CN114841801A - Credit wind control method and device based on user behavior characteristics - Google Patents
Credit wind control method and device based on user behavior characteristics Download PDFInfo
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Abstract
The invention provides a credit wind control method and a credit wind control device based on user behavior characteristics, which relate to the technical field of credit wind control and comprise the following steps: behavior feature data of overdue users are obtained; preprocessing behavior characteristic data of overdue users and processing derived variables to obtain derived variable data of the overdue users; training an initial wind control model by using derivative variable data of overdue users to obtain a target wind control model, wherein the initial wind control model comprises: the system comprises a wind control sub-model in a credit approval stage and a wind control sub-model in a loan approval stage; after obtaining a loan application and credit investigation authorization sent by a user to be analyzed through a loan program in terminal equipment, acquiring behavior characteristic data of the user to be analyzed; the risk rating of the user to be analyzed and a wind control decision result corresponding to the risk rating are determined based on the behavior characteristic data of the user to be analyzed and the target wind control model, and the technical problems of high timeliness and high cost of an existing credit wind control method are solved.
Description
Technical Field
The invention relates to the technical field of credit wind control, in particular to a credit wind control method and device based on user behavior characteristics.
Background
Credit wind control method, technology and system based on big data are the core of the financial science and technology field, and traditional banks, emerging internet banks and consumption financial institutions all pay considerable attention to research, development and application of related technologies and methods. The big data wind control technology can effectively integrate the multidimensional information data of the borrowing applicant, efficiently and accurately evaluate the credit or fraud risk of the borrower, and assist the financial institution to quickly give the credit granting and borrowing approval decision-making conclusion, so that the user experience of the borrowing applicant is improved, and the potential bad account loss risk of the financial institution is reduced.
In the current big data wind control technology, data sources mainly used comprise pedestrian, enterprise credit investigation, social security accumulation fund payment records, external multi-loan information data and the like, the data can reflect historical credit performance and repayment capacity of a client to a certain extent, certain time lag exists, the latest time point risk capture capacity of the client is weak, the risk that the client breaks through and tries by utilizing the difference of the timeliness of related credit reports exists, and potential loss is brought to financial institutions.
No effective solution has been proposed to the above problems.
Disclosure of Invention
In view of the above, the present invention provides a credit wind control method and apparatus based on user behavior characteristics, so as to alleviate the technical problems of timeliness and high cost of the existing credit wind control method.
In a first aspect, an embodiment of the present invention provides a credit wind control method based on user behavior characteristics, including: acquiring behavior feature data of overdue users, wherein the behavior feature data comprises: the method comprises the steps of obtaining terminal equipment information and buried point information of a front-end interactive interface of loan software; preprocessing the behavior characteristic data of the overdue user and processing derived variables to obtain derived variable data of the overdue user; training an initial wind control model by using the derived variable data of the overdue user to obtain a target wind control model, wherein the initial wind control model comprises: the system comprises a wind control sub-model in a credit approval stage and a wind control sub-model in a loan approval stage; after obtaining a loan application and credit investigation authorization sent by a user to be analyzed through a loan program in terminal equipment, acquiring behavior characteristic data of the user to be analyzed; and determining the risk rating of the user to be analyzed and a wind control decision result corresponding to the risk rating based on the behavior characteristic data of the user to be analyzed and the target wind control model.
Further, the terminal device information includes: attribute information of the terminal equipment, common geographical position and variation information, WIFI connection duration, position and stability information; the embedded point information of the front-end interactive interface comprises: the browsing times, the period, the duration, the time, the click behavior, the trial times and the failure times of the front-end interactive interface, wherein the front-end interactive interface comprises: the system comprises a loan opening interface, a loan application interface, a password inspection interface and a short message verification code input interface.
Further, the pre-processing comprises: abnormal value processing, null value processing, box dividing processing and evidence weight conversion processing; the derivative variable data includes: the method comprises the steps of browsing time of a welcome interface of loan software, browsing time of a loan application interface, WIFI connection records of workdays, common login address information, interval duration from successful credit granting to loan application, password input times and password input failure times.
