CN109472586A - Strategy determines method and device, storage medium, electronic device - Google Patents
Strategy determines method and device, storage medium, electronic device Download PDFInfo
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- CN109472586A CN109472586A CN201811270117.6A CN201811270117A CN109472586A CN 109472586 A CN109472586 A CN 109472586A CN 201811270117 A CN201811270117 A CN 201811270117A CN 109472586 A CN109472586 A CN 109472586A
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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
- G06Q20/00—Payment architectures, schemes or protocols
- G06Q20/22—Payment schemes or models
- G06Q20/24—Credit schemes, i.e. "pay after"
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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
- G06Q20/00—Payment architectures, schemes or protocols
- G06Q20/38—Payment protocols; Details thereof
- G06Q20/42—Confirmation, e.g. check or permission by the legal debtor of payment
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Abstract
The present invention provides a kind of strategies to determine method and device, storage medium, electronic device, wherein this method comprises: obtaining the characteristic index of borrower, wherein the characteristic index is used to indicate the loan repayment capacity of the borrower；The characteristic index is analyzed using model, determine that the borrower's urges money to score, wherein, the model is trained using multi-group data by machine learning, and every group of data in the multi-group data include: borrower's information and the refund information of the borrower；Money scoring is urged to determine to urge money strategy to the borrower according to described, by adopting the above technical scheme, it solves in the related technology, the collection strategy of collection system is too simple, lead to problems such as collection resource allocation unreasonable, and then model is introduced into during the determination for urging money to score, and then make reasonable collection strategy.
Description
Technical field
The present invention relates to the communications fields, determine method and device, storage medium, electronics in particular to a kind of strategy
Device.
Background technique
In order to meet society need, there are more and more pattern of lending, and then the collection work after loan also becomes numerous
It is miscellaneous, and in order to which better returned money, collection work seem most important, there are some simple collection systems in the related technology and carrys out generation
Collection work is realized for artificial, but the realization of these collection systems is also only that batch exhalation carries out the simply collection side such as collection
Formula, collection mode is simple, and without more careful collection strategy, and then collection resource allocation is also unreasonable.
For in the related technology, the collection strategy of collection system is too simple, causes collection resource allocation unreasonable etc. and asks
Topic, not yet puts forward effective solutions.
Summary of the invention
The embodiment of the invention provides a kind of strategies to determine method and device, storage medium, electronic device, at least to solve
The collection strategy of the relevant technologies collection system is too simple, leads to problems such as collection resource allocation unreasonable.
According to one embodiment of present invention, a kind of determining method of strategy is provided characterized by comprising
Obtain the characteristic index of borrower, wherein the characteristic index is used to indicate the loan repayment capacity of the borrower；Make
The characteristic index is analyzed with model, determines that the borrower's urges money to score, wherein the model is to use multiple groups
Data are trained by machine learning, and every group of data in the multi-group data include: borrower's information and the loaning bill
The refund information of people；
Money scoring is urged to determine to urge money strategy to the borrower according to described.
Optionally, money scoring is urged to determine to urge money strategy to the borrower according to described, comprising:
Obtain at least one the following information of the borrower: the overdue amount of money, debt time；
Money strategy is urged described in money scoring determination according to the information of acquisition and described urge.
Optionally, according to it is described urge money scoring determine to the borrower urge money strategy after, the method is also wrapped
It includes:
Urge the corresponding borrower's execution of money strategy is corresponding with the borrower to urge money strategy to described.
Optionally, the characteristic index includes at least following one: the income information of the borrower, the borrower institute
Job category, the refund loyalty information of the borrower, the credit information of the borrower.
According to another embodiment of the invention, a kind of strategy determination apparatus is additionally provided, comprising:
Module is obtained, for obtaining the characteristic index of borrower, wherein the characteristic index is used to indicate the borrower
Loan repayment capacity；
Using module, for being analyzed using model the characteristic index；
First determining module, for determining that the borrower's urges money to score, wherein first model is to use multiple groups
Data are trained by machine learning, and every group of data in the multi-group data include: borrower's information and the loaning bill
The refund information of people；
Second determining module, for urging money scoring to determine to urge money strategy to the borrower according to.
Optionally, second determining module, comprising:
Acquiring unit, at least one the following information for obtaining the borrower: the overdue amount of money, debt time；
Determination unit, for urging money strategy described in money scoring determination according to the information of acquisition and described urge.
