CN110020938A

CN110020938A - Exchange information processing method, device, equipment and storage medium

Info

Publication number: CN110020938A
Application number: CN201910062959.0A
Authority: CN
Inventors: 高利翠; 肖凯
Original assignee: Alibaba Group Holding Ltd
Current assignee: Advanced New Technologies Co Ltd; Advantageous New Technologies Co Ltd
Priority date: 2019-01-23
Filing date: 2019-01-23
Publication date: 2019-07-16
Anticipated expiration: 2039-01-23
Also published as: CN110020938B

Abstract

The embodiment of the present application provides a kind of exchange information processing method, device, equipment and storage medium.This method comprises: the trading activity data based on target subject generate the behavior sequence of target subject；The trading activity feature of target subject is extracted from the behavior sequence of target subject, wherein trading activity feature includes the main body variable information of trading activity data, and main body variable information includes the information for indicating the attaching information of target subject；Based on the dimensional information of trading activity feature, insertion vector corresponding with trading activity feature is generated in such a way that word is embedded in；It is counted based on each dimensional information of the main body variable information to insertion vector, to be predicted based on transaction risk of the statistical result to target subject.The technical solution of the embodiment of the present application can be improved the accuracy of the risk assessment to the unconspicuous trading activity of off-note, additionally it is possible to improve data-handling efficiency.

Description

Exchange information processing method, device, equipment and storage medium

Technical field

This application involves big data technical field more particularly to a kind of exchange information processing methods, Transaction Information processing dress It sets, Transaction Information processing equipment and storage medium.

Background technique

With the rapid development of Internet technology, more and more people's selection carries out various businesses transaction example by internet It such as does shopping, transfer accounts, transaction of remitting money, how ensureing that the safety of business transaction becomes focus concerned by people.

A kind of current transaction in technical solution, according to the historical trading behavioral data of bank account to the bank account The risk of behavior is assessed, and determines whether the current trading activity of the bank account is abnormal transaction based on assessment result.So And in this technical solution, the trading activity of for example new bank account of trading activity unconspicuous for off-note, it is difficult to Accurately identify transaction risk.

Summary of the invention

The purpose of the embodiment of the present application is to provide a kind of exchange information processing method, commerce information processor, transaction letter Processing equipment and storage medium are ceased, to solve to be difficult to accurately identify the transaction risk of the unconspicuous trading activity of off-note The problem of.

In order to solve the above technical problems, the embodiment of the present application is achieved in that

According to the embodiment of the present application in a first aspect, providing a kind of exchange information processing method, comprising: be based on target master The trading activity data of body generate the behavior sequence of the target subject；Described in being extracted from the behavior sequence of the target subject The trading activity feature of target subject, wherein the trading activity feature includes that the main body variable of the trading activity data is believed Breath, the main body variable information include the information for indicating the attaching information of the target subject；Based on the trading activity feature Dimensional information, word be embedded in by way of generate insertion vector corresponding with the trading activity feature；Based on the main body Variable information counts each dimensional information of the insertion vector, with the friendship based on statistical result to the target subject Easy risk is predicted.

In some embodiments of the present application, aforementioned schemes are based on, the dimensional information based on the trading activity feature is logical The mode for crossing word insertion generates insertion vector corresponding with the trading activity feature, comprising: to the trading activity feature Each characteristic item carries out clustering processing, and the dimensional information of the trading activity feature is determined based on cluster result；Based on the friendship The trading activity Feature Mapping is corresponding insertion vector by word incorporation model by the dimensional information of easy behavioural characteristic.

In some embodiments of the present application, aforementioned schemes are based on, institute's predicate incorporation model is that shot and long term remembers LSTM mould The trading activity Feature Mapping is corresponding by word incorporation model by type, the dimensional information based on the trading activity feature It is embedded in vector, comprising: the dimensional information of the trading activity feature and the trading activity feature is input to the LSTM Model；Dimensional information based on the trading activity feature is by the hidden layer of the LSTM model from the trading activity feature It is middle to extract corresponding dimensional characteristics；Each dimensional characteristics of the trading activity feature of extraction are output to default full connection Layer presets the corresponding insertion vector of full articulamentum generation by described.

In some embodiments of the present application, it is based on aforementioned schemes, further includes: obtain the historical trading of multiple target subjects Behavioral data；The trading activity feature of the multiple target subject is extracted from the historical trading behavioral data；Based on described The dimensional information of the trading activity feature of multiple target subjects and the trading activity feature instructs the LSTM model Practice.

In some embodiments of the present application, be based on aforementioned schemes, based on the main body variable information to it is described be embedded in Each dimensional information of amount is counted, comprising: the complete information based on the main body variable information is to the insertion vector Each dimensional information is counted；And/or the attaching information pair for the target subject for based on the main body variable information including Each dimensional information of the insertion vector is counted.

In some embodiments of the present application, aforementioned schemes are based on, the main body variable information is bank account information, base In the target subject that the main body variable information includes attaching information to it is described insertion vector each dimensional information into Row statistics, comprising: the bank identification number for including based on bank account information carries out each dimensional information of the insertion vector Statistics.

In some embodiments of the present application, aforementioned schemes are based on, each dimensional information of the insertion vector is carried out Statistics, comprising: maximum value, minimum value or the average value of each dimensional information of the insertion vector are counted.

In some embodiments of the present application, it is based on aforementioned schemes, the transaction based on statistical result to the target subject Risk is predicted, comprising: the target subject is divided into New Account or old debts family based on the main body variable information；If described Target subject is New Account, then based on the corresponding system of the attaching information of the target subject that includes with the main body variable information Meter result predicts the transaction risk of the target subject；If the target subject is old debts family, it is based on and the master The corresponding statistical result of the complete information of body variable information predicts the transaction risk of the target subject.

In some embodiments of the present application, it is based on aforementioned schemes, the transaction based on statistical result to the target subject Risk is predicted, comprising: obtains the statistical result of each dimension of the insertion vector corresponding with the main body variable；Base It is predicted in the statistical result by transaction risk of the decision-tree model to the target subject.

According to the second aspect of the embodiment of the present application, a kind of commerce information processor is provided, comprising: sequence generates single Member generates the behavior sequence of the target subject for the trading activity data based on target subject；Feature extraction unit is used for The trading activity feature of the target subject is extracted from the behavior sequence of the target subject, wherein the trading activity is special Sign includes the main body variable information of the trading activity data, and the main body variable information includes to indicate returning for the target subject Belong to the information of information；It is embedded in vector generation unit, for the dimensional information based on the trading activity feature, is embedded in by word Mode generates insertion vector corresponding with the trading activity feature；Statistic unit, for being based on the main body variable information pair Each dimensional information of the insertion vector is counted, to be carried out based on transaction risk of the statistical result to the target subject Prediction.

In some embodiments of the present application, aforementioned schemes are based on, the insertion vector generation unit includes: dimensional information Determination unit carries out clustering processing for each characteristic item to the trading activity feature, based on described in cluster result determination The dimensional information of trading activity feature；Map unit is embedded in for the dimensional information based on the trading activity feature by word The trading activity Feature Mapping is corresponding insertion vector by model.

In some embodiments of the present application, aforementioned schemes are based on, institute's predicate incorporation model is that shot and long term remembers LSTM mould Type, the map unit include: input unit, for by the dimension of the trading activity feature and the trading activity feature Information input is to the LSTM model；Feature extraction unit passes through institute for the dimensional information based on the trading activity feature The hidden layer for stating LSTM model extracts corresponding dimensional characteristics from the trading activity feature；Feature output unit, being used for will The each dimensional characteristics for the trading activity feature extracted, which are output to, presets full articulamentum, presets full articulamentum life by described At corresponding insertion vector.

