CN113643118B - Method and device for client layering - Google Patents
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- CN113643118B CN113643118B CN202110750015.XA CN202110750015A CN113643118B CN 113643118 B CN113643118 B CN 113643118B CN 202110750015 A CN202110750015 A CN 202110750015A CN 113643118 B CN113643118 B CN 113643118B
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Abstract
The application aims to provide a method and a device for client layering. The method comprises the following steps: determining layering evaluation of the user in a plurality of periods based on the historical performance data of the user to be evaluated and pre-stored layering boundary information; calculating layering mobility of a plurality of layering based on layering evaluation of the user and other plurality of users in a plurality of periods; if the layering mobility does not meet the preset condition, continuously increasing the boundary value range of each layering of the current layering boundary information to recalculate the layering mobility until the calculated layering mobility meets the preset condition. The embodiment of the application has the following advantages: when predicting the client layering of the user, the client layering of the user in a plurality of periods is obtained, and the boundary of the client layering is widened continuously in an iterative adjustment feedback mode, so that the finally obtained client layering is stable, and serious deviation of risk estimation of the client layering is avoided when the client state changes greatly.
Description
Technical Field
The present application relates to the field of computer technology, and in particular, to a method and apparatus for client layering.
Background
Based on the prior art, after a client registers an application on a platform for providing credit service for the first time, the platform distributes the client to a receiving institution to complete the primary matching of the capital assets. The client finishes a borrowing or forms the lending and lending action for a certain time on the platform, at this time, the platform can periodically reevaluate the risk of the client according to the lending and returning history information, external credit information and the like of the client, and the platform can appropriately adjust the risk of the client to provide a service which is more matched with the risk. The risk of the client is rapidly deteriorated, measures are taken in time to avoid the loss of resources, and in addition, the risk judgment of the client is expected to have certain timeliness from the perspective of the client or the cooperation mechanism, so that the continuity of the operation of the client is ensured. The technology adopted by the current banking financial institutions and science and technology companies is mainly to define a certain boundary value (cut-off) through credit risk scores, and directly layer clients or cross other rule variables.
However, this approach may cause client layering to change with time, and the stability of layering is poor, which mainly includes: 1) The grading is formulated by adopting the scoring of the risk model in the credit; model design targets of model personnel when developing a risk model are not completely matched with layering targets, the targets of the risk model in lending are the risk positions of clients in all client groups at a certain time point, and layering requires continuity in a certain time while completing risk level division; 2) Model prediction risk deviation caused by state change of clients; the model predicts risk from a group of clients in similar states, and the group characteristic distribution difference is not excessive in combination with the variables such as the behavior of the clients, but the transition of the client state of the clients in the credit, such as the transition from the active client to the inactive client, can cause serious deviation of risk estimation of the clients.
Disclosure of Invention
The application aims to provide a method and a device for client layering.
According to an embodiment of the present application, there is provided a method for client layering, wherein the method comprises the steps of:
determining layering evaluation of the user in a plurality of periods based on the historical performance data of the user to be evaluated and pre-stored layering boundary information;
calculating layering mobility of a plurality of layering based on layering evaluation of the user and other plurality of users in a plurality of periods;
if the layering mobility does not meet the preset condition, continuously increasing the boundary value range of each layering of the current layering boundary information to recalculate the layering mobility until the calculated layering mobility meets the preset condition.
According to an embodiment of the present application, there is provided an apparatus for client layering, wherein the apparatus includes:
means for determining a hierarchical rating for the user over a plurality of cycles based on historical performance data of the user to be evaluated and pre-stored hierarchical boundary information;
means for calculating a hierarchical mobility of the plurality of hierarchies based on the hierarchical evaluation of the user and the other plurality of users over a plurality of cycles;
and means for continuously increasing the boundary value range of each layer of the current layer boundary information to recalculate the layer mobility until the calculated layer mobility satisfies the predetermined condition if the layer mobility does not satisfy the predetermined condition.
According to an embodiment of the present application, there is provided a computer device including a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of the embodiment of the present application when executing the program.
According to an embodiment of the present application, there is provided a computer-readable storage medium having stored thereon a computer program, characterized in that the program, when executed by a processor, implements a method of an embodiment of the present application.
