WO2020019895A1 - 后付费交易数据处理方法、装置、处理设备、及服务器 - Google Patents

后付费交易数据处理方法、装置、处理设备、及服务器 Download PDF

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Publication number
WO2020019895A1
WO2020019895A1 PCT/CN2019/091092 CN2019091092W WO2020019895A1 WO 2020019895 A1 WO2020019895 A1 WO 2020019895A1 CN 2019091092 W CN2019091092 W CN 2019091092W WO 2020019895 A1 WO2020019895 A1 WO 2020019895A1
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Prior art keywords
transaction
postpaid
data processing
payment channel
risk
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PCT/CN2019/091092
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English (en)
French (fr)
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王骏
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阿里巴巴集团控股有限公司
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Priority to SG11202010625RA priority Critical patent/SG11202010625RA/en
Publication of WO2020019895A1 publication Critical patent/WO2020019895A1/zh
Priority to US17/103,139 priority patent/US11373161B2/en

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/12—Accounting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/22—Payment schemes or models
    • G06Q20/24—Credit schemes, i.e. "pay after"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/01—Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computing arrangements based on specific mathematical models
    • G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/30—Payment architectures, schemes or protocols characterised by the use of specific devices or networks
    • G06Q20/36—Payment architectures, schemes or protocols characterised by the use of specific devices or networks using electronic wallets or electronic money safes
    • G06Q20/367—Payment architectures, schemes or protocols characterised by the use of specific devices or networks using electronic wallets or electronic money safes involving electronic purses or money safes
    • G06Q20/3678—Payment architectures, schemes or protocols characterised by the use of specific devices or networks using electronic wallets or electronic money safes involving electronic purses or money safes e-cash details, e.g. blinded, divisible or detecting double spending
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/38—Payment protocols; Details thereof
    • G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
    • G06Q20/401—Transaction verification
    • G06Q20/4014—Identity check for transactions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/38—Payment protocols; Details thereof
    • G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
    • G06Q20/401—Transaction verification
    • G06Q20/4016—Transaction verification involving fraud or risk level assessment in transaction processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/38—Payment protocols; Details thereof
    • G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
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    • G06Q20/4037—Remote solvency checks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/38—Payment protocols; Details thereof
    • G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
    • G06Q20/405—Establishing or using transaction specific rules
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G—PHYSICS
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    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/02—Banking, e.g. interest calculation or account maintenance

Definitions

  • This specification belongs to the technical field of risk prevention and control, and particularly relates to a method and device for processing postpaid transaction data.
  • e-wallet As users may have many funding channels in the e-wallet, the information between the e-wallet and the merchant cannot be exchanged. Under the pre-paid model, the merchant cannot determine whether all the funds in the user's e-wallet can pay for the purchase of goods. Of the amount. Fraudsters may use e-wallet accounts with little or less money to purchase pre-paid services or goods from merchants. When merchants request debits from e-wallet platforms, debit failures may occur.
  • the purpose of this specification is to provide a post-paid transaction data processing method and device, which improves the reliability of post-paid transaction risk control, reduces the risk of post-paid transactions, reduces the capital loss of post-paid transactions, and improves post-paid transactions User experience.
  • an embodiment of the present specification provides a method for processing postpaid transaction data, including:
  • the risk inquiry information includes: transaction amount, transaction user identification;
  • a default evaluation result of the postpaid transaction is determined.
  • the risk query information further includes a product type
  • the method further includes:
  • the result of determining the default assessment of the postpaid transaction includes:
  • a default evaluation result of the postpaid transaction is determined according to the risk coefficient and the payable prediction value.
  • determining the payable predictive value of the payment channel for the transaction amount based on the payment channel and the transaction amount includes:
  • a payable predicted value of the transaction amount by the payment channel is determined.
  • determining, based on the payment channel and the transaction amount, the predictable value of the payment channel for the transaction amount further includes:
  • a payable predicted value of the transaction amount by the payment channel is determined.
  • determining the risk coefficient corresponding to the product type includes:
  • a risk coefficient corresponding to the commodity type is determined.
  • determining the default evaluation result of the postpaid transaction based on the payable predictive value includes:
  • a default evaluation result of the postpaid transaction is determined by using at least one method of a decision tree and a logistic regression.
  • the method further includes:
  • this specification provides a postpaid transaction data processing device, including:
  • the method further includes:
  • the risk query information further includes a product type
  • the apparatus further includes:
  • a commodity risk analysis module configured to determine a risk coefficient corresponding to the commodity type
  • the decision module is specifically configured to:
  • a default evaluation result of the postpaid transaction is determined according to the risk coefficient and the payable prediction value.
  • the quota prediction module is specifically configured to:
  • a payable predictive value for the transaction amount by the payment channel is determined according to the predicted amount of funds of the payment channel and the transaction amount.
  • the quota prediction module is specifically configured to:
  • a payable predicted value of the transaction amount by the payment channel is determined.
  • the commodity risk analysis module is specifically configured to:
  • a risk coefficient corresponding to the commodity type is determined.
  • the decision module is specifically configured to:
  • a default evaluation result of the postpaid transaction is determined by using at least one method of a decision tree and a logistic regression.
  • the device further includes a payment processing module, configured to:
  • an embodiment of the present specification provides a postpaid transaction data processing device, including: at least one processor and a memory for storing processor-executable instructions, where the processor executes the instructions to implement the first aspect Postpaid transaction data processing method.
  • an embodiment of the present specification provides a post-paid transaction data processing system, including: a post-paid transaction data processing device, a risk consulting interface, and a payment interface.
  • the post-paid transaction data processing system includes at least one processor and A memory storing a processor-executable instruction, and when the processor executes the instruction, the post-paid transaction data processing method of the first aspect is implemented.
  • an embodiment of the present specification provides a method for processing postpaid transaction data, including:
  • the risk inquiry information After receiving the postpaid transaction service request, sending risk inquiry information to the postpaid transaction data processing device, the risk inquiry information includes: product type, transaction amount, and transaction user identification;
  • an embodiment of the present specification provides a postpaid transaction data processing terminal, including:
  • the query information sending module is configured to receive a postpaid transaction service request, and send risk query information to the postpaid transaction data processing device, where the risk query information includes: product type, transaction amount, and transaction user identification;
  • a default information receiving module configured to receive a default evaluation result of a postpaid transaction sent by the postpaid transaction data processing device
  • the transaction passing module is configured to send request information for passing the postpaid transaction to the postpaid transaction data processing device if the default evaluation result is less than a preset probability threshold.
  • an embodiment of the present specification provides a postpaid transaction data processing device, including: at least one processor and a memory for storing processor-executable instructions, and the processor implements when the instructions are executed:
  • the risk inquiry information After receiving the postpaid transaction service request, sending risk inquiry information to the postpaid transaction data processing device, the risk inquiry information includes: product type, transaction amount, and transaction user identification;
  • an embodiment of the present specification provides a computer storage medium on which a computer program is stored.
  • the computer program is executed, the following method is implemented:
  • the risk inquiry information After receiving the postpaid transaction service request, sending risk inquiry information to the postpaid transaction data processing device, the risk inquiry information includes: product type, transaction amount, and transaction user identification;
  • an embodiment of the present specification provides a data processing server for an electronic wallet, including at least one processor and a memory for storing processor-executable instructions.
  • the processor executes the instructions, the first aspect is implemented.
  • Post-paid transaction data processing method Post-paid transaction data processing method.
  • the post-paid transaction data processing method, device, processing equipment, and server provided in this manual analyze the payment channels of the transaction users of the post-paid transactions before the post-paid transaction is concluded, and determine the payable forecast value of the transaction amount by the payment channels. According to the payable forecast value of the payment channel, the default evaluation result of the transaction amount of the post-paid transaction that the transaction user's account can pay is determined. Predictive analysis of payment possibilities based on payment channels improves the accuracy and precision of postpaid transaction default prediction, improves the reliability of postpaid transaction risk control, reduces the risk of postpaid transactions, and reduces the risk of postpaid transactions. The loss of funds improves the user experience of postpaid transactions.
  • FIG. 1 is a schematic flowchart of a post-paid transaction data processing method in an embodiment provided in this specification
  • FIG. 2 is a schematic flowchart of a post-paid transaction data processing method in another embodiment of the present specification
  • FIG. 3 is a schematic structural diagram of a module of an embodiment of a postpaid transaction data processing device provided in this specification;
  • FIG. 4 is a schematic structural diagram of a module of a postpaid transaction data processing device according to another embodiment of the present specification.
  • FIG. 5 is a schematic structural diagram of a module of a postpaid transaction data processing device according to another embodiment of the present specification.
  • FIG. 6 is a schematic structural diagram of a postpaid transaction data processing system in an embodiment of the present specification.
  • FIG. 7 is a block diagram of a hardware structure of a data processing server of an electronic wallet according to an embodiment of the present invention.
  • FIG. 8 is a schematic flowchart of a post-paid transaction data processing method in another embodiment of the present specification.
  • FIG. 9 is a schematic structural diagram of a postpaid transaction data processing terminal in an embodiment of the present specification.
  • More and more merchants will provide first-come and later-pay services.
  • first-come and post-pay services usually merchants will access third-party electronic wallets as one of the payment methods.
  • third-party electronic wallets In order to improve the user's shopping and payment experience, users, merchants, and e-wallets will sign secret-free withholding agreements, allowing merchants to automatically deduct users' assets in third-party e-wallets.
  • the user can try the product for a certain period of time. After a certain period of time, the user does not return the product, and the merchant can automatically deduct the corresponding amount in the electronic wallet through a third-party electronic wallet.
  • the post-paid transaction data processing method provided in the embodiment of the present specification can process the transaction data before the transaction is completed during the pre-paid and post-paid transaction, and judge the default evaluation result of the current transaction.
  • the merchant can determine whether to pass the default evaluation result The current pay-as-you-go transaction. Improved risk prevention and control of prepaid and postpaid transactions, reduced capital loss, and improved user experience.
  • the pre-paid transaction data processing method may be performed on a client such as a smart phone, a tablet computer, a smart wearable device (a smart watch, a virtual reality glasses, a virtual reality helmet, etc.) and other electronic devices.
  • a client such as a smart phone, a tablet computer, a smart wearable device (a smart watch, a virtual reality glasses, a virtual reality helmet, etc.) and other electronic devices.
  • FIG. 1 is a schematic flowchart of a post-paid transaction data processing method in an embodiment provided in this specification.
  • a post-paid transaction data processing method provided in an embodiment of this specification includes:
  • a merchant may first call a risk consulting interface of a postpaid transaction data processing system, and send risk inquiry information to the postpaid transaction data processing device through the risk consulting interface.
  • the risk query information may include information about the current postpaid transaction, such as: transaction amount, transaction user identification, transaction merchant identification, transaction time, and transaction location.
  • the transaction user ID may include: the account name of the transaction user (eg, the account name of the electronic wallet, the account name of the trading platform, etc.), and the transaction merchant ID may include the account name of the transaction merchant (eg, the account name of the transaction merchant).
  • the risk query information may also include other information such as: product type, and the specific content of the risk query information may be adjusted according to actual needs, which are not specifically limited in the embodiments of this specification.
  • the sending and receiving of the risk query information may be performed through wireless network transmission, Bluetooth, wired network, etc., and may be specifically set according to actual needs, which are not specifically limited in the embodiments of this specification.
  • the payment channel of the account of the transaction user corresponding to the transaction user ID may be queried according to the transaction user ID in the risk query information.
  • Payment channels can include payment channels in electronic wallets such as: bound bank card accounts, credit card accounts, prepaid cards, red envelope coupons, etc.
  • the predictable value for payment can indicate the ability of the payment channel to pay for the transaction amount of the postpaid transaction.
  • the predictable value for payment can be expressed as a percentage or a multiple, but of course, it can also be expressed in other ways.
  • the predictable payment value of the transaction amount by the payment channel can be determined according to the flow of funds or other information in the payment channel and the transaction amount of the current postpaid transaction. For example, if the amount in the payment channel is half of the transaction amount, it can be considered that the user account of the postpaid transaction may only pay half of the transaction amount, and the payable forecast value may be determined as 50%.
  • determining the payable predictive value of the payment channel for the transaction amount based on the payment channel and the transaction amount includes:
  • a payable predicted value of the transaction amount by the payment channel is determined.
  • historical billing data can indicate the flow of funds in the payment channel within a specified time, and can include the inflow and outflow of funds from the payment channel, such as the flow of funds of bank card A within one month before the transaction time.
