CN110363658A - Processing method and processing device, storage medium and the electronic device of credit data - Google Patents
Processing method and processing device, storage medium and the electronic device of credit data Download PDFInfo
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Abstract
The present invention provides processing method and processing device, storage medium and the electronic devices of a kind of credit data, wherein this method comprises: obtaining information relevant to credit refund, wherein, the relevant information of refunding to credit includes: the asset size of platform, the weighted connections of neural network model, history loan repayment statistical information, industry loan repayment account of the history, the following designated time period；By the information input relevant to credit refund into the neural network model；Obtain the first output result of the neural network model, wherein the first output result is used to indicate loan repayment situation of the platform in the following designated time period.Through the invention, solve the problems, such as that the experience of operation management personnel and mark post value are normally based in lending platforms in the related technology to be carried out loan repayment volume and estimate the management flowed with assets.
Description
Technical field
The present invention relates to computer fields, are situated between in particular to a kind of processing method and processing device of credit data, storage
Matter and electronic device.
Background technique
With socio-economic development, loan has become commonly used service in people's life, loan platform assets rule
Mould, amount of money sustainable growth, customer traffic rapid increase, it is possible to create financial industry risk, will also show complication, multiplicity
Information technology has been difficult to be utilized in change trend, operation management personnel, and the situation of refunding of spending money to loan does detailed analysis, only with operation
The flowing of experience managed fund is more and more difficult.Therefore operation management personnel need the operation more refined.
Traditional personal credit's platform operation administrative staff can estimate loan repayment volume, management money by experience and mark post value
Produce flowing.But the problem of traditional operation management mode is: operation management personnel often guard operation, reserve excess fund,
Leave unused capital is caused, so that it cannot make maximum revenue；When Industry risk occurs, operation management personnel cannot integrate history
Data correctly estimate loan repayment volume, reserve very few fund, business development is caused to be glided, or even fall into financial difficulty.
In view of the above problems in the related art, not yet there is effective solution at present.
Summary of the invention
The embodiment of the invention provides processing method and processing device, storage medium and the electronic device of a kind of credit data, with
It at least solves the experience for being normally based on operation management personnel in lending platforms in the related technology and mark post value carries out loan repayment
Volume estimates the problem of management with assets flowing.
According to one embodiment of present invention, a kind of processing method of credit data is provided, comprising: obtain with credit also
The relevant information of money, wherein the relevant information of refunding to credit includes: the asset size of platform, neural network model
Weighted connections, history loan repayment statistical information, industry loan repayment account of the history, the following designated time period；It will described and letter
It borrows and refunds relevant information input into the neural network model；Obtain the neural network model first output as a result,
Wherein, the first output result is used to indicate loan repayment situation of the platform in the following designated time period.
According to another embodiment of the invention, a kind of processing unit of credit data is provided, comprising: first obtains mould
Block, for obtaining relevant to credit refund information, wherein the relevant information of refunding to credit includes: the assets of platform
Scale, the weighted connections of neural network model, history loan repayment statistical information, industry loan repayment account of the history, future refer to
It fixes time section；Input module, for the information input relevant to credit refund by described in into the neural network model；Second
Module is obtained, for obtaining the first output result of the neural network model, wherein the first output result is used to indicate
Loan repayment situation of the platform in the following designated time period.
According to still another embodiment of the invention, a kind of storage medium is additionally provided, meter is stored in the storage medium
Calculation machine program, wherein the computer program is arranged to execute the step in any of the above-described embodiment of the method when operation.
According to still another embodiment of the invention, a kind of electronic device, including memory and processor are additionally provided, it is described
Computer program is stored in memory, the processor is arranged to run the computer program to execute any of the above-described
Step in embodiment of the method.
Through the invention, relevant to credit refund information is obtained, and the relevant information input that will refund to credit is to refreshing
In network model, obtained neural network model is used to indicate loan repayment situation of the platform in the following designated time period
First output is as a result, to solve the experience and mark post for being normally based on operation management personnel in lending platforms in the related technology
Value carries out the problem of loan repayment volume estimates the management with assets flowing, has reached the loan repayment feelings for improving some the following time
Condition prediction effect.