Compared with the prior art, the embodiment of the application has the following advantages: when predicting the client layering of the user, the client layering of the user in a plurality of periods is obtained, and the boundary of the client layering is widened continuously in an iterative adjustment feedback mode, so that the finally obtained client layering is stable, and serious deviation of risk estimation of the client layering is avoided when the client state changes greatly.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the detailed description of non-limiting embodiments, made with reference to the accompanying drawings in which:
FIG. 1 illustrates a flow chart of a method for client layering according to an embodiment of the application;
fig. 2 shows a schematic structural diagram of an apparatus for client layering according to an embodiment of the present application.
The same or similar reference numbers in the drawings refer to the same or similar parts.
Detailed Description
Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart depicts operations as a sequential process, many of the operations can be performed in parallel, concurrently, or at the same time. Furthermore, the order of the operations may be rearranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figures. The processes may correspond to methods, functions, procedures, subroutines, and the like.
In this context, the term "computer device", also called a "computer", refers to an intelligent electronic device that can execute a predetermined process such as numerical computation and/or logic computation by executing a predetermined program or instruction, and may include a processor and a memory, the predetermined process being executed by the processor executing a stored instruction stored in the memory, or the predetermined process being executed by hardware such as ASIC, FPGA, DSP, or a combination of both. Computer devices include, but are not limited to, servers, personal computers, notebook computers, tablet computers, smart phones, and the like.
The computer device includes a user device and a network device. Wherein the user equipment includes, but is not limited to, a computer, a smart phone, a PDA, etc.; the network device includes, but is not limited to, a single network server, a server group of multiple network servers, or a Cloud based Cloud computing (Cloud computing) consisting of a large number of computers or network servers, where Cloud computing is one of distributed computing, and is a super virtual computer consisting of a group of loosely coupled computer sets. The computer device can be used for realizing the application by running alone, and can also be accessed into a network and realized by interaction with other computer devices in the network. Wherein the network where the computer device is located includes, but is not limited to, the internet, a wide area network, a metropolitan area network, a local area network, a VPN network, and the like.
It should be noted that the user device, the network, etc. are only examples, and other computer devices or networks that may be present in the present application or in the future are applicable to the present application, and are also included in the scope of the present application and are incorporated herein by reference.
The methods discussed below (some of which are illustrated by flowcharts) may be implemented by hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof. When implemented in software, firmware, middleware or microcode, the program code or code segments to perform the necessary tasks may be stored in a machine or computer readable medium such as a storage medium. The processor(s) may perform the necessary tasks.
Specific structural and functional details disclosed herein are merely representative and are for purposes of describing exemplary embodiments of the application. The application may be embodied in many alternate forms and should not be construed as limited to only the embodiments set forth herein.
It will be understood that, although the terms "first," "second," etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of example embodiments. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may be present. In contrast, when an element is referred to as being "directly connected" or "directly coupled" to another element, there are no intervening elements present. Other words used to describe relationships between units (e.g., "between" versus "directly between," "adjacent to" versus "directly adjacent to," etc.) should be interpreted in a similar manner.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used herein, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be noted that, in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may in fact be executed substantially concurrently or the figures may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
The application is described in further detail below with reference to the accompanying drawings.
FIG. 1 illustrates a flow chart of a method for client layering according to an embodiment of the application. The method comprises the steps of S1, S2 and S3.
Referring to fig. 1, in step S1, hierarchical evaluation of a user to be evaluated over a plurality of cycles is determined based on historical performance data of the user and pre-stored hierarchical boundary information.
The historical performance data comprise the balance, overdue amount and the like of each subsequent month.
The layering boundary information comprises a plurality of preset client layering and boundary value ranges corresponding to the client layering.
Wherein the hierarchical evaluation includes various information that can be used to indicate the user's hierarchy over a certain period, such as the name or identification of the hierarchy, etc.
According to one embodiment, the customer hierarchy is used to indicate risk levels for different customers, and the boundary values of the customer hierarchy score customers, such as a credit score for a customer, etc.
It should be noted that, those skilled in the art should be familiar with, the client score may be obtained based on various types of reference data and evaluation criteria, for example, the active model score or the inactive model is selected to obtain the client score according to whether the state in which the client is active. Moreover, those skilled in the art should be familiar with the fact that the number of client hierarchies and the boundary value ranges of the respective hierarchies can be set based on the actual requirements, and will not be described herein.
In step S2, the hierarchical mobility is calculated based on the hierarchical evaluation of the user and the other plurality of users over a plurality of cycles.
