CN111242767A - User on-schedule payment prediction method and device and electronic equipment - Google Patents

User on-schedule payment prediction method and device and electronic equipment Download PDF

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CN111242767A
CN111242767A CN201911271318.2A CN201911271318A CN111242767A CN 111242767 A CN111242767 A CN 111242767A CN 201911271318 A CN201911271318 A CN 201911271318A CN 111242767 A CN111242767 A CN 111242767A
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user
repayment
determining
distribution
payment
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陈博
黎文杰
郑盛麟
刘禹彤
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Shanghai Qiyue Information Technology Co Ltd
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Shanghai Qiyue Information Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/03—Credit; Loans; Processing thereof
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"

Abstract

The disclosure relates to a method and a device for predicting user on-schedule payment, electronic equipment and a computer readable medium. The method comprises the following steps: acquiring user data of a target user and a repayment amount and repayment time corresponding to the user data; determining an adjustment parameter; determining a credit score corresponding to the user data according to the user data; and determining the repayment date distribution and the repayment amount distribution of the repayment by date of the target user based on the repayment amount, the adjusting parameter and the credit score. According to the user payment-by-date prediction method, the user payment-by-date prediction device, the electronic equipment and the computer readable medium, the number of the users who pay by date and the overdue amount can be screened out, the total number of future payments per day is calculated, the redundancy degree of cash preparation is reduced, and the cash cost is effectively saved.

Description

User on-schedule payment prediction method and device and electronic equipment
Technical Field
The disclosure relates to the field of computer information processing, in particular to a method and a device for predicting a user return amount by time, an electronic device and a computer readable medium.
Background
For the companies of the internet financial services, different types of borrowing products are provided to meet the requirements of different borrowing users, the products have different repayment modes, and the users using the products also have different repayment characteristics. Since the total financial resources are limited for a relatively fixed time, it is particularly important to allocate financial resources in different businesses reasonably. The financial resources refer to the sum or aggregate of a series of objects related to the structure, quantity, scale, distribution and effects and interaction relation of financial service subjects and objects in the financial field, and in production and life, the financial and economic sustainable development can be realized only when the financial resources are configured efficiently. For companies that provide internet financial services, the financial resource may be the total amount of funds, or the amount of assets equivalent to funds, or the like.
For individual users, due to individual differences of the individual users, an internet financial service company can hardly predict plans and time of financial resource demands of the individual users in advance, and if the plan can predict whether the users can pay according to schedule or not, the payment behaviors of different types of products can be predicted, the financial service demands of the individual users can be better predicted, and the financial resources of the individual users can be more reasonably distributed.
Therefore, a new method, apparatus, electronic device and computer readable medium for predicting a user's due on-schedule payment is needed.
The above information disclosed in this background section is only for enhancement of understanding of the background of the disclosure and therefore it may contain information that does not constitute prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
In view of the above, the present disclosure provides a method, an apparatus, an electronic device and a computer readable medium for predicting a user's due payment, which can screen out the number of users who pay due.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.
According to an aspect of the present disclosure, a method for predicting a user's due payment on schedule is provided, the method including: acquiring user data of a target user and a repayment amount and repayment time corresponding to the user data; determining an adjustment parameter; determining a credit score corresponding to the user data according to the user data; and determining the repayment date distribution and the repayment amount distribution of the repayment by date of the target user based on the repayment amount, the adjusting parameter and the credit score.
Optionally, the method further comprises: acquiring payment date distribution and payment amount of all target users on a preset date; and predicting the liquidity of the financial resources based on the payment date distribution and the payment amount of all the target users.
Optionally, the obtaining user data of the target user and a repayment amount and a repayment time corresponding to the user data includes: before the repayment time of the user is preset time, and when the arrearage state of the user is a non-stage state; determining the user as the target user.
Optionally, determining an adjustment parameter, including at least one of: determining a user composition coefficient; determining a cycle factor coefficient; determining a lunar factor coefficient; determining a market environment coefficient; and determining a running behavior coefficient.
Optionally, determining a credit score corresponding to the user data according to the user data includes: inputting the user data into a user credit assessment model to generate credit assessment parameters; acquiring historical arrearage data of the target user; acquiring historical repayment data of the target user; and generating the credit score from the credit assessment parameter, the historical arrearage data, the historical repayment data; wherein the user credit assessment model is generated by a machine learning model.