  • the historical billing data of the payment channel can be obtained, and the fund flow history of the payment channel can be obtained. Based on the fund flow history of the payment channel, the predicted funding amount of the payment channel at the current transaction time can be predicted.
  • time series analysis and prediction algorithms such as regression method, average method, model prediction, etc.
  • the time series analysis can be used to calculate the relationship between the current value and the past value of a variable.
  • the time series analysis can be used to predict the current funds of the payment channel at the current transaction time based on the past flow of funds in the payment channel. According to the predicted amount of funds of the payment channel determined and comparing the transaction amount of the postpaid transaction, the payable forecast value of the payment channel to the transaction amount can be determined.
  • the historical billing data of each payment channel can be obtained separately. Based on the historical billing data of each payment channel, the corresponding payment channel can be determined at the time of the transaction. Predict the amount of funds, and further determine the predictable value of payment for each payment channel.
  • the payable forecast values of each payment channel can be superimposed to determine the total payable forecast value of all payment channels of the transaction user. Of course, the total payable forecast value of the payment channel may also be determined according to the sum of the predicted funds corresponding to each payment channel.
  • a forecast model for the number of funds can be constructed based on the historical bill data of the payment channel in advance, that is, the relationship between the number of funds in the payment channel and the change over time can be analyzed and summarized.
  • the fund number prediction models of different payment channels can be the same or different. Specifically, they can be constructed according to the transaction habits and fund flow characteristics of each payment channel. The constructed fund number prediction model is used to predict the fund number of each payment channel.
  • the historical billing data of bank card A and the historical billing data of credit card B can be obtained separately, based on the acquired historical billing data . You can get the bank card history of bank card A and credit card B respectively.
  • Time series analysis and prediction algorithms can be used to draw the time-varying curve of bank card A and credit card B's funds. Time-varying curve of funds can be used to analyze the relationship between the flow of funds in bank card A and credit card B over time and predict. When the current transaction time is shown, the predicted funds in bank card A and credit card B.
  • the predictable payable values of bank card A and credit card B are determined, respectively.
  • the payable predicted values of bank card A and credit card B are superimposed to determine the total payable predicted value of the account of the transaction user of the current postpaid transaction.
  • the determining the payable predictive value of the payment channel for the transaction amount based on the payment channel and the transaction amount further includes:
  • a payable predicted value of the transaction amount by the payment channel is determined.
  • the transaction behavior of the post-paid transaction user using the payment channel can be obtained according to the historical billing data of each payment channel.
  • a transaction user may represent a user who purchases a product in a postpaid transaction, that is, a user corresponding to a transaction user ID.
  • Transaction behaviors can include: the sustainability of consumption, whether there has been a default transaction (such as: overdue payment) and so on.
  • the acquired transaction behavior, the predicted amount of funds of the payment channel, and the transaction amount can be used to determine the payable predicted value of the transaction amount by the payment channel.
  • the user's transaction behavior, the number of predicted funds, and the functional relationship between the final payable amount can be analyzed, and a predictable model that can pay the predicted value can be constructed through model training and other methods.
  • the predictable model can be used to determine the payable amount of the payment channel, and further determine the payable forecast value of the payment channel to the transaction amount.
  • the amount of funds in the payment channel fluctuates greatly, and the user's consumption time is relatively large, it can be considered that the user's consumption can be Poor persistence.
  • the predicted number A of payment channels corresponds to the transaction behavior of the user, it can be determined that the actual amount of funds that the transaction user's payment channel can pay is lower than A.
  • a forecasting algorithm can be used to comprehensively analyze the predicted amount of funds and the user's transaction behavior to determine the predictable value that the payment channel can cover for the transaction amount.
  • the credit rating or credit score of the transaction user can be determined, which can provide a reference basis for determining the predictable value of the transaction amount that can be covered by the payment channel.
  • the embodiment of the present specification analyzes the transaction behavior of the transaction user by acquiring historical bill data of the payment channel of the transaction user, and predicts the payable predicted value of the transaction amount by the payment channel based on the transaction behavior and the predicted amount of funds in the payment channel. Combined with the user's transaction behavior, the probability of a user's possible default can be analyzed more accurately, providing an accurate data basis for the risk prevention and control of postpaid transactions.
  • the default evaluation result can indicate the possibility of insufficient funds or no payment amount in the account of the transaction user of the post-paid transaction.
  • the default evaluation result can adopt the default probability or default level.
  • the default level can be divided into 10 levels. The higher the level, the greater the risk of default in the transaction, or the default assessment result can be expressed in terms of probability values. The greater the probability, the higher the risk of default in the transaction.
  • a method such as a prediction algorithm can be used to determine the default evaluation result of the current postpaid transaction. For example, the functional relationship between the payable forecast value and the probability of default can be analyzed by means of numerical simulation, model analysis, and so on.
  • model training can be used to construct a model of default probability prediction and the probability of default.
  • the prediction model may include a functional relationship between the payable prediction value and the probability of default.
  • the payable predictive value of the current postpaid transaction can be input into the default probability prediction model to determine the corresponding default probability.
  • At least one of a decision tree and a logistic regression method may be used to determine a default evaluation result of a postpaid transaction.
  • a decision tree can represent a prediction model and can represent a mapping relationship between object attributes and object values.
  • Logistic regression is a generalized linear regression analysis model. It is often used in data mining, automatic disease diagnosis, and economic forecasting. It can be used to predict the probability of something happening. The use of decision trees and logistic regression methods can accurately predict the default evaluation results of postpaid transactions, and provide an accurate data basis for the risk prevention and control of postpaid transactions.
  • the obtained default evaluation result may be sent to the postpaid transaction data processing terminal corresponding to the transaction merchant in the postpaid transaction, and the postpaid transaction data
  • the processing terminal may be an electronic device such as a smart phone or a tablet.
  • the post-payment transaction data processing terminal can determine whether the risk of the current default evaluation result is acceptable, and if it is acceptable, it can send the post-payment transaction request information to the post-payment transaction data processing device.
  • the post-paid transaction data processing device can perform payment processing on the post-paid transaction, for example, the transaction amount can be deducted from the account of the transaction user at the predetermined payment time of the post-paid transaction and transferred to the account of the transaction merchant.
  • the post-paid transaction data processing method analyzes the payment channel of the transaction user of the post-paid transaction before the post-paid transaction is concluded, and determines the predictable payment value of the transaction amount by the payment channel.
  • the payment prediction value determines the default evaluation result of the transaction amount of the transaction user's account that can pay the postpaid transaction.
  • Predictive analysis of payment possibilities based on payment channels improves the accuracy and precision of postpaid transaction default prediction, improves the reliability of postpaid transaction risk control, reduces the risk of postpaid transactions, and reduces the risk of postpaid transactions.
  • the loss of funds improves the user experience of postpaid transactions.
  • FIG. 2 is a schematic flowchart of a post-paid transaction data processing method in another embodiment of the present specification. As shown in FIG. 2, based on the above embodiment, in one embodiment of the present specification, the post-paid transaction data processing method may further include: :
  • Receive risk query information which includes: product type, transaction amount, and transaction user identification.
  • the product type may indicate the type and category of the product, such as a certain brand of mobile phone, a certain brand of clothing, etc., and may also include the type of service such as: housekeeping service, travel service, and so on.
  • Different commodity types may correspond to different risk coefficients, that is, the risk of default for different commodity types may be different. Based on historical transaction data of postpaid transactions, the risk situation of different commodity types can be analyzed, and different risk factors can be set for different commodity types.
  • determining the risk coefficient corresponding to the product type may include:
  • a risk coefficient corresponding to the commodity type is determined.
  • historical transaction data may include transaction data of postpaid transactions within a preset time period (eg, one month before the current transaction time).
  • transaction data of postpaid transactions within a preset time period may include: Commodity type, transaction amount, default, etc. It is possible to analyze the default transaction ratio of the commodity type of the current postpaid transaction in historical transaction data, and determine the risk factor of the commodity type of the current postpaid transaction according to the default transaction proportion. For example, if the default rate of the product type of the current post-paid transaction is 50%, the risk system corresponding to the product type can be determined as 0.5. Of course, the risk factor of the product type can also be determined according to other methods. The risk factor can also be adjusted and updated based on real-time historical transaction data.
  • the payable forecast value of each payment channel for the transaction amount under the current transaction time can be calculated, and the payable forecast value corresponding to all payment channels can be further calculated.
  • the method for determining the payable predictive value corresponding to the payment channel reference may be made to the description of the foregoing embodiment, and details are not described herein again.
  • the risk factor of the product type and the payable forecast value of the payment channel are comprehensively considered, and the decision tree or logistic regression can be used Method to predict the default evaluation result of the post-paid transaction.
  • the expression form of the default evaluation result reference may be made to the records in the foregoing embodiment, and details are not described herein again.
  • a risk factor for the type of product, a predictable payment value for the payment channel, and a prediction model or algorithm for the evaluation of the default of the postpaid transaction can be constructed.
  • the prediction model or algorithm can be used to predict the current postpaid transaction. Default assessment results.
  • the risk factor of the product type of the post-paid transaction and the funding situation of the transaction channel corresponding to the transaction user are comprehensively considered, and the default evaluation result of the post-paid transaction can be accurately determined, which improves the The risk prevention and control of post-paid transactions reduces the loss of funds and improves the user's post-paid transaction experience.
  • one or more embodiments of the present specification also provide a post-paid transaction data processing device.
  • the device may include a system (including a distributed system), software (application), module, component, server, client, etc. that uses the method described in the embodiments of the present specification, and a device that implements necessary hardware.
  • the devices in one or more embodiments provided in the embodiments of this specification are as described in the following embodiments. Since the implementation solution of the device to solve the problem is similar to the method, the implementation of the specific device in the embodiment of this specification may refer to the implementation of the foregoing method, and the duplicated details are not described again.
  • unit or “module” may be a combination of software and / or hardware that realizes a predetermined function.
  • the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and conceived.
  • FIG. 3 is a schematic diagram of a module structure of an embodiment of a postpaid transaction data processing device provided in this specification.
  • the postpaid transaction data processing device provided in this specification includes an information receiving module 31 and a payment channel.
  • the information receiving module 31 may be configured to receive risk query information, where the risk query information includes: a transaction amount and a transaction user identifier;
  • the payment channel query module 32 may be configured to obtain a payment channel corresponding to the transaction user identifier according to the transaction user identifier;
  • An amount prediction module 33 may be configured to determine a payable predicted value of the transaction amount by the payment channel based on the payment channel and the transaction amount;
  • the decision module 34 may be configured to determine a default evaluation result of the postpaid transaction according to the payable prediction value.
  • the post-paid transaction data processing device analyzes the payment channel of the transaction user of the post-paid transaction before the post-paid transaction is determined, and determines the predictable payment value of the transaction amount by the payment channel.
  • the payment prediction value determines the default evaluation result of the transaction amount of the transaction user's account that can pay the postpaid transaction. Predictive analysis of payment possibilities based on payment channels improves the accuracy and precision of postpaid transaction default prediction, improves the reliability of postpaid transaction risk control, reduces the risk of postpaid transactions, and reduces the risk of postpaid transactions. The loss of funds improves the user experience of postpaid transactions.
  • FIG. 4 is a schematic diagram of a module structure of a postpaid transaction data processing device according to another embodiment of the present specification. As shown in FIG. 4, based on the above embodiment, the risk query information further includes a product type;
  • the apparatus further includes:
  • the commodity risk analysis module 41 may be configured to determine a risk coefficient corresponding to the commodity type
  • the decision module 34 is specifically configured to:
  • a default evaluation result of the postpaid transaction is determined according to the risk coefficient and the payable prediction value.
  • the post-paid transaction data processing device provided in the embodiment of this specification can comprehensively consider the risk factor of the product type of the post-paid transaction and the funding situation of the transaction channel corresponding to the transaction user before the post-paid transaction, and can accurately determine the post-paid
  • the result of the transaction default assessment improves the risk prevention and control of post-paid transactions, reduces capital losses, and improves the user's post-paid transaction experience.
  • the quota prediction module is specifically configured to:
  • a payable predictive value for the transaction amount by the payment channel is determined according to the predicted amount of funds of the payment channel and the transaction amount.
  • the post-paid transaction data processing device determines the payable predicted value of each post-paid transaction for the current post-paid transaction by analyzing the historical bill data of each payment channel, which is the default assessment for subsequent post-paid transactions. The prediction of the results provides an accurate data base.
