WO2018014812A1 - 风险识别方法、风险识别装置、云风险识别装置及系统 - Google Patents

风险识别方法、风险识别装置、云风险识别装置及系统 Download PDF

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Publication number
WO2018014812A1
WO2018014812A1 PCT/CN2017/093194 CN2017093194W WO2018014812A1 WO 2018014812 A1 WO2018014812 A1 WO 2018014812A1 CN 2017093194 W CN2017093194 W CN 2017093194W WO 2018014812 A1 WO2018014812 A1 WO 2018014812A1
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Prior art keywords
risk identification
risk
cloud
service data
processing request
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PCT/CN2017/093194
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English (en)
French (fr)
Inventor
顾曦
李才伟
夏巨鹏
Original Assignee
阿里巴巴集团控股有限公司
顾曦
李才伟
夏巨鹏
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Application filed by 阿里巴巴集团控股有限公司, 顾曦, 李才伟, 夏巨鹏 filed Critical 阿里巴巴集团控股有限公司
Priority to JP2019503330A priority Critical patent/JP6692000B2/ja
Priority to EP17830446.5A priority patent/EP3490216B1/en
Priority to SG11201900526WA priority patent/SG11201900526WA/en
Priority to KR1020197005334A priority patent/KR102134547B1/ko
Publication of WO2018014812A1 publication Critical patent/WO2018014812A1/zh
Priority to US16/254,473 priority patent/US20190156343A1/en
Priority to US16/725,751 priority patent/US20200250677A1/en

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00—Network architectures or network communication protocols for network security
    • H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
    • H04L63/1408—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic by monitoring network traffic
    • H04L63/1416—Event detection, e.g. attack signature detection
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/50—Network services
    • H04L67/51—Discovery or management thereof, e.g. service location protocol [SLP] or web services
    • 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
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/08—Insurance

Definitions

  • the present application relates to the field of Internet information processing technologies, and in particular, to a risk identification method, a risk identification device, a cloud risk identification device, and a system.
  • the front end of the Internet financial service platform collects various data.
  • the various data here include device data, environmental data, behavior data, etc.
  • the front end transmits various data collected to the risk control server through the network, and the risk control server receives the data.
  • the various data to be processed or calculated, and then the risk identification and processing of the business behavior of the user end, and finally the risk identification result is returned to the front end to achieve effective risk control.
  • the front end has high requirements for network transmission, background computing resources and data storage of the whole system, so that the risk control server is receiving. After the large amount of data sent to the front end, the data calculation takes a long time and reduces the efficiency of risk control.
  • the embodiment of the present application provides a risk identification method, a risk identification device, a cloud risk identification device, and a system, which are used to solve the problem in the prior art that the risk control server processes the received data.
  • the embodiment of the present application provides a risk identification method, including:
  • the risk identification request including the service data is sent to the cloud risk identification device, where the risk identification request is used to request the cloud risk identification device to risk the service processing request for generating the service data. Identification.
  • the embodiment of the present application provides a risk identification method, where the risk identification rule stored in the cloud risk identification device is different from the risk identification rule stored in the terminal device, and the method includes:
  • the cloud risk identification device performs risk identification on the service processing request for generating the service data based on the stored risk identification rule or risk identification model, in combination with the service data.
  • the embodiment of the present application provides a risk identification device applied to a terminal device, where the risk identification device includes:
  • the collecting unit collects service data, and the service data is generated according to the service processing request;
  • the risk identification unit based on the stored risk identification rule or the risk identification model, combines the service data to perform risk identification on the service processing request;
  • a sending unit when the risk identification result cannot be determined, sending a risk identification request including the service data to the cloud risk identification device, where the risk identification request is used to request the cloud risk identification device to perform service processing on the service data Request for risk identification.
  • the embodiment of the present application provides a cloud risk identification device, where the risk identification rule stored in the cloud risk identification device is different from the risk identification rule stored in the terminal device, where the cloud risk identification device includes:
  • a risk identification unit based on a stored risk identification rule or a risk identification model, combined with said The business data is subjected to risk identification for a business processing request that generates the business data.