The layering mobility is an index for measuring layering variation, a certain group is taken as a study object, and the duty ratio of clients belonging to a certain layering in a certain group at a certain moment and migrating to other layering after a plurality of subsequent periods is counted.
In step S3, if the layered mobility does not satisfy the predetermined condition, the range of boundary values of the respective layers of the current layered boundary information is continuously increased to recalculate the layered mobility until the calculated layered mobility satisfies the predetermined condition.
Wherein the predetermined condition is used for judging the stability of the current client layering result based on layering mobility. The maximum value or average value of mobility or the like may be compared with a predetermined threshold value as a reference based on the layered mobility of the plurality of layers.
Preferably, the predetermined condition includes a maximum value of the layered mobilities of the plurality of layers being less than a predetermined threshold.
For example, assuming that the client hierarchy includes 3 hierarchies, l_1 to L3, after obtaining hierarchies of all clients in the client group over a plurality of cycles, calculating the hierarchy mobility from one hierarchy of l_1 to L3 to the other hierarchies, and obtaining the hierarchy mobility from L1 to L2 as the maximum value, it is determined whether a predetermined condition is satisfied by comparing the maximum value with a predetermined threshold value.
According to one embodiment, the step S3 further includes a step S301, a step S302, a step S303, and a step S304.
In step S301, if the layered mobility does not satisfy a predetermined condition, the boundary value range of each layered of the current layered boundary information is increased, thereby determining new layered boundary information.
In step S302, a new hierarchical evaluation of the user over a plurality of cycles is determined based on the historical performance data of the user under evaluation and the new hierarchical boundary information.
Specifically, if the hierarchical evaluation of the user to be evaluated changes in a certain period based on the new hierarchical boundary information, and the new hierarchical evaluation may cause the degradation of the hierarchical mobility, the hierarchical evaluation of the user in the period is changed.
In step S303, the layered mobilities of the plurality of layers are recalculated based on the new layered evaluation.
In step S304, the above steps are repeated until the calculated layered mobility satisfies a predetermined condition.
According to one embodiment, the method comprises a step S4,
in step S4, if the calculated hierarchical mobility satisfies a predetermined condition, the current hierarchical evaluation is taken as the final hierarchical evaluation of the user.
As will be described below with reference to examples, according to this example, the method is applied to a platform that provides credit services, and hierarchical boundary information in which 5 evaluation levels a to E are set based on annual balance failure rates to correspond to 1%,5%,9%,12%, and 16% of annual balance failure rates, respectively, is prestored in the platform. The 5 customer hierarchies corresponding to the 5 evaluation levels and their respective scoring boundary value ranges are respectively expressed as: tier a [600, 890], tier B [550, 600), tier C [500, 550), tier D [290, 500), and tier E [230, 290).
For a user_1 to be evaluated, historical performance data of the user is firstly obtained, wherein the historical performance data comprise the paying month paying principal, and the balance and overdue amount of each subsequent month. In step S1, based on the historical performance data of the user to be evaluated and the pre-stored hierarchical boundary information, a hierarchical evaluation of the user in 12 periods, which correspond to 12 moments, respectively, denoted T1, T2, T3, …, T12, respectively, is determined. Assume that the resulting 12 cycle hierarchical evaluation is expressed as: f1 =a, f2=a, f3=b, …, f12=b.
Next, in step S2, the layering mobility is calculated based on the obtained layering evaluation of 12 cycles. If the maximum mobility in each layering is lower than 60%, the current layering evaluation is directly output as the final layering evaluation of the client.
If the maximum mobility is higher than 60%, the bandwidth of the boundary value of each layer of the current layer boundary information is increased by 10 to buffer, for example, the layer boundary value of layers a and B is 600, the buffer boundary of the lower probe for layer a is 590, and the buffer boundary of the upper probe for layer B is 610 in step S3. The new hierarchical boundary information obtained based on this approach is expressed as: tier a [590, 890], tier B [540, 610), tier C [490, 560), tier D [280, 510), and tier E [230, 300).
Next, a new hierarchical rating for the user over a plurality of cycles is determined based on the historical performance data of the user and the new hierarchical boundary information. Assuming that the client tier corresponding to the user at the initial time T1 is B, the client score corresponding to the time T2 is 607, and the tier rating corresponding to the time T2 is f2=a according to the original tier boundary information, but the rating interval of tier B is changed to 540 to 610 according to the new tier boundary information after the buffer value is added, so that the tier rating corresponding to the time T2 can be f2=b. It can be seen that if the layering evaluation corresponding to the time T2 can be f2=b, the layering of the successive times T1 and T2 is adjusted from b→a to b→b, so that mobility is reduced, and thus the layering evaluation f2=a at the initial time T2 is changed to f2=b.