Further, training the initial wind control model by using the derived variable data of the overdue user to obtain a target wind control model, comprising: and respectively training the wind control sub-model of the credit approval stage of the initial wind control model and the wind control sub-model of the borrowing approval stage of the initial wind control model by using the derived variable data of the overdue user to obtain the target wind control model.
Further, determining a risk rating of the user to be analyzed and a wind control decision result corresponding to the risk rating based on the behavior feature data of the user to be analyzed and the target wind control model, including: preprocessing the behavior characteristic data of the user to be analyzed and processing derivative variables to obtain derivative variable data of the user to be analyzed; and inputting the derived variable data of the user to be analyzed into the target wind control model to obtain the risk rating of the user to be analyzed and a wind control decision result corresponding to the risk rating.
Further, the method further comprises: and sending the risk rating of the user to be analyzed and the wind control decision result corresponding to the risk rating to the terminal equipment of the user to be analyzed.
Further, the wind control model is a decision tree algorithm model which is generated based on the information entropy, the maximum information gain ratio and the kini index.
In a second aspect, an embodiment of the present invention further provides a credit wind control device based on user behavior characteristics, including: the system comprises an acquisition unit, a processing unit, a training unit, an acquisition unit and an air control unit, wherein the acquisition unit is used for acquiring behavior characteristic data of overdue users, wherein the behavior characteristic data comprises: the method comprises the steps of obtaining terminal equipment information and buried point information of a front-end interactive interface of loan software; the processing unit is used for preprocessing the behavior characteristic data of the overdue user and processing derived variables to obtain derived variable data of the overdue user; the training unit is used for training an initial wind control model by using the derived variable data of the overdue user to obtain a target wind control model, wherein the initial wind control model comprises: the system comprises a wind control sub-model in a credit approval stage and a wind control sub-model in a loan approval stage; the system comprises a collecting unit and a processing unit, wherein the collecting unit is used for collecting behavior characteristic data of a user to be analyzed after obtaining a loan application and credit investigation authorization sent by the user to be analyzed through a loan program in terminal equipment; and the wind control unit is used for determining the risk rating of the user to be analyzed and a wind control decision result corresponding to the risk rating based on the behavior characteristic data of the user to be analyzed and the target wind control model.
In a third aspect, an embodiment of the present invention further provides an electronic device, which includes a memory and a processor, where the memory is used to store a program that supports the processor to execute the method in the first aspect, and the processor is configured to execute the program stored in the memory.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored.
In the embodiment of the present invention, behavior feature data of an overdue user is obtained, where the behavior feature data includes: the method comprises the steps of obtaining terminal equipment information and buried point information of a front-end interactive interface of loan software; preprocessing the behavior characteristic data of the overdue user and processing derived variables to obtain derived variable data of the overdue user; training an initial wind control model by using the derived variable data of the overdue user to obtain a target wind control model, wherein the initial wind control model comprises: the system comprises a wind control sub-model in a credit approval stage and a wind control sub-model in a loan approval stage; after obtaining a loan application and credit investigation authorization sent by a user to be analyzed through a loan program in terminal equipment, acquiring behavior characteristic data of the user to be analyzed; and determining the risk rating of the user to be analyzed and a wind control decision result corresponding to the risk rating based on the behavior characteristic data of the user to be analyzed and the target wind control model, so that the aim of wind control of the user without third-party wind control data and pain-creating wind control data is fulfilled, and the technical problems of high timeliness and high cost of the conventional credit wind control method are solved, so that the technical effects of improving the timeliness of the credit wind control method and reducing the cost of the credit wind control method are achieved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flowchart of a credit wind control method based on user behavior characteristics according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a credit wind control device based on user behavior characteristics according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The first embodiment is as follows:
in accordance with an embodiment of the present invention, there is provided an embodiment of a credit windmilling method based on user behavior characteristics, it being noted that the steps illustrated in the flowchart of the figures may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be performed in an order different than presented herein.
Fig. 1 is a flowchart of a credit wind control method based on user behavior characteristics according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
step S102, behavior feature data of overdue users are obtained, wherein the behavior feature data comprise: the method comprises the steps of obtaining terminal equipment information and buried point information of a front-end interactive interface of loan software;
the terminal device information includes: attribute information of the terminal equipment, common geographical position and change information, and WIFI connection duration, position and stability information.
