CN110659984A - Credit limit management method and device based on user life cycle prediction and electronic equipment - Google Patents
Credit limit management method and device based on user life cycle prediction and electronic equipment Download PDFInfo
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Abstract
The embodiment of the specification provides a credit line management method based on user life cycle prediction, which comprises the steps of obtaining data of a sample user, wherein the data of the sample user comprises basic information, credit worthiness information, performance information and a life cycle of the sample user, training a life cycle prediction model based on the data, wherein the life cycle prediction model is used for predicting the life cycle of a current user, obtaining data of the current user, inputting the data of the current user into the life cycle prediction model, obtaining a prediction result of the life cycle of the current user, and performing credit granting on the current user based on the prediction result. The life cycle prediction model is trained through the data of the sample user, the life cycle of the current user is predicted by the life cycle prediction model, namely the decline period of the current user is predicted, the limit validity period of the current user is set before the decline period comes, and therefore the user is guided to examine and approve when the decline period comes, and risks are reduced.
Description
Technical Field
The application relates to the field of computers, in particular to a credit line management method and device based on user life cycle prediction and electronic equipment.
Background
The credit granting management is a management mode which is developed aiming at the credit granting business and has wide coverage, higher efficiency, obvious economic benefit and great development potential. When the financial institution approves the credit application of the user, the credit limit of the user needs to be calculated. The credit line is a credit line which is given to a client for use within a certain period of time by public commitment or internal agreement after being approved by an internal program by a financial institution. The financial institution applies for the amount of the financial demand provided by the client, considers the recent fund supply and demand condition of the institution and the future cash flow of the client, and examines the risk limit which can be used for the credit transaction of the client in a period under the highest comprehensive credit line. After the credit is granted, the user is likely to be immobile for a long time or a long time has passed after the approval when the loan is made again, and the user may deteriorate at this time, that is, the user qualification is greatly changed compared with the user qualification during the approval, and the user risk may not be controlled only by the amount of money and price means.
Disclosure of Invention
The embodiment of the specification provides a credit line management method and device based on user life cycle prediction and electronic equipment. The method and the device are used for solving the problems that in the prior art, the user is not movable for a long time after credit granting, the quality of the user is possibly changed, and the user risk is possibly not controlled for preventing the user from deteriorating.
The embodiment of the specification provides a credit line management method based on user life cycle prediction, which comprises the following steps:
acquiring data of a sample user, wherein the data of the sample user comprises basic information, credit information, performance information and a life cycle of the sample user;
training a life cycle prediction model based on the data, wherein the life cycle prediction model is used for predicting the life cycle of the current user;
acquiring data of a current user;
inputting the data of the current user into the life cycle prediction model, and obtaining the prediction result of the life cycle of the current user;
and based on the prediction result, granting credit to the current user.
Optionally, the life cycle is a period from a credit date to a decline period.
Optionally, the decline period of the sample user is obtained based on the overdue number and the overdue amount of the sample user.
Optionally, the obtaining the decline period of the sample user based on the overdue number and the overdue amount of the sample user includes:
establishing a overdue threshold comprising a number of overdue thresholds and/or an overdue amount threshold;
the sample users exceeding the overdue threshold enter a decline period.
Optionally, the acquiring data of the current user includes:
the basic information, credit information and performance information of the current user.
Optionally, the granting, based on the prediction result, the current user includes:
and setting up a corresponding limit validity period of the current user according to the prediction result of the life cycle of the current user.
Optionally, the granting the current user based on the prediction result further includes:
before the limit validity period of the current user expires, displaying the limit validity period of the current user;
and guiding the current user with the line validity period about to expire to re-enter the credit granting process.
Optionally, the granting the current user based on the prediction result further includes:
and leading the user who transacts outside the validity period of the limit or the user who sleeps after credit and suddenly transacts to enter the credit process.