After obtaining new layering evaluations of the user in a plurality of periods, calculating layering mobility again. If the maximum mobility of each period is lower than 60%, the current hierarchical evaluation is directly output as the final hierarchical evaluation of the client. If the maximum mobility is higher than 60%, the bandwidth of the boundary value of each layer of the current layer boundary information is continuously increased, for example, 1 or 5 is increased, and the steps are repeated until the calculated maximum mobility in each layer is lower than 60%.
It should be noted that the foregoing examples are only for better illustrating the technical solution of the present application, and not for limiting the present application, and those skilled in the art should understand that any implementation manner of recalculating the layered mobility by continuously increasing the boundary value range of each layer of the current layered boundary information until the calculated layered mobility satisfies the predetermined condition is included in the scope of the present application.
According to the method provided by the embodiment of the application, when the client layering of the user is predicted, the client layering of the user in a plurality of periods is obtained, and the boundary of the client layering is continuously widened in an iterative adjustment feedback mode, so that the finally obtained client layering is stable, and serious deviation of risk estimation of the client layering is avoided when the client state is changed greatly.
Fig. 2 shows a schematic structural diagram of an apparatus for client layering according to an embodiment of the present application. The device comprises: means for determining a hierarchical evaluation of the user in a plurality of cycles based on the historical performance data of the user to be evaluated and the pre-stored hierarchical boundary information (hereinafter referred to as "hierarchical determining means 1"), means for calculating a hierarchical mobility of a plurality of hierarchies based on the hierarchical evaluation of the user and other plurality of users in a plurality of cycles (hereinafter referred to as "migration calculating means 2"), and means for continuously increasing a boundary value range of each hierarchy of the current hierarchical boundary information to recalculate the hierarchical mobility until the calculated hierarchical mobility satisfies a predetermined condition (hereinafter referred to as "hierarchical adjusting means 3"), if the hierarchical mobility does not satisfy the predetermined condition.
Referring to fig. 2, the hierarchical determining device 1 determines hierarchical evaluation of a user to be evaluated over a plurality of cycles based on historical performance data of the user and pre-stored hierarchical boundary information.
The historical performance data comprise the balance, overdue amount and the like of each subsequent month.
The layering boundary information comprises a plurality of preset client layering and boundary value ranges corresponding to the client layering.
Wherein the hierarchical evaluation includes various information that can be used to indicate the user's hierarchy over a certain period, such as the name or identification of the hierarchy, etc.
According to one embodiment, the customer hierarchy is used to indicate risk levels for different customers, and the boundary values of the customer hierarchy score customers, such as a credit score for a customer, etc.
It should be noted that, those skilled in the art should be familiar with, the client score may be obtained based on various types of reference data and evaluation criteria, for example, the active model score or the inactive model is selected to obtain the client score according to whether the state in which the client is active. Moreover, those skilled in the art should be familiar with the fact that the number of client hierarchies and the boundary value ranges of the respective hierarchies can be set based on the actual requirements, and will not be described herein.
The migration calculating means 2 calculates layered mobility based on the layered evaluation of the user and the other plurality of users over a plurality of cycles.
The layering mobility is an index for measuring layering variation, a certain group is taken as a study object, and the duty ratio of clients belonging to a certain layering in a certain group at a certain moment and migrating to other layering after a plurality of subsequent periods is counted.
If the layered mobility does not satisfy the predetermined condition, the layered adjustment apparatus 3 continuously increases the boundary value range of each layered of the current layered boundary information to recalculate the layered mobility until the calculated layered mobility satisfies the predetermined condition.
Wherein the predetermined condition is used for judging the stability of the current client layering result based on layering mobility. The maximum value or average value of mobility or the like may be compared with a predetermined threshold value as a reference based on the layered mobility of the plurality of layers.
Preferably, the predetermined condition includes a maximum value of the layered mobilities of the plurality of layers being less than a predetermined threshold.
For example, assuming that the client hierarchy includes 3 hierarchies, l_1 to L3, after obtaining hierarchies of all clients in the client group over a plurality of cycles, calculating the hierarchy mobility from one hierarchy of l_1 to L3 to the other hierarchies, and obtaining the hierarchy mobility from L1 to L2 as the maximum value, it is determined whether a predetermined condition is satisfied by comparing the maximum value with a predetermined threshold value.