  • the quota prediction module is specifically configured to:
  • a payable predicted value of the transaction amount by the payment channel is determined.
  • the embodiment of the present specification analyzes the transaction behavior of the transaction user by acquiring historical bill data of the payment channel of the transaction user, and predicts the payable predicted value of the transaction amount by the payment channel based on the transaction behavior and the predicted amount of funds in the payment channel. Combined with the user's transaction behavior, the probability of a user's possible default can be analyzed more accurately, providing an accurate data basis for the risk prevention and control of postpaid transactions.
  • the commodity risk analysis module is specifically configured to:
  • a risk coefficient corresponding to the commodity type is determined.
  • the embodiments of this specification analyze the risk coefficients corresponding to different commodity types, and provide a more accurate data basis for subsequent determination of the default evaluation results of the current postpaid transaction.
  • the decision module is specifically configured to:
  • a default evaluation result of the postpaid transaction is determined by using at least one method of a decision tree and a logistic regression.
  • a decision tree and a logistic regression method can be used to accurately predict the default evaluation results of postpaid transactions, and provide an accurate data basis for the risk prevention and control of postpaid transactions.
  • FIG. 5 is a schematic diagram of a module structure of a postpaid transaction data processing device according to another embodiment of the present specification. As shown in FIG. 5, based on the above embodiment, the device further includes a payment processing module 51 for:
  • the default evaluation result determined based on the type of the postpaid transaction, the transaction amount, and the relevant information of the payment channel of the transaction user is sent to the client of the transaction merchant, so that the transaction merchant can use the current postpayment
  • the result of the transaction default assessment to determine whether to pass the transaction improves the accuracy of risk prevention and control of post-paid transactions and reduces the loss of funds of the transaction merchant.
  • An embodiment of the present specification also provides a post-paid transaction data processing device, including: at least one processor and a memory for storing processor-executable instructions. When the processor executes the instructions, the post-paid transaction in the foregoing embodiment is implemented.
  • Data processing methods such as:
  • the risk inquiry information includes: transaction amount, transaction user identification;
  • a default evaluation result of the postpaid transaction is determined.
  • the storage medium may include a physical device for storing information. Generally, the information is digitized and then stored by using a medium such as electricity, magnetism, or optics.
  • the storage medium may include: a device that stores information using electrical energy, such as various types of memory, such as RAM, ROM, and the like; a device that uses magnetic energy to store information, such as hard disks, floppy disks, magnetic tapes, magnetic core memories, magnetic bubble memories, U disk; a device that uses optical means to store information, such as a CD or DVD.
  • electrical energy such as various types of memory, such as RAM, ROM, and the like
  • a device that uses magnetic energy to store information such as hard disks, floppy disks, magnetic tapes, magnetic core memories, magnetic bubble memories, U disk
  • a device that uses optical means to store information such as a CD or DVD.
  • quantum memory graphene memory, and so on.
  • processing device described above may also include other implementations according to the description of the method embodiment, such as combining the risk factor corresponding to the type of product of the postpaid transaction and the payable forecast value of the payment channel to comprehensively predict Out of the default assessment of the transaction.
  • other implementations such as combining the risk factor corresponding to the type of product of the postpaid transaction and the payable forecast value of the payment channel to comprehensively predict Out of the default assessment of the transaction.
  • the system may be a separate server, or may include a server cluster, a system (including a distributed system), software (application), one or more of the methods or one or more embodiments of the device described in this specification, Practical operating devices, logic gate circuit devices, quantum computers, etc. combined with necessary terminal devices that implement hardware.
  • the offline shopping recommendation content generation system may include at least one processor and a memory storing computer-executable instructions. When the processor executes the instructions, the steps of the method in any one or more of the foregoing embodiments are implemented.
  • FIG. 6 is a schematic structural diagram of a postpaid transaction data processing system in an embodiment of the present specification.
  • the postpaid transaction data processing system in the embodiment of the present specification may represent the electronic wallet system in FIG. 6, and may specifically include: Post-paid transaction data processing device, risk consulting interface, and payment interface.
  • the post-paid transaction data processing system may further include a post-payment transaction data processing terminal corresponding to the transaction merchant.
  • the postpaid transaction data processing device may include a commodity risk analysis module, a quota prediction module, a decision module, a risk consulting interface and a payment interface for data communication with a postpaid transaction data processing terminal corresponding to a transaction merchant.
  • the postpaid transaction data processing system includes at least one processor and a memory for storing processor-executable instructions. When the processor executes the instructions, the postpaid transaction data processing method in the foregoing embodiment is implemented, such as:
  • the risk inquiry information includes: transaction amount, transaction user identification;
  • a default evaluation result of the postpaid transaction is determined.
  • FIG. 7 is a hardware structural block diagram of a data processing server of an electronic wallet to which an embodiment of the present invention is applied.
  • the server 10 may include one or more (only one shown in the figure) a processor 100 (the processor 100 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), A memory 200 for storing data, and a transmission module 300 for communication functions.
  • a processor 100 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA
  • a memory 200 for storing data
  • a transmission module 300 for communication functions.
  • the server 10 may further include more or fewer components than those shown in FIG. 7, and may further include other processing hardware, such as a database or a multi-level cache, a GPU, or have a configuration different from that shown in FIG. 9.
  • the memory 200 may be used to store software programs and modules of application software, such as program instructions / modules corresponding to the postpaid transaction data processing method in the embodiment of the present specification.
  • the processor 100 runs the software programs and modules stored in the memory 200, thereby Perform various functional applications and data processing.
  • the memory 200 may include a high-speed random access memory, and may further include a non-volatile memory, such as one or more magnetic storage devices, a flash memory, or other non-volatile solid-state memory.
  • the memory 200 may further include memories remotely provided with respect to the processor 100, and these remote memories may be connected to the computer terminal 10 through a network. Examples of the above network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.
  • the transmission module 300 is configured to receive or send data via a network.
  • a specific example of the above network may include a wireless network provided by a communication provider of the computer terminal 10.
  • the transmission module 300 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet.
  • the transmission module 300 may be a radio frequency (RF) module, which is used to communicate with the Internet in a wireless manner.
  • RF radio frequency
  • the above-mentioned processing system and server according to the description of the method embodiment may also include other implementations, such as a combination of a risk factor corresponding to a product type of a postpaid transaction and a predictable value of a payment channel, Comprehensively predict the default assessment results of the transaction.
  • implementations such as a combination of a risk factor corresponding to a product type of a postpaid transaction and a predictable value of a payment channel, Comprehensively predict the default assessment results of the transaction.
  • FIG. 8 is a schematic flowchart of a post-paid transaction data processing method in another embodiment of this specification. As shown in FIG. 8, an embodiment of this specification also provides a transaction data processing method of a transaction merchant side in a post-paid transaction, including: :
  • T2 After receiving the postpaid transaction service request, send risk inquiry information to the postpaid transaction data processing device, where the risk inquiry information includes: product type, transaction amount, and transaction user identification.
  • the transaction merchant can use the post-payment transaction data processing terminal corresponding to the transaction merchant such as smartphones, tablets, smart portable devices, etc., and the post-payment transaction data processing device such as electronic wallet system or post-payment
  • the payment transaction data processing system sends a risk inquiry request.
  • the postpaid transaction data processing terminal can send risk query information to the postpaid transaction data processing system through the risk consulting interface in FIG. 6, and the postpaid transaction data processing system will receive the risk.
  • the query information is sent to the postpaid transaction data processing device.
  • the risk query information may include: a product type, a transaction amount, and a transaction user identifier.
  • the specific content of the risk query information may refer to the records in the foregoing embodiments, and details are not described herein again.
  • T4 Receive a default evaluation result of the postpaid transaction sent by the postpaid transaction data processing device.
  • the postpaid transaction data processing device predicts the current default evaluation result of the postpaid transaction based on the received risk query information, and sends the determined default evaluation result To the post-payment transaction data processing terminal of the transaction merchant.
  • the transaction merchant can determine whether to accept the current postpaid transaction based on the default assessment result received by the postpaid transaction data processing terminal. For example, the default assessment result and the size of a preset probability threshold can be used to determine whether to accept the postpaid transaction. If the result of the default assessment is less than a preset probability threshold, the transaction merchant can accept such a risk, and then the postpaid transaction data processing terminal may send the postpaid transaction processing request information to the postpaid transaction processing device.
  • the post-paid transaction processing device can perform payment processing after receiving the request information of the post-paid transaction sent by the transaction merchant through the post-paid transaction processing terminal, for example, it can be generated from the transaction user's account at the payment time agreed upon by the post-paid transaction. The transaction amount is deducted and transferred to the account of the trading merchant.
  • the transaction merchant If the result of the default assessment received is relatively high, and the transaction merchant believes that it cannot accept the default risk of the current postpaid transaction, it can reject the postpaid transaction, and can send a request message to the electronic wallet system through the postpaid transaction terminal to reject the postpaid transaction. .
  • the analysis of the post-paid transaction's transaction product type and the payment channel of the transaction user is performed before the post-paid transaction is concluded to determine the default evaluation result of the post-paid transaction.
  • the transaction merchant is based on the determined default evaluation result. Determine whether to accept the postpaid transaction. Improved the accuracy of risk prevention and control for post-paid transactions, reduced capital losses for merchants, and improved the user experience of post-paid transactions.
  • FIG. 9 is a schematic structural diagram of a post-paid transaction data processing terminal in an embodiment of the present specification. As shown in FIG. 9, in an embodiment of the specification, a post-paid transaction data processing terminal may be provided, including:
  • the query information sending module 91 may be configured to receive a postpaid transaction service request, and send risk query information to a postpaid transaction data processing device, where the risk query information includes: a product type, a transaction amount, and a transaction user identifier;
  • the default information receiving module 92 may be configured to receive a default evaluation result of the postpaid transaction sent by the postpaid transaction data processing device;
  • the transaction passing module 93 may be configured to send request information for passing the postpaid transaction to the postpaid transaction data processing device if the default evaluation result is less than a preset probability threshold.
  • the analysis of the post-paid transaction's transaction product type and the payment channel of the transaction user is performed before the post-paid transaction is concluded to determine the default evaluation result of the post-paid transaction.
  • the transaction merchant is based on the determined default evaluation result. Determine whether to accept the postpaid transaction. Improved the accuracy of risk prevention and control for post-paid transactions, reduced capital losses for merchants, and improved the user experience of post-paid transactions.
  • a postpaid transaction data processing device such as a smart phone terminal, a tablet computer, a smart wearable device, etc.
  • a postpaid transaction data processing device including: at least one processor and a processor for storing executable instructions of the processor.
  • a memory, and the processor implements the post-paid transaction data processing method in the foregoing embodiment when the processor executes the instruction, such as:
  • the risk inquiry information After receiving the postpaid transaction service request, sending risk inquiry information to the postpaid transaction data processing device, the risk inquiry information includes: product type, transaction amount, and transaction user identification;
  • a computer storage medium may also be provided, in which a computer program is stored.
  • the method for processing video data in the foregoing embodiment is implemented.
  • the following method may be implemented:
  • the risk inquiry information After receiving the postpaid transaction service request, sending risk inquiry information to the postpaid transaction data processing device, the risk inquiry information includes: product type, transaction amount, and transaction user identification;
  • the storage medium may include a physical device for storing information. Generally, the information is digitized and then stored by using a medium such as electricity, magnetism, or optics.
  • the storage medium may include: a device that stores information using electrical energy, such as various types of memory, such as RAM, ROM, and the like; a device that uses magnetic energy to store information, such as hard disk, floppy disk, magnetic tape, magnetic core memory, magnetic bubble memory, U disk; a device that uses optical means to store information, such as a CD or DVD.
  • electrical energy such as various types of memory, such as RAM, ROM, and the like
  • a device that uses magnetic energy to store information such as hard disk, floppy disk, magnetic tape, magnetic core memory, magnetic bubble memory, U disk
  • a device that uses optical means to store information such as a CD or DVD.
  • quantum memory graphene memory, and so on.
  • the method or device described in the foregoing embodiments provided in this specification may implement business logic through a computer program and record it on a storage medium, and the storage medium may be read and executed by a computer to achieve the effect of the solution described in the embodiments of this specification.
  • the above-mentioned postpaid transaction data processing method or device provided in the embodiments of this specification may be implemented by a processor executing corresponding program instructions in a computer, such as using a C ++ language of a Windows operating system on a PC, a Linux system, or other such as Use android, iOS system programming language to realize in the intelligent terminal, and realize the processing logic based on quantum computer.
