CN114298825A - Method and device for extremely evaluating repayment volume - Google Patents
Method and device for extremely evaluating repayment volume Download PDFInfo
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Abstract
The present disclosure provides a payment aggressiveness assessment method. The method comprises the steps that based on pre-acquired user data, the user data comprise user attribute label data and user lending behavior label data; determining the repayment proportion of the repayment amount of the user in a preset time threshold value to the total amount of the required repayment according to the user data; and determining the repayment positive degree score of the user through a pre-constructed repayment volume extreme evaluation model based on the repayment proportion and the user data. The repayment positive degree evaluation method disclosed by the invention improves the prediction effect on the premise of ensuring the prediction stability, is applied to future repayment positive degree evaluation and extreme repayment volume monitoring in the borrowing process of the client, has high automation degree, is easy to realize and has low iterative maintenance cost.
Description
Technical Field
The disclosure relates to the technical field of computers, in particular to a repayment positive degree assessment method and device.
Background
Most existing technologies in risk control in transactions employ a behavior score + rules strategy to identify the transaction risk of a customer and make a pass or no decision.
The result of this is that high risk people with low repayment enthusiasm or increased and worsened queries at large are still misjudged by the current risk system after a period of time by the customers in the product of the circulating credit.
Therefore, how to provide a technical solution for automatically identifying dynamic risks of customers and accurately judging the risks of customers is a problem that needs to be solved by those skilled in the art.
Disclosure of Invention
The embodiment of the disclosure provides a repayment volume extreme evaluation method and device, which can improve the prediction effect on the premise of ensuring the prediction stability.
In a first aspect of the embodiments of the present disclosure, a method for assessing payment aggressiveness is provided, where the method includes:
based on pre-acquired user data, wherein the user data comprises user attribute label data and user lending behavior label data;
determining the repayment proportion of the repayment amount of the user in a preset time threshold value to the total amount of the required repayment according to the user data;
and determining the repayment positive degree score of the user through a pre-constructed repayment volume extreme evaluation model based on the repayment proportion and the user data.
In an alternative embodiment of the method according to the invention,
the user lending behavior data comprises at least one of data representing installments payment, normal payment, advanced payment, overdue payment and credit line;
the preset time threshold includes at least one of 100 days, 3 months, and 6 months.
In an alternative embodiment of the method according to the invention,
the method for determining the payment volume excessiveness score of the user through a pre-constructed payment volume excessiveness evaluation model based on the payment proportion and the user data comprises the following steps:
and determining the payment positive degree score of the user through a boost promotion algorithm XGB based on the payment proportion and the user data.
In an alternative embodiment of the method according to the invention,
the method further comprises training the repayment extreme assessment model, and the method for training the repayment extreme assessment model comprises the following steps:
based on a training data set acquired in advance, wherein the training data set comprises user attribute training label data, user loan behavior training label data and a plurality of repayment proportions of repayment amount to total amount of required repayment;
and training the user attribute training label data and the user loan and loan behavior training label data in the training data set and the repayment proportion of a plurality of repayment amount accounts for the total amount of the required repayment as input variables by using a boost algorithm XGB, and training the repayment positive degree evaluation model by using the repayment positive degree score as an output variable.
In an alternative embodiment of the method according to the invention,
the method further comprises the following steps:
and taking the repayment positive degree score of the user as an input variable, and taking the repayment positive degree score as one of the input variables of the pre-constructed financial risk analysis model, so that the financial risk analysis model performs financial risk analysis on the user.
In a second aspect of the embodiments of the present disclosure, there is also provided a payment aggressiveness evaluating device, the device including:
the system comprises a first unit, a second unit and a third unit, wherein the first unit is used for acquiring user data in advance, and the user data comprises user attribute label data and user lending behavior label data;
the second unit is used for determining the repayment proportion of the repayment amount of the user in a preset time threshold value to the total amount of the required repayment according to the user data;
and the third unit is used for determining the repayment positive degree score of the user through a pre-constructed repayment volume extreme evaluation model based on the repayment proportion and the user data.
In an alternative embodiment of the method according to the invention,
the user lending behavior data comprises at least one of data representing installments payment, normal payment, advanced payment, overdue payment and credit line;
the preset time threshold includes at least one of 100 days, 3 months, and 6 months.