  • the embodiment of the present application provides a risk identification system, where the risk identification system includes: a terminal device and a cloud risk identification device, wherein:
  • the terminal device collects service data, and the service data is generated according to a service processing request; and based on the stored risk identification rule or risk identification model, combined with the service data, performs risk identification on the service processing request; when the risk cannot be determined Transmitting a result, sending a risk identification request including the service data to the cloud risk identification device;
  • the cloud risk identification device receives a risk identification request sent by the terminal device, and performs a service processing request for generating the service data based on the stored risk identification rule and the service data included in the risk identification request. Risk Identification.
  • the terminal device When collecting the service data, the terminal device performs risk identification on the service processing request for generating the service data based on the stored risk identification rule or the risk identification model, and triggers the cloud risk identification device to generate the location when the risk identification result cannot be determined.
  • the business processing request of the business data is subjected to risk identification.
  • a distributed risk identification architecture is proposed, which effectively avoids the problem that the risk server in the prior art takes a long time to process the received data, resulting in low risk control efficiency.
  • the risk identification reduces the computing pressure of the risk server, reduces the overhead of system resources, and completes risk identification on the terminal device, which can shorten the time of risk identification and improve the user experience of the user.
  • FIG. 1 is a schematic flowchart diagram of a risk identification method according to an embodiment of the present application
  • FIG. 2 is a schematic flowchart of a risk identification method according to an embodiment of the present application.
  • FIG. 3 is a schematic flowchart diagram of a risk identification method according to an embodiment of the present application.
  • FIG. 4 is a schematic diagram of a scenario of a risk identification method according to an embodiment of the present application.
  • FIG. 5 is a schematic structural diagram of a risk identification apparatus applied to a terminal device according to an embodiment of the present disclosure
  • FIG. 6 is a schematic structural diagram of a risk identification apparatus applied to a terminal device according to an embodiment of the present disclosure
  • FIG. 7 is a schematic structural diagram of a cloud risk identification apparatus according to an embodiment of the present application.
  • FIG. 8 is a schematic structural diagram of a cloud risk identification apparatus according to an embodiment of the present application.
  • FIG. 9 is a schematic structural diagram of a risk identification system according to an embodiment of the present application.
  • FIG. 10 is a schematic structural diagram of an intelligent platform device according to an embodiment of the present application.
  • an embodiment of the present application provides a risk identification method, a risk identification device, a cloud risk identification device, and a system, and when a terminal device collects service data, based on a stored risk identification rule or a risk identification model, The service processing request for generating the service data is identified by the risk, and when the risk identification result cannot be determined, the cloud risk identification device triggers the risk identification of the service processing request for generating the service data.
  • a distributed risk identification architecture is proposed, which effectively avoids the problem that the risk server in the prior art takes a long time to process the received data, resulting in low risk control efficiency.
  • the risk identification reduces the computing pressure of the risk server, reduces the overhead of system resources, and completes risk identification on the terminal device, which can shorten the time of risk identification and improve the user experience of the user.
  • the cloud risk identification device described in the embodiment of the present invention may be a cloud-based risk identification device or a service-side air control system, which is not specifically limited herein.
  • the risk identification device described in the embodiment of the present invention may be a risk identification terminal device based on the user end, and may be integrated in the application software of the user end, and can support the requirements of different operating systems, and is not specifically limited herein.
  • the risk identification device here can be carried on the mobile terminal device or can be carried in the PC device.
  • the risk identification method applied to the terminal device described in the embodiment of the present application may also be applied to the App client installed in the mobile terminal device, or may be a control in the PC device, which is not specifically limited herein.
  • the risk identification rule stored in the cloud risk identification device is different from the risk identification rule stored in the terminal device.
  • FIG. 1 is a schematic flowchart diagram of a risk identification method according to an embodiment of the present application.
  • the method can be as follows.
  • the execution body of the embodiment of the present application may be a terminal device.
  • Step 101 Collect service data, where the service data is generated according to a service processing request.
  • the terminal device receives the service processing request sent by the user, and starts the risk identification operation when receiving the service processing request. This ensures timely risk control during business processing.
  • the service data generated by the service processing request is used in real time or periodically.
  • the service data here includes but is not limited to: device data (for example: device identification, data generated when the device is running, etc.), environmental data, user behavior data generated during user execution of a business process, transaction data generated during a business execution process, and the like. .
  • Step 102 Perform risk identification on the service processing request based on the stored risk identification rule or risk identification model and the service data.
  • the terminal device after collecting the service data, analyzes the service data; and based on the stored risk identification rule or the risk identification model, combined with the analysis result obtained by the analysis, performs the service processing request. Risk Identification.