  • the embodiments of the present specification are not limited to situations that must conform to industry communication standards, standard computer data processing and data storage rules, or one or more embodiments described in this specification. Certain industry standards or implementations that are slightly modified based on implementations described in custom methods or embodiments can also achieve the same, equivalent or similar, or predictable implementation effects of the above embodiments. The embodiments obtained by applying these modified or deformed data acquisitions, storages, judgments, and processing methods may still fall within the scope of the optional implementations of the examples in this specification.
  • a programmable logic device Programmable Logic Device (PLD)
  • PLD Programmable Logic Device
  • FPGA Field Programmable Gate Array
  • HDL Hardware Description Language
  • VHDL Very-High-Speed Integrated Circuit Hardware Description Language
  • Verilog Verilog
  • the controller may be implemented in any suitable manner, for example, the controller may take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor. , Logic gates, switches, Application Specific Integrated Circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, With the Microchip PIC18F26K20 and Silicone Labs C8051F320, the memory controller can also be implemented as part of the control logic of the memory.
  • the controller may take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor. , Logic gates, switches, Application Specific Integrated Circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are
  • controller logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded controllers by logically programming the method steps. Microcontrollers, etc. to achieve the same function. Therefore, such a controller can be regarded as a hardware component, and a device included in the controller for implementing various functions can also be regarded as a structure within the hardware component. Or even, the means for implementing various functions can be regarded as a structure that can be both a software module implementing the method and a hardware component.
  • the system, device, module, or unit described in the foregoing embodiments may be specifically implemented by a computer chip or entity, or a product with a certain function.
  • a typical implementation device is a computer.
  • the computer may be, for example, a personal computer, a laptop computer, a vehicle-mounted human-computer interaction device, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, and a tablet.
  • a computer, a wearable device, or a combination of any of these devices may be, for example, a personal computer, a laptop computer, a vehicle-mounted human-computer interaction device, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, and a tablet.
  • a computer, a wearable device, or a combination of any of these devices are examples of any of these devices.
  • each module may be implemented in the same or multiple software and / or hardware, or the module that implements the same function may be implemented by a combination of multiple submodules or subunits, etc. .
  • the device embodiments described above are only schematic.
  • the division of the unit is only a logical function division.
  • multiple units or components may be combined or integrated.
  • the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or units, which may be electrical, mechanical or other forms.
  • These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to work in a particular manner such that the instructions stored in the computer-readable memory produce a manufactured article including an instruction device, the instructions
  • the device implements the functions specified in one or more flowcharts and / or one or more blocks of the block diagram.
  • These computer program instructions can also be loaded on a computer or other programmable data processing device, so that a series of steps can be performed on the computer or other programmable device to produce a computer-implemented process, which can be executed on the computer or other programmable device.
  • the instructions provide steps for implementing the functions specified in one or more flowcharts and / or one or more blocks of the block diagrams.
  • a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.
  • processors CPUs
  • input / output interfaces output interfaces
  • network interfaces network interfaces
  • memory volatile and non-volatile memory
  • Memory may include non-persistent memory, random access memory (RAM), and / or non-volatile memory in computer-readable media, such as read-only memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
  • RAM random access memory
  • ROM read-only memory
  • flash RAM flash memory
  • Computer-readable media includes both permanent and non-persistent, removable and non-removable media.
  • Information can be stored by any method or technology.
  • Information may be computer-readable instructions, data structures, modules of a program, or other data.
  • Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), and read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, read-only disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, Magnetic tape cartridges, magnetic tape magnetic disk storage, graphene storage, or other magnetic storage devices or any other non-transmission media can be used to store information that can be accessed by computing devices.
  • computer-readable media does not include temporary computer-readable media, such as modulated data signals and carrier waves.
  • one or more embodiments of the present specification may be provided as a method, a system, or a computer program product. Therefore, one or more embodiments of the present specification may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, one or more embodiments of the present specification may adopt a computer program implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code therein. The form of the product.
  • computer-usable storage media including but not limited to disk storage, CD-ROM, optical storage, etc.
