CN108460690A - Claims Resolution Risk Forecast Method, system, equipment and storage medium - Google Patents
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Abstract
The present invention relates to a kind of Claims Resolution Risk Forecast Method, system, equipment and storage mediums to calculate the Claims Resolution risk index of the order, this method comprises the following steps after client submits order：Obtain order essential characteristic；Calculate the derivative feature based on essential characteristic；Corresponding receipts, the history of sender Claims Resolution data are obtained according to essential characteristic；Essential characteristic, derivative feature and history Claims Resolution data are subjected to fusion treatment and input the risk forecast model for being in advance based on XGBOOST algorithms and building, obtain Claims Resolution risk index.It is modeled by using XGBOOST algorithms, Comprehensive Evaluation is carried out to order data information, can be after client place an order, order calculates the Claims Resolution risk index of the order before striking a bargain, corresponding precautionary measures are made to be directed to the high order of Claims Resolution risk index in advance, reduce loss.
Description
Technical field
The present invention relates to risk profile technical fields, are especially suitable for the Claims Resolution risk profile of logistics field.
Background technology
With the development of the industries such as internet, logistics, transport, more and more people select shopping at network, or using soon
Company is passed to carry out article mailing.In a large amount of express mails generated daily, some a small number of express mails due to itself article particularity, or
Person in transit link, send link with charge free etc. for some reason, or due to the special circumstances etc. of client itself, and generate different journeys
The damage of degree, this, which just will produce, settles a claim to these express mails, and different degrees of loss can be all caused to client and express company.
In the prior art, it is difficult to accomplish to screen the Claims Resolution risk of express mail in advance, therefore, it is necessary to a kind of Claims Resolution risk profile sides
Method calculates the Claims Resolution risk index of the express mail after client places an order, before receiving pickup of dispatching officers.
Invention content
In order to calculate the Claims Resolution risk index of order after client places an order, before order conclusion of the business, the present invention provides
A kind of prediction technique, system, equipment and the storage medium that the Claims Resolution risk of order is calculated based on XGBOOST algorithms.
The present invention relates to a kind of Claims Resolution Risk Forecast Methods, include the following steps：Obtain order essential characteristic；Calculating is based on
The derivative feature of essential characteristic；Corresponding receipts, the history of sender Claims Resolution data are obtained according to essential characteristic；By essential characteristic,
Derivative feature and history Claims Resolution data carry out fusion treatment and input the risk profile mould for being in advance based on XGBOOST algorithms structure
Type obtains Claims Resolution risk index.
Preferably, essential characteristic includes sender, addressee, posts part address, posting address, and support posts species type and timeliness letter
Breath.
Preferably, derivative feature includes receiving, posting part address distance, and association is received, posted at least one in part regional historical Claims Resolution rate
.
Preferably, history Claims Resolution data packet claim times, post piece number, at least one in frequency of supporting value and address change number
.
Preferably, it is described by essential characteristic, derivative feature and history settle a claim Data Fusion, including by essential characteristic,
Derivative feature and history Claims Resolution data are directly combined as a feature vector.
Preferably, risk forecast model is by that will include the history of essential characteristic, derivative feature and history Claims Resolution data
Input of the order data collection as XGBOOST algorithms, the risk forecast model of structure, wherein History Order data set is to going through
The data acquisition system that the express delivery order of history counts.
Preferably, Claims Resolution Risk Forecast Method of the invention further includes pushing to Claims Resolution risk index in preset time
Receipts send terminal.
Preferably, it is included in preset time after order triggering to before pulling receipts.
Preferably, Claims Resolution Risk Forecast Method of the invention further includes that feedback pulls receipts for what high risk Claims Resolution support posted object
Strategy.
The invention further relates to a kind of Claims Resolution Risk Forecast Systems, including
Essential characteristic acquisition module is configured to obtain order essential characteristic；
Derivative feature acquisition module is configured to calculate the derivative feature based on essential characteristic；
Claims Resolution data acquisition module is configured to obtain based on the corresponding receipts of essential characteristic, the history of sender Claims Resolution number
According to；
Risk profile module is configured to essential characteristic, derivative feature and history Claims Resolution data carrying out fusion treatment simultaneously
Input is in advance based on the risk forecast model of XGBOOST algorithms structure, obtains Claims Resolution risk index.
The invention further relates to a kind of equipment, which includes：One or more processors；Memory is stored thereon with one
A or multiple programs；When one or more processors execute one or more programs, above-mentioned Claims Resolution risk profile side is realized
The step of method.
