CN112927064A

CN112927064A - Deferred payment data processing method and device, electronic equipment and storage medium

Info

Publication number: CN112927064A
Application number: CN202110220891.1A
Authority: CN
Inventors: 张远
Original assignee: Ping An Puhui Enterprise Management Co Ltd
Current assignee: Ping An Puhui Enterprise Management Co Ltd
Priority date: 2021-02-26
Filing date: 2021-02-26
Publication date: 2021-06-08

Abstract

The invention relates to the technical field of big data, and provides a deferred payment data processing method, a deferred payment data processing device, electronic equipment and a storage medium, wherein the method comprises the following steps: analyzing the credit loss data of each user to obtain deferred payment information, follow-up information and credit loss grade calculation rules, and inputting the information into a pre-trained credit loss grade model to obtain a target credit loss grade; when the target loss of credit level is greater than a preset loss of credit level threshold value, identifying each digital asset and the asset type of each digital asset in each account information of each user; calculating a target risk level of each user delay repayment; and performing deferred payment analysis based on the target risk level of deferred payment of each user and all account information of each user, and executing deferred payment according to a target payment strategy when the obtained target analysis result is that the payment is competent. According to the invention, the efficiency and the accuracy of deferred payment processing are improved by comprehensively analyzing the assets of all account information of the user.

Description

Deferred payment data processing method and device, electronic equipment and storage medium

Technical Field

The invention relates to the technical field of big data, in particular to a deferred payment data processing method and device, electronic equipment and a storage medium.

Background

With the development of internet technology, users usually adopt an instant messaging tool to perform various operations such as communication, payment and account transfer, and after borrowing actions occur through the instant messaging tool, the prior art adopts a unified processing mode for deferred payment caused by any reason by uniformly setting payment standards, aiming at deferred payment conditions, the user experience is poor, the phenomenon that the user probably forgets to pay the deferred payment is not considered, and the accuracy and the effectiveness of deferred payment processing are low.

Disclosure of Invention

In view of the above, it is necessary to provide a deferred payment data processing method, device, electronic device, and storage medium, which improve efficiency and accuracy of deferred payment processing by performing asset comprehensive analysis on all account information of a user.

The first aspect of the invention provides a deferred payment data processing method, which comprises the following steps:

crawling a lost credit user list and lost credit data of each user in the lost credit user list from a plurality of data sources by using a web crawler;

analyzing the credit loss data of each user to obtain deferred payment information, follow-up information and credit loss grade calculation rules of each user;

inputting the deferred repayment information, follow-up urging information and the loss level calculation rule of each user into a pre-trained loss level model to obtain a target loss level of each user;

when the target loss of credit level is greater than a preset loss of credit level threshold value, acquiring all account information of each user, and identifying each digital asset and an asset type corresponding to each digital asset in each account information of each user;

calculating a target risk level of deferred repayment of each user according to each digital asset in each account information of each user and the asset type corresponding to each digital asset;

performing deferred payment analysis based on the target risk level of deferred payment of each user and all account information of each user to obtain a target analysis result of each user;

and when the target analysis result of each user is that the payment is available, identifying a target payment strategy of each user, and executing deferred payment according to the target payment strategy of each user.

Optionally, the calculating a target risk level of deferred repayment of each user according to each digital asset in each account information of each user and an asset type corresponding to each digital asset includes:

acquiring all first digital assets with asset types as first asset types and asset risk conversion coefficients preset by each first digital asset, calculating the product of the asset risk conversion coefficients of each first digital asset and each first digital asset to obtain a first product, and calculating the sum of all the first products to obtain the first asset;

acquiring all second digital assets with the asset type being a second asset type, an income risk conversion coefficient preset by each second digital asset and a repayment period of each second digital asset, calculating the product of the income risk conversion coefficient preset by each second digital asset and the repayment period of each second digital asset to obtain a second product, and calculating the sum of all the second products to obtain the second asset;

calculating according to all account information of each user and a preset calculation rule to obtain a third asset of each user;

calculating the sum of the first asset and the second asset, and subtracting a third asset to obtain a fourth asset;

and calculating the product of the current target total repayment amount acquired from the deferred repayment information of each user and the multiplication of a preset risk grade coefficient, and dividing the product by the fourth asset for rounding to obtain the target risk grade of the deferred repayment of each user.