  • the terminal device determines, according to the collected service data, a service indicator corresponding to the service data.
  • the service indicator described in the embodiment of the present application may include, but is not limited to, a count value, an added value, a start value, an end value, a difference value, an average value, a standard deviation, and a maximum value of the service data in a predetermined time window. And / or minimum.
  • the terminal device analyzes the frequency characteristics of the service operation request and/or the operating environment characteristics of the terminal device based on the determined service indicator and the service data.
  • the terminal device triggers the rule engine and/or the model engine to perform logic analysis and/or probability analysis on the determined service indicators, thereby obtaining the analysis result.
  • the terminal device determines a risk identification result of the service processing request according to the analysis result.
  • the risk identification rule stored in the terminal device described in the embodiment of the present application may be sent by the background server, or may be requested from the background server, and is not limited herein.
  • the risk identification solution described in the embodiment of the present application may further include an intelligent platform device, where the risk identification rule, the risk control policy, and the like are stored, and the terminal device may also obtain the risk identification rule from the smart platform device. There is no specific limit here.
  • the smart platform device described in the embodiment of the present application may be the same device or a different device as the backend server in the prior art.
  • the intelligent platform device described in the example can be developed and maintained as a service platform, which can be used to develop and maintain variables, rules and models, etc., and the functions are more comprehensive than the background server.
  • the processing mode supported by the terminal device may be adopted, and the specific processing manner is not specifically limited.
  • Step 103 Determine the operation result of step 102. If the risk identification result cannot be determined, execute step 104; if the risk identification result can be determined, perform step 105 and/or step 106.
  • step 102 the terminal device processes the service.
  • the terminal device processes the service.
  • the first case the risk identification result can be determined; the second case: the risk identification result cannot be determined.
  • the output result is a clear result (for example, Accept or Reject), it indicates that the risk identification result can be determined
  • the risk identification result cannot be determined.
  • the terminal device will trigger to perform different operations.
  • Step 104 When the risk identification result cannot be determined, the risk identification request including the service data is sent to the cloud risk identification device.
  • the risk identification request is used to request the cloud risk identification device to perform risk identification on a service processing request that generates the service data.
  • the terminal device if the risk identification result cannot be determined in step 102, the terminal device cannot accurately identify the risk behavior that occurs, and the cloud risk identification device needs to perform the judgment. At this time, the terminal device needs to send the cloud risk identification device to the cloud risk identification device.
  • the risk identifies the request and carries the collected business data in the risk identification request.
  • Step 105 When the risk identification result can be determined, the determined risk identification result and the service data are synchronized to the cloud risk identification device.
  • the step 105 may be performed at the same time as one of the situations described in the step 106, or may be performed sequentially, and is not specifically limited herein.
  • the critical data amount in the service data may be selected and sent to the cloud risk identification device, so that To achieve synchronization, it can also ensure that the amount of data transferred is small, the data transmission rate is increased, and system resource consumption is reduced.
  • Step 106 Perform risk on the service processing request according to the determined risk identification result control.
  • the operation of performing step 106 may be based on the following two situations:
  • step 102 the risk identification result can be determined, that is, a clear risk identification result is obtained.
  • the terminal device sends the obtained explicit risk identification result to the service processing unit in the terminal device, and performs effective risk control on the service processing request.
  • step 105 can also be triggered.
  • the second case the risk identification result cannot be determined in step 102.
  • step 104 the terminal device sends a risk identification request to the cloud risk identification device when the risk identification result cannot be determined.
  • the terminal device may receive the risk identification result according to the received risk. , performing risk control on the business processing request.
  • the terminal device sends the received risk identification result to the service processing unit in the terminal device, and performs effective risk control on the service processing request.
  • the terminal device when collecting the service data, the terminal device performs risk identification on the service processing request for generating the service data based on the stored risk identification or risk identification model, and when the risk identification result cannot be determined, The risk identification device triggers the risk identification of the service processing request for generating the service data.
  • a distributed risk identification architecture is proposed, which effectively avoids the problem that the risk server in the prior art takes a long time to process the received data, resulting in low risk control efficiency.
  • the risk identification reduces the computing pressure of the risk server, reduces the overhead of system resources, and completes risk identification on the terminal device, which can shorten the time of risk identification and improve the user experience of the user.
  • FIG. 2 is a schematic flowchart of a risk identification method according to an embodiment of the present application.