The invention further relates to a kind of computer readable storage mediums, are stored thereon with computer program, which is handled
The step of device realizes above-mentioned Claims Resolution Risk Forecast Method when executing.
Claims Resolution Risk Forecast Method, system, equipment and the storage medium of the present invention is carried out by using XGBOOST algorithms
Modeling carries out Comprehensive Evaluation to order data information, can be after client places an order, and order calculates the reason of the order before striking a bargain
Risk index is paid for, corresponding precautionary measures is made to be directed to the high order of Claims Resolution risk index in advance, reduces loss.
Description of the drawings
Below with reference to the accompanying drawings the preferred embodiment of the present invention described, attached drawing in order to illustrate the preferred embodiment of the present invention without
It is to limit the purpose of the present invention.In attached drawing,
Fig. 1 is the overall procedure block diagram of the embodiment of the present invention.
Fig. 2 is the flow chart that Claims Resolution risk forecast model is built based on XGBOOST algorithms of the embodiment of the present invention.
Specific implementation mode
The specific implementation mode of the present invention is used for illustrating the present invention, but is not limited to the specific implementation mode.
The present invention is specifically described by taking express delivery order as an example in following embodiment.
Fig. 1 is the overall procedure block diagram of the embodiment of the present invention.
As shown in Figure 1, the Claims Resolution Risk Forecast Method of the present embodiment, includes the following steps：
Step S1：Obtain order essential characteristic；
The data of client's express delivery order are obtained from addressee system, express delivery order data is expressed with json formats, packet
It has included and the relevant various information of express waybill.After client places an order, parsing and extracting from the json character strings of the order needs
The field wanted and corresponding field value, obtain the essential characteristic of the order.
The essential characteristic of acquisition includes sender, addressee, post part address, posting address, support post species type and timeliness letter
Breath etc..
Step S2：Calculate the derivative feature based on essential characteristic.
The relevant derivative feature of the order is calculated in real time according to the essential characteristic obtained in step S1.According to derivative feature
The essential characteristic of the order carries out the feature that correlation computations obtain, such as：Post part address and posting address physical distance and
With addressee mapped with posting part relevant history Claims Resolution rate etc..The mapping of history Claims Resolution rate refers to being compiled according to the region of current waybill
Number search its corresponding history Claims Resolution rate.History Claims Resolution rate is to settle a claim data to the corresponding reason in different regions according to passing history
Odds are done statistics and are preserved and obtain.
Step S3：Corresponding receipts, the history of sender Claims Resolution data are obtained according to essential characteristic.
According to the basic feature information obtained in step S1, real-time query and the history Claims Resolution for obtaining sender and addressee
Data, including claim times, post piece number, address change number etc..
Step S4：Essential characteristic, derivative feature and history Claims Resolution data are carried out fusion treatment and inputted to be in advance based on
The risk forecast model of XGBOOST algorithms structure obtains Claims Resolution risk index.
The history that will be obtained in derivative feature and step 3 that the essential characteristic of the obtained orders of step S1, step S2 are obtained
Data of settling a claim carry out fusion treatment, the mode of processing features above can be directly combined as the feature of a bigger dimension to
Amount.Such as：Essential characteristic has an a dimensions, and derivative feature has b dimensions, history Claims Resolution data to have c dimensions, then, directly by essential characteristic, spread out
Raw feature and history Claims Resolution data are combined into the feature vector T that a dimension is a+b+c.
The method that embodiment that the present invention will be described in detail below builds risk forecast model.
Fig. 2 is the flow chart that the present embodiment builds risk forecast model based on XGBOOST algorithms.
Risk forecast model is by that will include the History Order data of essential characteristic, derivative feature and history Claims Resolution data
Collect the input as XGBOOST algorithms, the risk forecast model of structure, wherein History Order data set is the express delivery to history
The data acquisition system that order counts.
Risk forecast model is built, is exactly the training process according to History Order data set by XGBOOST algorithms.
As shown in Fig. 2, structure risk forecast model includes following method：
Step S41, is extracted and sample history express delivery order data collection is gathered as training.
History express delivery order data collection refers to the bills data of all express delivery orders caused by history, including Claims Resolution order
With non-Claims Resolution order.For example, carrying out stochastical sampling to non-Claims Resolution order so that Claims Resolution order and non-Claims Resolution in final training set
Order numbers are compared to about 1:10.
Step S42, construction feature set.