Optionally, the obtaining, by calculation according to a preset calculation rule, a third asset of each user according to all account information of each user includes:

acquiring historical expenditure bills of a plurality of preset periods from each account information of each user;

dividing the historical expenditure bill of each preset period into a first consumption type and a second consumption type; calculating the sum of expenditure assets corresponding to the first consumption type in each preset period to obtain a first consumption asset, and calculating the sum of expenditure assets corresponding to the second consumption type in each preset period to obtain a second consumption asset;

calculating the average value of the first consumption assets in the preset periods to obtain a third consumption asset, and calculating the average value of the second consumption assets in the preset periods to obtain a fourth consumption asset;

calculating the product of the third consumption asset and a preset first weight value to obtain a first target consumption asset;

calculating the product of the fourth consumption asset and a preset second weight value to obtain a second target consumption asset;

and calculating the sum of the first target consumption asset and the second target consumption asset to obtain a third asset of each user.

Optionally, the performing deferred payment analysis based on the target risk level of deferred payment of each user and all account information of each user to obtain a target analysis result of each user includes:

determining the residual assets of each account of each user according to the information of each account of each user;

processing the historical expenditure bill in each account information of each user by using a preset neural network model to obtain a preset expenditure asset of each account in the next repayment period;

respectively calculating available assets of each account according to the residual assets of each account and preset expenditure assets of each account in the next repayment period;

calculating the sum of the available assets of all accounts of each user to obtain the target available asset of each user;

when the target available assets of each user are matched with the target risk level of the deferred payment of each user, determining that the target analysis result of each user is the ability to pay; or

And when the target available assets of each user do not match the target risk level of the deferred repayment of each user, determining the target analysis result of each user as incapability repayment.

Optionally, after the deferred payment is executed according to the target payment policy of each user, the method further includes:

judging whether the target risk level of each user is greater than a preset risk level or not;

when the target risk level of each user is greater than or equal to the preset risk level, judging whether the deferred repayment date of each user exceeds a fine discount period;

when the delayed repayment date of each user exceeds the fine discount period, calculating the fine days of each user according to the delayed repayment date and the fine discount expiration date of each user, and adjusting the credit level of each user according to the fine days of each user; or

When the delayed repayment date of each user does not exceed the fine discount period, the credit rating of each user is maintained.

Optionally, the analyzing the credit loss data of each user to obtain deferred payment information, follow-up information and credit loss level calculation rules of each user includes:

extracting a plurality of first key fields corresponding to deferred payment information from the credit loss data of each user, and extracting a plurality of second key field words corresponding to the payment information from the credit loss data of each user;

converting the first key fields into a plurality of first structured data of a preset type, and converting the second key fields into a plurality of first structured data of a preset type;

and determining a confidence loss level calculation rule of each user according to the plurality of first structured data and the plurality of first structured data.

Optionally, the method further comprises:

and when the target analysis result of each user is incapability repayment, sending an alarm notification to each user.

A second aspect of the present invention provides a deferred payment data processing apparatus, comprising:

the system comprises a crawling module, a searching module and a display module, wherein the crawling module is used for crawling a credit loss user list and credit loss data of each user in the credit loss user list from a plurality of data sources by utilizing a web crawler;

the acquisition module is used for analyzing the credit loss data of each user to acquire deferred repayment information, follow-up urging information and credit loss grade calculation rules of each user;

the input module is used for inputting the deferred payment information, the follow-up information and the loss level calculation rule of each user into a pre-trained loss level model to obtain a target loss level of each user;

the identification module is used for acquiring all account information of each user when the target loss level is greater than a preset loss level threshold value, and identifying each digital asset and an asset type corresponding to each digital asset in each account information of each user;

the calculation module is used for calculating a target risk level of deferred repayment of each user according to each digital asset in each account information of each user and the asset type corresponding to each digital asset;

the analysis module is used for carrying out deferred payment analysis based on the target risk level of deferred payment of each user and all account information of each user to obtain a target analysis result of each user;

and the execution module is used for identifying the target payment strategy of each user and executing deferred payment according to the target payment strategy of each user when the target analysis result of each user is that the payment is available.