The embedded point information of the front-end interactive interface comprises: the browsing times, the period, the duration, the time, the click behavior, the trial times and the failure times of the front-end interactive interface, wherein the front-end interactive interface comprises: the system comprises a loan opening interface, a loan application interface, a password inspection interface and a short message verification code input interface.
Step S104, preprocessing the behavior characteristic data of the overdue user and processing derived variables to obtain derived variable data of the overdue user;
it should be noted that the preprocessing includes: abnormal value processing, null value processing, box processing and evidence weight conversion processing.
The above-mentioned derivative variable data include: the method comprises the steps of browsing time of a welcome interface of loan software, browsing time of a loan application interface, WIFI connection records of workdays, common login address information, interval duration from successful credit granting to loan application, password input times and password input failure times.
In addition, the browsing time of the welcome interface of the loan software and the browsing time of the loan application interface are used for judging whether the overdue user browses the loan software on a working day. The WIFI connection record for the workday is used to determine if the workplace of the overdue user is fixed. The common login address information is used to determine whether the overdue user frequently changes the place of residence or work. The length of the interval between the successful crediting and borrowing applications is used to determine whether the overdue user is at risk of excessive fund hunger and thirst.
Step S106, training an initial wind control model by using the derivative variable data of the overdue user to obtain a target wind control model, wherein the initial wind control model comprises the following steps: the system comprises a wind control sub-model in a credit approval stage and a wind control sub-model in a loan approval stage;
it should be noted that the wind control model is a decision tree algorithm model generated based on the information entropy, the maximum information gain ratio, and the kini index.
The model based on the decision tree algorithm can effectively process various data (such as sparse, biased, continuous, classified and missing values), has better interpretability and is a common modeling tool in the field of credit wind control. The decision tree is trained by using the behavior feature data of the overdue user, various behavior feature variables of the user can be divided into a plurality of decision tree nodes according to an optimal threshold, each node has a corresponding historical overdue rate statistical condition, and therefore corresponding high, medium and low risk grade division can be conducted according to the risk tolerance of the business.
Step S108, after obtaining a loan application and credit investigation authorization sent by a user to be analyzed through a loan program in terminal equipment, collecting behavior characteristic data of the user to be analyzed;
step S110, determining the risk rating of the user to be analyzed and a wind control decision result corresponding to the risk rating based on the behavior characteristic data of the user to be analyzed and the target wind control model.
In the embodiment of the present invention, behavior feature data of an overdue user is obtained, where the behavior feature data includes: the method comprises the steps of obtaining terminal equipment information and buried point information of a front-end interactive interface of loan software; preprocessing the behavior characteristic data of the overdue user and processing derived variables to obtain derived variable data of the overdue user; training an initial wind control model by using the derived variable data of the overdue user to obtain a target wind control model, wherein the initial wind control model comprises: the system comprises a wind control sub-model in a credit approval stage and a wind control sub-model in a loan approval stage; after obtaining a loan application and credit investigation authorization sent by a user to be analyzed through a loan program in terminal equipment, acquiring behavior characteristic data of the user to be analyzed; and determining the risk rating of the user to be analyzed and a wind control decision result corresponding to the risk rating based on the behavior characteristic data of the user to be analyzed and the target wind control model, so that the aim of wind control of the user without third-party wind control data and pain-creating wind control data is fulfilled, and the technical problems of high timeliness and high cost of the conventional credit wind control method are solved, so that the technical effects of improving the timeliness of the credit wind control method and reducing the cost of the credit wind control method are achieved.
In the embodiment of the present invention, step S110 includes the following steps:
step S11, preprocessing the behavior characteristic data of the user to be analyzed and processing derived variables to obtain derived variable data of the user to be analyzed;
step S12, inputting the derived variable data of the user to be analyzed into the target wind control model to obtain the risk rating of the user to be analyzed and a wind control decision result corresponding to the risk rating.