Optionally, determining a repayment date distribution and a repayment amount distribution of the target user's repayment by date based on the repayment amount, the adjustment parameter, and the credit score, comprising: determining a probability distribution of repayment of the target user based on the adjusting parameter and the credit score, wherein the probability distribution represents the repayment probability distribution of the user in time sequence; and determining the amount distribution of the repayment of the target user based on the repayment amount and the credit score.
Optionally, the payment date distribution comprises:
Xi＝{xi，T，xi，T+1，xi，T+2……xi，T+n}
Xiis a probability distribution, x, of repaymenti，TThe probability of repayment for the i user on day T.
Optionally, wherein the payment amount distribution comprises:
Yi＝QaXi＝Qa{xi，T，xi，T+1，xi，T+2……xi，T+n}
Yifor repayment amount distribution, xi，TAnd (4) the probability of repayment of the user on the T day is i, the payment sum of the user is a, and Q is an adjusting coefficient.
Optionally, acquiring payment date distribution and payment amount of all target users on a predetermined date comprises: taking the arrearage users on the preset date as target users; acquiring user data of a plurality of target users and corresponding repayment amounts and repayment time; determining credit scores for the plurality of target users; and determining a repayment date distribution and a repayment amount distribution of the target users based on the repayment amounts of the plurality of targets, the adjustment parameter and the credit score.
Optionally, predicting liquidity of the financial resource based on the payment date distribution and the payment amount of all the target users comprises: calculating repayment prediction data for the financial resource on a predetermined date based on the repayment amounts for the plurality of targets, the adjustment parameter, and the credit score; acquiring borrowing prediction data of financial resources on a preset date; and predicting liquidity of the financial resource based on the repayment prediction data and the borrowing prediction data.
According to an aspect of the present disclosure, there is provided a user on-schedule payment prediction apparatus, including: the data module is used for acquiring the user data of the target user and the corresponding repayment amount and repayment time; a parameter module for determining an adjustment parameter; the scoring module is used for determining a credit score corresponding to the user data according to the user data; and the distribution module is used for determining the repayment date distribution and the repayment amount distribution of the target user on-time repayment based on the repayment amount, the adjusting parameter and the credit score.
Optionally, the method further comprises: the prediction module is used for acquiring the repayment date distribution and the repayment amount of all target users on a preset date; and predicting the liquidity of the financial resources based on the payment date distribution and the payment amount of all the target users.
Optionally, the data module includes: the judging unit is used for judging whether the payment time of the user is the preset time or not and whether the arrearage state of the user is the non-stage state or not; determining the user as the target user.
Optionally, adjusting parameters, including at least one of: a user composition coefficient; a cycle factor coefficient; a monthly factor coefficient; market environment factor; and a running behavior coefficient.
Optionally, the scoring module includes: a model unit for inputting the user data into a user credit assessment model to generate credit assessment parameters; the history unit is used for acquiring historical arrearage data of the target user; acquiring historical repayment data of the target user; and a scoring unit for generating the credit score by the credit evaluation parameter, the historical arrearage data, and the historical reimbursement data; wherein the user credit assessment model is generated by a machine learning model.
Optionally, the distribution module includes: a probability unit, configured to determine a probability distribution of repayment of the target user based on the adjustment parameter and the credit score, where the probability distribution represents a repayment probability distribution of the user in a time series; and the money unit is used for determining money distribution of the repayment of the target user based on the repayment money and the credit score.
Optionally, the payment date distribution comprises:
Xi＝{xi，T，xi，T+1，xi，T+2……xi，T+n}
Xiis a probability distribution, x, of repaymenti，TThe probability of repayment for the i user on day T.
Optionally, the repayment amount distribution includes:
Yi＝QaXi＝Qa{xi，T，xi，T+1，xi，T+2……xi，T+n}
Yifor repayment amount distribution, xi，TAnd (4) the probability of repayment of the user on the T day is i, the payment sum of the user is a, and Q is an adjusting coefficient.
Optionally, the prediction module includes: the calculating unit is used for taking the arrearage users on the preset date as target users; acquiring user data of a plurality of target users and corresponding repayment money amounts and repayment time; determining credit scores for the plurality of target users; and determining a repayment date distribution and a repayment amount distribution of the target users based on the repayment amounts of the plurality of targets, the adjustment parameter and the credit score.