The application also provides a credit limit management device based on user life cycle prediction, the device includes:
a first obtaining module: the data acquisition module is used for acquiring data of a sample user, wherein the data of the sample user comprises basic information, credit information, performance information, overdue information and a life cycle of the sample user;
a training module: training a life cycle prediction model based on the data, the life cycle prediction model being used for predicting the life cycle of the current user;
a second obtaining module: the data acquisition module is used for acquiring data of a current user;
a prediction module: the life cycle prediction model is used for inputting the data of the current user into the life cycle prediction model and acquiring the prediction result of the life cycle of the current user;
a credit granting module: and the user authorization module is used for authorizing the current user based on the prediction result.
Optionally, the life cycle is a period from a credit date to a decline period.
Optionally, the decline period of the sample user is obtained based on the overdue number and the overdue amount of the sample user.
Optionally, the obtaining the decline period of the sample user based on the overdue number and the overdue amount of the sample user includes:
establishing a overdue threshold comprising a number of overdue thresholds and/or an overdue amount threshold;
the sample users exceeding the overdue threshold enter a decline period.
Optionally, the acquiring data of the current user includes:
the basic information, credit information and performance information of the current user.
Optionally, the granting, based on the prediction result, the current user includes:
and setting up a corresponding limit validity period of the current user according to the prediction result of the life cycle of the current user.
Optionally, the granting the current user based on the prediction result further includes:
before the limit validity period of the current user expires, displaying the limit validity period of the current user;
and guiding the current user with the line validity period about to expire to re-enter the credit granting process.
Optionally, the granting the current user based on the prediction result further includes:
and leading the user who transacts outside the validity period of the limit or the user who sleeps after credit and suddenly transacts to enter the credit process.
The present application further provides an electronic device, wherein the electronic device includes:
a processor; and the number of the first and second groups,
a memory storing computer-executable instructions that, when executed, cause the processor to perform any of the methods described above.
The present application also provides a computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement any of the methods described above.
The method comprises the steps of obtaining data of a sample user, wherein the data of the sample user comprises basic information, credit worthiness information, performance information and a life cycle of the sample user, training a life cycle prediction model based on the data, wherein the life cycle prediction model is used for predicting the life cycle of a current user, obtaining data of the current user, inputting the data of the current user into the life cycle prediction model, obtaining a prediction result of the life cycle of the current user, and granting credit to the current user based on the prediction result. Firstly, a life cycle prediction model is trained through data of a sample user, the life cycle of the current user is predicted by the life cycle prediction model, namely, the decline period of the current user is predicted, the limit validity period of the current user is set before the decline period comes, so that the user is guided to examine and approve when the decline period comes, and the problem that the user risk cannot be controlled due to the fact that the user quality changes is avoided.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a schematic diagram illustrating a method for managing credit based on user lifecycle prediction according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram illustrating a method for managing credit based on user lifecycle prediction according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of an electronic device provided in an embodiment of the present disclosure;
fig. 4 is a schematic diagram of a computer-readable medium provided in an embodiment of the present specification.
Detailed Description
For the user with the credit granted, after the credit is granted, the user is probably immobile for a long time or a long time has passed after the approval is passed during the loan again, and the user may go bad at this time, that is, the current qualification of the user is greatly changed compared with the qualification of the user during the approval. The invention provides a method for reducing user risk, which is characterized in that the period of the change, namely the decline period, is predicted, and the limit validity period of a user is set before the decline period comes, so that the user is guided to examine and approve when the decline period comes.
The embodiment of the specification provides a credit line management method based on user life cycle prediction, which comprises the following steps:
acquiring data of a sample user, wherein the data of the sample user comprises basic information, credit information, performance information and a life cycle of the sample user;
training a life cycle prediction model based on the data, wherein the life cycle prediction model is used for predicting the life cycle of the current user;
acquiring data of a current user;
inputting the data of the current user into the life cycle prediction model, and obtaining the prediction result of the life cycle of the current user;
and based on the prediction result, granting credit to the current user.