In the embodiment of the invention, after the derived variable data of the user to be analyzed is obtained, the derived variable data of the user to be analyzed is input into the wind control submodel in the trust approval stage in the target wind control model, the wind control submodel in the trust approval stage outputs the corresponding decision tree node of the user to be analyzed according to the derived variable data of the user to be analyzed, and then the node is mapped into the trust approval risk rating by comparing and referring to the default rate condition of the overdue user corresponding to the node, and the trust approval risk rating is divided into low risk, medium risk and high risk.
If the wind control sub-model in the credit approval stage predicts that the customer to be analyzed belongs to high risk rating, outputting a wind control decision as approval refusal; if the wind control sub-model in the credit approval stage predicts that the client to be analyzed is at medium and high risk, performing certain pressure drop and limitation on the credit of the client to be analyzed; and if the wind control sub-model in the credit approval stage predicts medium and low risks of the customer to be analyzed, outputting a decision conclusion that the approval is passed, and enabling the customer to be analyzed to perform the next loan application operation.
The method comprises the steps that derived variable data of a user to be analyzed are input into a pneumatic control submodel of a loan approval stage in a target pneumatic control model, the pneumatic control submodel of the loan approval stage outputs a corresponding decision tree node of the user to be analyzed according to the derived variable data of the user to be analyzed, the node is mapped into loan approval risk ratings by comparing and referring to default rate conditions of overdue users corresponding to the node, and the loan approval risk ratings are divided into low risk, medium risk and high risk.
It should be noted that, during the loan approval stage, it may also be obtained whether the loan application time of the customer is an abnormal application (e.g., early morning hours), and whether fraud or telecom fraud risk exists is determined by combining the relevant variables. The specific variable segmentation threshold and risk grade division are also determined according to behavior feature data of overdue users.
If the wind control sub-model in the loan approval stage predicts that the user to be analyzed belongs to high risk rating, outputting a wind control decision result as loan interception; if the wind control sub-model in the loan approval stage predicts that the user to be analyzed is at medium and high risk, further risk assessment can be carried out on the user to be analyzed by combining external third-party data, or manual auditing and the like can be carried out; and if the wind control sub-model in the loan approval stage predicts that the user to be analyzed is at medium or low risk, outputting a wind control decision result that the approval is passed, and carrying out bank card selection, withdrawal and other operations by the user to be analyzed.
And finally, after the risk rating of the user to be analyzed and the wind control decision result corresponding to the risk rating are obtained, feeding the risk rating of the user to be analyzed and the wind control decision result corresponding to the risk rating back to the user to be analyzed.
The user to be analyzed needs to simultaneously pass through the wind control submodel in the credit granting approval stage and the wind control submodel rule in the loan approval stage based on the user behavior characteristics from the credit granting to the withdrawal success, so that the behavior characteristics of the user to be analyzed can be comprehensively judged from multiple dimensions and angles, and the risk of the whole service can be controlled.
In the embodiment of the invention, the operation result of the wind control model comprises passing, rejecting, derating, manual auditing, re-evaluating and the like, and a corresponding execution strategy is configured according to the output result of the model.
More specifically, the method can be independently used as a separate wind control strategy, and can also be used for carrying out comprehensive risk evaluation on the borrowing application user by combining credit investigation data of other dimensions so as to obtain more accurate risk evaluation.
In summary, the credit wind control method based on the user behavior characteristics provided by the embodiment of the invention does not rely on external third-party wind control data, so that the credit investigation cost can be reduced, and meanwhile, the real-time abnormal behavior risk characteristics of the user can be effectively identified;
the main decision point of the current big data wind control method system is the client credit application point, and the wind control evaluation in the client loan application and withdrawal links is weak, so that the risk reevaluation cannot be performed by effectively utilizing the multidimensional information data which may have large changes from credit to withdrawal of the client;
the method comprises the steps that a data source used by the traditional wind control comprises a pedestrian, an enterprise credit investigation, a social security public deposit fund payment record, external multi-head borrowing information data and the like, the data can reflect historical credit performance and repayment capacity of a user to be analyzed to a certain extent, certain time lag exists, the latest time point risk capture capacity of the user to be analyzed is weak, and the risk of the user to be analyzed utilizing the difference in time effectiveness of a related credit investigation report to carry out breakthrough attempt exists.