Optionally, the prediction module includes: a prediction unit for calculating repayment prediction data of the financial resource on a predetermined date based on the repayment amount of the plurality of targets, the adjustment parameter, and the credit score; acquiring borrowing prediction data of financial resources on a preset date; and predicting liquidity of the financial resource based on the repayment prediction data and the borrowing prediction data.
According to an aspect of the present disclosure, an electronic device is provided, the electronic device including: one or more processors; storage means for storing one or more programs; when executed by one or more processors, cause the one or more processors to implement a method as above.
According to an aspect of the disclosure, a computer-readable medium is proposed, on which a computer program is stored, which program, when being executed by a processor, carries out the method as above.
According to the on-schedule payment predicting method and device for the user, the electronic equipment and the computer readable medium, user data of the target user and the corresponding payment amount and payment time are obtained; determining an adjustment parameter; determining a credit score corresponding to the user data according to the user data; and determining the distribution of the repayment date and the repayment amount of the target user according to the term repayment based on the repayment amount, the adjusting parameters and the credit score, screening out the number of the users according to the term repayment and the overdue amount, further calculating the total amount of the repayment of the future each day, reducing the redundancy degree of cash preparation, and effectively saving the cash cost.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The above and other objects, features and advantages of the present disclosure will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings. The drawings described below are merely some embodiments of the present disclosure, and other drawings may be derived from those drawings by those of ordinary skill in the art without inventive effort.
Fig. 1 is a flow chart illustrating a method for predicting a user's due by date payment according to an exemplary embodiment.
Fig. 2 is a flow chart illustrating a method for predicting a user's due on schedule according to another exemplary embodiment.
Fig. 3 is a flow chart illustrating a method for predicting a user's due on schedule according to another exemplary embodiment.
Fig. 4 is a block diagram illustrating a user pay-by-date prediction apparatus according to an example embodiment.
FIG. 5 is a block diagram illustrating an electronic device in accordance with an example embodiment.
FIG. 6 is a block diagram illustrating a computer-readable medium in accordance with an example embodiment.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The same reference numerals denote the same or similar parts in the drawings, and thus, a repetitive description thereof will be omitted.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations, or operations have not been shown or described in detail to avoid obscuring aspects of the disclosure.
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 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.
It will be understood that, although the terms first, second, third, etc. may be used herein to describe various components, these components should not be limited by these terms. These terms are used to distinguish one element from another. Thus, a first component discussed below may be termed a second component without departing from the teachings of the disclosed concept. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It is to be understood by those skilled in the art that the drawings are merely schematic representations of exemplary embodiments, and that the blocks or processes shown in the drawings are not necessarily required to practice the present disclosure and are, therefore, not intended to limit the scope of the present disclosure.
FIG. 1 is a flow chart illustrating a method for predicting a user's due on schedule according to an exemplary embodiment. The user payment-on-schedule prediction method 10 at least includes steps S102 to S108.
As shown in fig. 1, in S102, user data of the target user and the corresponding payment amount and payment time are acquired. The method comprises the following steps: before the repayment time of the user is preset time, and when the owing state of the user is a non-stage state; determining the user as the target user.
In one embodiment, the occupation state, occupation period and occupation object of the financial resource can be obtained; and screening the target users from the occupied objects based on the occupied states and the occupied periods. It is noted that the target occupation object can be a user, and can also be other financial institutions, other companies and the like, and the payment prediction of the user part is only concerned in the disclosure.
In one embodiment, the cash flow situation at the time of 15 is to be predicted, all users with the payment deadline of 15 are extracted and taken as target users.
In S104, the adjustment parameter is determined. The user composition coefficient may be determined, for example; determining a cycle factor coefficient; determining a lunar factor coefficient; determining a market environment coefficient; and determining a running behavior coefficient.
Theoretically, the users who should pay on time and the corresponding amount are fixed every day, but some users pay in advance and pay overdue, so the actual on-time payment amount is smaller than the actual on-time payment amount, and the actual on-time payment amount is highly related to the actual on-time payment amount. Therefore, a basic model can be established, if Y is the actual on-time payment amount, and a is the on-time payment amount, then the following are provided:
Y＝F(a)＝βa
then the actual normal repayment amount Y for day TTCan be expressed as:
YT＝βTaT
coefficient βTThe payment method is influenced by the user constitution to be paid, time factors, market conditions factors and operation factors, and therefore the factors are included in the model to be adjusted.