A third aspect of the present invention provides an electronic device, which includes a processor and a memory, wherein the processor is configured to implement the deferred payment data processing method when executing a computer program stored in the memory.

A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the deferred payment data processing method.

In summary, according to the deferred payment data processing method, the deferred payment data processing apparatus, the electronic device and the storage medium of the present invention, on one hand, when the target loss level is greater than the preset loss level threshold, all account information of each user is obtained, each digital asset in each account information of each user and an asset type corresponding to each digital asset are identified, and by performing asset comprehensive analysis on all account information of each user, a potential risk of deferred payment is reduced, and efficiency and accuracy of deferred payment processing are improved; on the other hand, a target risk level of deferred payment of each user is calculated according to each digital asset in each account information of each user and an asset type corresponding to each digital asset, the target risk level of deferred payment of each user is calculated by comprehensively analyzing the asset condition of all accounts of each user and asset value reduction loss and daily fixed overhead of each period, the accuracy of the calculated target risk level of each user is improved, the accuracy of deferred payment risk prediction is improved, finally deferred payment analysis is carried out on the basis of the target risk level of deferred payment of each user and all account information of each user, a target analysis result of each user is obtained, and whether each user has the ability of payment is determined according to the matching result by matching the target available asset of each user with the target risk level of deferred payment of each user, whether each user has the ability to pay is not determined singly according to the target risk level of deferred payment of each user, and the accuracy of the target analysis result is improved.

Drawings

Fig. 1 is a flowchart of a deferred payment data processing method according to an embodiment of the present invention.

Fig. 2 is a structural diagram of a deferred payment data processing apparatus according to a second embodiment of the present invention.

Fig. 3 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention.

Detailed Description

In order that the above objects, features and advantages of the present invention can be more clearly understood, a detailed description of the present invention will be given below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present invention and features of the embodiments may be combined with each other without conflict.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

Example one

In this embodiment, the deferred payment data processing method may be applied to an electronic device, and for an electronic device that needs to perform deferred payment data processing, the deferred payment data processing function provided by the method of the present invention may be directly integrated on the electronic device, or may be run in the electronic device in a Software Development Kit (SDK) form.

As shown in fig. 1, the deferred payment data processing method specifically includes the following steps, and the order of the steps in the flowchart may be changed and some may be omitted according to different requirements.

S11, crawling the lost credit user list and the lost credit data of each user in the lost credit user list from a plurality of data sources by using the web crawler.

In this embodiment, a plurality of data sources may be preset, a lost-credit user list is crawled from a lost-credit database of each data source by using a web crawler technology, and lost-credit data of each user in the lost-credit user list in each lost-credit database is acquired, and specifically, the data sources may be a third party platform, a credit investigation platform, and the like.

And S12, analyzing the credit loss data of each user to obtain the deferred payment information, the follow-up information and the credit loss grade calculation rule of each user.

In this embodiment, the credit loss data of each user includes a target total payment amount of the current deferred payment, historical payment data, follow-up information, and the like.

In an optional embodiment, the analyzing the credit loss data of each user to obtain deferred payment information, follow-up information, and credit loss level calculation rules of each user includes:

In this embodiment, the type of the first structured data may be preset, specifically, a plurality of first key fields corresponding to deferred payment information are extracted from the information lost data of each user, where the first key fields are 1: postponed date XXXX/XX/XX, first key field 2: converting the first key field 1 and the second key field 2 into first structure changing data of a preset type corresponding to the deferred date, wherein the deferred date is XXXX-XX-XX: XXXXXX-XX-XX.