In some embodiments of the present application, it is based on aforementioned schemes, commerce information processor further include: historical data obtains Unit is taken, for obtaining the historical trading behavioral data of multiple target subjects；Behavioural characteristic extraction unit is used for from the history The trading activity feature of the multiple target subject is extracted in trading activity data；Training unit, for being based on the multiple mesh The dimensional information of the trading activity feature and the trading activity feature of marking main body is trained the LSTM model.

In some embodiments of the present application, aforementioned schemes are based on, the statistic unit includes: the first statistic unit, is used It is counted in each dimensional information of the complete information based on the main body variable information to the insertion vector；And/or the Two statistic units, the attaching information of the target subject for including based on the main body variable information is to the insertion vector Each dimensional information counted.

In some embodiments of the present application, aforementioned schemes are based on, the main body variable information is bank account information, the Two statistic units are configured as: the bank identification number for including based on bank account information believes each dimension of the insertion vector Breath is counted.

In some embodiments of the present application, be based on aforementioned schemes, the statistic unit is configured as: to it is described be embedded in Maximum value, minimum value or the average value of each dimensional information of amount are counted.

In some embodiments of the present application, aforementioned schemes are based on, the statistic unit includes: division unit, is used for base The target subject is divided into New Account or old debts family in the main body variable information；First predicting unit, if being used for the mesh Mark main body is New Account, then based on the corresponding statistics of the attaching information of the target subject that includes with the main body variable information As a result the transaction risk of the target subject is predicted；Second predicting unit, if being old debts family for the target subject, Then carried out based on transaction risk of the statistical result corresponding with the complete information of the main body variable information to the target subject Prediction.

In some embodiments of the present application, aforementioned schemes are based on, the statistic unit is configured as: obtained and the master The statistical result of each dimension of the corresponding insertion vector of body variable；Pass through decision-tree model pair based on the statistical result The transaction risk of the target subject is predicted.

According to the third aspect of the embodiment of the present application, a kind of Transaction Information processing equipment is provided, comprising: processor；With And it is configured to store the memory of computer executable instructions, the computer executable instructions make the place when executed The step of reason device realizes exchange information processing method described in any one of above-mentioned first aspect.

According to the fourth aspect of the embodiment of the present application, a kind of storage medium is provided, computer is executable to be referred to for storing It enables, the computer executable instructions realize Transaction Information processing side described in any one of above-mentioned first aspect when executed The step of method.

Pass through the technical solution in the embodiment of the present application, on the one hand, mesh is determined based on the trading activity sequence of target subject Mark the trading activity feature comprising attaching information of main body, the transaction convenient for the attaching information based on target subject to target subject Behavioural characteristic is counted；On the other hand, the dimensional information based on trading activity feature is generated and is handed in such a way that word is embedded in The corresponding insertion vector of easy behavioural characteristic, can not only sufficiently refine the trading activity feature of target subject in each dimension, Calculation amount can also be reduced, data-handling efficiency is improved；In another aspect, based on main body variable information to each dimension of insertion vector Degree information is counted, and can be predicted based on transaction risk of the statistical result to fresh target main body, so as to improve pair The accuracy of the risk assessment of the unconspicuous trading activity of off-note.

Detailed description of the invention

In order to illustrate the technical solutions in the embodiments of the present application or in the prior art more clearly, to embodiment or will show below There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this The some embodiments recorded in application, for those of ordinary skill in the art, in the premise of not making the creative labor property Under, it is also possible to obtain other drawings based on these drawings.

Fig. 1 shows the signal of the application scenarios of the exchange information processing method provided according to some embodiments of the present application Block diagram；

Fig. 2 shows the flow diagrams of the exchange information processing method provided according to some embodiments of the present application；

Fig. 3 shows the flow diagram of the generation insertion vector provided according to some embodiments of the present application；

Fig. 4 shows the flow diagram of the exchange information processing method provided according to other embodiments of the application；

Fig. 5 shows the flow diagram of the exchange information processing method provided according to the other embodiment of the application；

Fig. 6 shows the schematic block diagram of the commerce information processor provided according to some embodiments of the present application；

Fig. 7 shows the schematic block diagram of the insertion vector generation unit provided according to some embodiments of the present application；

Fig. 8 shows the schematic block diagram of the map unit provided according to some embodiments of the present application；And

Fig. 9 shows the schematic block diagram of the Transaction Information processing equipment provided according to some embodiments of the present application.

Specific embodiment

In order to make those skilled in the art better understand the technical solutions in the application, below in conjunction with the application reality The attached drawing in example is applied, the technical scheme in the embodiment of the application is clearly and completely described, it is clear that described implementation Example is merely a part but not all of the embodiments of the present application.Based on the embodiment in the application, this field is common The application protection all should belong in technical staff's every other embodiment obtained without creative efforts Range.

Fig. 1 shows the signal of the application scenarios of the exchange information processing method provided according to some embodiments of the present application Block diagram.Shown in referring to Fig.1, which may include: at least one client 110 and server end 120.Client It is communicated between 110 and server end 120 by network 130.Bank or Third-party payment mechanism are installed in client 110 Payment application, user can be carried out under line by the payment application in client 110 or the trading activity that pays on line, such as The trading activities such as two dimensional code is paid, small amount exempts from close payment, transfers accounts or supplements with money are swept, and record friendship related with corresponding trading activity Easy behavioral data.The trading activity that server end 120 executes client 110 confirms, completes corresponding trading activity, and Record trading activity data related with the trading activity.

It should be noted that client 110 can calculate for mobile phone, tablet computer, desktop computer, Portable notebook type Machine or POS (Point Of Sales, point of sale) terminal etc..Server 120 can be the physical server comprising unique host, Perhaps for mainframe cluster carrying virtual server or be Cloud Server.Network 130 can be cable network or wireless network Network, for example, network 130 can be Public Switched Telephone Network (Public Switched Telephone Network, PSTN) Or internet.

It should be noted that step in exchange information processing method in the example embodiment of the application can part by Client 110 execute, partially executed, can also all be executed by server 120 by server 120, the present invention to this without Particular determination.

Below with reference to the application scenarios of Fig. 1, it is described with reference to Figure 2 the Transaction Information of the exemplary embodiment according to the application Processing method.It should be noted which is shown only for the purpose of facilitating an understanding of the spirit and principles of the present invention for above-mentioned application scenarios, this The embodiment of invention is unrestricted in this regard.On the contrary, the embodiment of the present invention can be applied to applicable any scene.

Fig. 2 shows the flow diagrams of the exchange information processing method provided according to some embodiments of the present application, should Exchange information processing method can be applied to the server 120 in Fig. 1.Referring to shown in Fig. 2, which includes Step S210 to step S240, is below described in detail the exchange information processing method in the example embodiment of Fig. 2.

Referring to shown in Fig. 2, in step S210, the trading activity data based on target subject generate the row of the target subject For sequence.

In the exemplary embodiment, target subject is the main body for being able to reflect the attaching information for the user for carrying out trading activity, The attaching information can be to carry out geographical location information locating for the user of trading activity, which can be to go with transaction For corresponding bank account information such as bank card number and bank card number, IP (Internet Protocol, network association View) address, telephone number, identification card number information, or other main bodys appropriate equipment for example corresponding with trading activity IMEI (International Mobile Equipment Identity, International Mobile Equipment Identity code), equipment MAC The main bodys such as (Media Access Control, media access control) address, this is equally within the scope of protection of this application.Below It is illustrated by taking bank card number as an example in section Example mode.