Detailed description of the invention
The drawings described herein are used to provide a further understanding of the present invention, constitutes part of this application, this hair
Bright illustrative embodiments and their description are used to explain the present invention, and are not constituted improper limitations of the present invention.In the accompanying drawings:
Fig. 1 is a kind of hardware block diagram of the terminal of the processing method of credit data of the embodiment of the present invention；
Fig. 2 is the processing method flow chart of credit data according to an embodiment of the present invention；
Fig. 3 is the structural block diagram of the processing unit of credit data according to an embodiment of the present invention；
Fig. 4 is the alternative construction block diagram one of the processing unit of credit data according to an embodiment of the present invention；
Fig. 5 is the alternative construction block diagram two of the processing unit of credit data according to an embodiment of the present invention.
Specific embodiment
Hereinafter, the present invention will be described in detail with reference to the accompanying drawings and in combination with Examples.It should be noted that not conflicting
In the case of, the features in the embodiments and the embodiments of the present application can be combined with each other.
Firstly, being illustrated to the term being related in the application；
Tensorflow lstm:TensorFlow is one based on data flow programming (dataflow programming)
Symbolic mathematical system, be widely used in each neural network (machine learning) algorithm programming realize.Long
Short Term network, is generally just called LSTM, is a kind of type that RNN is special, can learn long-term Dependency Specification.
Facebook prophef: high-precision time series forecasting is carried out with the parameter of simple, intuitive, and is supported certainly
Define the influence in season and festivals or holidays
ARIMA model (Autoregressive Integrated Moving Average model): difference integration moves
Dynamic average autoregression model also known as integrates rolling average autoregression model (mobile also referred to as to slide), time series forecasting point
One of analysis method.
Decision tree: (Decision Tree) be it is known it is various happen probability on the basis of, pass through constitute decision tree
Seek the probability that the desired value of net present value (NPV) is more than or equal to zero, assessment item risk judges the method for decision analysis of its feasibility,
It is a kind of intuitive graphical method for using probability analysis.
Logistic regression: logarithm probability returns, can export result and can also export its corresponding probability.
Mark post value: the amount to pay counted according to mode of repayment and mentions the loan and overdue loan for not looking to the future newly-increased
The case where preceding repaying.
The segmented industry: the segmented industry is economic term, and society is divided into various industries, especially by the difference of the division of labor in society
It is economic aspect, is divided into industry, agricultural, service trade, the primary industry, secondary industry, tertiary industry etc., in the division of labor of big type
Afterwards, it is not able to satisfy the needs of social management, then divides again, has manufacturing industry in industry, energy industry is commercially divided into internal trade, and foreign trade can
It is not enough, then to classify again, with the needs of social development, new industry is continuously added social and economic activities, the division of labor in society
More and more, more and more carefully, the continuous differentiation of this industry is exactly industry subdivision.
It should be noted that description and claims of this specification and term " first " in above-mentioned attached drawing, "
Two " etc. be to be used to distinguish similar objects, without being used to describe a particular order or precedence order.
Embodiment 1
Embodiment of the method provided by the embodiment of the present application one can be filled in terminal, terminal or similar operation
Set middle execution.For running at the terminal, Fig. 1 is a kind of terminal of the processing method of credit data of the embodiment of the present invention
Hardware block diagram.As shown in Figure 1, terminal 10 may include one or more (only showing one in Fig. 1) processor 102 (places
Reason device 102 can include but is not limited to the processing unit of Micro-processor MCV or programmable logic device FPGA etc.) and for storing
The memory 104 of data, optionally, above-mentioned terminal can also include defeated for the transmission device 106 of communication function and input
Equipment 108 out.It will appreciated by the skilled person that structure shown in FIG. 1 is only to illustrate, not to above-mentioned terminal
Structure causes to limit.For example, terminal 10 may also include the more perhaps less component than shown in Fig. 1 or have and Fig. 1 institute
Show different configurations.