The users to be paid are formed, and the user proportion of the users with the preference of normal payment in the group of users is mainly seen, and is set as C;
the payment period is affected by the frequency and month, so the frequency factor W needs to be setTAnd monthly factor MT. The periodic factors are estimated by mean comparison, namely:
WT＝E(αT/(E(T-3,T+3)(αT))；
MT＝E(αT/(E(T-15,T+15)(αT))；
wherein, WTIs the coefficient of the cycle factor, MTα, where E is the expected value, T is the current dateTTo pay back data on time.
The repayment behavior of the user is also influenced by the market environment, and the coefficient can be set to be V; the operation behavior also influences the repayment of the user to a certain extent, and the coefficient can be set to be O;
YT＝CTMTWTVTOTβTaT。
aTthe amount of money to be paid can be calculated directly according to the time and amount of money to be paid by the user, and the other coefficients are fitted according to historical data and can be also calculated according to YTMaking predictions of subsequent dates, e.g. Y(T+1)。
In S106, the credit score corresponding to the user data is determined according to the user data. The method comprises the following steps: inputting the user data into a user credit assessment model to generate credit assessment parameters; acquiring historical arrearage data of the target user; acquiring historical repayment data of the target user; and generating the credit score through the credit evaluation parameter, the historical arrearage data and the historical repayment data; wherein the user credit assessment model is generated by a machine learning model.
In S108, a repayment date distribution and a repayment amount distribution of the target user on-time repayment are determined based on the repayment amount, the adjustment parameter and the credit score. The method comprises the following steps: determining a probability distribution of repayment of the target user based on the adjustment parameter and the credit score, wherein the probability distribution represents the repayment probability distribution of the user in time sequence; and determining the amount distribution of the repayment of the target user based on the repayment amount and the credit score.
The payment date distribution comprises:
Xi＝{xi，T，xi，T+1，xi，T+2……xi，T+n}
Xiis a probability distribution, x, of repaymenti，TThe probability of repayment for the i user on day T.
The repayment amount distribution comprises:
Yi＝QaXi＝Qa{xi，T，xi，T+1，xi，T+2……xi，T+n}
Q＝CTMTWTVTOTβT
Yifor repayment amount distribution, xi，TAnd (b) the probability of repayment of the user on the T day is i, and the payment sum of the user is a.
According to the on-time payment forecasting method for the user, user data of a target user and corresponding payment amount and payment time are obtained; determining an adjustment parameter; determining a credit score corresponding to the user data according to the user data; and determining the repayment date distribution and the repayment amount distribution of the target user according to the term repayment based on the repayment amount, the adjusting parameters and the credit score, so that the number of the users according to the term repayment and the overdue amount can be screened out, the total amount of the future repayment every day is calculated, the redundancy degree of cash preparation is reduced, and the cash cost is effectively saved.
It should be clearly understood that this disclosure describes how to make and use particular examples, but the principles of this disclosure are not limited to any details of these examples. Rather, these principles can be applied to many other embodiments based on the teachings of the present disclosure.
Fig. 2 is a flowchart illustrating a method for predicting a user's due payment on schedule according to another exemplary embodiment. The flow shown in fig. 2 is a detailed description of "determining the credit score corresponding to the user data" at S106 in the flow shown in fig. 1.
As shown in fig. 2, in S202, the user data is input into a user credit assessment model to generate credit assessment parameters. Wherein the user credit assessment model is generated by a machine learning model.
In S204, historical arrears data of the target user is obtained.
In S206, historical repayment data of the target user is acquired.
In S208, the credit score is generated from the credit assessment parameter, the historical arrears data, and the historical repayment data.
And finally generating the credit characteristic of the target object by combining the personal historical repayment characteristic of the target object according to the credit evaluation data of the user, wherein the credit characteristic can comprehensively reflect the probability of the repayment of the user according to the date.
Fig. 3 is a flowchart illustrating a method for predicting a user's due payment on schedule according to another exemplary embodiment.
As shown in fig. 3, in S302, the payment date distribution and the payment amount of all the target users on the predetermined date are acquired. The method specifically comprises the following steps: taking the arrearage users on the preset date as target users; acquiring user data of a plurality of target users and corresponding repayment amounts and repayment time; determining credit scores for the plurality of target users; and determining a repayment date distribution and a repayment amount distribution of the target users based on the repayment amounts of the plurality of targets, the adjustment parameter and the credit score.