The method comprises the steps of training a life cycle prediction model based on data of a sample user, wherein the sample user data comprises basic information, credit worthiness information, performance information and a life cycle of the sample user, the life cycle prediction model is used for predicting the life cycle of a current user, obtaining data of the current user, inputting the data of the current user into the life cycle prediction model, obtaining a prediction result of the life cycle of the current user, and granting credit to the current user based on the prediction result. In the embodiments described in the specification, the life cycle prediction model is trained through the data of the sample user, the life cycle of the current user is predicted by using the life cycle prediction model, namely, the decline period of the current user is predicted, and the limit validity period of the current user is set before the decline period comes, so that the user is guided to examine and approve again when the decline period comes, and the risk is reduced.
Exemplary embodiments of the present invention will now be described more fully with reference to the accompanying drawings. The exemplary embodiments, however, may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these exemplary embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the invention to those skilled in the art. The same reference numerals denote the same or similar elements, components, or parts in the drawings, and thus their repetitive description will be omitted.
Features, structures, characteristics or other details described in a particular embodiment do not preclude the fact that the features, structures, characteristics or other details may be combined in a suitable manner in one or more other embodiments in accordance with the technical idea of the invention.
In describing particular embodiments, the present invention has been described with reference to features, structures, characteristics or other details that are within the purview of one skilled in the art to provide a thorough understanding of the embodiments. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific features, structures, characteristics, or other details.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The term "and/or" and/or "includes all combinations of any one or more of the associated listed items.
Fig. 1 is a schematic diagram illustrating a principle of a credit line management method based on user lifecycle prediction according to an embodiment of the present disclosure, where the method may include:
s101: and acquiring data of the sample user, wherein the data of the sample user comprises basic information, credit information, performance information and life cycle of the sample user.
In the embodiments of the present disclosure, the sample user is an overdue user among early trust users, and is not specifically illustrated and limited herein.
In the embodiment of the present specification, the basic information, credit worthiness information and performance information of the sample user may include historical credit granting information of the sample user and performance information and credit worthiness information of the sample user in a longer period.
Because the credit worthiness information and the performance information of the sample user can reflect the credit and asset conditions of the sample user and the stability of the life cycle of the sample user to a certain extent, the information is utilized, and the decay period of the sample user can be calculated by utilizing the existing data more efficiently.
Wherein the life cycle is a period from a credit date to a decline period.
Wherein the decline period may include:
and acquiring the decay period of the sample user based on the overdue times and the overdue amount of the sample user. Correspondingly, obtaining the decline period of the sample user based on the overdue times and the overdue amount of the sample user may include: establishing a overdue threshold comprising a number of overdue thresholds and/or an overdue amount threshold;
the sample users exceeding the overdue threshold enter a decline period.
In the embodiment of the specification, data of a sample user is firstly acquired, whether the sample user enters the decline period or not is judged by integrating the overdue times and the overdue amount of the sample user, and for the sample user entering the decline period, the life cycle of the sample user is calculated, wherein the life cycle is specifically the time difference from the credit date to the decline period.
S102: based on the data, a lifecycle prediction model is trained, the lifecycle prediction model for predicting a lifecycle of a current user.
In the embodiment of the present specification, after the data and the life cycle of the sample user are acquired, the data of the sample user is used as an input layer, and the life cycle of the sample user is used as an output layer, so as to train the life cycle prediction model.
After the life cycle prediction model is trained, the life cycle of the user can be acquired by directly using the life cycle prediction model. Particularly, when the data of the user is input, the life cycle of the user can be acquired according to the data of the user, and then the corresponding limit validity period is set. Therefore, the risk of the quality deterioration of the user is reduced to a certain extent, and a reasonable limit validity period is set for the user.
In the embodiment of the present specification, for a sample user entering a decline period, the sample user is classified according to a time difference from a credit date to the decline period, for example: the specific time is not limited, machine learning is carried out on the probability that the user possibly enters the decline period in each time period, and the person with the largest probability is the validity period of the limit displayed to the user during the credit granting.