The data of each history express delivery order are expressed by feature set.History express delivery order data collection is being extracted as training
After set, the feature vector of each History Order in training set is extracted, this feature vector passes through the History Order
Essential characteristic, derivative feature and history Claims Resolution data carry out fusion treatment and obtain.By all History Order data sets of extraction
Feature vector composition characteristic set.
Different characteristic in characteristic set has different data category numbers, for example, posting the data of part area code
Class number refers to just all regional areas code.
Step S43, XGBOOST model training.Using the training set of step S41, the characteristic set of step S42 passes through
XGBOOST algorithms train Claims Resolution risk forecast model.
XGBOOST algorithms are made of regression tree, and each regression tree includes multiple leaf nodes, and each leaf node is corresponding
The training set is assigned to each leaf node by one score, regression tree.
The training set of the History Order is assigned to each leaf node, the spy of input by regression tree according to the feature of input
Sign is a feature in the feature set of training set.The result of XGBOOST algorithms is exactly that all regression tree values are superimposed together work
For final predicted value.
XGBOOST algorithms are the study for having label to training set.For example, we pass through to history express delivery order big data
It is extracted 100,000 History Order data in information, each history express delivery order has the feature set that 50 features constitute and one
Label, label herein is Claims Resolution high risk, Claims Resolution medium risk and Claims Resolution low-risk, if stipulations are numerical value 2,1 and 0, that
The value of the express delivery order for high risk of settling a claim is 2, and the value for medium risk of settling a claim is 1, and the value for low-risk of settling a claim is then 0.
XGBOOST algorithms determine the best features of Claims Resolution risk forecast model by object function, and best features are will to go through
History order data collection is completely divided into Claims Resolution high risk, the feature of Claims Resolution medium risk and low-risk of settling a claim, and determines best features
Afterwards, it is known Claims Resolution risk forecast model just to obtain parameter all.
Assuming that the history express waybill data set of input is D={ (xi,yi), xiIndicate the feature of input waybill data, yi
Indicate whether the waybill of input settles a claim, the calculation formula of the object function of XGBOOST is as follows：
Wherein, l is adjustable parameter.
In formula, first part is training error, in XGBOOST, is trained (additive training) using addition
Training method carrys out learning model, i.e., retains that original model is constant each time, and a new function (regression tree) is added and arrives model
In.
Addition training is optimization object function step by step, and first, the one tree in optimized regression tree is over excellent again later
Change second tree, until having optimized all regression trees.Entire calculating process is as follows：
Value be preceding t-1 take turns model prediction result, in addition a new function ft(xi), ft(xi) it is existing t-1 time
On the basis of Gui Shu so that regression tree of object function minimum.
ft(x) set of all regression trees of set expression is characterized with following formula：
ft(x)=wq(x),w∈RT,q:Rd- ＞ 1,2 ..., T }
In this formula, T is the leaf number of regression tree, that is, according to the feature of input by history express delivery order numbers
According to the number for the Sub Data Set that collection is divided into；W is the weight portion of leaf, that is, the score value of each leaf, w ∈ RTCharacterize leaf
Vector；Q (x) expressions have been assigned to the data of history express delivery order data collection on some leaf node, that is, characterize the structural portion of tree
Point, q:Rd- ＞ 1,2 ..., and T } characterize the structure set；wq(x)It is exactly predicted value of this regression tree to express delivery order data,
It is exactly the prediction result to express delivery order：Claims Resolution high risk, Claims Resolution medium risk and Claims Resolution low-risk.So ft(x) it is to be directed to
Model f after t-1 iterationt-1(x) residual error of predicted value and actual value, resettles regression tree and is learnt, to constantly
Approach actual value.
Second part Ω (f in object functiont) characterization regression tree complexity, indicated with following formula：
Wherein γ, λ are the adjustable parameter of algorithm, and the γ of setting, the bigger expression of λ value is more desirable to obtain simple in structure
Regression tree.
The value w of each leaf nodejBetween be independent from each other, at this time, it will be able to find out the best of each leaf node
Value and the at this time value of object function, the value of object function is smaller, and the structure for representing regression tree is better, to obtain Claims Resolution risk
Prediction model.
XGBOOST models in calculating each time, object function Obj(t)Will according to last round of result come it is corresponding from
The parameter (study for having supervision) of dynamic adjustment epicycle, to achieve the effect that continue to optimize model.
After the structure of tree is determined (i.e. q (x) is determined), in order to make the minimization of object function, its derivative to w can be enabled
It is 0, the optimum prediction score for solving each leaf node is w*, to which characteristic parameter is adjusted to optimal, obtain optimal
Claims Resolution risk forecast model.