In S304, repayment prediction data for the financial resource on a predetermined date is calculated based on the repayment amounts of the plurality of targets, the adjustment parameter, and the credit score.
In S306, borrowing prediction data of the financial resource on a predetermined date is acquired. Financial resource borrowing data for a predetermined date may be derived, for example, by historical data fitting.
The daily occurrence of the cash flow for borrowing can be considered as a function of the credit line granted to all the users, and is a simple linear function, let the cash flow for borrowing be Y, the credit line be X, β be the ratio of the credit to the amount of borrowing (abbreviated as borrowing rate), then:
Y＝f(X)＝βX
the users who borrow on the same day can be classified according to the credit date of the users. Setting:
ya，bthe credit user is granted on day b, the amount of the loan on day a (b of a),
xbthe credit amount of the credit user is granted on the b day,
βa，bnamely the loan rate of the credit user on the b day on the a day,
then there are: y isa，b＝βa，bxb
The amount of money to be borrowed Y of day TTCan be expressed as:
Figure BDA0002314273700000111
therefore, when the T +1 day debit amount is predicted, the historical credit amount is known, and the T +1 credit amount needs to be estimated
Figure BDA0002314273700000112
And historical daily credit user inBorrowing rate for T +1 day
Figure BDA0002314273700000113
I.e. YT+1Can be expressed as:
Figure BDA0002314273700000114
wherein
Figure BDA0002314273700000115
(b belongs to [0, T +1 ]]) And
Figure BDA0002314273700000116
to be predicted, xb(b is of [0, T ]]) Is a known value.
To predict
Figure BDA0002314273700000117
Historical borrowing and credit granting data of a user need to be input, time period factors, operation factors and wind control adjusting factors are considered, and time series prediction is carried out through an ARIMA model.
If the time period factor variable is w, the operation factor is o, and the wind control adjustment factor is z, the loan rate can be expressed as:
Figure BDA0002314273700000118
wherein f isT+1(βT，b) The ARIMA (differential integrated moving average self-regression model) model prediction values for the T +1 borrowing rate are shown.
To predict
Figure BDA0002314273700000119
The number of the credit providers is determined according to the actual service condition and the number of the credit providers set in the current month. Because the final number of credits per month is strongly correlated with the target number set at the beginning of the month, the final number of credits may be influenced by time period, operational factors, and wind control.
Assuming that the target of the number of credit awards per day set in the current month is p, the predicted value of the number of credit awards (amount of money) can be expressed as:
Figure BDA00023142737000001110
will be provided with
Figure BDA00023142737000001111
And fT+1(βT，b) Into YT+1In the formula, the predicted value of the borrowing amount in the T +1 day can be obtained, and the borrowing amount in the future 30 days can be continuously calculated and iteratively predicted step by step.
In S308, liquidity of the financial resource is predicted based on the repayment prediction data and the borrowing prediction data.
Those skilled in the art will appreciate that all or part of the steps implementing the above embodiments are implemented as computer programs executed by a CPU. When executed by the CPU, performs the functions defined by the methods provided by the present disclosure. The program may be stored in a computer readable storage medium, which may be a read-only memory, a magnetic or optical disk, or the like.
Furthermore, it should be noted that the above-mentioned figures are merely schematic illustrations of processes involved in methods according to exemplary embodiments of the present disclosure, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
The following are embodiments of the disclosed apparatus that may be used to perform embodiments of the disclosed methods. For details not disclosed in the embodiments of the apparatus of the present disclosure, refer to the embodiments of the method of the present disclosure.
Fig. 4 is a block diagram illustrating a user pay-by-date prediction apparatus according to an example embodiment. As shown in fig. 4, the user's repayment-by-date prediction apparatus 40 includes: a data module 402, a parameter module 404, a scoring module 406, a distribution module 408, and a prediction module 410.
The data module 402 is configured to obtain user data of a target user and a repayment amount and a repayment time corresponding to the user data; the data module 402 includes: the judging unit is used for judging whether the payment time of the user is the preset time or not and whether the arrearage state of the user is the non-stage state or not; determining the user as the target user.