Optionally, described device further include:
Execution module, for urging the corresponding borrower's execution of money strategy is corresponding with the borrower to urge money plan to described
Slightly.
Optionally, the characteristic index includes at least following one: the income information of the borrower, the borrower institute
Job category, the refund loyalty information of the borrower, the credit information of the borrower.
According to another embodiment of the invention, a kind of storage medium is additionally provided, meter is stored in the storage medium
Calculation machine program, wherein execute any of the above-described strategy when the computer program is arranged to operation and determine method.
According to 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
Strategy determines method.
By means of the invention it is possible to analyze using characteristic index of the model to the borrower of acquisition, borrower is determined
Urge money to score, and then according to urging money scoring to determine the technical solution for urging money strategy of borrower, solve in the related technology,
The collection strategy of collection system is too simple, leads to problems such as collection resource allocation unreasonable, and then model is introduced into and urges money
During the determination of scoring, and then make reasonable collection strategy.
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 the flow chart that strategy according to an embodiment of the present invention determines method；
Fig. 2 is another flow chart that strategy according to an embodiment of the present invention determines method；
Fig. 3 is the structural block diagram of strategy determination apparatus according to an embodiment of the present invention；
Fig. 4 is another structural block diagram of strategy determination apparatus 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.
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
A kind of determining method of strategy is provided in the present embodiment, and Fig. 1 is tactful determination side according to an embodiment of the present invention
The flow chart of method, as shown in Figure 1, the process includes the following steps:
Step S102 obtains the characteristic index of borrower, wherein the characteristic index is used to indicate going back for the borrower
Money ability；
Step S104 analyzes the characteristic index using model, determines that the borrower's urges money to score,
In, the model is trained using multi-group data by machine learning, and every group of data in the multi-group data include:
The refund information of borrower's information and the borrower；
Step S106 urges money scoring to determine to urge money strategy to the borrower according to described.
Through the above steps, it is able to use model to analyze the characteristic index of the borrower of acquisition, determines to borrow money
People's urges money to score, and then according to the technical solution for urging money strategy for urging money scoring to determine borrower, solves the relevant technologies
In, the collection strategy of collection system is too simple, leads to problems such as collection resource allocation unreasonable, and then model is introduced into and is urged
During the determination of money scoring, and then make reasonable collection strategy.
In embodiments of the present invention, features described above index can include at least following one: the income letter of the borrower
Breath, the job category where the borrower, the refund loyalty information of the borrower, the credit information of the borrower, tool
Body, features described above index are mainly used to characterize the loan repayment capacity of borrower, in requisition for can also include other information, the present invention
Embodiment is not construed as limiting this, and the division that grace is more careful to features described above index, can refer to following table 1.
Table 1
It should be noted that the setting of value range can also be adjusted flexibly according to actual needs in table 1, the present invention is implemented
Example is equally not construed as limiting this.
It, can preferably shot and long term memory network (Long Short-Term for model used in S104 step
Memory, referred to as LSTM) model, wherein LSTM model is based on periodical neural network (Recurrent Neural
Network, referred to as RNN) on the basis of a kind of optimization neural network model, advantage is to can handle real-life one
It is a little to need the long-term scene for relying on historical trace.The core of shot and long term memory network is to joined a judgement in RNN algorithm
Information whether useful " processor ", including input gate, forgetting door and out gate, wherein only meeting the information of Model Condition
It can be left, remaining information can be removed by forgeing door.LSTM model is applied in chain transaction scene, can be more preferably located in
The incidence relation of reason and record trading activity on a timeline, and some abnormal trading activities are distinguished.
Step S106 can be accomplished by the following way: obtain in one alternate embodiment there are many implementation
Take at least one the following information of the borrower: the overdue amount of money, debt time；According to the information of acquisition and described urge money
Scoring urges money strategy described in determining, can it is possible to further urge money scoring to determine that first urges money strategy according to the overdue amount of money
To urge money scoring to determine that second urges money strategy, the debt time, money can also be urged to comment according to the overdue amount of money according to the debt time
Divide to determine that third urges money strategy, first urges money strategy, and second urges money strategy, and it is identical that third urges money strategy can be, can also
It is different to be set as needed.