In an alternative embodiment of the method according to the invention,
the third unit is further configured to:
and determining the payment positive degree score of the user through a boost promotion algorithm XGB based on the payment proportion and the user data.
In an alternative embodiment of the method according to the invention,
the apparatus further comprises a fourth unit for:
based on a training data set acquired in advance, wherein the training data set comprises user attribute training label data, user loan behavior training label data and a plurality of repayment proportions of repayment amount to total amount of required repayment;
and training the user attribute training label data and the user loan and loan behavior training label data in the training data set and the repayment proportion of a plurality of repayment amount accounts for the total amount of the required repayment as input variables by using a boost algorithm XGB, and training the repayment positive degree evaluation model by using the repayment positive degree score as an output variable.
In an alternative embodiment of the method according to the invention,
the apparatus further comprises a fifth unit for:
and taking the repayment positive degree score of the user as an input variable, and taking the repayment positive degree score as one of the input variables of the pre-constructed financial risk analysis model, so that the financial risk analysis model performs financial risk analysis on the user.
The repayment volume extreme evaluation method comprises the steps of obtaining user data in advance, wherein the user data comprise user attribute label data and user lending behavior label data;
determining the repayment proportion of the repayment amount of the user in a preset time threshold value to the total amount of the required repayment according to the user data;
and determining the repayment positive degree score of the user through a pre-constructed repayment volume extreme evaluation model based on the repayment proportion and the user data.
According to the repayment positive degree evaluation method, the repayment volume of the client is extremely quantized by establishing the repayment positive degree evaluation model, the repayment positive degree prediction effect on the client is good, the prediction is stable, the automation degree is high, the method is easy to realize, and the iterative maintenance cost is low. Not only is a repayment positive degree scoring model established, but also extreme changes of the repayment volume of the client can be monitored in real time, and the loan risk is effectively controlled.
Drawings
FIG. 1 is a schematic flow chart illustrating a payment volume extreme evaluation method according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of a payment volume extreme evaluation device according to an embodiment of the present disclosure.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present disclosure more clear, the technical solutions of the embodiments of the present disclosure will be described clearly and completely with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are only a part of the embodiments of the present disclosure, not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims of the present disclosure and in the drawings described above, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the disclosure described herein are capable of operation in sequences other than those illustrated or otherwise described herein.
It should be understood that, in various embodiments of the present disclosure, the sequence numbers of the processes do not mean the execution sequence, and the execution sequence of the processes should be determined by the functions and the inherent logic of the processes, and should not constitute any limitation on the implementation process of the embodiments of the present disclosure.
It should be understood that in the present disclosure, "including" and "having" and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in the present disclosure, "plurality" means two or more. "and/or" is merely an association describing an associated object, meaning that three relationships may exist, for example, and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "comprises A, B and C" and "comprises A, B, C" means that all three of A, B, C comprise, "comprises A, B or C" means that one of A, B, C comprises, "comprises A, B and/or C" means that any 1 or any 2 or 3 of A, B, C comprises.
It should be understood that in the present disclosure, "B corresponding to A", "A and B
The corresponding "or" B corresponds to A "means that B is associated with A, from which B can be determined. Determining B from a does not mean determining B from a alone, but may be determined from a and/or other information. And the matching of A and B means that the similarity of A and B is greater than or equal to a preset threshold value.
As used herein, "if" may be interpreted as "at … …" or "when … …" or "in response to a determination" or "in response to a detection", depending on the context.
The technical solution of the present disclosure is explained in detail below with specific examples. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.
Fig. 1 is a schematic flow chart illustrating a repayment limit assessment method according to an embodiment of the present disclosure, as shown in fig. 1, the method includes:
step S101, based on the pre-acquired user data
Wherein the user data comprises user attribute tag data and user lending behavior tag data;
in an optional embodiment, the user loan behavior data comprises at least one of data representing an installment payment, a normal payment, an advance payment, an overdue payment and a credit line;
the preset time threshold includes at least one of 100 days, 3 months, and 6 months.
Illustratively, if the user borrows from the financial platform at a certain time point, data related to loan activities of the user within 3 months of the current time point, such as installments, normal payments, advanced payments, overdue payments, credit lines and the like, are acquired in the previous 3 months of the current time point.