S103: and acquiring the data of the current user.
The method comprises the steps of training a life cycle prediction model based on data of a sample user, wherein the sample user data comprises basic information, credit worthiness information, performance information and a life cycle of the sample user, the life cycle prediction model is used for predicting the life cycle of a current user, obtaining data of the current user, inputting the data of the current user into the life cycle prediction model, obtaining a prediction result of the life cycle of the current user, and granting credit to the current user based on the prediction result.
The life cycle prediction model is trained through the data of the sample user, the life cycle of the current user is predicted by using the life cycle prediction model, and the corresponding limit validity period is set, so that the risk of the user can be controlled, the limit validity period can be reasonably set, and in addition, the user experience can be improved.
In an embodiment of this specification, acquiring data of the current user includes:
the basic information, credit information and performance information of the current user.
The basic information, credit worthiness information and performance information of the current user may include historical credit application data generated by the current user and three-party data of the current user.
In this embodiment of the present specification, the user for whom the current user applies for the credit authorization on the service platform may include: the specific service platform of the historical credit application users and the new users who do not apply for credit on the service platform is not elaborated herein.
And S104, inputting the data of the current user into the life cycle prediction model, and obtaining the prediction result of the life cycle of the current user.
In the embodiment of the specification, the basic information, credit worthiness information and performance information of the current user are used as input layers of the life cycle prediction model and input into the life cycle prediction model, the life cycle performance value of the current user is obtained by using the life cycle prediction model, and the life cycle of the current user is predicted according to the life cycle performance value of the current user.
Wherein, the life cycle of the current user is used as a standard for setting up the validity period of the quota. Specifically, a corresponding quota validity period is set for the current user according to the life cycle of the current user.
The life cycle may include a specific duration, for example: one or two years. The life cycle of a specific duration can be calculated from the model.
And S105, based on the prediction result, granting credit to the current user.
The granting trust to the current user based on the prediction result comprises:
and setting up a corresponding limit validity period of the current user according to the prediction result of the life cycle of the current user.
The granting credit to the current user based on the prediction result further comprises:
before the limit validity period of the current user expires, displaying the limit validity period of the current user;
and guiding the current user with the line validity period about to expire to re-enter the credit granting process.
The granting credit to the current user based on the prediction result further comprises:
and leading the user who transacts outside the validity period of the limit or the user who sleeps after credit and suddenly transacts to enter the credit process.
In an embodiment of this specification, the granting, based on the prediction result, the trust to the current user further includes:
in a longer life cycle, the qualification of the current user may change, for example, the qualification of the current user becomes poor, so that the decline period of the current user needs to be predicted, and the validity period of the quota of the current user is set before the decline period comes, so that the current user is redirected to examine and approve when the decline period comes, and the risk is reduced.
In this embodiment of the present specification, the quality degradation of the current user may include that the current user conducts a transaction outside the validity period of the credit line or conducts a transaction suddenly for a user who is asleep after the credit is granted (no transaction is conducted within the validity period of the credit line), and the current user is guided to re-enter the credit granting process. This way, the risk of default due to poor user quality can be reduced.
Based on the same inventive concept, the embodiment of the specification also provides a credit line management device based on the user life cycle prediction.
Fig. 2 is a schematic diagram of a credit line management device based on user lifecycle prediction according to an embodiment of the present disclosure, where the device may include:
the first obtaining module 201: the data acquisition module is used for acquiring data of a sample user, wherein the data of the sample user comprises basic information, credit information, performance information, overdue information and a life cycle of the sample user;
the training module 202: training a life cycle prediction model based on the data, the life cycle prediction model being used for predicting the life cycle of the current user;
the second obtaining module 203: the data acquisition module is used for acquiring data of a current user;
the prediction module 204: the life cycle prediction model is used for inputting the data of the current user into the life cycle prediction model and acquiring the prediction result of the life cycle of the current user;
the trust module 205: and the user authorization module is used for authorizing the current user based on the prediction result.