Optionally, according to it is described urge money scoring determine to the borrower urge money strategy after, the method is also wrapped
Include: Xiang Suoshu urges the corresponding borrower's execution of money strategy is corresponding with the borrower to urge money strategy.
Above-mentioned tactful determination process is explained below in conjunction with an optional example, as shown in Fig. 2, including following
Step:
S202: building includes the neural network model of LSTM network；
S204: several inputs of the main feature of risk as neural network model are chosen；Wherein, feature of risk can be with
It is the feature enumerated in table 1；
S206: the neural network model built is trained using training sample；Training sample in this step includes
Borrower and borrower's refund information include normal refund and the overdue borrower not refunded.
S208: collection scoring is obtained by trained neural network model.
In addition, be explained herein for above-mentioned second collection strategy and third collection strategy, example of the present invention, the
One collection strategy is using similar method of determination, and example of the present invention is without repeating.
Scored first according to collection and the risk situation of early stage overdue client assessed, by client be divided into high risk, in
Etc. risks, low-risk three classes establish a two dimension further according to overdue time (the debt time for being equivalent to above-described embodiment) length
Matrix formulates different collection strategies, specifically, as shown in table 2 below according to the position of client in a matrix.
Table 2
On the basis of two dimensions of collection scoring and debt time, it is further added by the overdue amount of money, client is divided into high risk
(H), four class of medium risk (M), low-risk (L) and not collection (N), and different collection modes is formulated, as shown in table 3 below.
Table 3
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.) execute method described in each embodiment of the present invention.
Embodiment 2
A kind of strategy determination apparatus is additionally provided in the present embodiment, and the device is real for realizing above-described embodiment and preferably
Mode is applied, the descriptions that have already been made will not be repeated.As used below, the soft of predetermined function may be implemented in term " module "
The combination of part and/or hardware.Although device described in following embodiment is preferably realized with software, hardware, or
The realization of the combination of software and hardware is also that may and be contemplated.
Fig. 3 is the structural block diagram of strategy determination apparatus according to an embodiment of the present invention, as shown in figure 3, the device includes:
Module 40 is obtained, for obtaining the characteristic index of borrower, wherein the characteristic index is used to indicate the loaning bill
The loan repayment capacity of people；
Using module 42, for being analyzed using model the characteristic index；
First determining module 44, for determining that the borrower's urges money to score, wherein first model is using more
Group data are trained by machine learning, and every group of data in the multi-group data include: borrower's information and described borrow
The refund information of money people；
Second determining module 46, for urging money scoring to determine to urge money strategy to the borrower according to.
By above-mentioned apparatus, it is able to use model and the characteristic index of the borrower of acquisition is analyzed, determine to borrow money
People's urges money to score, and then according to the technical solution for urging money strategy for urging money scoring to determine borrower, solves the relevant technologies
In, the collection strategy of collection system is too simple, leads to problems such as collection resource allocation unreasonable, and then model is introduced into and is urged
During the determination of money scoring, and then make reasonable collection strategy.
Optionally, as shown in figure 4, the second determining module 46, comprising:
Acquiring unit 462, at least one the following information for obtaining the borrower: the overdue amount of money, debt time；
Determination unit 464, for urging money strategy described in money scoring determination according to the information of acquisition and described urge.
Optionally, as shown in figure 4, described device further include:
Execution module 48, for urging the corresponding borrower's execution of money strategy is corresponding with the borrower to urge money plan to described
Slightly.
Optionally, the characteristic index includes at least following one: the income information of the borrower, the borrower institute
Job category, the refund loyalty information of the borrower, the credit information of the borrower.
Embodiment 3
The embodiments of the present invention also provide a kind of storage medium, which includes the program of storage, wherein above-mentioned
Program executes method described in any of the above embodiments when running.
Optionally, in the present embodiment, above-mentioned storage medium can be set to store the journey for executing following steps
Sequence code:
S1 obtains the characteristic index of borrower, wherein the characteristic index is used to indicate the refund energy of the borrower
Power；
S2 analyzes the characteristic index using model, determines that the borrower's urges money to score, wherein described
Model is trained using multi-group data by machine learning, and every group of data in the multi-group data include: borrower
The refund information of information and the borrower；
S3: money scoring is urged to determine to urge money strategy to the borrower according to described.
Optionally, storage medium is also configured to store the program code for executing following steps:
S4 obtains at least one following information of the borrower: the overdue amount of money, debt time；
S5 urges money strategy described in money scoring determination according to the information of acquisition and described urge.