In the exemplary embodiment, the trading activity data of available target subject within a predetermined period of time, the pre- timing Between section can be the periods such as 15 days, 1 month, 2 months or 3 months.The trading activity data of target subject may include target Transfer information, payment information and the Receiving information of main body also may include that other data related with trading activity are for example stepped on Data, load value data or purchase data are recorded, the application is to this without particular determination.

Further, the behavior of the target subject is generated based on the trading activity data of target subject within a predetermined period of time Sequence, the behavior sequence of target subject are the transaction row according to the target subject in some time section of transaction time of origin sequence For data, for example, the trading activity data set based on target subject in predetermined amount of time such as 1 month generate the target subject Behavior sequence, then the trading activity data in the predetermined amount of time can be divided based on predetermined space such as 1 day, be obtained To the trading activity data in some time section, and according to time of origin by the trading activity data in each time interval into Row sequence, generates the behavior sequence in the predetermined amount of time corresponding with the target subject.

In step S220, the trading activity feature of the target subject is extracted from the behavior sequence of the target subject, In, which includes the main body variable information of target subject corresponding with trading activity data, main body variable letter The information of attaching information of the breath comprising indicating the target subject.

In the exemplary embodiment, the trading activity feature of target subject may include target subject main body variable information, Transaction amount, beneficiary information or paying party information, loco, exchange hour and type of transaction feature also may include The characteristic informations such as other trading activity features appropriate such as means of payment, coupon use information.The main body of target subject becomes Measure information include indicate target subject attaching information information, such as target subject be bank card when, the main body of target subject Variable information includes bank's card number, bank card BIN (Bank Identification Number, the bank that bank's card number includes Identification code) it can indicate the attaching information of bank card, bank card BIN is usually indicated by 6 bit digitals, appears in bank card number First 6, such as the card BIN of business bank, Hefei City is 603601, the card BIN of Wuxi City business bank is 603265, Changshu City The card BIN of Rural Commercial Bank is 603694, the card BIN of business bank, Daliang City is 603708.It is identity card in target subject When, the main body variable information of target subject includes identification card number, and first 6 of identification card number can indicate the ownership letter of identity card Breath, such as Beijing are 110000, Tianjin 120000, Hebei province 130000, Shanxi Province 140000, and the Inner Mongol is autonomous Area is 150000.

Further, in the exemplary embodiment, the trading activity data in the behavior sequence of target subject are segmented Processing extracts from the trading activity data in the behavior sequence after participle and identifies related word such as target master with transaction risk The information such as the main body variable of body, transaction amount, type of transaction, exchange hour, to extract with transaction risk identify related word into Row coding such as one-hot one-hot coding, generate the feature of corresponding with the behavior sequence of target subject trading activity feature to Amount.For example, when identifying that related word includes the main body variable of target subject, transaction amount, type of transaction with transaction risk, mesh The one-hot coding for marking the main body variable of main body is [1,0,0,0], and the one-hot coding of transaction amount is [0,1,0,0], type of transaction One-hot coding be [0,0,1,0], based on identifying that the coding generation of related word is corresponding in trading activity data with transaction risk Trading activity feature feature vector.

It should be noted that, although trading activity data are encoded using one-hot coding in this exemplary embodiment, But in the example embodiment of the application, trading activity data can also be encoded using other coding modes appropriate Such as bag of words, TF-IDF (Term Frequency-Inverse Document Frequency, term frequency-inverse document frequency Rate) model etc., the application is to this without particular determination.

Next, in step S230, the dimensional information of the trading activity feature based on target subject is embedded in by word Mode generates insertion vector corresponding with the trading activity feature.

In the exemplary embodiment, the dimensional information of the trading activity feature of target subject is special for identifying the trading activity The same or similar characteristic information in sign, the dimensional information of the trading activity feature of target subject may include: that trading activity is returned Belong to one of dimensional information, trading activity type dimension information, transaction amount dimensional information and exchange hour dimensional information Or it is a variety of.For example, trading activity ownership dimensional information may include bank card number information, IP address information, cell-phone number information, Identification card number information etc. belongs to dimensional information；Trading activity type dimension information may include: consumer transaction, insurance class friendship Easily, financing class trades, borrows the type dimensions information such as the transaction of refund class, the transaction of wage class；Transaction amount dimensional information may include Multiple amount of money sections such as amount of money dimension such as [0,5000], [5000,10000], [10000,20000], [20000,50000] is believed Breath；Exchange hour dimensional information may include that multiple time intervals set predetermined amount of time for example as 2018.1.1 to 2018.5.1, Then multiple time intervals can for [2018.1.1,2018.2.1], [2018.2.1,2018.3.1], [2018.3.1, 2018.4.1], the time dimensions information such as [2018.4.1,2018.5.1].

Further, the dimensional information based on trading activity feature generates and the trading activity in such a way that word is embedded in The corresponding insertion vector of feature.Word insertion is that a kind of distribution of word indicates, can be by a high dimension in such a way that word is embedded in Word space reflection into the vector space of a low-dimensional number.In the exemplary embodiment, the dimension of trading activity feature is being determined It spends after information, word incorporation model such as LSTM (Long Short-Term Memory, shot and long term memory) model can be passed through By the trading activity Feature Mapping of multidimensional to the insertion vector of low-dimensional number corresponding with the dimensional information, calculated so as to reduce Amount improves data-handling efficiency.Such as, it is determined that the dimensional information of trading activity feature includes the information of four dimensions, that is, is handed over Easy is to belong to dimensional information, trading activity type dimension information, transaction amount dimensional information and exchange hour dimensional information, It can be by word incorporation model by the insertion vector of trading activity Feature Mapping to the four dimensions.

It should be noted that in this exemplary embodiment, the mode of word insertion can be with LSTM model, or Word2Vec model or Glove model, can also for other neural network models appropriate such as Skip-Gram model or The models such as CBOW (Continuous Bag-of-Words, continuous bag of words), this is equally within the scope of protection of this application.

In step S240, the main body variable information based on target subject unites to each dimensional information of insertion vector Meter, to be predicted based on transaction risk of the statistical result to the target subject.

In the exemplary embodiment, when target subject is bank's card number, the main body variable information of target subject includes bank Card number information, bank's card number information include attaching information, that is, bank card card BIN information of target subject, can be based on bank card Number complete information, that is, entire card number to insertion vector each dimensional information count, can also be based on the card of bank card BIN counts each dimensional information of insertion vector.When target subject is ID card information, the main body of target subject becomes Amount includes identification card number information, ground of the identification card number information comprising 6 expressions before attaching information, that is, identification card number of target subject Location information can be united with each dimensional information of the complete information, that is, entire identification card number of identity-based card number to insertion vector Meter can also count each dimensional information of insertion vector with first 6 of identity-based card number.

It for example, in the exemplary embodiment, can be based on each of target subject when target subject is bank's card number Maximum value, minimum value or the average value of dimensional information are counted, for example, can count within a predetermined period of time with each card number Maximum value, minimum value or the average value of corresponding transaction amount can also count corresponding with various card BIN within a predetermined period of time Transaction amount maximum value, minimum value or average value.Further, it is also possible to statistical calculation is combined to each dimensional information, Trading activity type dimension, transaction amount dimension, trading activity ownership dimension etc. can be combined statistical calculation, example Such as, the total of corresponding with each card number consumer within a predetermined period of time or insurance class type of transaction transaction amount can be counted Volume, average value, or each card BIN of statistics is corresponding consumer or insures the total value of the transaction amount of class type of transaction, is averaged Value.