In this embodiment, the credit loss level calculation rule of each user is determined according to different deferred payment information and follow-up payment information, and the different deferred payment information and the credit loss level calculation rule corresponding to the follow-up payment information are different, so that the diversity of the credit loss level calculation rule is improved.

And S13, inputting the deferred payment information, the follow-up information and the loss level calculation rule of each user into a pre-trained loss level model to obtain the target loss level of each user.

In this embodiment, the delay repayment information, follow-up information and the delay level calculation rule of each user can be obtained by training the delay repayment level model in advance, and then the delay repayment information, follow-up information and the delay level calculation rule of each user are obtained by inputting the delay repayment information, follow-up information and follow-up information of each user into the pre-trained delay level model to obtain the target delay level of each user.

Specifically, the training process of the confidence level model includes:

obtaining deferred repayment information, follow-up information and loss level calculation rules of a plurality of users as a sample data set;

dividing a training set and a testing set from the sample data set;

inputting the training set into a preset neural network for training to obtain a confidence losing grade model;

inputting the test set into the confidence losing level model for testing, and calculating the test passing rate;

if the test passing rate is larger than a preset passing rate threshold value, determining that the training of the loss of confidence level model is finished; and if the test passing rate is smaller than the preset passing rate threshold value, increasing the number of training sets, and re-training the confidence losing level model.

In this embodiment, in the subsequent service process, the deferred payment information, the follow-up information, and the loss level calculation rule of each user are used as new data to increase the number of the data sets, and the loss level model is retrained based on the new data sets. Namely, the loss of credit grade model is continuously updated, so that the accuracy of the loss of credit grade value is continuously improved.

And S14, when the target loss level is greater than a preset loss level threshold, acquiring all account information of each user, and identifying each digital asset and an asset type corresponding to each digital asset in each account information of each user.

In this embodiment, a credit loss level threshold may be preset, when the target credit loss level is greater than the preset credit loss level threshold, it is determined that potential risks may exist in the deferred repayment, and all account information of each user is acquired to perform asset comprehensive analysis, so as to reduce the potential risks of the deferred repayment and improve efficiency and accuracy of deferred repayment processing.

And S15, calculating the target risk level of each user for deferred repayment according to each digital asset in each account information of each user and the asset type corresponding to each digital asset.

In this embodiment, the target risk level refers to a risk level of deferred payment of each user, and a risk brought to business transaction by deferred payment of each user can be predicted through the target risk level.

In an optional embodiment, the calculating a target risk level of deferred repayment of each user according to each digital asset in each account information of each user and an asset type corresponding to each digital asset includes:

In this embodiment, the first asset type includes assets that can be withheld, such as financing assets, stock assets, and live assets, the second asset type includes stable assets, such as payroll assets, income assets, and house assets, and the third asset refers to an overhead asset of each user, and specifically, the overhead asset includes life expenses, family expenses, and the like, and is obtained by calculating assets that are lost by asset deduction and assets that are lost by daily fixed expenses. In this embodiment, the asset risk conversion factor is greater than 0 and less than 1; the income risk conversion coefficient is greater than 0 and less than 1, and the risk grade coefficient can be a natural number and is preset according to the actual situation of each user.

Exemplarily, the risk level is determined to be 0-10, 0 is the lowest risk level, 10 is the highest risk level, the repayment period of the user a is 6 days, the preset risk level coefficient is 5, the total target repayment amount of the current deferred repayment is 100 ten thousand, the user a has two account numbers, and the account number 1 includes: assets of the first type: asset 1 is 50 ten thousand, asset risk conversion factor for asset 1: a1 ═ 0.9, asset 2 ═ 40 ten thousand, asset 2's asset risk reduction factor: a2 is 0.9; asset of the second type: asset 3 is 2 ten thousand, and the income risk conversion factor b1 of asset 3 is 0.01; the account 2 includes: assets of the first type: asset 4 is 50, asset risk conversion factor for asset 4: a3 is 0.8; asset of the second type: asset 5 is 5, and the revenue risk conversion factor b2 for asset 5 is 0.09; calculating according to the historical expenditure assets in the account 1 and the account 2 and according to a preset calculation rule to obtain a third asset C of each user, calculating to obtain a first asset C of 50 × 0.9+40 × 0.9+50 × 0.8 of 121 ten thousand, calculating to obtain a second asset C of 2 × 0.01 × 6+5 × 0.09 × 6 of 2.82 ten thousand, calculating to obtain a target risk level of each user deferred payment of 100 × 5 ÷ (121+2.82-2) ═ 4.1, and rounding to obtain a target risk level of each user deferred payment of 4.