Memory 104 can be used for storing computer program, for example, the software program and module of application software, such as this hair
The corresponding computer program of processing method of credit data in bright embodiment, processor 102 are stored in memory by operation
Computer program in 104 realizes above-mentioned method thereby executing various function application and data processing.Memory 104
May include high speed random access memory, may also include nonvolatile memory, as one or more magnetic storage device, flash memory,
Or other non-volatile solid state memories.In some instances, memory 104 can further comprise relative to processor 102
Remotely located memory, these remote memories can pass through network connection to terminal 10.The example of above-mentioned network include but
It is not limited to internet, intranet, local area network, mobile radio communication and combinations thereof.
Transmission device 106 is used to that data to be received or sent via a network.Above-mentioned network specific example may include
The wireless network that the communication providers of terminal 10 provide.In an example, transmission device 106 includes a network adapter
(Network Interface Controller, referred to as NIC), can be connected by base station with other network equipments so as to
It is communicated with internet.In an example, transmission device 106 can be radio frequency (Radio Frequency, referred to as RF)
Module is used to wirelessly be communicated with internet.
A kind of processing method of credit data for running on above-mentioned terminal is provided in the present embodiment, and Fig. 2 is according to this
The processing method flow chart of the credit data of inventive embodiments, as shown in Fig. 2, the process includes the following steps:
Step S202 obtains information relevant to credit refund, wherein information relevant to credit refund includes: platform
Asset size, the weighted connections of neural network model, history loan repayment statistical information, industry loan repayment account of the history,
The following designated time period；
Step S204, the relevant information input that will refund to credit is into neural network model；
Step S206 obtains the first output result of neural network model, wherein the first output result is used to indicate platform
Loan repayment situation in the following designated time period.
Wherein, it should be noted that the loan repayment situation in the following designated time period is preferred are as follows: loan repayment Target area
Between, industry loan repayment situation, seasonal loans refund situation.It can certainly be other loan repayment situations, herein no longer
It repeats.
S202 to step S206 through the above steps obtains information relevant to credit refund, and will be with credit refund phase
For the information input of pass into neural network model, obtain neural network model is used to indicate platform in the following designated time period
Loan repayment situation the first output as a result, being normally based on operation management to solving in lending platforms in the related technology
The experience and mark post value of personnel carry out the problem of loan repayment volume estimates the management with assets flowing, reached raising it is following some
The loan repayment situation prediction effect of time.
In the optional embodiment of the present embodiment, the weighted connections for the neural network model being related in the application pass through
Following manner determines:
The accuracy rate of the asset size of platform and the history output valve of neural network model is input to nerve by step S11
In network, and obtain the second output result；
Step S12 determines the weighted connections of asset size and neural network model according to the second output result.
For above-mentioned steps S11 and step S12, since platform asset size will affect the output valve of neural network model
Predictablity rate, therefore will predict accuracy rate, the platform asset size of the history output valve of the neural network model of refund situation
The neural network model that input divides asset size section obtains the period lower platform asset size section according to output valve
With the weighted connections of the neural network model of prediction refund situation.
In addition, being determined in the following manner for loan repayment section and predictablity rate: obtaining history loan repayment
Statistical information carries out pretreatment operation, various dimensions processing, noise data removal including outlier processing, missing values etc., input
The neural network model of the prediction refund situation of training in advance, the loan repayment section of certain period before exporting under current time
And predictablity rate.
In another optional embodiment of the present embodiment, the present processes step can also include:
Step S200 gets nerve net using preset data training before obtaining information relevant to credit refund
Network module, wherein preset data collection includes: history loan repayment statistical information.
It should be noted that the history loan repayment statistical information in the application includes at least following one: client's total amount
With according to the repayment amount total values of client characteristics distribution statistics, promise breaking amount of money total value, overdue rate of refunding, rate in advance of refunding.It is above-mentioned
Client characteristics distribution includes at least following one: client age distribution, Regional Distribution, Sex distribution, marital status distribution, credit
Distribution of grades, credit line are distributed, are distributed with opening an account, the distribution of Annual distribution of opening an account, mode of repayment, providing amount of money distribution, credit
Category disposition；
Industry loan repayment account of the history includes at least following one: according to the repayment amount of industry disaggregated classification distribution statistics
Total value, promise breaking amount of money total value, overdue rate of refunding, refund rate, industry seasonality refund rule in advance, upstream relevant industries disaggregated classification
Change rate with the statistical information of downstream relevant industries disaggregated classification relative to mark post value.