It should be noted that in this exemplary embodiment, the main body variable of target subject such as bank card and identity card is believed Breath is preceding 6 information, but the example embodiment of the application is without being limited thereto, for example, the main body variable information of bank card can be with It is 5 before bank's card number, the main body variable information of identity card can also be for 9 before identification card number i.e. comprising the position of year of birth Number, as long as the main body variable information of target subject can distinguish the ownership of target subject, the application is to this without spy It is different to limit.

Further, in the exemplary embodiment, the statistical result of each dimensional information based on insertion vector is to the target The transaction risk of main body is predicted, for example, determining the transaction row after getting the current trading activity data of target subject For the transaction amount under each trading activity type dimension in data maximum value whether be more than the target subject the transaction The maximum value of historical statistics result under behavior type dimension, if being more than maximum value, it is determined that the transaction row of the target subject To issue the user with risk prompting message with risk.According to the technical solution in the implementation of this example, even if new main body is for example new The transaction data of card is seldom, as long as example card BIN the corresponding dimension of the new main body occurred accordingly, it will be able to determine that this is new The transaction risk of main body improves the accuracy of the risk assessment of the trading activity of new main body.

In addition, in the exemplary embodiment, being also based on the trading activity data pair of the target subject in preset time period Risk classifications, that is, the high risk or low-risk of the target subject are marked, and the risk classifications of the target subject based on label will be with The corresponding insertion vector of target subject is divided into positive sample and negative sample, is carried out based on positive sample and negative sample to risk evaluation model Training, is predicted based on transaction risk of the training result of the risk evaluation model to the current trading activity of target subject, The risk evaluation model can be neural network model, decision-tree model or supporting vector machine model, the application to this not into Row particular determination.

The exchange information processing method in example embodiment according to fig. 2, on the one hand, the trading activity based on target subject Sequence determines the trading activity feature comprising attaching information of target subject, convenient for the attaching information based on target subject to target The trading activity feature of main body is counted；On the other hand, the side that the dimensional information based on trading activity feature is embedded in by word Formula generates insertion vector corresponding with trading activity feature, and the transaction of target subject can not only be sufficiently refined in each dimension Behavioural characteristic, additionally it is possible to reduce calculation amount, improve data-handling efficiency；In another aspect, based on main body variable information to insertion to Each dimensional information of amount is counted, and can be predicted based on transaction risk of the statistical result to fresh target main body, thus It can be improved the accuracy of the risk assessment to the unconspicuous trading activity of off-note.

Fig. 3 shows the flow diagram of the generation insertion vector provided according to some embodiments of the present application.

Referring to shown in Fig. 3, in step s310, each characteristic item of the trading activity feature of target subject is clustered Processing, the dimensional information of trading activity feature is determined based on cluster result.

It in the exemplary embodiment, can be in the case where the dimensional information of the trading activity feature of uncertain target subject Word segmentation processing is carried out to the trading activity data in the behavior sequence of target subject, from the transaction row in the behavior sequence after participle Related word is identified with transaction risk to extract in data, is generated corresponding term vector, is calculated the distance between each term vector, Determine that each and transaction risk in trading activity data identifies the class cluster of related word based on the distance of calculating, by each class cluster Dimensional information as trading activity feature.For example, cluster after class cluster may include main body variable class, transaction amount class, Type of transaction class, exchange hour class etc., using these types as the dimensional information of trading activity feature.

It should be noted that the distance between each term vector can be Hamming distances, Euclidean distance, COS distance, but It is that distance in the exemplary embodiment of the application is without being limited thereto, such as distance can also be mahalanobis distance, manhatton distance etc..

In step s 320, the dimensional information based on trading activity feature is reflected trading activity feature by word incorporation model It penetrates as corresponding insertion vector.

In the exemplary embodiment, word incorporation model is LSTM model, which is a kind of deep learning model, can be with Including input layer, hidden layer and output layer.Input layer is used to receive the trading activity feature and trading activity feature extracted Dimensional information, hidden layer extracts corresponding dimension for the dimensional information based on trading activity feature from trading activity feature Feature, output layer presets full articulamentum for being output to each dimensional characteristics of the trading activity feature of extraction, pre- by this If full articulamentum generates insertion vector corresponding with trading activity feature.

In addition, in the exemplary embodiment, the historical trading behavioral data based on target subject instructs word incorporation model Practice, based on the word incorporation model after training by the corresponding insertion vector of trading activity Feature Mapping.For example, when available default Between multiple target subjects in section historical trading behavioral data, extracted from the historical trading behavioral data of multiple target subjects Corresponding trading activity feature, trading activity feature and corresponding dimensional information based on target subject to word incorporation model into Row training.

Fig. 4 shows the flow diagram of the exchange information processing method provided according to other embodiments of the application.

Referring to shown in Fig. 4, in step S410, the trading activity data based on target subject generate the behavior of target subject Sequence.

In the exemplary embodiment, which can be bank card, and the trading activity data of target subject can be receipts Money behavioral data, the behavior sequence of target subject are gathering sequence, that is, gathering event 1 to gathering event t.

In the step s 420, the trading activity feature of the target subject is extracted from the behavior sequence of target subject.

In the exemplary embodiment, the trading activity feature of the target subject can for target subject main body variable information, The features such as transaction amount, beneficiary information or paying party information, loco, exchange hour and type of transaction, also can wrap Including also may include the characteristic informations such as other trading activity features appropriate such as means of payment, coupon use information.In Fig. 4 In, trading activity feature variable 1_1 to n_1, the variable 1_2 to variable n_2 of the target subject ..., variable 1_t to variable N_t indicates that each variable indicates a feature.

In step S430, the dimensional information of the trading activity feature based on target subject is raw in such a way that word is embedded in At insertion vector corresponding with the trading activity feature.

In the exemplary embodiment, the dimensional information of the trading activity feature of target subject may include: trading activity ownership One of dimensional information, trading activity type dimension information, transaction amount dimensional information and exchange hour dimensional information or It is a variety of.For example, trading activity ownership dimensional information may include bank card number information, IP address information, cell-phone number information, body The ownership dimensional information such as part card information.

Further, word insertion is that a kind of distribution of word indicates, can be by high dimension in such a way that word is embedded in Word space reflection is into the vector space of a low-dimensional number.In the exemplary embodiment, the dimension of trading activity feature is being determined After information, it can be incited somebody to action by word incorporation model such as LSTM (Long Short-Term Memory, shot and long term memory) model Insertion vector of the trading activity Feature Mapping of multidimensional to low-dimensional number corresponding with the dimensional information.Such as, it is determined that transaction row The dimensional information being characterized includes the information of four dimensions, i.e. trading activity ownership dimensional information, trading activity type dimension letter Breath, transaction amount dimensional information and exchange hour dimensional information, can be by word incorporation model by trading activity Feature Mapping To the insertion vector of the four dimensions.

The LSTM model is a kind of deep learning model, may include input layer, hidden layer and output layer.Input layer is used In the dimensional information for receiving the trading activity feature and trading activity feature extracted, hidden layer is used to be based on trading activity feature Dimensional information extract corresponding dimensional characteristics from trading activity feature, the trading activity feature that output layer is used to extract Each dimensional characteristics, which are output to, presets full articulamentum, presets full articulamentum by this and generates insertion corresponding with trading activity feature Vector.

In step S440, the main body variable information based on target subject unites to each dimensional information of insertion vector Meter.

Fig. 5 shows the flow diagram of the exchange information processing method provided according to the other embodiment of the application.

Referring to Figure 5, in step S510, the trading activity data of target subject within a preset period of time, example are obtained Trading activity data such as to target subject in 3 months are deposited and are cleaned, and the noise number in trading activity data is removed According to, and the trading activity data obtained to storage store the trading activity number of target subject for example in the form of off-line data table According to.