In the embodiment, the target risk level of the deferred payment of each user is calculated by comprehensively analyzing the asset conditions of all accounts of each user and the asset value reduction loss and daily fixed expense of each period, so that the accuracy of the calculated target risk level of each user is improved, and the accuracy of deferred payment risk prediction is further improved.

In an optional embodiment, the calculating the third asset of each user according to the preset calculation rule based on all account information of each user includes:

In this embodiment, the first consumption type refers to a fixed consumption asset, for example, a long-term fixed consumption such as a house rental, an education fund, and the like every month, and the second consumption type refers to a variable consumption asset, for example, a variable consumption asset such as clothes, cosmetics, and the like.

In this embodiment, different weight values are preset for different consumption types, for example, a first weight value is preset for a first consumption type, a second weight value is preset for a second consumption type, and the preset first weight value and the preset second weight value are set according to actual conditions of a user, specifically, since the first consumption type is a fixed expense per cycle, the first weight value may be set to 95%, the second consumption type may be variable, and the second weight value may be set to 80%, and by presetting different weight values for different consumption types, the accuracy of the calculated third asset may be improved.

And S16, performing deferred payment analysis based on the target risk level of deferred payment of each user and all account information of each user to obtain a target analysis result of each user.

In this embodiment, the target risk level is obtained by calculating according to digital assets in all account information of each user, and the target analysis result includes two types: incapability of payment and capacity for payment.

In some other embodiments, the target analysis result may be: forgetting to pay, overdue payment due to ineffectiveness, and the like.

In an optional embodiment, the performing deferred payment analysis based on the target risk level of deferred payment of each user and all account information of each user to obtain a target analysis result of each user includes:

In the embodiment, the target available assets of each user are matched with the target risk level of the deferred payment of each user, whether each user has the ability to pay is determined according to the matching result, and whether each user has the ability to pay is not determined directly according to the target risk level of the deferred payment of each user, so that the accuracy of the target analysis result is improved.

And S17, when the target analysis result of each user is that the payment is available, identifying the target payment strategy of each user, and executing deferred payment according to the target payment strategy of each user.

In this embodiment, the target payment policy includes, but is not limited to, one or more of the following:

target repayment policy a: and setting a multi-channel payment mode, for example, payment can be performed by a plurality of account numbers, and a payment sequence is set for the account numbers.

Target repayment policy B: when the cash is not available due to the expiration, the automatic payment of the high-liquidity product can be automatically changed and sold, such as a currency emergency.

Target repayment policy C: when no cash is due, the target user is prompted to pay by telephone or short message WeChat and other modes.

Target repayment policy D: and when no cash is due, automatically locking a response asset, and automatically releasing the response asset after the target user delays to pay, wherein the response asset is an asset which can be changed.

In an optional embodiment, after the deferred payment is executed according to the target payment policy of each user, the method further includes:

In this embodiment, because each user has the ability to pay but forgets to pay, deferred payment is caused, whether the date of deferred payment exceeds the fine discount period is judged, whether the credit rating of each user is reduced is determined according to the judgment result, when the date of deferred payment of each user exceeds the fine discount period, it is determined that the target user may be the time of the deferred payment, the credit rating of each user is adjusted according to the number of fine days, the validity of the credit rating of each user is improved, and meanwhile, the risk of deferred payment is reduced by assisting service handling.