In addition, the neural network model of prediction refund situation can be the nerve of a variety of neural network models being composed
Network model, corresponding weight and threshold value can be according to the carry out dynamic adjustment such as data classification, asset size, such as according to invention
Person has found that the loan repayment of the loan platform of smaller asset size predicts the neural network more suitable for being capable of handling temporal aspect
Model, such as ARIMA model；History loan repayment statistical information can input the multiple neural networks for being capable of handling temporal aspect
The neural network model being composed adapts to the statistical information of a variety of different types of user characteristics distributions, improves the standard of prediction
True property；Industry disaggregated classification type is more, and data classification is thinner, causes every kind of lower historical information of classification less, be based on decision tree into
Row prediction can be more accurate；Loan repayment account of the history can input the moulds such as Tensorflow lstm, Facebook prophef
Type.
The neural network model of prediction asset size demarcation interval can input disaggregated model, such as Logistic
Regression etc..
As it can be seen that aforesaid way through this embodiment, can more accurately predict the loan repayment of some the following time
Situation is analyzed loan repayment forecast interval, industry loan repayment situation, seasonal loans refund situation, to make to transport
Battalion person can reasonably formulate target volume of making loans, and accurately manage and control operation, to realize maximum revenue.
Through the above description of the embodiments, those skilled in the art can be understood that according to above-mentioned implementation
The method of example can be realized by means of software and necessary general hardware platform, naturally it is also possible to by hardware, but it is very much
In the case of the former be more preferably embodiment.Based on this understanding, technical solution of the present invention is substantially in other words to existing
The part that technology contributes can be embodied in the form of software products, which is stored in a storage
In medium (such as ROM/RAM, magnetic disk, CD), including some instructions are used so that a terminal device (can be mobile phone, calculate
Machine, server or network equipment etc.) method that executes each embodiment of the present invention.
Embodiment 2
Additionally provide a kind of processing unit of credit data in the present embodiment, the device for realizing above-described embodiment and
Preferred embodiment, the descriptions that have already been made will not be repeated.As used below, predetermined function may be implemented in term " module "
The combination of the software and/or hardware of energy.It is hard although device described in following embodiment is preferably realized with software
The realization of the combination of part or software and hardware is also that may and be contemplated.
Fig. 3 is the structural block diagram of the processing unit of credit data according to an embodiment of the present invention, as shown in figure 3, the device
It include: the first acquisition module 32, for obtaining information relevant to credit refund, wherein packet relevant to credit refund
Include: the asset size of platform, the weighted connections of neural network model, history loan repayment statistical information, industry loan repayment are gone through
History situation, the following designated time period；Input module 34, for the relevant information input that will refund to credit to neural network model
In；Second obtain module 36, be of coupled connections with input module 34, for obtain neural network model first output as a result, its
In, the first output result is used to indicate loan repayment situation of the platform in the following designated time period.
Fig. 4 is the alternative construction block diagram one of the processing unit of credit data according to an embodiment of the present invention, as shown in figure 4,
Device in the application further include: processing module 42, for the history of the asset size of platform and neural network model to be exported
The accuracy rate of value is input in neural network, and obtains the second output result；Determining module 44, with 44 company of coupling of processing module
It connects, for determining the weighted connections of asset size and neural network model according to the second output result.
Fig. 5 is the alternative construction block diagram two of the processing unit of credit data according to an embodiment of the present invention, as shown in figure 5,
Device further include: training module 52, for being got using preset data training before obtaining information relevant to credit refund
To neural network module, wherein preset data collection includes: history loan repayment statistical information.
It should be noted that history loan repayment statistical information includes at least following one: client's total amount and according to client
The repayment amount total value of feature distribution statistics, promise breaking amount of money total value, overdue rate of refunding, rate in advance of refunding.