In step S520, dimension behavioural characteristic corresponding with target subject, dimension row are determined based on trading activity data Be characterized DBF (Dimension Behavior Feature, dimension behavioural characteristic) refer to the detailed data of trading activity or The behavioural characteristic that behavior sequence is integrated in certain dimension after refining by processing.For example, available transaction The trading activity data of target subject are mapped to corresponding dimension behavior based on the dimensional information by the dimensional information of behavioral data Feature.

In the exemplary embodiment, step S520 may include step S524 to step S528, carry out below to these steps Detailed description.

In step S524, the trading activity data based on target subject generate the behavior sequence of target subject.Target master The behavior sequence of body is according to the trading activity data of the target subject in some time section of transaction time of origin sequence, example Such as, if the trading activity data based on target subject in predetermined amount of time such as 3 months generate the behavior sequence of the target subject Column, then can divide the trading activity data in the predetermined amount of time based on predetermined space such as 1 day, when obtaining several Between trading activity data in section, and the trading activity data in each time interval are ranked up according to time of origin, Generate the behavior sequence in the predetermined amount of time corresponding with the target subject.

In step S526, insertion vector corresponding with the behavior sequence of target subject is generated in such a way that word is embedded in. Word insertion is that a kind of distribution of word indicates, can be low to one by the word space reflection of a high dimension in such a way that word is embedded in In the vector space of dimension.In the exemplary embodiment, after the dimensional information that trading activity feature has been determined, word can be passed through Incorporation model such as LSTM model is by the trading activity Feature Mapping of multidimensional to the insertion of low-dimensional number corresponding with the dimensional information Vector.Such as, it is determined that the dimensional information of trading activity feature includes the information of four dimensions, i.e. trading activity ownership dimension letter Breath, trading activity type dimension information, transaction amount dimensional information and exchange hour dimensional information, can be embedded in mould by word Type is by the insertion vector of trading activity Feature Mapping to the four dimensions.

It is corresponding with target subject based on the corresponding insertion vector determination of behavior sequence of target subject in step S528 Dimension behavioural characteristic.In the exemplary embodiment, the main body variable information based on target subject believes each dimension of insertion vector Breath is counted, and determines dimension behavioural characteristic corresponding with target subject based on statistical result., it is bank's card number in target subject When, the main body variable information of target subject includes bank's card number information, and bank's card number information includes the attaching information of target subject That is the card BIN information of bank card, can the complete information based on bank's card number, that is, entire card number to insertion vector each dimension Information is counted, can also the card BIN based on bank card to insertion vector each dimensional information count.In target master When body is ID card information, the main body variable of target subject includes identification card number information, and identification card number information includes target subject Attaching information, that is, identification card number before 6 expression address informations, can with identity-based demonstrate,prove number complete information, that is, entire identity Card number counts each dimensional information of insertion vector, can also be with first 6 of identity-based card number to each of insertion vector A dimensional information is counted.

Next, being back to step S530, in step S530, the dimension behavioural characteristic based on target subject establishes risk Assessment models.For example, can with the dimension behavioural characteristic of combining target main body and normal risk variable to risk evaluation model into Row training, risk evaluation model can be the models such as neural network model, decision-tree model or supporting vector machine model, conventional Risk variable may include the variables such as the standard deviation of transaction amount, the average value of transaction amount and abnormal behavior variable.

Further, the dimension behavioural characteristic for being also based on target subject carries out a point group to target subject, for example, in mesh When mark main body is bank card, a point group can be carried out to target subject by the card BIN information of target subject, if the card of target subject BIN information is identical, these target subjects are divided into a group.It, can be by new main body by carrying out a point group to target subject Such as neocaine is divided into group appropriate, so as to improve new main body trading activity risk assessment accuracy.

In addition, the normal risk that the trading activity data based on target subject generate target subject becomes in step S540 Amount, for example, the trading activity data to target subject count, determines trade gold of the target subject in every type of transaction The normal risks variable such as average value, standard deviation, transaction probability of volume.

Next, determining model strategy corresponding with risk evaluation model in step 550.For different risk classes Type needs to construct different air control models and for example instead cheats model, counter usurps model, anti-cheating model etc..In every kind of risk class Under type for different types of service use different models, by it is counter cheat model for, can be further subdivided into and take advantage of offline Swindleness person's model transfers accounts online to account model, transfers accounts online to card mold type etc..For a type of service, model strategy can be with Certain regular parallel form is added using multiple model parallel forms, that is, each Model score threshold value, such as: model strategy 1: Model A score value is greater than 0.6 and transaction amount is greater than 1000 yuan；Model strategy 2 is that Model B score value is greater than 0.7 and trade gold Volume is greater than 10000 yuan.It is assessed by using transaction risk of multiple models to target subject, risk can be further increased The accuracy of assessment.

In step S560, based on transaction risk of the model strategy in step S550 to target subject carry out assessment and it is defeated The result of decision out.For example, integration exports synthesis point after the output score value of different models is standardized Value, is assessed based on transaction risk of the comprehensive scores to target subject, determines the grade of transaction risk, exports final decision Such as indicating risk or safety instruction.

Further, the decision in the face of risk log of target subject can also be precipitated, model exports score value, risk class label etc. Risk data further adjusts the mould of the risk evaluation model of the target subject with the risk data of the target subject based on deposition Type strategy improves the accuracy of risk assessment.

In the example embodiment of the application, a kind of commerce information processor is additionally provided.Referring to shown in Fig. 6, the friendship Easy information processing unit 600 include: sequence generating unit 610, feature extraction unit 620, insertion vector generation unit 630 and Statistic unit 640.Wherein, sequence generating unit 610 generates the target master for the trading activity data based on target subject The behavior sequence of body；Feature extraction unit 620 from the behavior sequence of the target subject for extracting the target subject Trading activity feature, wherein the trading activity feature includes the main body variable information of the trading activity data, the main body Variable information includes the information for indicating the attaching information of the target subject；Vector generation unit 630 is embedded in be used for based on described The dimensional information of trading activity feature generates insertion vector corresponding with the trading activity feature in such a way that word is embedded in； Statistic unit 640 is used to count based on each dimensional information of the main body variable information to the insertion vector, with base It is predicted in transaction risk of the statistical result to the target subject.

In some embodiments of the present application, aforementioned schemes are based on, referring to shown in Fig. 7, the insertion vector generation unit 630 include: dimensional information determination unit 710, carries out clustering processing, base for each characteristic item to the trading activity feature The dimensional information of the trading activity feature is determined in cluster result；Map unit 720, for being based on the trading activity feature Dimensional information by word incorporation model by the trading activity Feature Mapping be corresponding insertion vector.

In some embodiments of the present application, aforementioned schemes are based on, referring to shown in Fig. 8, institute's predicate incorporation model is length Phase remembers LSTM model, and the map unit 720 includes: input unit 722, for by the trading activity feature and described The dimensional information of trading activity feature is input to the LSTM model；Feature extraction unit 724, for being based on the trading activity The dimensional information of feature extracts corresponding dimensional characteristics from the trading activity feature by the hidden layer of the LSTM model； Feature output unit 726 presets full articulamentum for being output to each dimensional characteristics for the trading activity feature extracted, The corresponding insertion vector of full articulamentum generation is preset by described.