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 program code such as disk, magnetic or disk.
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.
The foregoing is only a preferred embodiment of the present invention, is not intended to restrict the invention, for the skill of this field
For art personnel, the invention may be variously modified and varied.It is all within principle of the invention, it is made it is any modification, etc.
With replacement, improvement etc., should all be included in the protection scope of the present invention.
Claims (10)
1. a kind of strategy determines method characterized by comprising
Obtain the characteristic index of borrower, wherein the characteristic index is used to indicate the loan repayment capacity of the borrower；
The characteristic index is analyzed using model, determines that the borrower's urges money to score, wherein the model is to make
It is trained with multi-group data by machine learning, every group of data in the multi-group data include: borrower's information and institute
State the refund information of borrower；
Money scoring is urged to determine to urge money strategy to the borrower according to described.
2. the method according to claim 1, wherein urging money scoring to determine to the borrower's according to described
Urge money strategy, comprising:
Obtain at least one the following information of the borrower: the overdue amount of money, debt time；
Money strategy is urged described in money scoring determination according to the information of acquisition and described urge.
3. the method according to claim 1, wherein urging money scoring to determine to the borrower's according to described
After urging money strategy, the method also includes:
Urge the corresponding borrower's execution of money strategy is corresponding with the borrower to urge money strategy to described.
4. method according to claim 1-3, which is characterized in that the characteristic index include at least it is following it
One: the income information of the borrower, the job category where the borrower, the refund loyalty information of the borrower, institute
State the credit information of borrower.
5. a kind of strategy determination apparatus characterized by comprising
Module is obtained, for obtaining the characteristic index of borrower, wherein the characteristic index is used to indicate going back for the borrower
Money ability；
Using module, for being analyzed using model the characteristic index；
First determining module, for determining that the borrower's urges money to score, wherein first model is to use multi-group data
It is trained by machine learning, every group of data in the multi-group data include: borrower's information and the borrower
Refund information；
Second determining module, for urging money scoring to determine to urge money strategy to the borrower according to.
6. device according to claim 5, which is characterized in that second determining module, comprising:
Acquiring unit, at least one the following information for obtaining the borrower: the overdue amount of money, debt time；
Determination unit, for urging money strategy described in money scoring determination according to the information of acquisition and described urge.
7. device according to claim 5, which is characterized in that described device further include:
Execution module, for urging the corresponding borrower's execution of money strategy is corresponding with the borrower to urge money strategy to described.
8. according to the described in any item devices of claim 5-7, which is characterized in that the characteristic index include at least it is following it
One: the income information of the borrower, the job category where the borrower, the refund loyalty information of the borrower, institute
State the credit information of borrower.
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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| CN111695988A (en) * | 2020-06-16 | 2020-09-22 | 中国工商银行股份有限公司 | Information processing method, information processing apparatus, electronic device, and medium |
| CN112541809A (en) * | 2019-09-04 | 2021-03-23 | 北京国双科技有限公司 | Money urging early warning method and device, equipment and storage medium |
| CN112907355A (en) * | 2021-03-03 | 2021-06-04 | 重庆度小满优扬科技有限公司 | Loan information processing method and device |
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| CN106952155A (en) * | 2017-03-08 | 2017-07-14 | 深圳前海纵腾金融科技服务有限公司 | A kind of collection method and device based on credit scoring |
| CN107424070A (en) * | 2017-03-29 | 2017-12-01 | 广州汇融易互联网金融信息服务有限公司 | A kind of loan user credit ranking method and system based on machine learning |
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| CN106897918A (en) * | 2017-02-24 | 2017-06-27 | 上海易贷网金融信息服务有限公司 | A kind of hybrid machine learning credit scoring model construction method |
| CN106952155A (en) * | 2017-03-08 | 2017-07-14 | 深圳前海纵腾金融科技服务有限公司 | A kind of collection method and device based on credit scoring |
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| CN112541809A (en) * | 2019-09-04 | 2021-03-23 | 北京国双科技有限公司 | Money urging early warning method and device, equipment and storage medium |
| CN111695988A (en) * | 2020-06-16 | 2020-09-22 | 中国工商银行股份有限公司 | Information processing method, information processing apparatus, electronic device, and medium |
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Application publication date: 20190315 |