Wherein, industry loan repayment account of the history includes at least following one: according to going back for industry disaggregated classification distribution statistics
Money amount of money total value, promise breaking amount of money total value, overdue rate of refunding, refund rate, industry seasonality refund rule in advance, upstream relevant industries
Change rate of the statistical information of disaggregated classification and downstream relevant industries disaggregated classification relative to mark post value.Client characteristics distribution is at least wrapped
Include following one: client age distribution, Regional Distribution, Sex distribution, marital status distribution, credit grade distribution, credit line
It is distributed, is distributed with opening an account, the distribution of Annual distribution of opening an account, mode of repayment, providing amount of money distribution, credit Category disposition.
It should be noted that above-mentioned modules can be realized by software or hardware, for the latter, Ke Yitong
Following manner realization is crossed, but not limited to this: above-mentioned module is respectively positioned in same processor；Alternatively, above-mentioned modules are with any
Combined form is located in different processors.
Embodiment 3
The embodiments of the present invention also provide a kind of storage medium, computer program is stored in the storage medium, wherein
The computer program is arranged to execute the step in any of the above-described embodiment of the method when operation.
Optionally, in the present embodiment, above-mentioned storage medium can be set to store by executing based on following steps
Calculation machine program:
S1 obtains relevant to credit refund information, wherein to credit refund relevant information include: platform assets
Scale, the weighted connections of neural network model, history loan repayment statistical information, industry loan repayment account of the history, future refer to
It fixes time section；
S2, the relevant information input that will refund to credit is into neural network model；
S3 obtains the first output result of neural network model, wherein the first output result will be used to indicate platform in future
Loan repayment situation in designated time period.
Optionally, in the present embodiment, above-mentioned storage medium can include but is not limited to: USB flash disk, read-only memory (Read-
Only Memory, referred to as ROM), it is random access memory (Random Access Memory, referred to as RAM), mobile hard
The various media that can store computer program such as disk, magnetic or disk.
The embodiments of the present invention also provide a kind of electronic device, including memory and processor, stored in the memory
There is computer program, which is arranged to run computer program to execute the step in any of the above-described embodiment of the method
Suddenly.
Optionally, above-mentioned electronic device can also include transmission device and input-output equipment, wherein the transmission device
It is connected with above-mentioned processor, which connects with above-mentioned processor.
Optionally, in the present embodiment, above-mentioned processor can be set to execute following steps by computer program:
S1 obtains relevant to credit refund information, wherein to credit refund relevant information include: platform assets
Scale, the weighted connections of neural network model, history loan repayment statistical information, industry loan repayment account of the history, future refer to
It fixes time section；
S2, the relevant information input that will refund to credit is into neural network model；
S3 obtains the first output result of neural network model, wherein the first output result will be used to indicate platform in future
Loan repayment situation in designated time period.
Optionally, the specific example in the present embodiment can be with reference to described in above-described embodiment and optional embodiment
Example, details are not described herein for the present embodiment.
Obviously, those skilled in the art should be understood that each module of the above invention or each step can be with general
Computing device realize that they can be concentrated on a single computing device, or be distributed in multiple computing devices and formed
Network on, optionally, they can be realized with the program code that computing device can perform, it is thus possible to which they are stored
It is performed by computing device in the storage device, and in some cases, it can be to be different from shown in sequence execution herein
Out or description the step of, perhaps they are fabricated to each integrated circuit modules or by them multiple modules or
Step is fabricated to single integrated circuit module to realize.In this way, the present invention is not limited to any specific hardware and softwares to combine.
These are only the preferred embodiment of the present invention, is not intended to restrict the invention, for those skilled in the art
For member, the invention may be variously modified and varied.It is all within principle of the invention, it is made it is any modification, equally replace
It changes, improve, should all be included in the protection scope of the present invention.
Claims (10)
1. a kind of processing method of credit data characterized by comprising
Obtain relevant to credit refund information, wherein the relevant information of refunding to credit includes: the assets rule of platform
Mould, the weighted connections of neural network model, history loan repayment statistical information, industry loan repayment account of the history, future are specified
Period；
By the information input relevant to credit refund into the neural network model；
Obtain the first output result of the neural network model, wherein the first output result is used to indicate the platform
Loan repayment situation in the following designated time period.
2. the method according to claim 1, wherein the weighted connections of the neural network model pass through with lower section
Formula determines:
The accuracy rate of the asset size of the platform and the history output valve of the neural network model is input to the nerve
In network, and obtain the second output result；
The weighted connections of the asset size and neural network model are determined according to the second output result.