In some embodiments of the present application, it is based on aforementioned schemes, commerce information processor 600 further include: history number According to acquiring unit, for obtaining the historical trading behavioral data of multiple target subjects；Behavioural characteristic extraction unit is used for from described The trading activity feature of the multiple target subject is extracted in historical trading behavioral data；Training unit, for based on described more The dimensional information of the trading activity feature of a target subject and the trading activity feature is trained the LSTM model.

In some embodiments of the present application, aforementioned schemes are based on, the statistic unit 640 includes: the first statistic unit, It is counted for each dimensional information of the complete information based on the main body variable information to the insertion vector；And/or Second statistic unit, the attaching information of the target subject for including based on the main body variable information to it is described be embedded in Each dimensional information of amount is counted.

In some embodiments of the present application, aforementioned schemes are based on, the statistic unit 640 is configured as: to described embedding Maximum value, minimum value or the average value of each dimensional information of incoming vector are counted.

In some embodiments of the present application, aforementioned schemes are based on, the statistic unit 640 includes: division unit, is used for The target subject is divided into New Account or old debts family based on the main body variable information；First predicting unit, if for described Target subject is New Account, then based on the corresponding system of the attaching information of the target subject that includes with the main body variable information Meter result predicts the transaction risk of the target subject；Second predicting unit, if being old debts for the target subject Family, then based on statistical result corresponding with the complete information of the main body variable information to the transaction risk of the target subject into Row prediction.

In some embodiments of the present application, aforementioned schemes are based on, the statistic unit 640 is configured as: acquisition and institute State the statistical result of each dimension of the corresponding insertion vector of main body variable；Pass through decision tree mould based on the statistical result Type predicts the transaction risk of the target subject.

According to the commerce information processor in the example embodiment of Fig. 6, on the one hand, the trading activity based on target subject Sequence determines the trading activity feature comprising attaching information of target subject, convenient for the attaching information based on target subject to target The trading activity feature of main body is counted；On the other hand, the side that the dimensional information based on trading activity feature is embedded in by word Formula generates insertion vector corresponding with trading activity feature, and the transaction of target subject can not only be sufficiently refined in each dimension Behavioural characteristic, additionally it is possible to reduce calculation amount, improve data-handling efficiency；In another aspect, based on main body variable information to insertion to Each dimensional information of amount is counted, and can be predicted based on transaction risk of the statistical result to fresh target main body, thus It can be improved the accuracy of the risk assessment to the unconspicuous trading activity of off-note.

Commerce information processor provided by the embodiments of the present application can be realized each process in preceding method embodiment, And reach identical function and effect, it is not repeated herein.

Further, the embodiment of the present application also provides a kind of Transaction Information processing equipments, as shown in Figure 9.

Transaction Information processing equipment can generate bigger difference because configuration or performance are different, may include one or one A above processor 901 and memory 902 can store one or more storage application programs in memory 902 Or data.Wherein, memory 902 can be of short duration storage or persistent storage.The application program for being stored in memory 902 can wrap One or more modules (diagram is not shown) are included, each module may include to a series of in Transaction Information processing equipment Computer executable instructions.Further, processor 901 can be set to communicate with memory 902, in Transaction Information processing The series of computation machine executable instruction in memory 902 is executed in equipment.Transaction Information processing equipment can also include one Or more than one power supply 903, one or more wired or wireless network interfaces 904, one or more input and output Interface 905, one or more keyboards 906 etc..

In a specific embodiment, Transaction Information processing equipment include memory and one or more Program, perhaps more than one program is stored in memory and one or more than one program may include for one of them One or more modules, and each module may include executable to the series of computation machine in Transaction Information processing equipment Instruction, and be configured to execute this or more than one program by one or more than one processor to include for carrying out Following computer executable instructions: the trading activity data based on target subject generate the behavior sequence of the target subject；From The trading activity feature of the target subject is extracted in the behavior sequence of the target subject, wherein the trading activity feature Main body variable information comprising the trading activity data, the main body variable information include the ownership for indicating the target subject The information of information；Based on the dimensional information of the trading activity feature, generated and the trading activity in such a way that word is embedded in The corresponding insertion vector of feature；It is counted based on each dimensional information of the main body variable information to the insertion vector, To be predicted based on transaction risk of the statistical result to the target subject.

Optionally, when executed, the dimensional information based on the trading activity feature passes through computer executable instructions The mode of word insertion generates insertion vector corresponding with the trading activity feature, comprising: to each of the trading activity feature A characteristic item carries out clustering processing, and the dimensional information of the trading activity feature is determined based on cluster result；Based on the transaction The trading activity Feature Mapping is corresponding insertion vector by word incorporation model by the dimensional information of behavioural characteristic.

Optionally, when executed, institute's predicate incorporation model is that shot and long term remembers LSTM model to computer executable instructions, The trading activity Feature Mapping is corresponding embedding by word incorporation model by the dimensional information based on the trading activity feature Incoming vector, comprising: the dimensional information of the trading activity feature and the trading activity feature is input to the LSTM mould Type；Dimensional information based on the trading activity feature is by the hidden layer of the LSTM model from the trading activity feature Extract corresponding dimensional characteristics；Each dimensional characteristics of the trading activity feature of extraction are output to and preset full articulamentum, The corresponding insertion vector of full articulamentum generation is preset by described.

Optionally, computer executable instructions are when executed, further includes: obtain the historical trading row of multiple target subjects For data；The trading activity feature of the multiple target subject is extracted from the historical trading behavioral data；Based on described more The dimensional information of the trading activity feature of a target subject and the trading activity feature is trained the LSTM model.

Optionally, computer executable instructions when executed, are based on the main body variable information to the insertion vector Each dimensional information counted, comprising: the complete information based on the main body variable information to it is described insertion vector it is each A dimensional information is counted；And/or the attaching information for the target subject for based on the main body variable information including is to institute The each dimensional information for stating insertion vector is counted.

Optionally, when executed, the main body variable information is bank account information to computer executable instructions, is based on The attaching information for the target subject that the main body variable information includes carries out each dimensional information of the insertion vector Statistics, comprising: the bank identification number for including based on bank account information unites to each dimensional information of the insertion vector Meter.

Optionally, computer executable instructions when executed, unite to each dimensional information of the insertion vector Meter, comprising: maximum value, minimum value or the average value of each dimensional information of the insertion vector are counted.

Optionally, computer executable instructions when executed, based on statistical result to the transaction wind of the target subject It is predicted danger, comprising: the target subject is divided by New Account or old debts family based on the main body variable information；If the mesh Mark main body is New Account, then based on the corresponding statistics of the attaching information of the target subject that includes with the main body variable information As a result the transaction risk of the target subject is predicted；If the target subject is old debts family, it is based on and the main body The corresponding statistical result of the complete information of variable information predicts the transaction risk of the target subject.

Optionally, computer executable instructions when executed, based on statistical result to the transaction wind of the target subject It is predicted danger, comprising: obtain the statistical result of each dimension of the insertion vector corresponding with the main body variable；It is based on The statistical result is predicted by transaction risk of the decision-tree model to the target subject.

Transaction Information processing equipment provided by the embodiments of the present application can be realized each process in preceding method embodiment, And reach identical function and effect, it is not repeated herein.

In addition, the embodiment of the present application also provides a kind of storage medium, for storing computer executable instructions, a kind of tool In the embodiment of body, which can be USB flash disk, CD, hard disk etc., the computer executable instructions of storage medium storage When being executed by processor, be able to achieve following below scheme: the trading activity data based on target subject generate the target subject Behavior sequence；The trading activity feature of the target subject is extracted from the behavior sequence of the target subject, wherein the friendship Easy behavioural characteristic includes the main body variable information of the trading activity data, and the main body variable information includes to indicate the target The information of the attaching information of main body；Based on the dimensional information of the trading activity feature, generated in such a way that word is embedded in and institute State the corresponding insertion vector of trading activity feature；Based on the main body variable information to each dimensional information of the insertion vector It is counted, to be predicted based on transaction risk of the statistical result to the target subject.