According to one embodiment, the hierarchical adjustment means 3 are adapted to perform the following operations: if the layering mobility does not meet the preset condition, increasing the boundary value range of each layering of the current layering boundary information, so as to determine new layering boundary information;
determining a new hierarchical evaluation of the user in a plurality of periods based on the historical performance data and the new hierarchical boundary information of the user to be evaluated; specifically, if the layering evaluation of the user to be evaluated changes in a certain period based on the new layering boundary information, and the layering mobility of the user is reduced due to the new layering evaluation, the layering evaluation of the user in the period is changed;
recalculating the layering mobilities of the plurality of layering based on the new layering evaluation;
repeating the above operation until the calculated layered mobility meets the predetermined condition.
According to one embodiment, the apparatus includes means for regarding the current hierarchical evaluation as a final hierarchical evaluation of the user (hereinafter referred to as "hierarchical result means") if the calculated hierarchical mobility satisfies a predetermined condition.
And if the calculated layering mobility meets the preset condition, the layering result device takes the current layering evaluation as the final layering evaluation of the user.
As will be described below with reference to examples, according to this example, the apparatus is applied to a platform that provides credit services, and hierarchical boundary information in which 5 evaluation levels a to E are set based on annual balance failure rates to correspond to 1%,5%,9%,12%, and 16% of annual balance failure rates, respectively, is prestored in the platform. The 5 customer hierarchies corresponding to the 5 evaluation levels and their respective scoring boundary value ranges are respectively expressed as: tier a [600, 890], tier B [550, 600), tier C [500, 550), tier D [290, 500), and tier E [230, 290).
For a user_1 to be evaluated, historical performance data of the user is firstly obtained, wherein the historical performance data comprise the paying month paying principal, and the balance and overdue amount of each subsequent month. The hierarchical determination device 1 determines a hierarchical evaluation of the user at 12 periods, which correspond to 12 times respectively, respectively denoted as T1, T2, T3, …, T12, based on the historical performance data of the user to be evaluated and the pre-stored hierarchical boundary information. Assume that the resulting 12 cycle hierarchical evaluation is expressed as: f1 =a, f2=a, f3=b, …, f12=b.
Next, the computing device 2 calculates the layered mobility based on the obtained layered evaluation of 12 cycles. If the maximum mobility in each layering is lower than 60%, the current layering evaluation is directly output as the final layering evaluation of the client.
If the maximum mobility is higher than 60%, the hierarchical adjustment means 3 increases the bandwidth of the boundary value of each hierarchy of the current hierarchy boundary information by 10 for buffering, for example, the hierarchy boundary value of hierarchies a and B is 600, the buffering boundary for hierarchy a is 590, and the buffering boundary for hierarchy B is 610. The new hierarchical boundary information obtained based on this approach is expressed as: tier a [590, 890], tier B [540, 610), tier C [490, 560), tier D [280, 510), and tier E [230, 300).
Next, the hierarchical adjustment apparatus 3 determines a new hierarchical evaluation of the user over a plurality of cycles based on the historical performance data of the user and the new hierarchical boundary information. Assuming that the client tier corresponding to the user at the initial time T1 is B, the client score corresponding to the time T2 is 607, and the tier rating corresponding to the time T2 is f2=a according to the original tier boundary information, but the rating interval of tier B is changed to 540 to 610 according to the new tier boundary information after the buffer value is added, so that the tier rating corresponding to the time T2 can be f2=b. It can be seen that if the layering evaluation corresponding to the time T2 can be f2=b, the layering of the successive times T1 and T2 is adjusted from b→a to b→b so that the mobility is reduced, and thus the layering adjustment device 3 changes the layering evaluation f2=a at the initial time T2 to f2=b.
After obtaining new layering evaluations of the user in a plurality of periods, calculating layering mobility again. If the maximum mobility of each period is lower than 60%, the current hierarchical evaluation is directly output as the final hierarchical evaluation of the client. If the maximum mobility is higher than 60%, the hierarchical adjustment means 3 continues to increase the bandwidth of the boundary value of each hierarchical layer of the current hierarchical boundary information, for example by 1 or 5, and repeats the above steps until the calculated maximum mobility in each hierarchical layer is lower than 60%.