  • One or more embodiments of the specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules.
  • program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types.
  • One or more embodiments of the present specification may also be practiced in distributed computing environments in which tasks are performed by remote processing devices connected through a communication network.
  • program modules may be located in local and remote computer storage media, including storage devices.

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Abstract

本说明书提供一种后付费交易数据处理方法、装置、处理设备、及服务器。所述方法包括：接收风险查询信息，所述风险查询信息包括：交易金额、交易用户标识；根据所述交易用户标识获取所述交易用户标识对应的支付渠道；基于所述支付渠道和所述交易金额，确定所述支付渠道对所述交易金额的可支付预测值；根据所述可支付预测值，确定出后付费交易的违约评估结果。利用本说明书中各个实施例，提高了后付费交易违约预测的准确性和精度，提高了后付费交易风险防控的可靠性，降低了后付费交易的风险，减少了后付费交易的资金损失，提高了后付费交易的用户体验。

Description

后付费交易数据处理方法、装置、处理设备、及服务器 技术领域
本说明书属于风险防控技术领域，尤其涉及一种后付费交易数据处理方法及装置。
背景技术
随着科学技术的发展，使用电子钱包进行先享后付进行交易的用户越来越多，商户开通先享后付服务后，可以通过第三方电子钱包进行收款。
由于用户在电子钱包里可能会有很多资金渠道，电子钱包与商户之间的信息不能互通，在先享后付的模式下，商户不能确定用户的电子钱包中的所有资金渠道是否能够支付购买商品的金额。欺诈分子可能会利用没有钱或者钱比较少的电子钱包账户在商户处购买先享后付的服务或者商品，当商户向电子钱包平台请求扣款时，可能就会发生扣款失败。
发明内容
本说明书目的在于提供一种后付费交易数据处理方法及装置，提高了后付费交易风险防控的可靠性，降低了后付费交易的风险，减少了后付费交易的资金损失，提高了后付费交易的用户体验。
第一方面本说明书实施例提供了一种后付费交易数据处理方法，包括：
接收风险查询信息，所述风险查询信息包括：交易金额、交易用户标识；
根据所述交易用户标识获取所述交易用户标识对应的支付渠道；
基于所述支付渠道和所述交易金额，确定所述支付渠道对所述交易金额的可支付预测值；
根据所述可支付预测值，确定出后付费交易的违约评估结果。
进一步地，所述方法的另一个实施例中，所述风险查询信息还包括商品类型；
相应地，所述方法还包括：
确定出所述商品类型对应的风险系数；
相应地，所述确定出后付费交易的违约评估结果，包括：
根据所述风险系数和所述可支付预测值，确定出所述后付费交易的违约评估结果。
进一步地，所述方法的另一个实施例中，所述基于所述支付渠道和所述交易金额，确定所述支付渠道对所述交易金额的可支付预测值，包括：
获取所述支付渠道的历史账单数据；
根据所述历史账单数据，确定出在交易时间时所述支付渠道对应的预测资金数；
根据所述交易金额、所述支付渠道的预测资金数，确定出所述支付渠道对所述交易金额的可支付预测值。
进一步地，所述方法的另一个实施例中，所述基于所述支付渠道和所述交易金额，确定所述支付渠道对所述交易金额的可支付预测值，还包括：
根据所述历史账单数据，获取所述支付渠道对应的交易用户的交易行为；
根据所述交易行为、所述交易金额、所述支付渠道的预测资金数，确定出所述支付渠道对所述交易金额的可支付预测值。
进一步地，所述方法的另一个实施例中，所述确定出所述商品类型对应的风险系数，包括：
获取历史交易数据，根据所述历史交易数据获取所述商品类型对应的违约交易比例；
根据所述违约交易比例，确定出所述商品类型对应的风险系数。
进一步地，所述方法的另一个实施例中，所述根据所述可支付预测值，确定出后付费交易的违约评估结果，包括：
根据所述可支付预测值，利用决策树、逻辑回归中的至少一种方法，确定出后付费交易的违约评估结果。
进一步地，所述方法的另一个实施例中，所述方法还包括：
将所述违约评估结果发送至后付费交易数据处理终端，所述后付费交易数据处理终端与所述后付费交易的交易商户对应；
接收通过所述后付费交易的请求信息，对所述后付费交易进行支付处理。
第二方面，本说明书提供了后付费交易数据处理装置，包括：
所述方法还包括：
将所述违约评估结果发送至后付费交易数据处理终端，所述后付费交易数据处理终端与所述后付费交易的交易商户对应；
接收通过所述后付费交易的请求信息，对所述后付费交易进行支付处理。
进一步地，所述装置的另一个实施例中，所述风险查询信息还包括商品类型；
相应地，所述装置还包括：
商品风险分析模块，用于确定出所述商品类型对应的风险系数；
相应地，所述决策模块具体用于：
根据所述风险系数和所述可支付预测值，确定出所述后付费交易的违约评估结果。
进一步地，所述装置的另一个实施例中，所述额度预测模块具体用于：
获取所述支付渠道的历史账单数据；
根据所述历史账单数据，确定出在交易时间时所述支付渠道对应的预测资金数；
根据所述支付渠道的预测资金数和所述交易金额，确定出所述支付渠道对所述交易金额的可支付预测值。
进一步地，所述装置的另一个实施例中，所述额度预测模块具体用于：
根据所述历史账单数据，获取所述支付渠道对应的交易用户的交易行为；
根据所述交易行为、所述交易金额、所述支付渠道的预测资金数，确定出所述支付渠道对所述交易金额的可支付预测值。
进一步地，所述装置的另一个实施例中，所述商品风险分析模块具体用于：
获取历史交易数据，根据所述历史交易数据获取所述商品类型对应的违约交易比例；
根据所述违约交易比例，确定出所述商品类型对应的风险系数。
进一步地，所述装置的另一个实施例中，所述决策模块具体用于：
根据所述可支付预测值，利用决策树、逻辑回归中的至少一种方法，确定出后付费交易的违约评估结果。
进一步地，所述装置的另一个实施例中，所述装置还包括支付处理模块，用于：
将所述违约评估结果发送至后付费交易数据处理终端，所述后付费交易数据处理终端与所述后付费交易的交易商户对应；
接收通过所述后付费交易的请求信息，对所述后付费交易进行支付处理。
第三方面，本说明书实施例提供了后付费交易数据处理设备，包括：至少一个处理器以及用于存储处理器可执行指令的存储器，所述处理器执行所述指令时实现上述第一方面的后付费交易数据处理方法。
第四方面，本说明书实施例提供了后付费交易数据处理系统，包括：后付费交易数据处理装置、风险咨询接口、支付接口，所述后付费交易数据处理系统中包括至少一个处理器以及用于存储处理器可执行指令的存储器，所述处理器执行所述指令时实现上述第一方面的后付费交易数据处理方法。
第五方面，本说明书实施例提供了一种后付费交易数据处理方法，包括：
接收后付费交易服务请求，向后付费交易数据处理装置发送风险查询信息，所述风险查询信息包括：商品类型、交易金额、交易用户标识；
接收所述后付费交易数据处理装置发送的后付费交易的违约评估结果；
若所述违约评估结果小于预设概率阈值，则向所述后付费交易数据处理装置发送通过所述后付费交易的请求信息。
第六方面，本说明书实施例提供了一种后付费交易数据处理终端，包括：
查询信息发送模块，用于接收后付费交易服务请求，向后付费交易数据处理装置发送风险查询信息，所述风险查询信息包括：商品类型、交易金额、交易用户标识；
违约信息接收模块，用于接收所述后付费交易数据处理装置发送的后付费交易的违约评估结果；
交易通过模块，用于若所述违约评估结果小于预设概率阈值，则向所述后付费交易数据处理装置发送通过所述后付费交易的请求信息。
第七方面，本说明书实施例提供了一种后付费交易数据处理设备，包括：至少一个处理器以及用于存储处理器可执行指令的存储器，所述处理器执行所述指令时实现：
接收后付费交易服务请求，向后付费交易数据处理装置发送风险查询信息，所述风险查询信息包括：商品类型、交易金额、交易用户标识；
接收所述后付费交易数据处理装置发送的后付费交易的违约评估结果；
若所述违约评估结果小于预设概率阈值，则向所述后付费交易数据处理装置发送通 过所述后付费交易的请求信息。
第八方面，本说明书实施例提供了一种计算机存储介质，其上存储有计算机程序，所述计算机程序被执行时，实现下述方法：
接收后付费交易服务请求，向后付费交易数据处理装置发送风险查询信息，所述风险查询信息包括：商品类型、交易金额、交易用户标识；
接收所述后付费交易数据处理装置发送的后付费交易的违约评估结果；
若所述违约评估结果小于预设概率阈值，则向所述后付费交易数据处理装置发送通过所述后付费交易的请求信息。
第九方面，本说明书实施例提供了一种电子钱包的数据处理服务器，包括至少一个处理器以及用于存储处理器可执行指令的存储器，所述处理器执行所述指令时实现上述第一方面的后付费交易数据处理方法。
本说明书提供的后付费交易数据处理方法、装置、处理设备、服务器，在后付费交易成交之前，通过后付费交易的交易用户的支付渠道进行分析，确定支付渠道对交易金额的可支付预测值，根据支付渠道的可支付预测值，确定出交易用户的账户可以支付后付费交易的交易金额的违约评估结果。基于支付渠道进行支付可能性的预测分析，提高了后付费交易违约预测的准确性和精度，提高了后付费交易风险防控的可靠性，降低了后付费交易的风险，减少了后付费交易的资金损失，提高了后付费交易的用户体验。
附图说明
为了更清楚地说明本说明书实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本说明书中记载的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动性的前提下，还可以根据这些附图获得其他的附图。
图1是本说明书提供的一个实施例中的后付费交易数据处理方法的流程示意图；
图2是本说明书又一个实施例中后付费交易数据处理方法的流程示意图；
图3是本说明书提供的后付费交易数据处理装置一个实施例的模块结构示意图；
图4是本说明书又一个实施例提供的后付费交易数据处理装置的模块结构示意图；
图5是本说明书又一个实施例提供的后付费交易数据处理装置的模块结构示意图；
图6是本说明书一个实施例中后付费交易数据处理系统的结构示意图；
图7是应用本发明实施例的一种电子钱包的数据处理服务器的硬件结构框图；
图8是本说明书又一个实施例中后付费交易数据处理方法的流程示意图；
图9是本说明书一个实施例中后付费交易数据处理终端的结构示意图。
具体实施方式
为了使本技术领域的人员更好地理解本说明书中的技术方案，下面将结合本说明书实施例中的附图，对本说明书实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本说明书一部分实施例，而不是全部的实施例。基于本说明书中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都应当属于本说明书保护的范围。
越来越多的商户会提供先享后付的服务，在使用先享后付服务时，通常商户会接入第三方电子钱包作为付款方式之一。为了提高用户的购物和支付体验，用户、商户、电子钱包会签订免密代扣协议，允许商户自动扣减用户在第三方电子钱包的资产。在进行先享后付交易时，用户可以先试用商品一定时间，在一定时间之后，用户没有退回商品，商户可以通过第三方电子钱包自动扣减电子钱包中的相应金额。
由于商户和第三方电子钱包平台属于独立运营的主体，数据信息不是互通的，在进行免密代扣时，可能会出现电子钱包中的资金不足，导致扣款失败，造成商户的NSF(Non-sufficient fund risk，资金风险不足)资金损失。
本说明书实施例提供的后付费交易数据处理方法，在进行先享后付交易时，可以在交易成交之前对交易数据进行处理，判断当前交易的违约评估结果，商户可以根据违约评估结果确定是否通过当前的先享后付交易。提高了先享后付交易的风险防控力度，减少了资金损失，提高了用户体验。
本申请实施例中先享后付交易数据处理方法可以在客户端上进行如：智能手机、平板电脑、智能可穿戴设备(智能手表、虚拟现实眼镜、虚拟现实头盔等)等电子设备。
具体地，图1是本说明书提供的一个实施例中的后付费交易数据处理方法的流程示意图，如图1所示，本说明书实施例提供的后付费交易数据处理方法，包括：
S2、接收风险查询信息，所述风险查询信息包括：交易金额、交易用户标识。
在具体的实施过程中，商户在接收到一个后付费的服务请求后，可以先调用后付费交易数据处理系统的风险咨询接口，通过风险咨询接口将风险查询信息发送至后付费交易数据处理装置。风险查询信息可以包括当前的后付费交易的相关信息如：交易金额、交易用户标识、交易商户标识、交易时间、交易地点等。交易用户标识可以包括：交易用户的账户名(如：电子钱包的账户名、交易平台的账户名等)，交易商户标识可以包括交易商户的账户名(如：交易商家的账户名)。当然，风险查询信息还可以包括其他的信息如：商品类型，风险查询信息的具体内容可以根据实际需要进行调整，本说明书实施例不作具体限定。
风险查询信息的发送和接收可以通过无线网络传输、蓝牙、有线网络等方式，具体可以根据实际需要进行设置，本说明书实施例不作具体限定。
S4、根据所述交易用户标识获取所述交易用户标识对应的支付渠道。
在具体的实施过程中，接收到风险查询信息后，可以根据风险查询信息中的交易用户标识，查询交易用户标识对应的交易用户的账户的支付渠道。支付渠道可以包括电子钱包中的支付渠道如：绑定的银行卡账户、信用卡账户、预付卡、红包券等。
S6、基于所述支付渠道和所述交易金额，确定所述支付渠道对所述交易金额的可支付预测值。