The life cycle prediction model is trained through the data of the sample user, the life cycle of the current user is predicted by the life cycle prediction model, namely the decline period of the current user is predicted, the limit validity period of the current user is set before the decline period comes, and therefore the user is guided to examine and approve when the decline period comes, and risks are reduced.
The first obtaining module 201: the method specifically comprises the following steps:
and acquiring data of the sample user, wherein the data of the sample user comprises basic information, credit information, performance information and life cycle of the sample user.
In the embodiments of the present disclosure, the sample user is a overdue user of the early trust user, and is not specifically illustrated and limited herein.
In the embodiment of the present specification, the basic information, credit worthiness information and performance information of the sample user may include historical credit granting information of the sample user and performance information and credit worthiness information of the sample user in a longer period.
Because the credit worthiness information and the performance information of the sample user can reflect the credit and asset conditions of the sample user and the stability of the life cycle of the sample user to a certain extent, the information is utilized, and the decay period of the sample user can be calculated by utilizing the existing data more efficiently.
Wherein the life cycle is a period from a credit date to a decline period.
Wherein the decline period may include:
and acquiring the decay period of the sample user based on the overdue times and the overdue amount of the sample user. Correspondingly, obtaining the decline period of the sample user based on the overdue times and the overdue amount of the sample user may include: establishing a overdue threshold comprising a number of overdue thresholds and/or an overdue amount threshold;
the sample users exceeding the overdue threshold enter a decline period.
In the embodiment of the specification, data of a sample user is firstly acquired, whether the sample user enters the decline period or not is judged by integrating the overdue times and the overdue amount of the sample user, and for the sample user entering the decline period, the life cycle of the sample user is calculated, wherein the life cycle is specifically the time difference from the credit date to the decline period.
The training module 202: the method specifically comprises the following steps:
based on the data, a lifecycle prediction model is trained, the lifecycle prediction model for predicting a lifecycle of a current user.
In the embodiment of the present specification, after the data and the life cycle of the sample user are acquired, the data of the sample user is used as an input layer, and the life cycle of the sample user is used as an output layer, so as to train the life cycle prediction model.
In the embodiment of the present specification, for a sample user entering a decline period, the sample user is classified according to a time difference from a credit date to the decline period, for example: the specific time is not limited, machine learning is carried out on the probability that the user possibly enters the decline period in each time period, and the person with the largest probability is the validity period of the limit displayed to the user during the credit granting.
The second obtaining module 203: the method specifically comprises the following steps:
and acquiring the data of the current user.
The method comprises the steps of training a life cycle prediction model based on data of a sample user, wherein the sample user data comprises basic information, credit worthiness information, performance information and a life cycle of the sample user, the life cycle prediction model is used for predicting the life cycle of a current user, obtaining data of the current user, inputting the data of the current user into the life cycle prediction model, obtaining a prediction result of the life cycle of the current user, and granting credit to the current user based on the prediction result.
The life cycle prediction model is trained through the data of the sample user, the life cycle of the current user is predicted by the life cycle prediction model, namely the decline period of the current user is predicted, the limit validity period of the current user is set before the decline period comes, and therefore the user is guided to examine and approve when the decline period comes, and risks are reduced.
In an embodiment of this specification, acquiring data of the current user includes:
the basic information, credit information and performance information of the current user.
The prediction module 204 may specifically include:
and inputting the data of the current user into the life cycle prediction model, and obtaining the prediction result of the life cycle of the current user.
In the embodiment of the specification, the basic information, credit worthiness information and performance information of the current user are input into the life cycle prediction model, the life cycle performance value of the current user is obtained, and the life cycle of the current user is predicted according to the life cycle performance value of the current user.