It should be noted that the foregoing examples are only for better illustrating the technical solution of the present application, and not for limiting the present application, and those skilled in the art should understand that any implementation manner of recalculating the layered mobility by continuously increasing the boundary value range of each layer of the current layered boundary information until the calculated layered mobility satisfies the predetermined condition is included in the scope of the present application.
According to the device provided by the embodiment of the application, when the client layering of the user is predicted, the client layering of the user in a plurality of periods is obtained, and the boundary of the client layering is continuously widened in an iterative adjustment feedback mode, so that the finally obtained client layering is stable, and serious deviation of risk estimation of the client layering is avoided when the client state is changed greatly.
The software program of the present application may be executed by a processor to perform the steps or functions described above. Likewise, the software programs of the present application (including associated data structures) may be stored on a computer readable recording medium, such as RAM memory, magnetic or optical drive or diskette and the like. In addition, some of the steps or functions of the present application may be implemented in hardware, for example, as circuitry that cooperates with the processor to perform various functions or steps.
Furthermore, portions of the present application may be implemented as a computer program product, such as computer program instructions, which when executed by a computer, may invoke or provide methods and/or techniques in accordance with the present application by way of operation of the computer. Program instructions for invoking the inventive methods may be stored in fixed or removable recording media and/or transmitted via a data stream in a broadcast or other signal bearing medium and/or stored within a working memory of a computer device operating according to the program instructions. An embodiment according to the application comprises an apparatus comprising a memory for storing computer program instructions and a processor for executing the program instructions, wherein the computer program instructions, when executed by the processor, trigger the apparatus to operate a method and/or a solution according to the embodiments of the application as described above.
It will be evident to those skilled in the art that the application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the system claims can also be implemented by means of software or hardware by means of one unit or means. The terms first, second, etc. are used to denote a name, but not any particular order.
Claims (12)
1. A method for client layering, wherein the method comprises the steps of:
determining layered evaluation of the user in a plurality of periods based on historical performance data of the user to be evaluated and pre-stored layered boundary information, wherein the historical performance data comprises the money release principal of a money release month, and the balance and overdue amount of each subsequent month;
calculating layering mobility of a plurality of layering based on layering evaluation of the user and other users in a plurality of periods, wherein the layering mobility is an index for measuring layering variation, and taking a certain group as a research object, counting the duty ratio of clients belonging to a certain layering in a certain time group and migrating to other layering after a plurality of subsequent periods;
if the layering mobility does not meet the preset condition, continuously increasing the boundary value range of each layering of the current layering boundary information to recalculate the layering mobility until the calculated layering mobility meets the preset condition.
2. The method of claim 1, wherein the hierarchical boundary information comprises a preset plurality of client hierarchies and boundary value ranges corresponding to the respective client hierarchies.
3. The method of claim 1, wherein if the layered mobility does not satisfy the predetermined condition, increasing the boundary value range of each layered in the current layered boundary information until the calculated layered mobility satisfies the predetermined condition comprises:
if the layering mobility does not meet the preset condition, increasing the boundary value range of each layering of the current layering boundary information, so as to determine new layering boundary information;
determining a new hierarchical evaluation of the user in a plurality of periods based on the historical performance data and the new hierarchical boundary information of the user to be evaluated;
recalculating the layering mobilities of the plurality of layering based on the new layering evaluation;
repeating the steps until the calculated layering mobility meets the preset condition.
4. The method of claim 1, wherein the predetermined condition comprises a maximum of the layered mobilities of the plurality of layers being less than a predetermined threshold.
5. The method according to any one of claims 1 to 4, wherein the method comprises:
and if the calculated layering mobility meets the preset condition, taking the current layering evaluation as the final layering evaluation of the user.
6. An apparatus for client layering, wherein the apparatus comprises:
means for determining a hierarchical evaluation of the user over a plurality of periods based on historical performance data of the user to be evaluated and pre-stored hierarchical boundary information, wherein the historical performance data includes a refund principal for a refund month, a balance for each subsequent month, and an overdue amount;
means for calculating a hierarchical mobility of a plurality of hierarchies based on hierarchical evaluations of the user and other plurality of users over a plurality of cycles, wherein the hierarchical mobility is an index for measuring hierarchical variation, and statistics is made of the duty ratios of clients belonging to a certain hierarchy in a certain time group and migrating to other hierarchies after a plurality of subsequent cycles by taking the certain group as a study object;
and means for continuously increasing the boundary value range of each layer of the current layer boundary information to recalculate the layer mobility until the calculated layer mobility satisfies the predetermined condition if the layer mobility does not satisfy the predetermined condition.