3. described the method according to claim 1, wherein before obtaining relevant to credit refund information
Method further include:
The neural network module is got using preset data training, wherein the preset data collection includes: that history loan is gone back
Money statistical information.
4. according to the method in any one of claims 1 to 3, which is characterized in that
The history loan repayment statistical information includes at least following one: client's total amount and according to client characteristics distribution statistics
Repayment amount total value, promise breaking amount of money total value, overdue rate of refunding, rate in advance of refunding；
The client characteristics distribution includes at least following one: client age distribution, Regional Distribution, Sex distribution, marital status
Distribution, credit grade distribution, credit line are distributed, are distributed with opening an account, the distribution of Annual distribution of opening an account, mode of repayment, providing the amount of money
Distribution, credit Category disposition；
The industry loan repayment account of the history includes at least following one: according to the repayment amount of industry disaggregated classification distribution statistics
Total value, promise breaking amount of money total value, overdue rate of refunding, refund rate, industry seasonality refund rule in advance, upstream relevant industries disaggregated classification
Change rate with the statistical information of downstream relevant industries disaggregated classification relative to mark post value.
5. a kind of processing unit of credit data characterized by comprising
First obtains module, for obtaining information relevant to credit refund, wherein the packet relevant to credit refund
Include: the asset size of platform, the weighted connections of neural network model, history loan repayment statistical information, industry loan repayment are gone through
History situation, the following designated time period；
Input module, for the information input relevant to credit refund by described in into the neural network model；
Second obtains module, for obtaining the first output result of the neural network model, wherein the first output result
It is used to indicate loan repayment situation of the platform in the following designated time period.
6. device according to claim 5, which is characterized in that described device further include:
Processing module, for the accuracy rate of the asset size of the platform and the history output valve of the neural network model is defeated
Enter into the neural network, and obtains the second output result；
Determining module, for determining that the weighting of the asset size and neural network model is closed according to the second output result
System.
7. device according to claim 6, which is characterized in that described device further include:
Training module, for getting the mind using preset data training before obtaining information relevant to credit refund
Through network module, wherein the preset data collection includes: history loan repayment statistical information.
8. device according to any one of claims 5 to 7, which is characterized in that
The history loan repayment statistical information includes at least following one: client's total amount and according to client characteristics distribution statistics
Repayment amount total value, promise breaking amount of money total value, overdue rate of refunding, rate in advance of refunding；
The client characteristics distribution includes at least following one: client age distribution, Regional Distribution, Sex distribution, marital status
Distribution, credit grade distribution, credit line are distributed, are distributed with opening an account, the distribution of Annual distribution of opening an account, mode of repayment, providing the amount of money
Distribution, credit Category disposition；
The industry loan repayment account of the history includes at least following one: according to the repayment amount of industry disaggregated classification distribution statistics
Total value, promise breaking amount of money total value, overdue rate of refunding, refund rate, industry seasonality refund rule in advance, upstream relevant industries disaggregated classification
Change rate with the statistical information of downstream relevant industries disaggregated classification relative to mark post value.
9. a kind of storage medium, which is characterized in that be stored with computer program in the storage medium, wherein the computer
Program is arranged to execute method described in any one of Claims 1-4 when operation.
10. a kind of electronic device, including memory and processor, which is characterized in that be stored with computer journey in the memory
Sequence, the processor are arranged to run the computer program to execute side described in any one of Claims 1-4
Method.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111583015A (en) * | 2020-04-09 | 2020-08-25 | 上海淇毓信息科技有限公司 | Credit application classification method and device and electronic equipment |
| CN112199359A (en) * | 2020-09-18 | 2021-01-08 | 中国建设银行股份有限公司 | Data checking method and device, electronic equipment and storage medium |
| CN113449873A (en) * | 2020-03-25 | 2021-09-28 | 北京同邦卓益科技有限公司 | Data processing method and device, electronic equipment and computer storage medium |
| CN113935826A (en) * | 2021-10-21 | 2022-01-14 | 阿尔法时刻科技(深圳)有限公司 | Credit account management method and system based on user privacy |
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