The payment activity scoring model can predict payment behaviors or payment volume excesses of the client within 3 months, and can also predict payment volume excesses of the client within different preset time periods, such as 6 months or 100 days. The preset time can be adjusted according to the needs of the financial platform
The establishment of the repayment positive degree evaluation model requires acquiring a large amount of data similar to customers, the data of group customers are used for training when the model is trained, and after the model is online, the labels are updated according to the continuous historical actions of each user.
Step S102, determining a repayment proportion of a repayment amount of a user in a preset time threshold value to a total amount of the required repayment according to the user data;
in an optional implementation manner, the existing manner of calculating the payment positivity of the user is to calculate the number of times of payment of the user, determine the payment positivity of the user according to the number of times of payment of the user, and determine that the more the number of times of payment of the user is, the higher the payment positivity of the user is.
In practical application, however, the user does not necessarily achieve true positive payment after making multiple payments, for example, the total amount of payments required by the user is 1 ten thousand, and the user makes 10 payments, but each payment is 100 yuan, and the total payment amount is 1000 yuan, which is far lower than the total amount of payments actually required.
Based on this, the embodiment of the present disclosure determines, according to the data of the user, a repayment proportion of the repayment amount of the user in the preset time threshold to the total required repayment amount, for example, the user can repay for 2 times, but the repayment amount is 2000 yuan, and in this case, it can be considered that the repayment volume of the user is extremely higher than that of the former repayment mode. By acquiring the repayment proportion of the repayment amount of the users in the preset time threshold value to the total amount of the required repayment, the model for extremely evaluating the repayment volume can be effectively trained.
And S103, determining the payment positive degree score of the user through a pre-constructed payment volume extreme evaluation model based on the payment proportion and the user data.
In an optional embodiment, the method for determining the payment volume excessiveness score of the user through a pre-constructed payment volume excessiveness evaluation model based on the payment proportion and the user data includes:
and determining the payment positive degree score of the user through a boost promotion algorithm XGB based on the payment proportion and the user data.
In an alternative embodiment of the method according to the invention,
the method further comprises training the repayment extreme assessment model, and the method for training the repayment extreme assessment model comprises the following steps:
based on a training data set acquired in advance, wherein the training data set comprises user attribute training label data, user loan behavior training label data and a plurality of repayment proportions of repayment amount to total amount of required repayment;
and training the user attribute training label data and the user loan and loan behavior training label data in the training data set and the repayment proportion of a plurality of repayment amount accounts for the total amount of the required repayment as input variables by using a boost algorithm XGB, and training the repayment positive degree evaluation model by using the repayment positive degree score as an output variable.
Illustratively, a repayment aggressiveness assessment model may be constructed using the boost lifting algorithm XGB to predict a repayment volume limit of the user over a certain time, wherein the certain time may include at least one of 100 days, 3 months, and 6 months.
The repayment volume of the prediction result can be extremely divided into 5 groups by using a box dividing method, labels of A, B, C, D, E are respectively 80% probability, 60% probability, 40% probability, 20% probability and 20% probability, and the type falling into A is determined according to a set threshold value of 80%. The higher the repayment aggressiveness, the lower the customer risk.
The repayment volume extreme evaluation model can calculate the importance of the label in the label data of the client and rank according to the importance of the label. In the using process of the model, the weight of the label can be adjusted through the importance of the label, and further more accurate repayment positive degree score is obtained.
According to the real-time updating payment aggressiveness evaluation method, after the back-stage model is online, as long as the client moves, a label can be printed according to the movement, so that the model can update the score of each user in real time.
In an alternative embodiment of the method according to the invention,
the method further comprises the following steps:
and taking the repayment positive degree score of the user as an input variable, and taking the repayment positive degree score as one of the input variables of the pre-constructed financial risk analysis model, so that the financial risk analysis model performs financial risk analysis on the user.
Further, the user data includes at least one of data generated online, data generated and stored in advance, and data received from the outside through an input device or a transmission medium.
After the repayment positive degree scoring model is established, the model can be used for extremely predicting the repayment volume of the user, and at the moment, user data to be evaluated needs to be acquired. Sources of user data include: at least one of data generated online, data generated and stored in advance, and data received from the outside through an input device or a transmission medium.
The financial platform can obtain attribute tag data of the user, such as gender, age, location, academic calendar, mobile phone brand, and the like.