The parameter module 404 is used to determine adjustment parameters; adjusting parameters, including: a user composition coefficient; a cycle factor coefficient; a monthly factor coefficient; market environment factor; and a running behavior coefficient.
The scoring module 406 is configured to determine a credit score corresponding to the user data according to the user data; the scoring module 406 includes: a model unit for inputting the user data into a user credit assessment model to generate credit assessment parameters; the history unit is used for acquiring historical arrearage data of the target user; acquiring historical repayment data of the target user; the scoring unit is used for generating the credit score through the credit evaluation parameter, the historical arrearage data and the historical repayment data; wherein the user credit assessment model is generated by a machine learning model.
The distribution module 408 is configured to determine a repayment date distribution and a repayment amount distribution of the current repayment of the target user based on the repayment amount, the adjustment parameter, and the credit score. The distribution module 408 includes: a probability unit, configured to determine a probability distribution of repayment of the target user based on the adjustment parameter and the credit score, where the probability distribution represents a repayment probability distribution of the user in a time sequence; and the money unit is used for determining money distribution of the repayment of the target user based on the repayment money and the credit score.
The payment date distribution comprises:
Xi＝{xi，T，xi，T+1，xi，T+2……xi，T+n}
Xiis a probability distribution, x, of repaymenti，TThe probability of repayment for the i user on day T.
The repayment amount distribution comprises:
Yi＝QaXi＝Qa{xi，T，xi，T+1，xi，T+2……xi，T+n}
Yifor repayment amount distribution, xi，TAnd (b) the probability of repayment of the user on the T day is i, and the payment sum of the user is a.
The prediction module 410 is used for acquiring payment date distribution and payment amount of all target users on a preset date; and predicting the liquidity of the financial resources based on the payment date distribution and the payment amount of all the target users.
The prediction module 410 includes: the calculating unit is used for taking the arrearage users on the preset date as target users; acquiring user data of a plurality of target users and corresponding repayment amounts and repayment time; determining credit scores for the plurality of target users; and determining the repayment date distribution and the repayment amount distribution of the target user based on the repayment amounts of the plurality of targets, the adjustment parameter and the credit score. A prediction unit for calculating repayment prediction data of the financial resource on a predetermined date based on the repayment amount of the plurality of targets, the adjustment parameter, and the credit score; acquiring borrowing prediction data of financial resources on a preset date; and predicting liquidity of the financial resource based on the repayment prediction data and the borrowing prediction data.
According to the on-schedule payment predicting device for the user, user data of a target user and corresponding payment amount and payment time are obtained; determining an adjustment parameter; determining a credit score corresponding to the user data according to the user data; and determining the repayment date distribution and the repayment amount distribution of the target user according to the term repayment based on the repayment amount, the adjusting parameters and the credit score, so that the number of the users according to the term repayment and the overdue amount can be screened out, the total amount of the future repayment every day is calculated, the redundancy degree of cash preparation is reduced, and the cash cost is effectively saved.
FIG. 5 is a block diagram illustrating an electronic device in accordance with an example embodiment.
An electronic device 500 according to this embodiment of the disclosure is described below with reference to fig. 5. The electronic device 500 shown in fig. 5 is only an example and should not bring any limitation to the function and scope of use of the embodiments of the present disclosure.
As shown in fig. 5, the electronic device 500 is embodied in the form of a general purpose computing device. The components of the electronic device 500 may include, but are not limited to: at least one processing unit 510, at least one memory unit 520, a bus 530 connecting the various system components (including the memory unit 520 and the processing unit 510), a display unit 540, and the like.
Wherein the storage unit stores program code executable by the processing unit 510 to cause the processing unit 510 to perform steps according to various exemplary embodiments of the present disclosure described in the electronic prescription flow-through processing method section above in this specification. For example, the processing unit 510 may perform the steps as shown in fig. 1, 2, 3.
The memory unit 520 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)5201 and/or a cache memory unit 5202, and may further include a read only memory unit (ROM) 5203.
The memory unit 520 may also include a program/utility 5204 having a set (at least one) of program modules 5205, such program modules 5205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, and in some combination, may comprise an implementation of a network environment.
Bus 530 may be any one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.
The electronic device 500 may also communicate with one or more external devices 500' (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 500, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 500 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 550. Also, the electronic device 500 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 560. The network adapter 560 may communicate with other modules of the electronic device 500 over the bus 530. It should be appreciated that although not shown, other hardware and/or software modules may be used in conjunction with the electronic device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, as shown in fig. 6, the technical solution according to the embodiment of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, or a network device, etc.) to execute the above method according to the embodiment of the present disclosure.