  • the execution subject of the embodiment of the present application may be a cloud risk identification device.
  • Step 201 The cloud risk identification device receives the risk identification sent by the terminal device.
  • the request includes the service data in the risk identification request.
  • the risk identification request sent by the terminal device received by the cloud risk identification device is sent when the terminal device cannot determine the risk identification result.
  • Step 202 The cloud risk identification device performs risk identification on the service processing request for generating the service data based on the stored risk identification rule or risk identification model and the service data.
  • the risk identification rules stored in the cloud risk identification device are different from the risk identification rules stored in the terminal device.
  • the cloud risk identification device can support the risk identification of the complex risk behavior
  • the risk identification request is extracted for the received risk identification request sent by the terminal device.
  • the business data included in the business data is analyzed, and the risk identification process is performed on the business processing request for generating the business data based on the stored risk identification rule or risk identification model and the analysis result obtained by the analysis.
  • Step 203 The cloud risk identification device sends a risk identification result to the terminal device, so that the terminal device performs risk control on the service processing request according to the risk identification result.
  • the cloud risk identification device described in the embodiment of the present application may further receive the risk identification result sent by the terminal device, and store the risk identification result and The business data of the risk identification result is obtained, so that the subsequent cloud risk identification device can use the stored risk identification result and the business data as training samples to optimize the risk identification rule, thereby improving the accuracy of the risk identification.
  • the cloud risk identification device when the terminal device cannot determine the risk identification result of a certain service processing request, the cloud risk identification device is configured to the received service data and the stored risk identification rule or risk identification model.
  • the service processing request performs risk identification, so that in the risk identification solution provided by the embodiment of the present invention, the “end+cloud” mode is adopted, which reduces the data calculation pressure of the cloud risk identification device on the one hand, and the cloud risk on the other hand.
  • the risk identification of complex risk behaviors ensures the accuracy of risk identification.
  • FIG. 3 is a schematic flowchart of a risk identification method according to an embodiment of the present application.
  • Step 301 The terminal device and the cloud risk identification device acquire the risk identification policy data packet from the smart platform device.
  • the intelligent platform device updates the stored risk identification policy data packet in real time or periodically, and transmits the risk identification policy data packet to the terminal device and the cloud risk identification device by using a push or pull method.
  • the terminal device and the cloud risk identification device synchronously update the locally stored risk identification policy data packet.
  • Step 302 The terminal device receives the service processing request sent by the user, and collects the service data generated by the service processing request during the execution process.
  • Step 303 The terminal device performs risk identification on the service processing request according to the stored risk identification rule or the risk identification model and the service data.
  • Step 304 The terminal device determines whether the risk identification result can be obtained. If yes, step 305 is performed; if not, step 307 is performed.
  • Step 305 The terminal device sends the risk identification result and the service data to the cloud risk identification device.
  • Step 306 The terminal device performs risk control on the service processing request according to the risk identification result.
  • Step 307 The terminal device sends a risk identification request to the cloud risk identification device, where the risk identification request includes service data.
  • Step 308 The cloud risk identification receives the risk identification request, and performs risk identification on the service processing request for generating the service data based on the stored risk identification policy data packet and the service data.
  • Step 309 The cloud risk identification sends the obtained risk identification result to the terminal device, and jumps Go to step 306.
  • FIG. 4 is a schematic diagram of a scenario of a risk identification method according to an embodiment of the present application.
  • the terminal device when collecting the service data, the terminal device performs risk identification on the service processing request for generating the service data based on the stored risk identification rule or the risk identification model, and triggers when the risk identification result cannot be determined.
  • the cloud risk identification device performs risk identification on the service processing request that generates the service data.
  • a distributed risk identification architecture is proposed, which effectively avoids the problem that the risk server in the prior art takes a long time to process the received data, resulting in low risk control efficiency.
  • the risk identification reduces the computing pressure of the risk server, reduces the overhead of system resources, and completes risk identification on the terminal device, which can shorten the time of risk identification and improve the user experience of the user.
  • FIG. 5 is a schematic structural diagram of a risk identification apparatus applied to a terminal device according to an embodiment of the present disclosure.
  • the risk identification device includes: an acquisition unit 51, a risk identification unit 52, and a transmission unit 53, wherein:
  • the collecting unit 51 collects service data, and the service data is generated according to a service processing request;
  • the risk identification unit 52 performs risk identification on the service processing request according to the stored risk identification rule or the risk identification model in combination with the service data;
  • the sending unit 53 sends a risk identification request including the service data to the cloud risk identification device when the risk identification result cannot be determined, where the risk identification request is used to request the cloud risk identification device to generate the service data Process requests for risk identification.