In an optional embodiment, the method further comprises:

and when the target risk level of each user is smaller than the preset risk level, keeping the credit level of each user.

In the embodiment, under the condition that each user has the ability to pay, deferred payment is executed according to the target payment strategy of each user, so that the processing efficiency of deferred payment is improved, meanwhile, the condition that the credit level is reduced due to the fact that the user forgets to pay due to negligence is avoided, and the user experience is improved.

Further, the method further comprises:

In the embodiment, when each user has no ability to pay, an alarm notification needs to be sent to each user to remind the user to process the deferred payment items in time, so that the processing efficiency of deferred payment is improved.

In summary, in the deferred payment data processing method according to this embodiment, a network crawler is used to crawl a credit loss user list and credit loss data of each user in the credit loss user list from a plurality of data sources; analyzing the credit loss data of each user to obtain deferred payment information, follow-up information and credit loss grade calculation rules of each user; inputting the deferred repayment information, follow-up urging information and the loss level calculation rule of each user into a pre-trained loss level model to obtain a target loss level of each user; when the target loss of credit level is greater than a preset loss of credit level threshold value, acquiring all account information of each user, and identifying each digital asset and an asset type corresponding to each digital asset in each account information of each user; calculating a target risk level of deferred repayment of each user according to each digital asset in each account information of each user and the asset type corresponding to each digital asset; performing deferred payment analysis based on the target risk level of deferred payment of each user and all account information of each user to obtain a target analysis result of each user; and when the target analysis result of each user is that the payment is available, identifying a target payment strategy of each user, and executing deferred payment according to the target payment strategy of each user.

In this embodiment, on one hand, when the target loss level is greater than the preset loss level threshold, all account information of each user is acquired, each digital asset in each account information of each user and an asset type corresponding to each digital asset are identified, and all account information of each user is subjected to asset comprehensive analysis, so that potential risk of deferred payment is reduced, and efficiency and accuracy of deferred payment processing are improved; on the other hand, a target risk level of deferred payment of each user is calculated according to each digital asset in each account information of each user and an asset type corresponding to each digital asset, the target risk level of deferred payment of each user is calculated by comprehensively analyzing the asset condition of all accounts of each user and asset value reduction loss and daily fixed overhead of each period, the accuracy of the calculated target risk level of each user is improved, the accuracy of deferred payment risk prediction is improved, finally deferred payment analysis is carried out on the basis of the target risk level of deferred payment of each user and all account information of each user, a target analysis result of each user is obtained, and whether each user has the ability of payment is determined according to the matching result by matching the target available asset of each user with the target risk level of deferred payment of each user, whether each user has the ability to pay is not determined singly according to the target risk level of deferred payment of each user, and the accuracy of the target analysis result is improved.

Example two

In some embodiments, the deferred payment data processing apparatus 20 may comprise a plurality of functional modules comprising program code segments. Program code of various program segments in the deferred payment data processing apparatus 20 may be stored in a memory of the electronic device and executed by the at least one processor to perform (see detailed description of fig. 1) the functions of deferred payment data processing.

In this embodiment, the deferred payment data processing apparatus 20 may be divided into a plurality of functional modules according to the functions executed by the apparatus. The functional module may include: the system comprises a crawling module 201, an obtaining module 202, an inputting module 203, a recognizing module 204, a calculating module 205, an analyzing module 206 and an executing module 207. The module referred to herein is a series of computer program segments capable of being executed by at least one processor and capable of performing a fixed function and is stored in memory. In the present embodiment, the functions of the modules will be described in detail in the following embodiments.

The crawling module 201 is configured to crawl a credit loss user list and credit loss data of each user in the credit loss user list from multiple data sources by using a web crawler.

The obtaining module 202 is configured to analyze the credit loss data of each user to obtain deferred payment information, follow-up information, and credit loss level calculation rules of each user.

In an optional embodiment, the obtaining module 202 analyzes the credit loss data of each user to obtain the deferred payment information, the follow-up information, and the credit loss level calculation rule of each user includes:

The input module 203 is configured to input the deferred payment information, the follow-up information, and the loss level calculation rule of each user into a pre-trained loss level model to obtain a target loss level of each user.