Optionally, the computer executable instructions of storage medium storage are based on the transaction when being executed by processor The dimensional information of behavioural characteristic generates insertion vector corresponding with the trading activity feature in such a way that word is embedded in, comprising: Clustering processing is carried out to each characteristic item of the trading activity feature, the trading activity feature is determined based on cluster result Dimensional information；The trading activity Feature Mapping is by the dimensional information based on the trading activity feature by word incorporation model Corresponding insertion vector.

Optionally, for the computer executable instructions of storage medium storage when being executed by processor, institute's predicate is embedded in mould Type is that shot and long term remembers LSTM model, and the dimensional information based on the trading activity feature passes through word incorporation model for the transaction Behavioural characteristic is mapped as corresponding insertion vector, comprising: by the trading activity feature and the dimension of the trading activity feature Information input is spent to the LSTM model；Dimensional information based on the trading activity feature is hidden by the LSTM model Layer extracts corresponding dimensional characteristics from the trading activity feature；Each dimension of the trading activity feature of extraction is special Sign, which is output to, presets full articulamentum, presets the corresponding insertion vector of full articulamentum generation by described.

Optionally, the computer executable instructions of storage medium storage are when being executed by processor, further includes: obtain more The historical trading behavioral data of a target subject；The friendship of the multiple target subject is extracted from the historical trading behavioral data Easy behavioural characteristic；The dimensional information pair of trading activity feature and the trading activity feature based on the multiple target subject The LSTM model is trained.

Optionally, the computer executable instructions of storage medium storage are based on the main body when being executed by processor Variable information counts each dimensional information of the insertion vector, comprising: based on the complete of the main body variable information Information counts each dimensional information of the insertion vector；And/or described in based on the main body variable information including The attaching information of target subject counts each dimensional information of the insertion vector.

Optionally, the computer executable instructions of storage medium storage are when being executed by processor, the main body variable Information is bank account information, and the attaching information for the target subject for including based on the main body variable information is to the insertion Each dimensional information of vector is counted, comprising: the bank identification number for including based on bank account information to it is described be embedded in Each dimensional information of amount is counted.

Optionally, the storage medium storage computer executable instructions when being executed by processor, to it is described be embedded in Each dimensional information of amount is counted, comprising: to the maximum value of each dimensional information of the insertion vector, minimum value or is put down Mean value is counted.

Optionally, the computer executable instructions of storage medium storage are based on statistical result when being executed by processor The transaction risk of the target subject is predicted, comprising: be divided into the target subject based on the main body variable information New Account or old debts family；If the target subject is New Account, based on the target for including with the main body variable information The corresponding statistical result of the attaching information of main body predicts the transaction risk of the target subject；If the target subject is Old debts family, then based on statistical result corresponding with the complete information of the main body variable information to the transaction wind of the target subject It is predicted danger.

Optionally, the computer executable instructions of storage medium storage are based on statistical result when being executed by processor The transaction risk of the target subject is predicted, comprising: obtain the insertion vector corresponding with the main body variable The statistical result of each dimension；It is carried out based on the statistical result by transaction risk of the decision-tree model to the target subject Prediction.

Computer readable storage medium provided by the embodiments of the present application can be realized each mistake in preceding method embodiment Journey, and reach identical function and effect, it is not repeated herein.

In the 1990s, the improvement of a technology can be distinguished clearly be on hardware improvement (for example, Improvement to circuit structures such as diode, transistor, switches) or software on improvement (improvement for method flow).So And with the development of technology, the improvement of current many method flows can be considered as directly improving for hardware circuit. Designer nearly all obtains corresponding hardware circuit by the way that improved method flow to be programmed into hardware circuit.Cause This, it cannot be said that the improvement of a method flow cannot be realized with hardware entities module.For example, programmable logic device (Programmable Logic Device, PLD) (such as field programmable gate array (Field Programmable Gate Array, FPGA)) it is exactly such a integrated circuit, logic function determines device programming by user.By designer Voluntarily programming comes a digital display circuit " integrated " on a piece of PLD, designs and makes without asking chip maker Dedicated IC chip.Moreover, nowadays, substitution manually makes IC chip, this programming is also used instead mostly " is patrolled Volume compiler (logic compiler) " software realizes that software compiler used is similar when it writes with program development, And the source code before compiling also write by handy specific programming language, this is referred to as hardware description language (Hardware Description Language, HDL), and HDL is also not only a kind of, but there are many kind, such as ABEL (Advanced Boolean Expression Language)、AHDL(Altera Hardware Description Language)、Confluence、CUPL(Cornell University Programming Language)、HDCal、JHDL (Java Hardware Description Language)、Lava、Lola、MyHDL、PALASM、RHDL(Ruby Hardware Description Language) etc., VHDL (Very-High-Speed is most generally used at present Integrated Circuit Hardware Description Language) and Verilog.Those skilled in the art also answer This understands, it is only necessary to method flow slightly programming in logic and is programmed into integrated circuit with above-mentioned several hardware description languages, The hardware circuit for realizing the logical method process can be readily available.

Controller can be implemented in any suitable manner, for example, controller can take such as microprocessor or processing The computer for the computer readable program code (such as software or firmware) that device and storage can be executed by (micro-) processor can Read medium, logic gate, switch, specific integrated circuit (Application Specific Integrated Circuit, ASIC), the form of programmable logic controller (PLC) and insertion microcontroller, the example of controller includes but is not limited to following microcontroller Device: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20 and Silicone Labs C8051F320 are deposited Memory controller is also implemented as a part of the control logic of memory.It is also known in the art that in addition to Pure computer readable program code mode is realized other than controller, can be made completely by the way that method and step is carried out programming in logic Controller is obtained to come in fact in the form of logic gate, switch, specific integrated circuit, programmable logic controller (PLC) and insertion microcontroller etc. Existing identical function.Therefore this controller is considered a kind of hardware component, and to including for realizing various in it The device of function can also be considered as the structure in hardware component.Or even, it can will be regarded for realizing the device of various functions For either the software module of implementation method can be the structure in hardware component again.

System, device, module or the unit that above-described embodiment illustrates can specifically realize by computer chip or entity, Or it is realized by the product with certain function.It is a kind of typically to realize that equipment is computer.Specifically, computer for example may be used Think personal computer, laptop computer, cellular phone, camera phone, smart phone, personal digital assistant, media play It is any in device, navigation equipment, electronic mail equipment, game console, tablet computer, wearable device or these equipment The combination of equipment.

For convenience of description, it is divided into various units when description apparatus above with function to describe respectively.Certainly, implementing this The function of each unit can be realized in the same or multiple software and or hardware when application.

It should be understood by those skilled in the art that, embodiments herein can provide as method, system or computer program Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the application Apply the form of example.Moreover, it wherein includes the computer of computer usable program code that the application, which can be used in one or more, The computer program implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) produces The form of product.

The application is referring to method, the process of equipment (system) and computer program product according to the embodiment of the present application Figure and/or block diagram describe.It should be understood that every one stream in flowchart and/or the block diagram can be realized by computer program instructions The combination of process and/or box in journey and/or box and flowchart and/or the block diagram.It can provide these computer programs Instruct the processor of general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce A raw machine, so that being generated by the instruction that computer or the processor of other programmable data processing devices execute for real The device for the function of being specified in present one or more flows of the flowchart and/or one or more blocks of the block diagram.