  • the risk identification device further includes: a control unit 54, wherein:
  • the control unit 54 performs risk control on the service processing request according to the risk identification result determined by the risk identification unit.
  • the risk identification device further includes: a synchronization unit 55, wherein:
  • the synchronization unit 55 when the risk identification unit is capable of determining a risk identification result, The determined risk identification result and the business data are synchronized to the cloud risk identification device.
  • control unit 54 performs risk control on the service processing request according to the determined risk identification result, including:
  • the risk identification unit 52 performs risk identification on the service processing request according to the stored risk identification rule or risk identification model, including the service data, including:
  • the business processing request is identified by risk.
  • the risk identification device may be implemented by using a hardware method or a software implementation, which is not specifically limited herein.
  • the risk identification device may be carried on the terminal device.
  • the terminal device performs risk identification on the service processing request for generating the service data based on the stored risk identification or risk identification model, and when the risk identification result cannot be determined. And triggering, by the cloud risk identification device, the risk identification of the service processing request that generates the service data.
  • the problem that the risk server in the prior art takes a long time to process the received data and the risk control efficiency is low is effectively avoided, and the multi-level risk identification is mitigated.
  • the computing pressure of the risk server reduces the overhead of system resources and completes risk identification on the terminal device, which can shorten the time of risk identification and enhance the user experience of the user.
  • FIG. 6 is a schematic structural diagram of a risk identification apparatus applied to a terminal device according to an embodiment of the present disclosure. Assuming that the functions described in FIG. 5 are integrated in the risk control module 61 of the risk identification device, the risk identification device shown in FIG. 6 includes, in addition to the risk control module 61, a security module 62 and a data module. 63 and communication module 64, wherein:
  • the data module 63 supports collecting device data, environmental data, behavior data, transaction data, and the like. Supports the collection of Socket data, service call information, etc.; supports the identification of abnormal environments, such as simulator recognition; supports secure storage and query of data; supports data processing and data operations, such as arithmetic operations, logic operations, Velocity calculations, etc.
  • the risk control module 61 supports the rule engine and the model engine on the terminal device, and analyzes and calculates the service data corresponding to the collected service processing request based on the rule engine and the model engine, thereby performing risk identification on the service processing request.
  • the security module 62 supports anti-cracking, anti-injection, and anti-adjustment, and can provide a virtual security environment (Virtual Secure Environment) for algorithms, variables, rules, models, and the like deployed on the terminal device.
  • Virtual Secure Environment Virtual Secure Environment
  • the communication module 64 supports receiving data, algorithms, rules, models, etc. pushed by the server; supports synchronous calling of the "cloud” wind control service; supports asynchronous calling of the "cloud” risk control service; payment and server side Conduct other forms of communication, such as condition monitoring.
  • sending a risk identification request to the cloud risk identification device For example, sending a risk identification request to the cloud risk identification device; receiving a risk identification result sent by the cloud risk identification device, and the like.
  • the terminal device described in the embodiment of the present application may also be referred to as an Edge-end air control device, and may be a mobile device-based wind control system that supports the Andriod and ISO operating platforms and is deployed to run on the user end.
  • the terminal device can perform the “follow-and-use” and timely identify the risk of the service processing request corresponding to the service data, thereby effectively avoiding the risk identification caused by transmitting the data to the background server.
  • the delay while reducing the resource consumption of the background server, improves the efficiency of the entire risk identification system.
  • FIG. 7 is a schematic structural diagram of a cloud risk identification apparatus according to an embodiment of the present application.
  • the cloud risk identification device includes: a receiving unit 71 and a risk identification unit 72, wherein:
  • the receiving unit 71 receives a risk identification request sent by the terminal device, where the risk identification request includes the service data;
  • the risk identification unit 72 performs risk identification on the service processing request for generating the service data based on the stored risk identification rule or the risk identification model in combination with the service data.
  • the cloud risk identification apparatus further includes: a sending unit 73, wherein:
  • the sending unit 73 sends a risk identification result to the terminal device, so that the terminal device performs risk control on the service processing request according to the risk identification result.