7. The apparatus of claim 6, wherein the hierarchical boundary information comprises a preset plurality of client hierarchies and boundary value ranges corresponding to the respective client hierarchies.
8. The apparatus of claim 6, wherein if the layered mobility does not satisfy the predetermined condition, increasing the boundary value range of each layered in the current layered boundary information until the calculated layered mobility satisfies the predetermined condition comprises:
if the layering mobility does not meet the preset condition, increasing the boundary value range of each layering of the current layering boundary information, so as to determine new layering boundary information;
determining a new hierarchical evaluation of the user in a plurality of periods based on the historical performance data and the new hierarchical boundary information of the user to be evaluated;
recalculating the layering mobilities of the plurality of layering based on the new layering evaluation;
repeating the steps until the calculated layering mobility meets the preset condition.
9. The apparatus of claim 6, wherein the predetermined condition comprises a maximum of a plurality of layered mobility of the plurality of layers being less than a predetermined threshold.
10. The apparatus according to any one of claims 6 to 9, wherein the apparatus comprises:
and means for taking the current hierarchical evaluation as the final hierarchical evaluation of the user if the calculated hierarchical mobility satisfies a predetermined condition.
11. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 5 when the program is executed by the processor.
12. A computer-readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any one of claims 1 to 5.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107563884A (en) * | 2017-07-28 | 2018-01-09 | 浙江邦盛科技有限公司 | A kind of method for reducing loss of assets rate |
| CN109903152A (en) * | 2019-01-24 | 2019-06-18 | 平安科技（深圳）有限公司 | Default loss analysis method, device and computer readable storage medium |
| CN110796542A (en) * | 2019-09-26 | 2020-02-14 | 北京淇瑀信息科技有限公司 | Financial risk control method, financial risk control device and electronic equipment |
| CN111199308A (en) * | 2019-12-12 | 2020-05-26 | 北京淇瑀信息科技有限公司 | Client prediction method and device for different overdue stages and electronic equipment |
| CN111798301A (en) * | 2020-06-28 | 2020-10-20 | 中国建设银行股份有限公司 | Resource transfer quota determining method, device and system |
| CN112200665A (en) * | 2020-11-17 | 2021-01-08 | 上海优扬新媒信息技术有限公司 | Method and device for determining credit limit |
| CN112862544A (en) * | 2021-03-15 | 2021-05-28 | 重庆度小满优扬科技有限公司 | Object information acquisition method and device and storage medium |
| CN112907356A (en) * | 2021-03-04 | 2021-06-04 | 深圳前海微众银行股份有限公司 | Overdue collection method, device and system and computer readable storage medium |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11392561B2 (en) * | 2018-09-28 | 2022-07-19 | Oracle International Corporation | Data migration using source classification and mapping |
-
2021
- 2021-07-02 CN CN202110750015.XA patent/CN113643118B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107563884A (en) * | 2017-07-28 | 2018-01-09 | 浙江邦盛科技有限公司 | A kind of method for reducing loss of assets rate |
| CN109903152A (en) * | 2019-01-24 | 2019-06-18 | 平安科技（深圳）有限公司 | Default loss analysis method, device and computer readable storage medium |
| CN110796542A (en) * | 2019-09-26 | 2020-02-14 | 北京淇瑀信息科技有限公司 | Financial risk control method, financial risk control device and electronic equipment |
| CN111199308A (en) * | 2019-12-12 | 2020-05-26 | 北京淇瑀信息科技有限公司 | Client prediction method and device for different overdue stages and electronic equipment |
| CN111798301A (en) * | 2020-06-28 | 2020-10-20 | 中国建设银行股份有限公司 | Resource transfer quota determining method, device and system |
| CN112200665A (en) * | 2020-11-17 | 2021-01-08 | 上海优扬新媒信息技术有限公司 | Method and device for determining credit limit |
| CN112907356A (en) * | 2021-03-04 | 2021-06-04 | 深圳前海微众银行股份有限公司 | Overdue collection method, device and system and computer readable storage medium |
| CN112862544A (en) * | 2021-03-15 | 2021-05-28 | 重庆度小满优扬科技有限公司 | Object information acquisition method and device and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 文维虎.风险分类偏离度与迁徙率检查.《中国农村信用合作》.2006,(第6期),第45-47页. * |
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