The life cycle may include a specific duration, for example: one or two years. The life cycle of a specific duration can be calculated from the model.
The trust module 205 may specifically include:
and based on the prediction result, granting credit to the current user.
The granting trust to the current user based on the prediction result comprises:
and setting up a corresponding limit validity period of the current user according to the prediction result of the life cycle of the current user.
The granting credit to the current user based on the prediction result further comprises:
before the limit validity period of the current user expires, displaying the limit validity period of the current user;
and guiding the current user with the line validity period about to expire to re-enter the credit granting process.
The granting credit to the current user based on the prediction result further comprises:
and leading the user who transacts outside the validity period of the limit or the user who sleeps after credit and suddenly transacts to enter the credit process.
In the embodiment of the specification, the life cycle of the current user is predicted when the current user gives credit; and showing the corresponding limit validity period to the current user; if the current user carries out transaction outside the validity period of the quota or the current user carrying out transaction suddenly for the user who sleeps after the credit service (the transaction is not carried out within the validity period of the quota), the current user is guided to enter the credit service flow again. This way, the risk of default due to poor user quality can be reduced.
Based on the same inventive concept, the embodiment of the specification further provides the electronic equipment.
In the following, embodiments of the electronic device of the present invention are described, which may be regarded as specific physical implementations for the above-described embodiments of the method and apparatus of the present invention. Details described in the embodiments of the electronic device of the invention should be considered supplementary to the embodiments of the method or apparatus described above; for details which are not disclosed in embodiments of the electronic device of the invention, reference may be made to the above-described embodiments of the method or the apparatus.
Fig. 3 is a schematic structural diagram of an electronic device provided in an embodiment of the present disclosure. An electronic device 300 according to this embodiment of the invention is described below with reference to fig. 3. The electronic device 300 shown in fig. 3 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 3, electronic device 300 is embodied in the form of a general purpose computing device. The components of electronic device 300 may include, but are not limited to: at least one processing unit 310, at least one memory unit 320, a bus 330 connecting the various system components (including the memory unit 320 and the processing unit 310), a display unit 340, and the like.
Wherein the storage unit stores program code executable by the processing unit 310 to cause the processing unit 310 to perform the steps according to various exemplary embodiments of the present invention described in the above-mentioned processing method section of the present specification. For example, the processing unit 310 may perform the steps as shown in fig. 1.
The storage unit 320 may include readable media in the form of volatile storage units, such as a random access memory unit (RAM)3201 and/or a cache storage unit 3202, and may further include a read only memory unit (ROM) 3203.
The storage unit 320 may also include a program/utility 3204 having a set (at least one) of program modules 3205, such program modules 3205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The electronic device 300 may also communicate with one or more external devices 400 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 300, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 300 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 350. Also, the electronic device 300 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) via the network adapter 360. Network adapter 360 may communicate with other modules of electronic device 300 via bus 330. It should be appreciated that although not shown in FIG. 3, other hardware and/or software modules may be used in conjunction with electronic device 300, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAI D systems, tape drives, and data backup storage systems, etc.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments of the present invention described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiment of the present invention can be embodied in the form of a software product, which can be stored in a computer-readable storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to make a computing device (which can be a personal computer, a server, or a network device, etc.) execute the above-mentioned method according to the present invention. The computer program, when executed by a data processing apparatus, enables the computer readable medium to implement the above-described method of the invention, namely: such as the method shown in fig. 1.
The embodiment of the specification trains a life cycle prediction model based on the data, the life cycle prediction model is used for predicting the life cycle of the current user, obtains the data of the current user, inputs the data of the current user into the life cycle prediction model, obtains the prediction result of the life cycle of the current user, and gives credit to the current user based on the prediction result by obtaining the data of the sample user, wherein the data of the sample user comprises basic information, credit information, performance information and the life cycle of the sample user. The life cycle prediction model is trained through the data of the sample user, the life cycle of the current user is predicted by the life cycle prediction model, namely the decline period of the current user is predicted, the limit validity period of the current user is set before the decline period comes, and therefore the user is guided to examine and approve when the decline period comes, and risks are reduced.