The software product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The 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 data signal propagating in baseband or as part of a carrier wave, which carries readable program code. 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 the present disclosure 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 of a variety of networks, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through an internet network using an internet service provider).
The computer readable medium carries one or more programs which, when executed by a device, cause the computer readable medium to perform the functions of: acquiring user data of a target user and a repayment amount and repayment time corresponding to the user data; determining an adjustment parameter; determining a credit score corresponding to the user data according to the user data; and determining the repayment date distribution and the repayment amount distribution of the target user on-time repayment based on the repayment amount, the adjusting parameter and the credit score.
Those skilled in the art will appreciate that the modules described above may be distributed in the apparatus according to the description of the embodiments, or may be modified accordingly in one or more apparatuses unique from the embodiments. The modules of the above embodiments may be combined into one module, or may be further split into a plurality of sub-modules.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by a combination of software and necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
Exemplary embodiments of the present disclosure are specifically illustrated and described above. It is to be understood that the present disclosure is not limited to the precise arrangements, instrumentalities, or instrumentalities described herein; on the contrary, the disclosure is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.

Claims (10)

1. A prediction method for on-schedule payment of a user is characterized by comprising the following steps:
acquiring user data of a target user and a repayment amount and repayment time corresponding to the user data;
determining an adjustment parameter;
determining a credit score corresponding to the user data according to the user data; and
and determining the repayment date distribution and the repayment amount distribution of the target user on-time repayment based on the repayment amount, the adjusting parameter and the credit score.
2. The method of claim 1, further comprising:
acquiring payment date distribution and payment amount of all target users on a preset date; and
and predicting the liquidity of the financial resources based on the payment date distribution and the payment amount of all the target users.
3. The method of claims 1-2, wherein obtaining user data of the target user and its corresponding payment amount and time comprises:
and determining the user as the target user before the repayment time of the user is preset time and when the arrearage state of the user is a non-stage state.
4. The method of claims 1-3, wherein determining an adjustment parameter comprises at least one of:
determining a user composition coefficient;
determining a cycle factor coefficient;
determining a lunar factor coefficient;
determining a market environment coefficient; and
and determining the operation behavior coefficient.
5. The method of claims 1-4, wherein determining from the user data its corresponding credit score comprises:
inputting the user data into a user credit assessment model to generate credit assessment parameters;
acquiring historical arrearage data of the target user;
acquiring historical repayment data of the target user; and
generating the credit score through the credit assessment parameter, the historical arrearage data, and the historical repayment data;
wherein the user credit assessment model is generated by a machine learning model.
6. The method of claims 1-5, wherein determining a payment date distribution and a payment amount distribution for the intended user's on-time payment based on the payment amount, the adjustment parameter, and the credit score comprises:
determining a probability distribution of repayment of the target user based on the adjusting parameter and the credit score, wherein the probability distribution represents the repayment probability distribution of the user in time sequence; and
and determining the amount distribution of the repayment of the target user based on the repayment amount and the credit score.
7. The method of claims 1-6, wherein the payment date distribution comprises:
Xi＝{xi，T，xi，T+1，xi，T+2……xi，T+n}
Xifor the probability distribution of repayment,xi，TThe probability of repayment for the i user on day T.
8. A user payment prediction apparatus, comprising:
the data module is used for acquiring the user data of the target user and the corresponding repayment amount and repayment time;
a parameter module for determining an adjustment parameter;
the scoring module is used for determining a credit score corresponding to the user data according to the user data; and
and the distribution module is used for determining the repayment date distribution and the repayment amount distribution of the target user on-time repayment based on the repayment amount, the adjusting parameter and the credit score.
9. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
10. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-7.
CN201911271318.2A 2019-12-12 2019-12-12 User on-schedule payment prediction method and device and electronic equipment Pending CN111242767A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113298635A (en) * 2021-04-28 2021-08-24 上海淇玥信息技术有限公司 Method and device for predicting quantity of unreturned resources of user based on tweed distribution

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113298635A (en) * 2021-04-28 2021-08-24 上海淇玥信息技术有限公司 Method and device for predicting quantity of unreturned resources of user based on tweed distribution

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