After the financial platform acquires the attribute data and the loan behavior data of the user, the repayment positive degree score of the user can be calculated by using the repayment positive degree scoring model, and further, the repayment behavior of the user in the future 3 months, 6 months or 100 days can be accurately predicted.
The repayment positive degree evaluation method updated in real time can label the action of the client when monitoring a new loan behavior of the user, for example, the user has an advance repayment, the repayment positive degree evaluation model can update label data related to the loan behavior of the user in real time in the repayment positive degree evaluation model, and then recalculate the repayment positive degree evaluation of the user, wherein the obtained evaluation is higher than the original evaluation.
If the user has an overdue period and the overdue days are high, the repayment positive degree scoring model can label the bad action of the client, label data related to the loan and loan behaviors of the user is updated in the repayment positive degree scoring model in real time, then the repayment positive degree score of the user is recalculated, and the obtained score is lower than the original score.
The repayment volume extreme evaluation method comprises the steps of obtaining user data in advance, wherein the user data comprise user attribute label data and user lending behavior label data;
determining the repayment proportion of the repayment amount of the user in a preset time threshold value to the total amount of the required repayment according to the user data;
and determining the repayment positive degree score of the user through a pre-constructed repayment volume extreme evaluation model based on the repayment proportion and the user data.
According to the repayment positive degree evaluation method, the repayment volume of the client is extremely quantized by establishing the repayment positive degree evaluation model, the repayment positive degree prediction effect on the client is good, the prediction is stable, the automation degree is high, the method is easy to realize, and the iterative maintenance cost is low. Not only is a repayment positive degree scoring model established, but also extreme changes of the repayment volume of the client can be monitored in real time, and the loan risk is effectively controlled.
Fig. 2 is a schematic structural diagram schematically illustrating a repayment limit assessment apparatus according to an embodiment of the present disclosure, and as shown in fig. 2, the apparatus includes:
a first unit 21, configured to obtain user data in advance, where the user data includes user attribute tag data and user lending behavior tag data;
the second unit 22 is used for determining a repayment proportion of a repayment amount of the user in a preset time threshold value to the total amount of the required repayment according to the user data;
a third unit 23, configured to determine a payment positive degree score of the user through a pre-constructed payment volume extreme evaluation model based on the payment proportion and the user data.
In an alternative embodiment of the method according to the invention,
the user lending behavior data comprises at least one of data representing installments payment, normal payment, advanced payment, overdue payment and credit line;
the preset time threshold includes at least one of 100 days, 3 months, and 6 months.
In an alternative embodiment of the method according to the invention,
the third unit 23 is further configured to:
and determining the payment positive degree score of the user through a boost promotion algorithm XGB based on the payment proportion and the user data.
In an alternative embodiment of the method according to the invention,
the apparatus further comprises a fourth unit for:
based on a training data set acquired in advance, wherein the training data set comprises user attribute training label data, user loan behavior training label data and a plurality of repayment proportions of repayment amount to total amount of required repayment;
and training the user attribute training label data and the user loan and loan behavior training label data in the training data set and the repayment proportion of a plurality of repayment amount accounts for the total amount of the required repayment as input variables by using a boost algorithm XGB, and training the repayment positive degree evaluation model by using the repayment positive degree score as an output variable.
In an alternative embodiment of the method according to the invention,
the apparatus further comprises a fifth unit for:
and taking the repayment positive degree score of the user as an input variable, and taking the repayment positive degree score as one of the input variables of the pre-constructed financial risk analysis model, so that the financial risk analysis model performs financial risk analysis on the user.
The present disclosure also provides a program product comprising execution instructions stored in a readable storage medium. The at least one processor of the device may read the execution instructions from the readable storage medium, and the execution of the execution instructions by the at least one processor causes the device to implement the methods provided by the various embodiments described above.
The readable storage medium may be a computer storage medium or a communication medium. Communication media includes any medium that facilitates transfer of a computer program from one place to another. Computer storage media may be any available media that can be accessed by a general purpose or special purpose computer. For example, a readable storage medium is coupled to the processor such that the processor can read information from, and write information to, the readable storage medium. Of course, the readable storage medium may also be an integral part of the processor. The processor and the readable storage medium may reside in an Application Specific Integrated Circuits (ASIC). Additionally, the ASIC may reside in user equipment. Of course, the processor and the readable storage medium may also reside as discrete components in a communication device. The readable storage medium may be a read-only memory (ROM), a random-access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
In the above embodiments of the terminal or the server, it should be understood that the Processor may be a Central Processing Unit (CPU), other general-purpose processors, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present disclosure may be embodied directly in a hardware processor, or in a combination of the hardware and software modules within the processor.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present disclosure, and not for limiting the same; while the present disclosure has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present disclosure.