The computer program may be stored on one or more computer readable media. The computer readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
In summary, the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functionality of some or all of the components in embodiments in accordance with the invention may be implemented in practice using a general purpose data processing device such as a microprocessor or a Digital Signal Processor (DSP). The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
While the foregoing embodiments have described the objects, aspects and advantages of the present invention in further detail, it should be understood that the present invention is not inherently related to any particular computer, virtual machine or electronic device, and various general-purpose machines may be used to implement the present invention. The invention is not to be considered as limited to the specific embodiments thereof, but is to be understood as being modified in all respects, all changes and equivalents that come within the spirit and scope of the invention.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. The credit line management method based on user life cycle prediction is characterized by comprising the following steps:
acquiring data of a sample user, wherein the data of the sample user comprises basic information, credit information, performance information and a life cycle of the sample user;
training a life cycle prediction model based on the data, wherein the life cycle prediction model is used for predicting the life cycle of the current user;
acquiring data of a current user;
inputting the data of the current user into the life cycle prediction model, and obtaining the prediction result of the life cycle of the current user;
and based on the prediction result, granting credit to the current user.
2. The method of claim 1, comprising:
the life cycle is a period from the credit date to the decline period.
3. The method of claim 2, comprising:
and acquiring the decay period of the sample user based on the overdue times and the overdue amount of the sample user.
4. The method of claim 3, wherein the obtaining the sample user's decay time based on the sample user's number of overdue times and amount of overdue comprises:
establishing a overdue threshold comprising a number of overdue thresholds and/or an overdue amount threshold;
the sample users exceeding the overdue threshold enter a decline period.
5. The method of claim 1, wherein the data of the current user comprises:
the basic information, credit information and performance information of the current user.
6. The method of claim 5, wherein said granting trust to the current user based on the prediction comprises:
and setting up a corresponding limit validity period of the current user according to the prediction result of the life cycle of the current user.
7. The method of claim 6, wherein said granting trust to said current user based on said prediction result further comprises:
before the limit validity period of the current user expires, displaying the limit validity period of the current user;
and guiding the current user with the line validity period about to expire to re-enter the credit granting process.
8. Credit limit management device based on user life cycle prediction, its characterized in that includes:
a first obtaining module: the data acquisition module is used for acquiring data of a sample user, wherein the data of the sample user comprises basic information, credit information, performance information, overdue information and a life cycle of the sample user;
a training module: training a life cycle prediction model based on the data, the life cycle prediction model being used for predicting the life cycle of the current user;
a second obtaining module: the data acquisition module is used for acquiring data of a current user;
a prediction module: the life cycle prediction model is used for inputting the data of the current user into the life cycle prediction model and acquiring the prediction result of the life cycle of the current user;
a credit granting module: and the user authorization module is used for authorizing the current user based on the prediction result.
9. An electronic device, comprising:
a processor; and the number of the first and second groups,
a memory storing computer-executable instructions that, when executed, cause the processor to perform the method of any of claims 1-7.