Specifically, the training process of the confidence level model includes:

dividing a training set and a testing set from the sample data set;

The identifying module 204 is configured to, when the target loss level is greater than a preset loss level threshold, acquire all account information of each user, and identify each digital asset and an asset type corresponding to each digital asset in each account information of each user.

And the calculating module 205 is configured to calculate a target risk level of deferred payment for each user according to each digital asset in each account information of each user and an asset type corresponding to each digital asset.

In an optional embodiment, the calculating module 205 calculates the target risk level of the deferred payment for each user according to each digital asset in each account information of each user and the asset type corresponding to each digital asset, where the target risk level includes:

And the analysis module 206 is configured to perform deferred payment analysis based on the target risk level of deferred payment of each user and all account information of each user, so as to obtain a target analysis result of each user.

In an optional embodiment, the analyzing module 206 performs deferred payment analysis based on the target risk level of deferred payment of each user and all account information of each user, and obtaining a target analysis result of each user includes:

And the execution module 207 is configured to identify a target payment policy of each user when the target analysis result of each user indicates that the payment is available, and execute deferred payment according to the target payment policy of each user.

In an optional embodiment, after the executing module 207 executes the deferred payment according to the target payment policy of each user, it is determined whether the target risk level of each user is greater than a preset risk level; when the target risk level of each user is greater than or equal to the preset risk level, judging whether the deferred repayment date of each user exceeds a fine discount period; when the delayed repayment date of each user exceeds the fine discount period, calculating the fine days of each user according to the delayed repayment date and the fine discount expiration date of each user, and adjusting the credit level of each user according to the fine days of each user; or when the delayed repayment date of each user does not exceed the fine offer period, keeping the credit rating of each user.

In an alternative embodiment, the credit rating of each user is maintained when the target risk rating of each user is less than the preset risk rating.

Further, when the target analysis result of each user is incapability repayment, an alarm notification is sent to each user.

In summary, the deferred payment data processing apparatus according to this embodiment crawls a credit loss user list and credit loss data of each user in the credit loss user list from a plurality of data sources by using a web crawler; analyzing the credit loss data of each user to obtain deferred payment information, follow-up information and credit loss grade calculation rules of each user; inputting the deferred repayment information, follow-up urging information and the loss level calculation rule of each user into a pre-trained loss level model to obtain a target loss level of each user; when the target loss of credit level is greater than a preset loss of credit level threshold value, acquiring all account information of each user, and identifying each digital asset and an asset type corresponding to each digital asset in each account information of each user; calculating a target risk level of deferred repayment of each user according to each digital asset in each account information of each user and the asset type corresponding to each digital asset; performing deferred payment analysis based on the target risk level of deferred payment of each user and all account information of each user to obtain a target analysis result of each user; and when the target analysis result of each user is that the payment is available, identifying a target payment strategy of each user, and executing deferred payment according to the target payment strategy of each user.

EXAMPLE III

Fig. 3 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention. In the preferred embodiment of the present invention, the electronic device 3 comprises a memory 31, at least one processor 32, at least one communication bus 33 and a transceiver 34.

It will be appreciated by those skilled in the art that the configuration of the electronic device shown in fig. 3 does not constitute a limitation of the embodiment of the present invention, and may be a bus-type configuration or a star-type configuration, and the electronic device 3 may include more or less other hardware or software than those shown, or a different arrangement of components.

In some embodiments, the electronic device 3 is an electronic device capable of automatically performing numerical calculation and/or information processing according to instructions set or stored in advance, and the hardware thereof includes but is not limited to a microprocessor, an application specific integrated circuit, a programmable gate array, a digital processor, an embedded device, and the like. The electronic device 3 may also include a client device, which includes, but is not limited to, any electronic product that can interact with a client through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device, for example, a personal computer, a tablet computer, a smart phone, a digital camera, and the like.