Claims (10)
1. A payment aggressiveness assessment method, the method comprising:
based on pre-acquired user data, wherein the user data comprises user attribute label data and user lending behavior label data;
determining the repayment proportion of the repayment amount of the user in a preset time threshold value to the total amount of the required repayment according to the user data;
and determining the repayment positive degree score of the user through a pre-constructed repayment volume extreme evaluation model based on the repayment proportion and the user data.
2. The extreme repayment volume assessment method according to claim 1,
the user lending behavior data comprises at least one of data representing installments payment, normal payment, advanced payment, overdue payment and credit line;
the preset time threshold includes at least one of 100 days, 3 months, and 6 months.
3. The repayment limit evaluation method according to claim 1, wherein the method of determining the repayment limit score of the user through a pre-constructed repayment limit evaluation model based on the repayment proportion and the user data comprises:
and determining the payment positive degree score of the user through a boost promotion algorithm XGB based on the payment proportion and the user data.
4. The payment aggressiveness assessment method according to any one of claims 1 to 3, further comprising training the payment aggressiveness assessment model, wherein the method of training the payment aggressiveness assessment model comprises:
based on a training data set acquired in advance, wherein the training data set comprises user attribute training label data, user loan behavior training label data and a plurality of repayment proportions of repayment amount to total amount of required repayment;
and training the user attribute training label data and the user loan and loan behavior training label data in the training data set and the repayment proportion of a plurality of repayment amount accounts for the total amount of the required repayment as input variables by using a boost algorithm XGB, and training the repayment positive degree evaluation model by using the repayment positive degree score as an output variable.
5. The extreme repayment volume assessment method according to claim 1, further comprising:
and taking the repayment positive degree score of the user as an input variable, and taking the repayment positive degree score as one of the input variables of the pre-constructed financial risk analysis model, so that the financial risk analysis model performs financial risk analysis on the user.
6. A payment aggressiveness evaluating device, the device comprising:
the system comprises a first unit, a second unit and a third unit, wherein the first unit is used for acquiring user data in advance, and the user data comprises user attribute label data and user lending behavior label data;
the second unit is used for determining the repayment proportion of the repayment amount of the user in a preset time threshold value to the total amount of the required repayment according to the user data;
and the third unit is used for determining the repayment positive degree score of the user through a pre-constructed repayment volume extreme evaluation model based on the repayment proportion and the user data.
7. The payment due extreme evaluation device according to claim 6,
the user lending behavior data comprises at least one of data representing installments payment, normal payment, advanced payment, overdue payment and credit line;
the preset time threshold includes at least one of 100 days, 3 months, and 6 months.
8. The payment due credit assessment device of claim 6, wherein the third unit is further configured to:
and determining the payment positive degree score of the user through a boost promotion algorithm XGB based on the payment proportion and the user data.
9. The payment due will assessment device according to any one of claims 6 to 8, further comprising a fourth unit for:
based on a training data set acquired in advance, wherein the training data set comprises user attribute training label data, user loan behavior training label data and a plurality of repayment proportions of repayment amount to total amount of required repayment;
and training the user attribute training label data and the user loan and loan behavior training label data in the training data set and the repayment proportion of a plurality of repayment amount accounts for the total amount of the required repayment as input variables by using a boost algorithm XGB, and training the repayment positive degree evaluation model by using the repayment positive degree score as an output variable.
10. The payment due extreme evaluation device of claim 6, further comprising a fifth unit for:
and taking the repayment positive degree score of the user as an input variable, and taking the repayment positive degree score as one of the input variables of the pre-constructed financial risk analysis model, so that the financial risk analysis model performs financial risk analysis on the user.
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| CN115660834A (en) * | 2022-12-23 | 2023-01-31 | 河北雄安舜耕数据科技有限公司 | Individual loan risk assessment method based on decision tree |
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| CN115660834A (en) * | 2022-12-23 | 2023-01-31 | 河北雄安舜耕数据科技有限公司 | Individual loan risk assessment method based on decision tree |
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