10. A computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement the method of any of claims 1-7.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111681094A (en) * | 2020-04-28 | 2020-09-18 | 上海淇馥信息技术有限公司 | Method and device for monitoring resource strategy abnormity and electronic equipment |
| CN111950770A (en) * | 2020-07-20 | 2020-11-17 | 上海淇馥信息技术有限公司 | Method and device for managing resource return auxiliary strategy and electronic equipment |
| CN112347343A (en) * | 2020-09-25 | 2021-02-09 | 北京淇瑀信息科技有限公司 | Customized information pushing method and device and electronic equipment |
| CN113422978A (en) * | 2021-07-14 | 2021-09-21 | 北京达佳互联信息技术有限公司 | Training method and device of dormancy early warning model and dormancy early warning method and device |
| CN113610631A (en) * | 2021-08-02 | 2021-11-05 | 北京淇瑀信息科技有限公司 | User policy adjustment method and device and electronic equipment |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8719151B2 (en) * | 2011-03-30 | 2014-05-06 | Bank Of America Corporation | Loan resolution modeling using discrete event simulation methodology |
| CN103942718A (en) * | 2014-04-14 | 2014-07-23 | 中国人民银行征信中心 | Enterprise credit information collection and integration method |
| CN108846520A (en) * | 2018-06-22 | 2018-11-20 | 北京京东金融科技控股有限公司 | Overdue loan prediction technique, device and computer readable storage medium |
| CN109447783A (en) * | 2018-09-21 | 2019-03-08 | 深圳市买买提信息科技有限公司 | Credit method, apparatus, terminal device and storage medium |
| CN109711981A (en) * | 2018-12-28 | 2019-05-03 | 上海点融信息科技有限责任公司 | The method, apparatus and storage medium of the accrediting amount are determined based on artificial intelligence |
-
2019
- 2019-09-30 CN CN201910941347.9A patent/CN110659984A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8719151B2 (en) * | 2011-03-30 | 2014-05-06 | Bank Of America Corporation | Loan resolution modeling using discrete event simulation methodology |
| CN103942718A (en) * | 2014-04-14 | 2014-07-23 | 中国人民银行征信中心 | Enterprise credit information collection and integration method |
| CN108846520A (en) * | 2018-06-22 | 2018-11-20 | 北京京东金融科技控股有限公司 | Overdue loan prediction technique, device and computer readable storage medium |
| CN109447783A (en) * | 2018-09-21 | 2019-03-08 | 深圳市买买提信息科技有限公司 | Credit method, apparatus, terminal device and storage medium |
| CN109711981A (en) * | 2018-12-28 | 2019-05-03 | 上海点融信息科技有限责任公司 | The method, apparatus and storage medium of the accrediting amount are determined based on artificial intelligence |
Non-Patent Citations (3)
| Title |
|---|
| 中国银行业协会银行业专业人员职业资格考试办公室编: "《个人贷款》", 31 July 2018, 中国金融出版社 * |
| 洪隽: "A互联网银行小微企业信贷风险控制策略优化研究", 《中国优秀硕士学位论文全文数据库（电子期刊） 经济与管理科学辑》 * |
| 立金银行培训中心著: "《银行客户经理基础信贷知识培训》", 31 October 2011, 中国金融出版社 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111681094A (en) * | 2020-04-28 | 2020-09-18 | 上海淇馥信息技术有限公司 | Method and device for monitoring resource strategy abnormity and electronic equipment |
| CN111681094B (en) * | 2020-04-28 | 2023-10-31 | 上海淇馥信息技术有限公司 | Method and device for monitoring resource policy abnormality and electronic equipment |
| CN111950770A (en) * | 2020-07-20 | 2020-11-17 | 上海淇馥信息技术有限公司 | Method and device for managing resource return auxiliary strategy and electronic equipment |
| CN112347343A (en) * | 2020-09-25 | 2021-02-09 | 北京淇瑀信息科技有限公司 | Customized information pushing method and device and electronic equipment |
| CN112347343B (en) * | 2020-09-25 | 2024-05-28 | 北京淇瑀信息科技有限公司 | Custom information pushing method and device and electronic equipment |
| CN113422978A (en) * | 2021-07-14 | 2021-09-21 | 北京达佳互联信息技术有限公司 | Training method and device of dormancy early warning model and dormancy early warning method and device |
| CN113422978B (en) * | 2021-07-14 | 2022-08-26 | 北京达佳互联信息技术有限公司 | Training method and device of dormancy early warning model and dormancy early warning method and device |
| CN113610631A (en) * | 2021-08-02 | 2021-11-05 | 北京淇瑀信息科技有限公司 | User policy adjustment method and device and electronic equipment |
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