可支付预测值可以表示支付渠道可能对后付费交易的交易金额的可以支付的能力，可支付预测值可以采用百分比或倍数值表示，当然，也可以采用其他方式表示。获取到交易用户标识对应的支付渠道后，可以根据支付渠道中的资金流水或其他信息，以及当前的后付费交易的交易金额，确定出支付渠道对交易金额的可支付预测值。如：若支付渠道中的金额为交易金额的一半，则可以认为后付费交易的用户的账户中可能只能支付交易金额的一半，可以将可支付预测值确定为50％。
本说明书一个实施例中，所述基于所述支付渠道和所述交易金额，确定所述支付渠道对所述交易金额的可支付预测值，包括：
获取所述支付渠道的历史账单数据；
根据所述历史账单数据，确定出在交易时间时所述支付渠道对应的预测资金数；
根据所述交易金额、所述支付渠道的预测资金数，确定出所述支付渠道对所述交易金额的可支付预测值。
在具体的实施过程中，历史账单数据可以表示指定时间内支付渠道的资金流动情况，可以包括支付渠道的资金流入和资金流出，如：银行卡A在交易时间前一个月内的资金流动情况。可以通过获取支付渠道在交易时间前预设时间段内的账单记录，获取到支付渠道的历史账单数据。在查询到交易用户标识的支付渠道后，可以获取支付渠道的历史账单数据，获取支付渠道的资金流历史，根据支付渠道的资金流历史可以预测出当前的交易时间时支付渠道的预测资金数。如：可以根据支付渠道的资金流历史，使用时序分析和预测算法(如：回归法、平均法、模型预测等)，计算出当前的交易时间下，支付渠道对交易金额的可覆盖头寸，获得支付渠道的预测资金数。时序分析可以用于计算一个变量的当前值与过去值之间的关系，可以利用时序分析根据支付渠道中过去的资金流水，预测当前的交易时间支付渠道的预测资金数。根据确定出的支付渠道的预测资金数，对比后付费交易的交易金额，可以确定出支付渠道对交易金额的可支付预测值。
若进行后付费交易的交易用户的账户中有多个支付渠道，则可以分别获取各个支付渠道的历史账单数据，根据各个支付渠道的历史账单数据，可以确定出在交易时间时各个支付渠道对应的预测资金数，进一步确定出各个支付渠道的可支付预测值。可以将各个支付渠道的可支付预测值进行叠加，确定出交易用户的所有支付渠道的总的可支付预测值。当然，也可以根据各个支付渠道对应的预测资金数的总和，确定出支付渠道的总的可支付预测值。
可以预先根据支付渠道的历史账单数据，构建出资金数预测模型，即分析总结出支付渠道中资金数随时间变化的关系。不同的支付渠道的资金数预测模型可以相同，也可以不同，具体可以根据各个支付渠道的交易习惯，资金流动特征进行构建，利用构建的资金数预测模型对各个支付渠道的资金数进行预测。
例如：若后付费交易的交易用户的账户中有两个支付渠道，银行卡A和信用卡B，可以分别获取银行卡A的历史账单数据、信用卡B的历史账单数据，根据获取到的历史账单数据，可以分别获取到银行卡A和信用卡B的资金流历史。可以采用时序分析和预测算法，绘制出银行卡A和信用卡B的资金随时间变化的曲线，可以根据资金随时间变化的曲线分析银行卡A和信用卡B中的资金流随时间的变化关系，预测出当前的交易时间时，银行卡A和信用卡B中的预测资金数。根据后付费交易的交易金额、银行卡A和信用卡B的预测资金数，分别确定出银行卡A和信用卡B的可支付预测值。将银行卡A和信用卡B的可支付预测值进行叠加，确定出当前的后付费交易的交易用户的账户的总的可支付预测值。
通过分析各个支付渠道的历史账单数据，准确的确定出各支付渠道对当前的后付费交易的可支付预测值，为后续当前的后付费交易的违约评估结果的预测提供了准确的数据基础。
本说明书一个实施例中，所述基于所述支付渠道和所述交易金额，确定所述支付渠道对所述交易金额的可支付预测值，还包括：
根据所述历史账单数据，获取所述支付渠道对应的交易用户的交易行为；
根据所述交易行为、所述交易金额、所述支付渠道的预测资金数，确定出所述支付渠道对所述交易金额的可支付预测值。
在具体的实施过程中，可以根据各个支付渠道的历史账单数据，获取后付费交易的交易用户使用支付渠道进行交易的交易行为。交易用户可以表示后付费交易中购买商品的用户，即交易用户标识对应的用户。交易行为可以包括：消费的可持续性、是否出现过违约交易(如：逾期未付款)等。可以利用获取到的交易行为、支付渠道的预测资金数以及交易金额，确定出支付渠道对交易金额的可支付预测值。例如：可以根据历史数据，分析用户的交易行为、预测资金数，与最终可支付金额之间的函数关系，可以通过模型训练等方法，构建出可支付预测值的预测模型。利用预测模型可以确定出支付渠道的可支付金额，进一步确定出支付渠道对交易金额的可支付预测值。
例如：若根据支付渠道的历史账单数据，获得交易用户的支付渠道没有固定的资金流入，支付渠道内的资金数波动比较大，并且用户的消费时间的间断性比较大，可以认为用户的消费可持续性比较差。若支付渠道对应的预测资金数A，综合考虑用户的交易行为，可以确定交易用户的支付渠道实际能够支付的资金数要低于A。可以利用预测算法等，综合分析预测资金数和用户的交易行为，确定出支付渠道对交易金额的可覆盖预测值。
此外，还可以根据交易用户的交易行为，确定出交易用户的信用等级或信用评分，为确定支付渠道对交易金额的可覆盖预测值提供参考依据。
本说明书实施例，通过获取交易用户支付渠道的历史账单数据，分析交易用户的交易行为，基于交易行为、以及支付渠道中的预测资金数，预测支付渠道对交易金额的可支付预测值。结合用户的交易行为，可以更准确的分析出用户可能违约的概率，为后付费交易的风险防控提供了准确的数据基础。
S8、根据所述可支付预测值，确定出后付费交易的违约评估结果。
违约评估结果可以表示后付费交易的交易用户在进行付款时，账户资金不足或没有付款金额的可能性，违约评估结果可以采用违约概率或违约等级的方式。如：可以将违约等级划分为10级，等级越高表示交易出现违约的风险越大，或者可以将违约评估结果使用概率值的方式表达，概率越大，则表示交易出现违约的风险越大。确定出支付渠道对支付金额的可支付预测值后，可以利用预测算法等方法确定出当前的后付费交易的违约评估结果。例如：可以通过数值模拟、模型分析等方式分析可支付预测值与违约概率之间的函数关系，如：可以根据后付费交易的历史交易数据，通过模型训练，构建出违约概率预测模型，违约概率预测模型可以包括可支付预测值与违约概率之间的函数关系。可以将当前的后付费交易的可支付预测值输入违约概率预测模型，确定出对应的违约概率。
本说明书一个实施例中，可以利用决策树、逻辑回归中的至少一种方法，确定出后付费交易的违约评估结果。决策树可以表示一种预测模型，可以表示对象属性与对象值之间的一种映射关系。逻辑回归是一种广义的线性回归分析模型，常用于数据挖掘，疾病自动诊断，经济预测等领域，可以用于预测某件事发生的概率。利用决策树、逻辑回归方法可以实现后付费交易违约评估结果的准确预测，为后付费交易的风险防控提供了准确的数据基础。
本说明书一个实施例中，在预测出后付费交易对应的违约评估结果后，可以将获得的违约评估结果发送至后付费交易中的交易商户对应的后付费交易数据处理终端上，后付费交易数据处理终端可以是智能手机、平板电脑等电子设备。后付费交易数据处理终端接收到违约评估结果后，可以判断当前的违约评估结果的风险是否能够接受，若可以接受，则可以向后付费交易数据处理装置发送通过该后付费交易的请求信息。后付费交易数据处理装置可以对该后付费交易进行支付处理，如：可以在后付费交易预定的付费时间从交易用户的账户中代扣交易金额，并转入交易商户的账户。
本说明书实施例提供的后付费交易数据处理方法，在后付费交易成交之前，通过后付费交易的交易用户的支付渠道进行分析，确定支付渠道对交易金额的可支付预测值，根据支付渠道的可支付预测值，确定出交易用户的账户可以支付后付费交易的交易金额的违约评估结果。基于支付渠道进行支付可能性的预测分析，提高了后付费交易违约预测的准确性和精度，提高了后付费交易风险防控的可靠性，降低了后付费交易的风险，减少了后付费交易的资金损失，提高了后付费交易的用户体验。
图2是本说明书又一个实施例中后付费交易数据处理方法的流程示意图，如图2 所示，在上述实施例的基础上，本说明书一个实施例中，后付费交易数据处理方法还可以包括：
B2、接收风险查询信息，所述风险查询信息包括：商品类型、交易金额、交易用户标识。
B4、确定出所述商品类型对应的风险系数。
在具体的实施过程中，商品类型可以表示商品的种类、类别如：某品牌手机、某品牌服装等，还可以包括服务的类别如：家政服务、旅游服务等。不同的商品类型可能对应不同的风险系数，即不同的商品类型违约的风险可能不同。可以根据后付费交易的历史交易数据，分析不同的商品类型的风险形势，为不同的商品类型设置出不同的风险系数。
本说明书一个实施例中，所述确定出所述商品类型对应的风险系数，可以包括：
获取历史交易数据，根据所述历史交易数据获取所述商品类型对应的违约交易比例；
根据所述违约交易比例，确定出所述商品类型对应的风险系数。
在具体的实施过程中，历史交易数据可以包括预设时间段内(如：当前的交易时间之前的一个月内)的后付费交易的交易数据如可以包括：预设时间段内后付费交易的商品类型、交易金额、违约情况等。可以分析历史交易数据中当前的后付费交易的商品类型的违约交易比例，根据违约交易比例可以确定出当前的后付费交易的商品类型的风险系数。如：若当前的后付费交易的商品类型的违约比例为50％，则可以将该商品类型对应的风险系统确定为0.5，当然，也可以根据其他的方法确定商品类型的风险系数。风险系数还可以根据实时的历史交易数据进行调整更新。
分析不同商品类型对应的风险系数，为后续确定当前的后付费交易的违约评估结果提供了更加准确的数据基础。
B6、根据所述交易用户标识获取所述交易用户标识对应的支付渠道。
可以扫描后付费交易的交易用户的账户在电子钱包里的所有的支付渠道。
B8、基于所述支付渠道和所述交易金额，确定所述支付渠道对所述交易金额的可支付预测值。
可以根据各个支付渠道的历史账单数据，使用时序分析法和预测算法，分别计 算当前的交易时间下，各个支付渠道对交易金额的可支付预测值，进一步计算出所有支付渠道对应的可支付预测值。支付渠道对应的可支付预测值的确定方法可以参考上述实施例的介绍，此处不再赘述。
B10、根据所述风险系数和所述可支付预测值，确定出所述后付费交易的违约评估结果。
在确定当前的后付费交易的商品类型对应的风险系数，以及支付渠道对应的可支付预测值后，综合考虑商品类型的风险系数和支付渠道的可支付预测值，可以通过决策树或逻辑回归等方法，预测出后付费交易的违约评估结果，违约评估结果的表现形式可以参考上述实施例的记载，此处不再赘述。如：可以根据历史交易数据，构建商品类型的风险系数、支付渠道的可支付预测值，与后付费交易违约评估结果的预测模型或算法，通过预测模型或算法，预测出当前的后付费交易的违约评估结果。
本说明书实施例，在进行后付费交易前，综合考虑了后付费交易的商品类型的风险系数和交易用户对应的交易渠道的资金情况，能够准确的确定出后付费交易的违约评估结果，提高了后付费交易的风险防控，减少了资金损失，提高了用户的后付费交易体验。
本说明书中上述方法的各个实施例均采用递进的方式描述，各个实施例之间相同相似的部分互相参见即可，每个实施例重点说明的都是与其他实施例的不同之处。相关之处参见方法实施例的部分说明即可。
基于上述所述的后付费交易数据处理方法，本说明书一个或多个实施例还提供一种后付费交易数据处理装置。所述的装置可以包括使用了本说明书实施例所述方法的系统(包括分布式系统)、软件(应用)、模块、组件、服务器、客户端等并结合必要的实施硬件的装置。基于同一创新构思，本说明书实施例提供的一个或多个实施例中的装置如下面的实施例所述。由于装置解决问题的实现方案与方法相似，因此本说明书实施例具体的装置的实施可以参见前述方法的实施，重复之处不再赘述。以下所使用的，术语“单元”或者“模块”可以实现预定功能的软件和/或硬件的组合。尽管以下实施例所描述的装置较佳地以软件来实现，但是硬件，或者软件和硬件的组合的实现也是可能并被构想的。
具体地，图3是本说明书提供的后付费交易数据处理装置一个实施例的模块结构示意图，如图3所示，本说明书中提供的后付费交易数据处理装置包括：信息接收模 块31、支付渠道查询模块32、额度预测模块33、决策模块34，其中：
信息接收模块31，可以用于接收风险查询信息，所述风险查询信息包括：交易金额、交易用户标识；
支付渠道查询模块32，可以用于根据所述交易用户标识获取所述交易用户标识对应的支付渠道；
额度预测模块33，可以用于基于所述支付渠道和所述交易金额，确定所述支付渠道对所述交易金额的可支付预测值；
决策模块34，可以用于根据所述可支付预测值，确定出后付费交易的违约评估结果。
本说明书实施例提供的后付费交易数据处理装置，在后付费交易确定之前，通过后付费交易的交易用户的支付渠道进行分析，确定支付渠道对交易金额的可支付预测值，根据支付渠道的可支付预测值，确定出交易用户的账户可以支付后付费交易的交易金额的违约评估结果。基于支付渠道进行支付可能性的预测分析，提高了后付费交易违约预测的准确性和精度，提高了后付费交易风险防控的可靠性，降低了后付费交易的风险，减少了后付费交易的资金损失，提高了后付费交易的用户体验。
图4是本说明书又一个实施例提供的后付费交易数据处理装置的模块结构示意图，如图4所示，在上述实施例的基础上，所述风险查询信息还包括商品类型；
相应地，所述装置还包括：
商品风险分析模块41，可以用于确定出所述商品类型对应的风险系数；
相应地，所述决策模块34具体用于：
根据所述风险系数和所述可支付预测值，确定出所述后付费交易的违约评估结果。
本说明书实施例提供的后付费交易数据处理装置，在进行后付费交易前，综合考虑了后付费交易的商品类型的风险系数和交易用户对应的交易渠道的资金情况，能够准确的确定出后付费交易的违约评估结果，提高了后付费交易的风险防控，减少了资金损失，提高了用户的后付费交易体验。
在上述实施例的基础上，所述额度预测模块具体用于：
获取所述支付渠道的历史账单数据；
根据所述历史账单数据，确定出在交易时间时所述支付渠道对应的预测资金数；
根据所述支付渠道的预测资金数和所述交易金额，确定出所述支付渠道对所述交易金额的可支付预测值。
本说明书实施例提供的后付费交易数据处理装置，通过分析各个支付渠道的历史账单数据，确定出各支付渠道对当前的后付费交易的可支付预测值，为后续当前的后付费交易的违约评估结果的预测提供了准确的数据基础。
在上述实施例的基础上，所述额度预测模块具体用于：
根据所述历史账单数据，获取所述支付渠道对应的交易用户的交易行为；
根据所述交易行为、所述交易金额、所述支付渠道的预测资金数，确定出所述支付渠道对所述交易金额的可支付预测值。