Example two:
the embodiment of the invention also provides a credit wind control device based on the user behavior characteristics, which is used for executing the credit wind control method based on the user behavior characteristics, provided by the embodiment of the invention, and the following is a specific introduction of the device provided by the embodiment of the invention.
As shown in fig. 2, fig. 2 is a schematic diagram of the credit wind control device based on the user behavior characteristics, and the credit wind control device based on the user behavior characteristics includes: the system comprises an acquisition unit 10, a processing unit 20, a training unit 30, an acquisition unit 40 and a wind control unit 50.
The acquiring unit is used for acquiring behavior feature data of an overdue user, wherein the behavior feature data comprises: the method comprises the steps of obtaining terminal equipment information and buried point information of a front-end interactive interface of loan software;
the processing unit is used for preprocessing the behavior characteristic data of the overdue user and processing derived variables to obtain derived variable data of the overdue user;
the training unit is used for training an initial wind control model by using the derived variable data of the overdue user to obtain a target wind control model, wherein the initial wind control model comprises: the system comprises a wind control submodel in a credit granting approval stage and a wind control submodel in a borrowing approval stage;
the system comprises a collecting unit and a processing unit, wherein the collecting unit is used for collecting behavior characteristic data of a user to be analyzed after obtaining a loan application and credit investigation authorization sent by the user to be analyzed through a loan program in terminal equipment;
and the wind control unit is used for determining the risk rating of the user to be analyzed and a wind control decision result corresponding to the risk rating based on the behavior characteristic data of the user to be analyzed and the target wind control model.
In the embodiment of the present invention, behavior feature data of an overdue user is obtained, where the behavior feature data includes: the method comprises the steps of obtaining terminal equipment information and buried point information of a front-end interactive interface of loan software; preprocessing the behavior characteristic data of the overdue user and processing derived variables to obtain derived variable data of the overdue user; training an initial wind control model by using the derived variable data of the overdue user to obtain a target wind control model, wherein the initial wind control model comprises: the system comprises a wind control sub-model in a credit approval stage and a wind control sub-model in a loan approval stage; after obtaining a loan application and credit investigation authorization sent by a user to be analyzed through a loan program in terminal equipment, acquiring behavior characteristic data of the user to be analyzed; and determining the risk rating of the user to be analyzed and a wind control decision result corresponding to the risk rating based on the behavior characteristic data of the user to be analyzed and the target wind control model, so that the aim of wind control of the user without third-party wind control data and pain-creating wind control data is fulfilled, and the technical problems of high timeliness and high cost of the conventional credit wind control method are solved, so that the technical effects of improving the timeliness of the credit wind control method and reducing the cost of the credit wind control method are achieved.
Example three:
an embodiment of the present invention further provides an electronic device, including a memory and a processor, where the memory is used to store a program that supports the processor to execute the method described in the first embodiment, and the processor is configured to execute the program stored in the memory.
Referring to fig. 3, an embodiment of the present invention further provides an electronic device 100, including: a processor 60, a memory 61, a bus 62 and a communication interface 63, wherein the processor 60, the communication interface 63 and the memory 61 are connected through the bus 62; the processor 60 is arranged to execute executable modules, such as computer programs, stored in the memory 61.
The Memory 61 may include a high-speed Random Access Memory (RAM) and may also include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 63 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like can be used.
The bus 62 may be an ISA bus, PCI bus, EISA bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 3, but this does not indicate only one bus or one type of bus.
The memory 61 is used for storing a program, the processor 60 executes the program after receiving an execution instruction, and the method executed by the apparatus defined by the flow process disclosed in any of the foregoing embodiments of the present invention may be applied to the processor 60, or implemented by the processor 60.
The processor 60 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 60. The Processor 60 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the device can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory 61, and the processor 60 reads the information in the memory 61 and, in combination with its hardware, performs the steps of the above method.
Example four:
the embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program performs the steps of the method in the first embodiment.