These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates, Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or block diagram or The function of being specified in multiple boxes.

These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that counting Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one The step of function of being specified in a box or multiple boxes.

In a typical configuration, calculating equipment includes one or more processors (CPU), input/output interface, net Network interface and memory.

Memory may include the non-volatile memory in computer-readable medium, random access memory (RAM) and/or The forms such as Nonvolatile memory, such as read-only memory (ROM) or flash memory (flash RAM).Memory is computer-readable medium Example.

Computer-readable medium includes permanent and non-permanent, removable and non-removable media can be by any method Or technology come realize information store.Information can be computer readable instructions, data structure, the module of program or other data. The example of the storage medium of computer includes, but are not limited to phase change memory (PRAM), static random access memory (SRAM), moves State random access memory (DRAM), other kinds of random access memory (RAM), read-only memory (ROM), electric erasable Programmable read only memory (EEPROM), flash memory or other memory techniques, read-only disc read only memory (CD-ROM) (CD-ROM), Digital versatile disc (DVD) or other optical storage, magnetic cassettes, tape magnetic disk storage or other magnetic storage devices Or any other non-transmission medium, can be used for storage can be accessed by a computing device information.As defined in this article, it calculates Machine readable medium does not include temporary computer readable media (transitory media), such as the data-signal and carrier wave of modulation.

It should also be noted that, the terms "include", "comprise" or its any other variant are intended to nonexcludability It include so that the process, method, commodity or the equipment that include a series of elements not only include those elements, but also to wrap Include other elements that are not explicitly listed, or further include for this process, method, commodity or equipment intrinsic want Element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that including described want There is also other identical elements in the process, method of element, commodity or equipment.

It will be understood by those skilled in the art that embodiments herein can provide as method, system or computer program product. Therefore, complete hardware embodiment, complete software embodiment or embodiment combining software and hardware aspects can be used in the application Form.It is deposited moreover, the application can be used to can be used in the computer that one or more wherein includes computer usable program code The shape for the computer program product implemented on storage media (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) Formula.

The application can describe in the general context of computer-executable instructions executed by a computer, such as program Module.Generally, program module includes routines performing specific tasks or implementing specific abstract data types, programs, objects, group Part, data structure etc..The application can also be practiced in a distributed computing environment, in these distributed computing environments, by Task is executed by the connected remote processing devices of communication network.In a distributed computing environment, program module can be with In the local and remote computer storage media including storage equipment.

All the embodiments in this specification are described in a progressive manner, same and similar portion between each embodiment Dividing may refer to each other, and each embodiment focuses on the differences from other embodiments.Especially for system reality For applying example, since it is substantially similar to the method embodiment, so being described relatively simple, related place is referring to embodiment of the method Part explanation.

The above description is only an example of the present application, is not intended to limit this application.For those skilled in the art For, various changes and changes are possible in this application.All any modifications made within the spirit and principles of the present application are equal Replacement, improvement etc., should be included within the scope of the claims of this application.

Claims

1. a kind of exchange information processing method characterized by comprising

Trading activity data based on target subject generate the behavior sequence of the target subject；

The trading activity feature of the target subject is extracted from the behavior sequence of the target subject, wherein the transaction row It is characterized the main body variable information comprising the trading activity data, the main body variable information includes to indicate the target subject Attaching information information；

Based on the dimensional information of the trading activity feature, generated in such a way that word is embedded in corresponding with the trading activity feature Insertion vector；

It is counted based on each dimensional information of the main body variable information to the insertion vector, to be based on statistical result pair The transaction risk of the target subject is predicted.

2. exchange information processing method according to claim 1, which is characterized in that the dimension based on the trading activity feature Degree information generates insertion vector corresponding with the trading activity feature in such a way that word is embedded in, comprising:

Clustering processing is carried out to each characteristic item of the trading activity feature, determines that the trading activity is special based on cluster result The dimensional information of sign；

The trading activity Feature Mapping is to correspond to by word incorporation model by the dimensional information based on the trading activity feature Insertion vector.

3. exchange information processing method according to claim 2, which is characterized in that institute's predicate incorporation model is shot and long term note Recall LSTM model, the dimensional information based on the trading activity feature passes through word incorporation model for the trading activity Feature Mapping For corresponding insertion vector, comprising:

The dimensional information of the trading activity feature and the trading activity feature is input to the LSTM model；

Dimensional information based on the trading activity feature is by the hidden layer of the LSTM model from the trading activity feature It is middle to extract corresponding dimensional characteristics；

Each dimensional characteristics of the trading activity feature of extraction are output to and preset full articulamentum, are connected entirely by described preset It connects layer and generates corresponding insertion vector.

4. exchange information processing method according to claim 3, which is characterized in that further include:

Obtain the historical trading behavioral data of multiple target subjects；

The trading activity feature of the multiple target subject is extracted from the historical trading behavioral data；

The dimensional information of trading activity feature and the trading activity feature based on the multiple target subject is to described LSTM model is trained.

5. exchange information processing method according to claim 1, which is characterized in that based on the main body variable information to institute The each dimensional information for stating insertion vector is counted, comprising:

Complete information based on the main body variable information counts each dimensional information of the insertion vector；And/or

Each dimension of the attaching information for the target subject for including based on the main body variable information to the insertion vector Information is counted.

6. exchange information processing method according to claim 5, which is characterized in that the main body variable information is bank's account Number information, each dimension of the attaching information of the target subject for including based on the main body variable information to the insertion vector Degree information is counted, comprising:

The bank identification number for including based on bank account information counts each dimensional information of the insertion vector.

7. exchange information processing method according to claim 5, which is characterized in that each dimension of the insertion vector Information is counted, comprising:

Maximum value, minimum value or the average value of each dimensional information of the insertion vector are counted.

8. exchange information processing method according to claim 5, which is characterized in that based on statistical result to the target master The transaction risk of body is predicted, comprising:

The target subject is divided into New Account or old debts family based on the main body variable information；

If the target subject is New Account, the ownership letter based on the target subject for including with the main body variable information Corresponding statistical result is ceased to predict the transaction risk of the target subject；

If the target subject is old debts family, based on statistical result pair corresponding with the complete information of the main body variable information The transaction risk of the target subject is predicted.

9. exchange information processing method according to any one of claim 1 to 8, which is characterized in that be based on statistical result The transaction risk of the target subject is predicted, comprising:

Obtain the statistical result of each dimension of the insertion vector corresponding with the main body variable；

It is predicted based on the statistical result by transaction risk of the decision-tree model to the target subject.

10. a kind of commerce information processor characterized by comprising

Sequence generating unit generates the behavior sequence of the target subject for the trading activity data based on target subject；

Feature extraction unit, the trading activity for extracting the target subject from the behavior sequence of the target subject are special Sign, wherein the trading activity feature includes the main body variable information of the trading activity data, the main body variable information packet Information containing the attaching information for indicating the target subject；

It is embedded in vector generation unit, for the dimensional information based on the trading activity feature, is generated in such a way that word is embedded in Insertion vector corresponding with the trading activity feature；

Statistic unit, for being counted based on each dimensional information of the main body variable information to the insertion vector, with It is predicted based on transaction risk of the statistical result to the target subject.

11. a kind of Transaction Information processing equipment characterized by comprising processor；And it is configured to store computer and can hold The memory of row instruction, the computer executable instructions make the processor realize the claims 1 to 9 when executed Any one of described in exchange information processing method the step of.

12. a kind of storage medium, for storing computer executable instructions, which is characterized in that the computer executable instructions The step of realizing exchange information processing method according to any one of claims 1 to 9 when executed.