本说明书实施例，通过获取交易用户支付渠道的历史账单数据，分析交易用户的交易行为，基于交易行为、以及支付渠道中的预测资金数，预测支付渠道对交易金额的可支付预测值。结合用户的交易行为，可以更准确的分析出用户可能违约的概率，为后付费交易的风险防控提供了准确的数据基础。
在上述实施例的基础上，所述商品风险分析模块具体用于：
获取历史交易数据，根据所述历史交易数据获取所述商品类型对应的违约交易比例；
根据所述违约交易比例，确定出所述商品类型对应的风险系数。
本说明书实施例，分析不同商品类型对应的风险系数，为后续确定当前的后付费交易的违约评估结果提供了更加准确的数据基础。
在上述实施例的基础上，所述决策模块具体用于：
根据所述可支付预测值，利用决策树、逻辑回归中的至少一种方法，确定出后付费交易的违约评估结果。
本说明书实施例，利用决策树、逻辑回归方法可以实现后付费交易违约评估结果的准确预测，为后付费交易的风险防控提供了准确的数据基础。
图5是本说明书又一个实施例提供的后付费交易数据处理装置的模块结构示意图，如图5所示，在上述实施例的基础上，所述装置还包括支付处理模块51，用于：
将所述违约评估结果发送至后付费交易数据处理终端，所述后付费交易数据处理终端与所述后付费交易的交易商户对应；
接收通过所述后付费交易的请求信息，对所述后付费交易进行支付处理。
本说明书实施例，将基于后付费交易的商品类型、交易金额、交易用户的支付渠道的相关信息等，确定出的违约评估结果发送至交易商户的客户端，使得交易商户可以根据当前的后付费交易的违约评估结果确定是否通过该交易，提高了后付费交易的风险防控的准确性，减少了交易商户的资金损失。
需要说明书的是，上述所述的装置根据方法实施例的描述还可以包括其他的实施方式。具体的实现方式可以参照相关方法实施例的描述，在此不作一一赘述。
本说明书实施例还提供一种后付费交易数据处理设备，包括：至少一个处理器以及用于存储处理器可执行指令的存储器，所述处理器执行所述指令时实现上述实施例中后付费交易数据处理方法，如：
接收风险查询信息，所述风险查询信息包括：交易金额、交易用户标识；
根据所述交易用户标识获取所述交易用户标识对应的支付渠道；
基于所述支付渠道和所述交易金额，确定所述支付渠道对所述交易金额的可支付预测值；
根据所述可支付预测值，确定出后付费交易的违约评估结果。
所述存储介质可以包括用于存储信息的物理装置，通常是将信息数字化后再以利用电、磁或者光学等方式的媒体加以存储。所述存储介质有可以包括：利用电能方式存储信息的装置如，各式存储器，如RAM、ROM等；利用磁能方式存储信息的装置如，硬盘、软盘、磁带、磁芯存储器、磁泡存储器、U盘；利用光学方式存储信息的装置如，CD或DVD。当然，还有其他方式的可读存储介质，例如量子存储器、石墨烯存储器等等。
需要说明的是，上述所述的处理设备根据方法实施例的描述还可以包括其他的实施方式，如还可以结合后付费交易的商品类型对应的风险系数和支付渠道的可支付预测值，综合预测出交易的违约评估结果。具体的实现方式可以参照相关方法实施例的描述，在此不作一一赘述。
本说明书还提供一种后付费交易数据处理系统，所述系统可以为单独的后付费 交易数据处理系统，也可以应用在多种数据分析处理系统中。所述的系统可以为单独的服务器，也可以包括使用了本说明书的一个或多个所述方法或一个或多个实施例装置的服务器集群、系统(包括分布式系统)、软件(应用)、实际操作装置、逻辑门电路装置、量子计算机等并结合必要的实施硬件的终端装置。所述线下购物推荐内容的生成系统可以包括至少一个处理器以及存储计算机可执行指令的存储器，所述处理器执行所述指令时实现上述任意一个或者多个实施例中所述方法的步骤。
图6是本说明书一个实施例中后付费交易数据处理系统的结构示意图，如图6所示，本说明书实施例中后付费交易数据处理系统可以表示图6中的电子钱包系统，具体可以包括：后付费交易数据处理装置、风险咨询接口、支付接口，后付费交易数据处理系统还可以包括交易商户对应的后付费交易数据处理终端。后付费交易数据处理装置可以包括商品风险分析模块、额度预测模块、决策模块，风险咨询接口和支付接口用于与交易商户对应的后付费交易数据处理终端进行数据通讯。所述后付费交易数据处理系统中包括至少一个处理器以及用于存储处理器可执行指令的存储器，所述处理器执行所述指令时实现上述实施例中后付费交易数据处理方法，如：
接收风险查询信息，所述风险查询信息包括：交易金额、交易用户标识；
根据所述交易用户标识获取所述交易用户标识对应的支付渠道；
基于所述支付渠道和所述交易金额，确定所述支付渠道对所述交易金额的可支付预测值；
根据所述可支付预测值，确定出后付费交易的违约评估结果。
本说明书实施例所提供的方法实施例可以在移动终端、计算机终端、服务器或者类似的运算装置中执行。以运行在服务器上为例，图7是应用本发明实施例的一种电子钱包的数据处理服务器的硬件结构框图。如图7所示，服务器10可以包括一个或多个(图中仅示出一个)处理器100(处理器100可以包括但不限于微处理器MCU或可编程逻辑器件FPGA等的处理装置)、用于存储数据的存储器200、以及用于通信功能的传输模块300。本邻域普通技术人员可以理解，图7所示的结构仅为示意，其并不对上述电子装置的结构造成限定。例如，服务器10还可包括比图7中所示更多或者更少的组件，例如还可以包括其他的处理硬件，如数据库或多级缓存、GPU，或者具有与图9所示不同的配置。
存储器200可用于存储应用软件的软件程序以及模块，如本说明书实施例中的 后付费交易数据处理方法对应的程序指令/模块，处理器100通过运行存储在存储器200内的软件程序以及模块，从而执行各种功能应用以及数据处理。存储器200可包括高速随机存储器，还可包括非易失性存储器，如一个或者多个磁性存储装置、闪存、或者其他非易失性固态存储器。在一些实例中，存储器200可进一步包括相对于处理器100远程设置的存储器，这些远程存储器可以通过网络连接至计算机终端10。上述网络的实例包括但不限于互联网、企业内部网、局域网、移动通信网及其组合。
传输模块300用于经由一个网络接收或者发送数据。上述的网络具体实例可包括计算机终端10的通信供应商提供的无线网络。在一个实例中，传输模块300包括一个网络适配器(Network Interface Controller，NIC)，其可通过基站与其他网络设备相连从而可与互联网进行通讯。在一个实例中，传输模块300可以为射频(Radio Frequency，RF)模块，其用于通过无线方式与互联网进行通讯。
需要说明的是，上述所述的处理系统、服务器根据方法实施例的描述还可以包括其他的实施方式，如还可以结合后付费交易的商品类型对应的风险系数和支付渠道的可支付预测值，综合预测出交易的违约评估结果。具体的实现方式可以参照相关方法实施例的描述，在此不作一一赘述。
图8是本说明书又一个实施例中后付费交易数据处理方法的流程示意图，如图8所示，本说明书一个实施例还提供了一种后付费交易中交易商户侧的交易数据处理方法，包括：
T2、接收后付费交易服务请求，向后付费交易数据处理装置发送风险查询信息，所述风险查询信息包括：商品类型、交易金额、交易用户标识。
交易商户在接收到后付费交易服务后，可以通过交易商户对应的后付费交易数据处理终端如：智能手机、平板电脑、智能随身设备等，向后付费交易数据处理装置如：电子钱包系统或后付费交易数据处理系统等发送风险查询请求。如：后付费交易数据处理终端在接收到后付费交易服务请求后，可以通过图6中的风险咨询接口向后付费交易数据处理系统发送风险查询信息，后付费交易数据处理系统将接收到的风险查询信息发送至后付费交易数据处理装置。风险查询信息中可以包括：商品类型、交易金额、交易用户标识，其中，风险查询信息的具体内容可以参考上述实施例的记载，此处不再赘述。
T4、接收所述后付费交易数据处理装置发送的后付费交易的违约评估结果。
交易商户将风险查询信息发送至后付费交易数据处理装置后，后付费交易数据 处理装置基于接收到的风险查询信息预测出当前的后付费交易的违约评估结果，并将确定出的违约评估结果发送至交易商户的后付费交易数据处理终端上。
T6、若所述违约评估结果小于预设概率阈值，则向所述后付费交易数据处理装置发送通过所述后付费交易的请求信息。
交易商户根据后付费交易数据处理终端接收到的违约评估结果，可以判断是否接受当前的后付费交易，如：可以通过违约评估结果与预设概率阈值的大小，决定是否接受该后付费交易。若违约评估结果小于预设概率阈值，交易商户能够接受这样的风险，则可以通过后付费交易数据处理终端向后付费交易处理装置发送通过当前的后付费交易的请求信息。后付费交易处理装置可以在接收到交易商户通过后付费交易处理终端发送的通过后付费交易的请求信息后，进行支付处理，如：可以在后付费交易约定的付费时间从交易用户的账户中代扣交易金额，并转入交易商户的账户。
若接收到的违约评估结果比较高，交易商户认为不能接受当前的后付费交易的违约风险，则可以拒绝该后付费交易，可以通过后付费交易终端向电子钱包系统发送拒绝后付费交易的请求信息。
本说明书实施例，通过在后付费交易成交前，对后付费交易的交易商品类型、交易用户的支付渠道进行分析，确定出该后付费交易的违约评估结果，交易商户基于确定出的违约评估结果确定是否接受该后付费交易。提高了后付费交易风险防控的准确性，减少了交易商户的资金损失，提高了后付费交易的用户体验。
图9是本说明书一个实施例中后付费交易数据处理终端的结构示意图，如图9所示，本说明书一个实施例中，可以提供一种后付费交易数据处理终端，包括：
查询信息发送模块91，可以用于接收后付费交易服务请求，向后付费交易数据处理装置发送风险查询信息，所述风险查询信息包括：商品类型、交易金额、交易用户标识；
违约信息接收模块92，可以用于接收所述后付费交易数据处理装置发送的后付费交易的违约评估结果；
交易通过模块93，可以用于若所述违约评估结果小于预设概率阈值，则向所述后付费交易数据处理装置发送通过所述后付费交易的请求信息。
本说明书实施例，通过在后付费交易成交前，对后付费交易的交易商品类型、交易用户的支付渠道进行分析，确定出该后付费交易的违约评估结果，交易商户基于确 定出的违约评估结果确定是否接受该后付费交易。提高了后付费交易风险防控的准确性，减少了交易商户的资金损失，提高了后付费交易的用户体验。
本说明书一个实施例中，还可以提供一种后付费交易数据处理设备(如：智能手机终端、平板电脑、智能穿戴设备等)，包括：至少一个处理器以及用于存储处理器可执行指令的存储器，所述处理器执行所述指令时实现上述实施例中后付费交易数据处理方法，如：
接收后付费交易服务请求，向后付费交易数据处理装置发送风险查询信息，所述风险查询信息包括：商品类型、交易金额、交易用户标识；
接收所述后付费交易数据处理装置发送的后付费交易的违约评估结果；
若所述违约评估结果小于预设概率阈值，则向所述后付费交易数据处理装置发送通过所述后付费交易的请求信息。
本说明书一个实施例中，还可以提供一种计算机存储介质，其上存储有计算机程序，所述计算机程序被执行时，实现上述实施例中视频数据的处理方法，例如可以实现如下方法：
接收后付费交易服务请求，向后付费交易数据处理装置发送风险查询信息，所述风险查询信息包括：商品类型、交易金额、交易用户标识；
接收所述后付费交易数据处理装置发送的后付费交易的违约评估结果；
若所述违约评估结果小于预设概率阈值，则向所述后付费交易数据处理装置发送通过所述后付费交易的请求信息。
所述存储介质可以包括用于存储信息的物理装置，通常是将信息数字化后再以利用电、磁或者光学等方式的媒体加以存储。所述存储介质有可以包括：利用电能方式存储信息的装置如，各式存储器，如RAM、ROM等；利用磁能方式存储信息的装置如，硬盘、软盘、磁带、磁芯存储器、磁泡存储器、U盘；利用光学方式存储信息的装置如，CD或DVD。当然，还有其他方式的可读存储介质，例如量子存储器、石墨烯存储器等等。
上述对本说明书特定实施例进行了描述。其它实施例在所附权利要求书的范围内。在一些情况下，在权利要求书中记载的动作或步骤可以按照不同于实施例中的顺序来执行并且仍然可以实现期望的结果。另外，在附图中描绘的过程不一定要求示出的特 定顺序或者连续顺序才能实现期望的结果。在某些实施方式中，多任务处理和并行处理也是可以的或者可能是有利的。
本说明书提供的上述实施例所述的方法或装置可以通过计算机程序实现业务逻辑并记录在存储介质上，所述的存储介质可以计算机读取并执行，实现本说明书实施例所描述方案的效果。
本说明书实施例提供的上述后付费交易数据处理方法或装置可以在计算机中由处理器执行相应的程序指令来实现，如使用windows操作系统的c++语言在PC端实现、linux系统实现，或其他例如使用android、iOS系统程序设计语言在智能终端实现，以及基于量子计算机的处理逻辑实现等。
需要说明的是说明书上述所述的装置、处理设备、计算机存储介质、系统根据相关方法实施例的描述还可以包括其他的实施方式，具体的实现方式可以参照方法实施例的描述，在此不作一一赘述。
本说明书中的各个实施例均采用递进的方式描述，各个实施例之间相同相似的部分互相参见即可，每个实施例重点说明的都是与其他实施例的不同之处。尤其，对于硬件+程序类实施例而言，由于其基本相似于方法实施例，所以描述的比较简单，相关之处参见方法实施例的部分说明即可。
本说明书实施例并不局限于必须是符合行业通信标准、标准计算机数据处理和数据存储规则或本说明书一个或多个实施例所描述的情况。某些行业标准或者使用自定义方式或实施例描述的实施基础上略加修改后的实施方案也可以实现上述实施例相同、等同或相近、或变形后可预料的实施效果。应用这些修改或变形后的数据获取、存储、判断、处理方式等获取的实施例，仍然可以属于本说明书实施例的可选实施方案范围之内。
在20世纪90年代，对于一个技术的改进可以很明显地区分是硬件上的改进(例如，对二极管、晶体管、开关等电路结构的改进)还是软件上的改进(对于方法流程的改进)。然而，随着技术的发展，当今的很多方法流程的改进已经可以视为硬件电路结构的直接改进。设计人员几乎都通过将改进的方法流程编程到硬件电路中来得到相应的硬件电路结构。因此，不能说一个方法流程的改进就不能用硬件实体模块来实现。例如，可编程逻辑器件(Programmable Logic Device,PLD)(例如现场可编程门阵列(Field Programmable Gate Array，FPGA))就是这样一种集成电路，其逻辑功能由用户对器件 编程来确定。由设计人员自行编程来把一个数字系统“集成”在一片PLD上，而不需要请芯片制造厂商来设计和制作专用的集成电路芯片。而且，如今，取代手工地制作集成电路芯片，这种编程也多半改用“逻辑编译器(logic compiler)”软件来实现，它与程序开发撰写时所用的软件编译器相类似，而要编译之前的原始代码也得用特定的编程语言来撰写，此称之为硬件描述语言(Hardware Description Language，HDL)，而HDL也并非仅有一种，而是有许多种，如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)等，目前最普遍使用的是VHDL(Very-High-Speed Integrated Circuit Hardware Description Language)与Verilog。本领域技术人员也应该清楚，只需要将方法流程用上述几种硬件描述语言稍作逻辑编程并编程到集成电路中，就可以很容易得到实现该逻辑方法流程的硬件电路。