It should be noted that the electronic device 3 is only an example, and other existing or future electronic products, such as those that can be adapted to the present invention, should also be included in the scope of the present invention, and are included herein by reference.

In some embodiments, the memory 31 is used for storing program codes and various data, such as the deferred payment data processing apparatus 20 installed in the electronic device 3, and realizes high-speed and automatic access to programs or data during the operation of the electronic device 3. The Memory 31 includes a Read-Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), a One-time Programmable Read-Only Memory (OTPROM), an electronically Erasable rewritable Read-Only Memory (Electrically-Erasable Programmable Read-Only Memory (EEPROM)), an optical Read-Only disk (CD-ROM) or other optical disk Memory, a magnetic disk Memory, a tape Memory, or any other medium readable by a computer capable of carrying or storing data.

In some embodiments, the at least one processor 32 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The at least one processor 32 is a Control Unit (Control Unit) of the electronic device 3, connects various components of the electronic device 3 by using various interfaces and lines, and executes various functions and processes data of the electronic device 3 by running or executing programs or modules stored in the memory 31 and calling data stored in the memory 31.

In some embodiments, the at least one communication bus 33 is arranged to enable connection communication between the memory 31 and the at least one processor 32 or the like.

Although not shown, the electronic device 3 may further include a power supply (such as a battery) for supplying power to each component, and optionally, the power supply may be logically connected to the at least one processor 32 through a power management device, so as to implement functions of managing charging, discharging, and power consumption through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device 3 may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.

It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.

The integrated unit implemented in the form of a software functional module may be stored in a computer-readable storage medium. The software functional module is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, an electronic device, or a network device) or a processor (processor) to execute parts of the methods according to the embodiments of the present invention.

In a further embodiment, in conjunction with fig. 2, the at least one processor 32 may execute operating means of the electronic device 3 and various installed applications (such as the deferred payment data processing apparatus 20), program codes, and the like, for example, the above-mentioned modules.

The memory 31 has program code stored therein, and the at least one processor 32 can call the program code stored in the memory 31 to perform related functions. For example, the modules illustrated in fig. 2 are program codes stored in the memory 31 and executed by the at least one processor 32, so as to implement the functions of the modules for the purpose of deferring payment data processing.

In one embodiment of the present invention, the memory 31 stores a plurality of instructions that are executed by the at least one processor 32 to implement the functionality of deferred payment data processing.

Specifically, the at least one processor 32 may refer to the description of the relevant steps in the embodiment corresponding to fig. 1, and details are not repeated here.

In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.

The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.

In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.

It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention 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 obvious that the word "comprising" does not exclude other elements or that the singular does not exclude the plural. A plurality of units or means recited in the present invention may also be implemented by one unit or means through software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.

Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A deferred payment data processing method, characterized in that the method comprises:

2. The deferred payment data processing method according to claim 1, wherein the calculating a target risk level of deferred payment for each user according to each digital asset in each account information of each user and an asset type corresponding to each digital asset comprises:

3. The deferred payment data processing method of claim 2, wherein the calculating the third asset of each user according to all account information of each user and a preset calculation rule comprises:

4. The deferred payment data processing method according to claim 1, wherein the deferred payment analysis is performed based on the target risk level of deferred payment of each user and all account information of each user, and obtaining a target analysis result of each user comprises:

5. The deferred payment data processing method according to claim 1, wherein after said deferred payment is performed in accordance with the target payment policy of each user, the method further comprises:

6. The deferred payment data processing method according to claim 1, wherein the analyzing the credit loss data of each user to obtain deferred payment information, follow-up information and credit loss level calculation rules of each user comprises:

7. The deferred payment data processing method according to any one of claims 1 to 6, wherein the method further comprises:

8. A deferred payment data processing apparatus, characterized in that the apparatus comprises:

9. An electronic device, characterized in that the electronic device comprises a processor and a memory, the processor being configured to implement the deferred payment data processing method according to any one of claims 1 to 7 when executing a computer program stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the deferred payment data processing method according to any one of claims 1 to 7.