When needing to carry out Claims Resolution risk profile, it is defeated that the practical express delivery order data that client submits is extracted in preset time
Enter risk forecast model of settling a claim, the Claims Resolution risk index of the order is calculated, then which is pushed to the receipts received and dispatched officers
Send terminal.
The preset time preferably submits order to be received in the time dispatched officers to visit and pulled before receiving after order triggering in client,
In this way, receiving the Claims Resolution risk index for dispatching officers to learn the express delivery before carrying out pulling receipts to express delivery.
It, can also be according to Claims Resolution risk index after risk forecast model calculates express delivery order Claims Resolution risk index
Feedback posts pulling for object for high risk Claims Resolution support and receives strategy.For example, corresponding different Claims Resolution value-at-risk, the operation that correspondence can be taken
Respectively：
Value-at-risk of settling a claim is 2, and risk is higher, therefore unpacks to test and show, shows identity card and takes pictures；
Value-at-risk of settling a claim is 1, and risk is medium, prompts to unpack to test to show；
Value-at-risk of settling a claim is 0, and risk is low, normal addressee.
Receipts are dispatched officers after receiving the Claims Resolution risk index and pulling receipts strategy, so that it may corresponding according to the height of risk, to take
Measure.
The present embodiment further relates to a kind of Claims Resolution Risk Forecast System, including
Essential characteristic acquisition module is configured to obtain order essential characteristic；
Derivative feature acquisition module is configured to calculate the derivative feature based on essential characteristic；
Claims Resolution data acquisition module is configured to obtain based on the corresponding receipts of essential characteristic, the history of sender Claims Resolution number
According to；
Risk profile module is configured to essential characteristic, derivative feature and history Claims Resolution data carrying out fusion treatment simultaneously
Input is in advance based on the risk forecast model of XGBOOST algorithms structure, obtains Claims Resolution risk index.
Acquisition and calculating about essential characteristic, derivative feature and history Claims Resolution data are in Risk Forecast Method of settling a claim
It is described in detail, details are not described herein again.
Essential characteristic acquisition module, derivative feature acquisition module, Claims Resolution data acquisition module and risk profile module can be
Background system carries out safeguarding the update with model parameter.
The workflow of risk profile module is as follows：
The data of client's express delivery order are obtained from addressee system, and carry out the parsing of certain format, and part is for example posted in acquisition
People, addressee post part address, posting address, and support posts species type, the essential characteristics such as age information.
It is calculated in real time according to the essential characteristic of acquisition and obtains the relevant derivative feature of the order.Derivative feature is with for example posting part
The physical distance of location and posting address, and with addressee mapped with posting part relevant history Claims Resolution rate etc..
According to the basic feature information of acquisition, real-time query simultaneously obtains sender spy related to the Claims Resolution of the history of addressee
Sign, such as claim times, post piece number, and the features such as number are changed in address.
Essential characteristic, derivative feature and history Claims Resolution data are subjected to the feature vector that fusion generates order, as the visitor
The Claims Resolution risk forecast model input feature vector at family.
The feature vector of acquisition is inputted into risk forecast model, obtains Claims Resolution value-at-risk in real time.
The invention further relates to a kind of equipment, which includes：One or more processors；Memory is stored thereon with one
A or multiple programs；When one or more processors execute one or more programs, above-mentioned Claims Resolution risk profile side is realized
The step of method.
The invention further relates to a kind of computer readable storage mediums, are stored thereon with computer program, which is handled
The step of device realizes above-mentioned Claims Resolution Risk Forecast Method when executing.
Claims Resolution Risk Forecast Method, system, equipment and the storage medium of the present invention is carried out by using XGBOOST algorithms
Modeling carries out Comprehensive Evaluation to order data information, can be after client places an order, and order calculates the reason of the order before striking a bargain
Risk index is paid for, corresponding precautionary measures is made to be directed to the high order of Claims Resolution risk index in advance, reduces loss.
It after client places an order, receives in this period dispatched officers before pickup, the Claims Resolution risk of the express mail is calculated
Index, and risk index is pushed to the handheld terminal received and dispatched officers.It dispatches officers before pickup in this way, receiving, so that it may to learn this
The risk index of express mail, if express mail Claims Resolution risk index is very high, receipts dispatch officers to take appropriate measures to avoid damaging
The generation of mistake, such as unpacking check, take pictures, repack.