控制器可以按任何适当的方式实现，例如，控制器可以采取例如微处理器或处理器以及存储可由该(微)处理器执行的计算机可读程序代码(例如软件或固件)的计算机可读介质、逻辑门、开关、专用集成电路(Application Specific Integrated Circuit，ASIC)、可编程逻辑控制器和嵌入微控制器的形式，控制器的例子包括但不限于以下微控制器：ARC 625D、Atmel AT91SAM、Microchip PIC18F26K20以及Silicone Labs C8051F320，存储器控制器还可以被实现为存储器的控制逻辑的一部分。本领域技术人员也知道，除了以纯计算机可读程序代码方式实现控制器以外，完全可以通过将方法步骤进行逻辑编程来使得控制器以逻辑门、开关、专用集成电路、可编程逻辑控制器和嵌入微控制器等的形式来实现相同功能。因此这种控制器可以被认为是一种硬件部件，而对其内包括的用于实现各种功能的装置也可以视为硬件部件内的结构。或者甚至，可以将用于实现各种功能的装置视为既可以是实现方法的软件模块又可以是硬件部件内的结构。
上述实施例阐明的系统、装置、模块或单元，具体可以由计算机芯片或实体实现，或者由具有某种功能的产品来实现。一种典型的实现设备为计算机。具体的，计算机例如可以为个人计算机、膝上型计算机、车载人机交互设备、蜂窝电话、相机电话、智能电话、个人数字助理、媒体播放器、导航设备、电子邮件设备、游戏控制台、平板计算机、可穿戴设备或者这些设备中的任何设备的组合。
虽然本说明书一个或多个实施例提供了如实施例或流程图所述的方法操作步骤， 但基于常规或者无创造性的手段可以包括更多或者更少的操作步骤。实施例中列举的步骤顺序仅仅为众多步骤执行顺序中的一种方式，不代表唯一的执行顺序。在实际中的装置或终端产品执行时，可以按照实施例或者附图所示的方法顺序执行或者并行执行(例如并行处理器或者多线程处理的环境，甚至为分布式数据处理环境)。术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、产品或者设备不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、产品或者设备所固有的要素。在没有更多限制的情况下，并不排除在包括所述要素的过程、方法、产品或者设备中还存在另外的相同或等同要素。第一，第二等词语用来表示名称，而并不表示任何特定的顺序。
为了描述的方便，描述以上装置时以功能分为各种模块分别描述。当然，在实施本说明书一个或多个时可以把各模块的功能在同一个或多个软件和/或硬件中实现，也可以将实现同一功能的模块由多个子模块或子单元的组合实现等。以上所描述的装置实施例仅仅是示意性的，例如，所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或单元的间接耦合或通信连接，可以是电性，机械或其它的形式。
本发明是参照根据本发明实施例的方法、装置(系统)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算 机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。
在一个典型的配置中，计算设备包括一个或多个处理器(CPU)、输入/输出接口、网络接口和内存。
内存可能包括计算机可读介质中的非永久性存储器，随机存取存储器(RAM)和/或非易失性内存等形式，如只读存储器(ROM)或闪存(flash RAM)。内存是计算机可读介质的示例。
计算机可读介质包括永久性和非永久性、可移动和非可移动媒体可以由任何方法或技术来实现信息存储。信息可以是计算机可读指令、数据结构、程序的模块或其他数据。计算机的存储介质的例子包括，但不限于相变内存(PRAM)、静态随机存取存储器(SRAM)、动态随机存取存储器(DRAM)、其他类型的随机存取存储器(RAM)、只读存储器(ROM)、电可擦除可编程只读存储器(EEPROM)、快闪记忆体或其他内存技术、只读光盘只读存储器(CD-ROM)、数字多功能光盘(DVD)或其他光学存储、磁盒式磁带，磁带磁磁盘存储、石墨烯存储或其他磁性存储设备或任何其他非传输介质，可用于存储可以被计算设备访问的信息。按照本文中的界定，计算机可读介质不包括暂存电脑可读媒体(transitory media)，如调制的数据信号和载波。
本领域技术人员应明白，本说明书一个或多个实施例可提供为方法、系统或计算机程序产品。因此，本说明书一个或多个实施例可采用完全硬件实施例、完全软件实施例或结合软件和硬件方面的实施例的形式。而且，本说明书一个或多个实施例可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。
本说明书一个或多个实施例可以在由计算机执行的计算机可执行指令的一般上下文中描述，例如程序模块。一般地，程序模块包括执行特定任务或实现特定抽象数据类型的例程、程序、对象、组件、数据结构等等。也可以在分布式计算环境中实践本本说明书一个或多个实施例，在这些分布式计算环境中，由通过通信网络而被连接的远程处理设备来执行任务。在分布式计算环境中，程序模块可以位于包括存储设备在内的本地和远程计算机存储介质中。
本说明书中的各个实施例均采用递进的方式描述，各个实施例之间相同相似的部分互相参见即可，每个实施例重点说明的都是与其他实施例的不同之处。尤其，对于 系统实施例而言，由于其基本相似于方法实施例，所以描述的比较简单，相关之处参见方法实施例的部分说明即可。在本说明书的描述中，参考术语“一个实施例”、“一些实施例”、“示例”、“具体示例”、或“一些示例”等的描述意指结合该实施例或示例描述的具体特征、结构、材料或者特点包含于本说明书的至少一个实施例或示例中。在本说明书中，对上述术语的示意性表述不必须针对的是相同的实施例或示例。而且，描述的具体特征、结构、材料或者特点可以在任一个或多个实施例或示例中以合适的方式结合。此外，在不相互矛盾的情况下，本领域的技术人员可以将本说明书中描述的不同实施例或示例以及不同实施例或示例的特征进行结合和组合。
以上所述仅为本说明书一个或多个实施例的实施例而已，并不用于限制本本说明书一个或多个实施例。对于本领域技术人员来说，本说明书一个或多个实施例可以有各种更改和变化。凡在本说明书的精神和原理之内所作的任何修改、等同替换、改进等，均应包含在权利要求范围之内。

Claims (21)

  1. 一种后付费交易数据处理方法，包括：
    接收风险查询信息，所述风险查询信息包括：交易金额、交易用户标识；
    根据所述交易用户标识获取所述交易用户标识对应的支付渠道；
    基于所述支付渠道和所述交易金额，确定所述支付渠道对所述交易金额的可支付预测值；
    根据所述可支付预测值，确定出后付费交易的违约评估结果。
  2. 如权利要求1所述的方法，所述风险查询信息还包括商品类型；
    相应地，所述方法还包括：
    确定出所述商品类型对应的风险系数；
    相应地，所述确定出后付费交易的违约评估结果，包括：
    根据所述风险系数和所述可支付预测值，确定出所述后付费交易的违约评估结果。
  3. 如权利要求1所述的方法，所述基于所述支付渠道和所述交易金额，确定所述支付渠道对所述交易金额的可支付预测值，包括：
    获取所述支付渠道的历史账单数据；
    根据所述历史账单数据，确定出在交易时间时所述支付渠道对应的预测资金数；
    根据所述交易金额、所述支付渠道的预测资金数，确定出所述支付渠道对所述交易金额的可支付预测值。
  4. 如权利要求3所述的方法，所述基于所述支付渠道和所述交易金额，确定所述支付渠道对所述交易金额的可支付预测值，还包括：
    根据所述历史账单数据，获取所述支付渠道对应的交易用户的交易行为；
    根据所述交易行为、所述交易金额、所述支付渠道的预测资金数，确定出所述支付渠道对所述交易金额的可支付预测值。
  5. 如权利要求2所述的方法，所述确定出所述商品类型对应的风险系数，包括：
    获取历史交易数据，根据所述历史交易数据获取所述商品类型对应的违约交易比例；
    根据所述违约交易比例，确定出所述商品类型对应的风险系数。
  6. 如权利要求1所述的方法，所述根据所述可支付预测值，确定出后付费交易的 违约评估结果，包括：
    根据所述可支付预测值，利用决策树、逻辑回归中的至少一种方法，确定出后付费交易的违约评估结果。
  7. 如权利要求1-6任一项所述的方法，所述方法还包括：
    将所述违约评估结果发送至后付费交易数据处理终端，所述后付费交易数据处理终端与所述后付费交易的交易商户对应；
    接收通过所述后付费交易的请求信息，对所述后付费交易进行支付处理。
  8. 一种后付费交易数据处理装置，包括：
    信息接收模块，用于接收风险查询信息，所述风险查询信息包括：交易金额、交易用户标识；
    支付渠道查询模块，用于根据所述交易用户标识获取所述交易用户标识对应的支付渠道；
    额度预测模块，用于基于所述支付渠道和所述交易金额，确定所述支付渠道对所述交易金额的可支付预测值；
    决策模块，用于根据所述可支付预测值，确定出后付费交易的违约评估结果。
  9. 如权利要求8所述的装置，所述风险查询信息还包括商品类型；
    相应地，所述装置还包括：
    商品风险分析模块，用于确定出所述商品类型对应的风险系数；
    相应地，所述决策模块具体用于：
    根据所述风险系数和所述可支付预测值，确定出所述后付费交易的违约评估结果。
  10. 如权利要求8所述的装置，所述额度预测模块具体用于：
    获取所述支付渠道的历史账单数据；
    根据所述历史账单数据，确定出在交易时间时所述支付渠道对应的预测资金数；
    根据所述支付渠道的预测资金数和所述交易金额，确定出所述支付渠道对所述交易金额的可支付预测值。
  11. 如权利要求10所述的装置，所述额度预测模块具体用于：
    根据所述历史账单数据，获取所述支付渠道对应的交易用户的交易行为；
    根据所述交易行为、所述交易金额、所述支付渠道的预测资金数，确定出所述支付渠道对所述交易金额的可支付预测值。
  12. 如权利要求9所述的装置，所述商品风险分析模块具体用于：
    获取历史交易数据，根据所述历史交易数据获取所述商品类型对应的违约交易比例；
    根据所述违约交易比例，确定出所述商品类型对应的风险系数。
  13. 如权利要求8所述的装置，所述决策模块具体用于：
    根据所述可支付预测值，利用决策树、逻辑回归中的至少一种方法，确定出后付费交易的违约评估结果。
  14. 如权利要求8-13任一项所述的装置，所述装置还包括支付处理模块，用于：
    将所述违约评估结果发送至后付费交易数据处理终端，所述后付费交易数据处理终端与所述后付费交易的交易商户对应；
    接收通过所述后付费交易的请求信息，对所述后付费交易进行支付处理。
  15. 一种后付费交易数据处理设备，包括：至少一个处理器以及用于存储处理器可执行指令的存储器，所述处理器执行所述指令时实现权利要求1-7任一项所述的方法。
  16. 一种后付费交易数据处理系统，包括：后付费交易数据处理装置、风险咨询接口、支付接口，所述后付费交易数据处理系统中包括至少一个处理器以及用于存储处理器可执行指令的存储器，所述处理器执行所述指令时实现权利要求1-7任一项所述的方法。
  17. 一种后付费交易数据处理方法，包括：
    接收后付费交易服务请求，向后付费交易数据处理装置发送风险查询信息，所述风险查询信息包括：商品类型、交易金额、交易用户标识；
    接收所述后付费交易数据处理装置发送的后付费交易的违约评估结果；
    若所述违约评估结果小于预设概率阈值，则向所述后付费交易数据处理装置发送通过所述后付费交易的请求信息。
  18. 一种后付费交易数据处理终端，包括：
    查询信息发送模块，用于接收后付费交易服务请求，向后付费交易数据处理装置发送风险查询信息，所述风险查询信息包括：商品类型、交易金额、交易用户标识；
    违约信息接收模块，用于接收所述后付费交易数据处理装置发送的后付费交易的违约评估结果；
    交易通过模块，用于若所述违约评估结果小于预设概率阈值，则向所述后付费交易数据处理装置发送通过所述后付费交易的请求信息。
  19. 一种后付费交易数据处理设备，包括：至少一个处理器以及用于存储处理器可执行指令的存储器，所述处理器执行所述指令时实现：
    接收后付费交易服务请求，向后付费交易数据处理装置发送风险查询信息，所述风险查询信息包括：商品类型、交易金额、交易用户标识；
    接收所述后付费交易数据处理装置发送的后付费交易的违约评估结果；
    若所述违约评估结果小于预设概率阈值，则向所述后付费交易数据处理装置发送通过所述后付费交易的请求信息。
  20. 一种计算机存储介质，其上存储有计算机程序，所述计算机程序被执行时，实现下述方法：
    接收后付费交易服务请求，向后付费交易数据处理装置发送风险查询信息，所述风险查询信息包括：商品类型、交易金额、交易用户标识；
    接收所述后付费交易数据处理装置发送的后付费交易的违约评估结果；
    若所述违约评估结果小于预设概率阈值，则向所述后付费交易数据处理装置发送通过所述后付费交易的请求信息。
  21. 一种电子钱包的数据处理服务器，包括至少一个处理器以及用于存储处理器可执行指令的存储器，所述处理器执行所述指令时实现权利要求1-7任一项所述的方法。
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