount as the final credit limit;
if not, determining to use the initial line of credit as the final line of credit.
5. An approval apparatus for a credit card, comprising:
the image acquisition module is used for acquiring an application scene image to be identified of a potential risk user during the application of the credit card; the potential risk users comprise users with good credit reports and no any one or more behavior types in illegal behaviors;
the information acquisition module is used for acquiring behavior characteristic information of the potential risk user;
the step of obtaining the behavior characteristic information of the potential risk user comprises the following steps:
Acquiring identity characteristics according to the personal identity information of the potential risk user;
acquiring social relationship characteristics according to personal social information of the potential risk user;
acquiring transaction behavior characteristics according to the personal transaction records of the potential risk users;
acquiring credit according to the personal credit report of the potential risk user;
the behavior characteristic information comprises any one or more of identity characteristics, social relation characteristics, transaction behavior characteristics and credibility;
the analysis module is used for inputting the behavior characteristic information of the potential risk user and the application scene picture to be identified into the trained risk identification model to output a risk score;
before inputting the behavior characteristic information of the potential risk user and the application scene picture to be identified into the trained risk identification model to output a risk score, the method comprises the following steps:
acquiring application scene sample pictures and behavior characteristic information of bad account users according to historical data of bad account type credit cards;
carrying out image recognition on the application scene sample picture through an image recognition algorithm to obtain bad account scene image features;
training to obtain a risk identification model according to the bad account scene image characteristics and the behavior characteristic information of the bad account user;
The processing module is used for judging whether to issue a credit card to the potential risk user according to the risk score;
the determining whether to issue a credit card to the potentially risky user based on the risk score includes:
comparing the risk score with a first preset risk threshold and a second preset risk threshold; the first preset risk threshold is smaller than the second preset risk threshold;
if the risk score is lower than the first preset risk threshold, rejecting to issue a credit card to the potential risk user;
if the risk score is higher than the second preset risk threshold, allowing a credit card to be issued to the potential risk user;
and if the risk score is higher than the first preset risk threshold and lower than the second preset risk threshold, judging whether to issue a credit card to the potential risk user again according to the repayment amount of the potential risk user.
6. An electronic device comprising a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, the memory being coupled to the processor, and the processor implementing the steps in the method of automatic approval of a credit card as claimed in any one of claims 1 to 4 when the computer program is executed by the processor.
7. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program, wherein the computer program when run controls a device in which the computer readable storage medium is located to perform the steps in the method for automated approval of a credit card according to any of claims 1 to 4.
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