In addition, in the description of the embodiments of the present invention, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A credit wind control method based on user behavior characteristics is characterized by comprising the following steps:
acquiring behavior feature data of overdue users, wherein the behavior feature data comprises: the method comprises the steps of obtaining terminal equipment information and buried point information of a front-end interactive interface of loan software;
preprocessing the behavior characteristic data of the overdue user and processing derived variables to obtain derived variable data of the overdue user;
training an initial wind control model by using the derived variable data of the overdue user to obtain a target wind control model, wherein the initial wind control model comprises: the system comprises a wind control submodel in a credit granting approval stage and a wind control submodel in a borrowing approval stage;
after obtaining a loan application and credit investigation authorization sent by a user to be analyzed through a loan program in terminal equipment, acquiring behavior characteristic data of the user to be analyzed;
and determining the risk rating of the user to be analyzed and a wind control decision result corresponding to the risk rating based on the behavior characteristic data of the user to be analyzed and the target wind control model.
2. The method of claim 1,
the terminal device information includes: attribute information of the terminal equipment, common geographical position and variation information, WIFI connection duration, position and stability information;
the embedded point information of the front-end interactive interface comprises: the browsing times, the period, the duration, the time, the click behavior, the trial times and the failure times of the front-end interactive interface, wherein the front-end interactive interface comprises: the system comprises a loan opening interface, a loan application interface, a password inspection interface and a short message verification code input interface.
3. The method of claim 1,
the pretreatment comprises the following steps: abnormal value processing, null value processing, box dividing processing and evidence weight conversion processing;
the derived variable data includes: the method comprises the steps of browsing time of a welcome interface of loan software, browsing time of a loan application interface, WIFI connection records of workdays, common login address information, interval duration from successful credit granting to loan application, password input times and password input failure times.
4. The method of claim 3, wherein training an initial wind control model with derivative variable data of the overdue user to obtain a target wind control model comprises:
and respectively training the wind control sub-model of the credit approval stage of the initial wind control model and the wind control sub-model of the borrowing approval stage of the initial wind control model by using the derived variable data of the overdue user to obtain the target wind control model.
5. The method according to claim 1, wherein determining a risk rating of the user to be analyzed and a wind control decision result corresponding to the risk rating based on the behavior feature data of the user to be analyzed and the target wind control model comprises:
preprocessing the behavior characteristic data of the user to be analyzed and processing derivative variables to obtain derivative variable data of the user to be analyzed;
and inputting the derived variable data of the user to be analyzed into the target wind control model to obtain the risk rating of the user to be analyzed and a wind control decision result corresponding to the risk rating.
6. The method of claim 1, further comprising:
and sending the risk rating of the user to be analyzed and the wind control decision result corresponding to the risk rating to the terminal equipment of the user to be analyzed.
7. The method of claim 1,
the wind control model is a decision tree algorithm model which is generated based on the information entropy, the maximum information gain ratio and the Gini index.
8. A credit wind control device based on user behavior characteristics, comprising: an acquisition unit, a processing unit, a training unit, an acquisition unit and a wind control unit, wherein,
the acquiring unit is used for acquiring behavior feature data of an overdue user, wherein the behavior feature data comprises: the method comprises the steps of obtaining terminal equipment information and buried point information of a front-end interactive interface of loan software;
the processing unit is used for preprocessing the behavior characteristic data of the overdue user and processing derived variables to obtain derived variable data of the overdue user;
the training unit is used for training an initial wind control model by using the derived variable data of the overdue user to obtain a target wind control model, wherein the initial wind control model comprises: the system comprises a wind control sub-model in a credit approval stage and a wind control sub-model in a loan approval stage;
the system comprises a collecting unit and a processing unit, wherein the collecting unit is used for collecting behavior characteristic data of a user to be analyzed after obtaining a loan application and credit investigation authorization sent by the user to be analyzed through a loan program in terminal equipment;
and the wind control unit is used for determining the risk rating of the user to be analyzed and a wind control decision result corresponding to the risk rating based on the behavior characteristic data of the user to be analyzed and the target wind control model.
9. An electronic device comprising a memory for storing a program that enables a processor to perform the method of any of claims 1 to 7 and a processor configured to execute the program stored in the memory.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of the claims 1 to 7.
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