Above example is specifically described the present invention using express delivery order, and the invention is not limited in express delivery orders
Claims Resolution risk profile has the order of Claims Resolution risk to be suitable for the present invention other field.
Above example is the preferred embodiment of the present invention, all the present invention's not to limit the purpose of the present invention
The modification and replacement carried out within spirit and principle, within the protection of the present invention.
Claims (12)
1. a kind of Claims Resolution Risk Forecast Method, which is characterized in that include the following steps
Obtain order essential characteristic；
Calculate the derivative feature based on essential characteristic；
Corresponding receipts, the history of sender Claims Resolution data are obtained according to essential characteristic；
Essential characteristic, derivative feature and history Claims Resolution data are carried out fusion treatment and inputted to be in advance based on XGBOOST algorithm structures
The risk forecast model built obtains Claims Resolution risk index.
2. Claims Resolution Risk Forecast Method according to claim 1, which is characterized in that
The essential characteristic includes sender, addressee, posts part address, posting address, and support posts species type and age information.
3. Claims Resolution Risk Forecast Method according to claim 1, which is characterized in that
The derivative feature includes receiving, posting part address distance, is associated with and at least one of receives, posts part regional historical Claims Resolution rate.
4. Claims Resolution Risk Forecast Method according to claim 1, which is characterized in that
The history Claims Resolution data packet claim times, post piece number, at least one of frequency of supporting value and address change number.
5. Claims Resolution Risk Forecast Method according to claim 1, which is characterized in that described by essential characteristic, derivative feature
And history Claims Resolution Data Fusion, including
Essential characteristic, derivative feature and history Claims Resolution data are directly combined as a feature vector.
6. Claims Resolution Risk Forecast Method according to claim 1, which is characterized in that
The risk forecast model is by that will include the History Order data of essential characteristic, derivative feature and history Claims Resolution data
Input of the collection as XGBOOST algorithms, the risk forecast model of structure,
Wherein, the History Order data set is the data acquisition system that the express delivery order to history counts.
7. according to claim 1-5 any one of them Claims Resolution Risk Forecast Method, which is characterized in that
Further include that Claims Resolution risk index is pushed to receipts in preset time to send terminal.
8. Claims Resolution Risk Forecast Method according to claim 7, which is characterized in that
It is included in the preset time after order triggering to before pulling receipts.
9. Claims Resolution Risk Forecast Method according to claim 7, which is characterized in that further include that feedback is settled a claim for high risk
Support posts pulling for object and receives strategy.
10. a kind of Claims Resolution Risk Forecast System, which is characterized in that including
Essential characteristic acquisition module is configured to obtain order essential characteristic；
Derivative feature acquisition module is configured to calculate the derivative feature based on essential characteristic；
Claims Resolution data acquisition module is configured to obtain based on the corresponding receipts of essential characteristic, the history of sender Claims Resolution data；
Risk profile module is configured to essential characteristic, derivative feature and history Claims Resolution data carrying out fusion treatment and input
It is in advance based on the risk forecast model of XGBOOST algorithms structure, obtains Claims Resolution risk index.
11. a kind of equipment, which is characterized in that the equipment includes：
One or more processors；
Memory is stored thereon with one or more programs；
When one or more of processors execute one or more of programs, method as described in the appended claim 1 is realized
The step of.
12. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the program is by processor
The step of method as described in the appended claim 1 is realized when execution.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109741065A (en) * | 2019-01-28 | 2019-05-10 | 广州虎牙信息科技有限公司 | A kind of payment risk recognition methods, device, equipment and storage medium |
| CN110147997A (en) * | 2019-04-16 | 2019-08-20 | 深圳壹账通智能科技有限公司 | Data processing method, device, equipment and storage medium |
| CN110543964A (en) * | 2019-07-19 | 2019-12-06 | 深圳市跨越新科技有限公司 | XGboost model-based abnormal singleweight early warning method and system |
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| CN102194177A (en) * | 2011-05-13 | 2011-09-21 | 南京柯富锐软件科技有限公司 | System for risk control over online payment |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109741065A (en) * | 2019-01-28 | 2019-05-10 | 广州虎牙信息科技有限公司 | A kind of payment risk recognition methods, device, equipment and storage medium |
| CN110147997A (en) * | 2019-04-16 | 2019-08-20 | 深圳壹账通智能科技有限公司 | Data processing method, device, equipment and storage medium |
| CN110543964A (en) * | 2019-07-19 | 2019-12-06 | 深圳市跨越新科技有限公司 | XGboost model-based abnormal singleweight early warning method and system |
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