  • the risk identification unit 72 performs risk identification on the service processing request for generating the service data based on the stored risk identification rule or the risk identification model, including:
  • the risk identification of the business processing request for generating the business data is performed.
  • the risk identification rule stored in the cloud risk identification device is different from the risk identification rule stored in the terminal device.
  • the cloud risk identification device provided by the embodiment of the present application may be implemented by using a software method or a hardware manner, and is not specifically limited herein.
  • the cloud risk identification device described in the embodiment of the present application when the terminal device cannot determine the risk identification result of a certain service processing request, requests the service for the received service data and the stored risk identification rule or risk identification model. Risk identification is performed, so that in the risk identification scheme provided by the embodiment of the present invention, the “end+cloud” mode is adopted, which reduces the data calculation pressure of the cloud risk identification device on the one hand, and the powerful cloud risk identification device on the other hand. Computational ability, risk identification of complex risk behaviors, ensuring the accuracy of risk identification.
  • FIG. 8 is a schematic structural diagram of a cloud risk identification apparatus according to an embodiment of the present application. Assuming that the functions described in 7 are integrated in the risk control module 81 of the cloud risk identification device, the cloud risk identification device shown in FIG. 8 includes, in addition to the risk control module 81, a data module 82 and a communication module. 83, where:
  • the risk control module 81 supports a rule engine and a model engine on the background server, and performs risk identification on the service processing request for generating the service data based on the stored risk identification rule and the service data included in the received risk identification request.
  • the data module 82 supports receiving various types of data, storing and querying various types of data, and performing data processing and data operations.
  • the communication module 83 receives the data (including service data, request message, and the like) sent by the terminal device, receives the risk identification policy data packet, the risk identification rule, the model, and the like sent by the intelligent platform device, and supports synchronization or asynchronous with the terminal device. Data call.
  • the cloud risk identification device described in the embodiment of the present application may be a server-based wind control system, and provides a RESTFUL and SOAP web service interface. With powerful computing power and large storage space, security performance is relatively high.
  • FIG. 9 is a schematic structural diagram of a risk identification system according to an embodiment of the present application.
  • the risk identification system includes: a terminal device 91 and a cloud risk identification device 92, wherein:
  • the terminal device 91 collects service data, and the service data is generated according to a service processing request; and based on the stored risk identification rule or risk identification model, combined with the service data, performs risk identification on the service processing request; Sending a risk identification request including the service data to the cloud risk identification device when the risk is recognized;
  • the cloud risk identification device 92 receives the risk identification request sent by the terminal device, and performs risk identification on the service processing request for generating the service data based on the stored risk identification rule or risk identification model and the service data. .
  • the risk identification system further includes: an intelligent platform device 93, wherein:
  • the smart platform device 93 monitors an operating status of the terminal device and the cloud risk identification device, and pushes a risk identification rule or a risk identification model to the terminal device and the cloud risk identification device, respectively.
  • the terminal device and the cloud risk control device described in the embodiments of the present application have the functions described in the above embodiments, and are not described in detail herein.
  • FIG. 10 is a schematic structural diagram of an intelligent platform device according to an embodiment of the present application.
  • the intelligent platform device included in the risk identification system in the embodiment of the present application may include the following modules: configuration Module 1001, monitoring module 1002, and communication module 1003, wherein:
  • the module 1001 is configured to configure various variables, rules, models, and the like;
  • the monitoring module 1002 supports monitoring the operating states of the terminal device and the cloud risk identification device.
  • the communication module 1003 supports pushing the risk identification rule to the terminal device and the cloud risk identification device respectively.
  • the intelligent platform device described in the embodiment of the present application may be a service platform that integrates development and maintenance, and can be used to develop and maintain variables, rules, models, and the like, and implement the risk identification system described in the embodiments of the present application. Dynamic update of risk identification strategies.
  • the "end + cloud” wind control solution which fully utilizes the terminal device to realize the “follow-and-use” of the data, and avoids transferring a large amount of data back to the background server, thereby reducing network overhead and background server resources;
  • the key data and data related to complex risk behaviors are transmitted back to the back-end server, and the powerful computing resources of the “cloud” background are used to process the risk identification requests that require a large amount of calculations, thereby ensuring the accuracy of the wind control identification.
  • risk identification can be completed on the “end”. For this part of the risk control identification, network delay and background operation time will no longer occur. The user experience will be significantly improved, and on the other hand, the background service congestion will be alleviated.
  • embodiments of the present invention can be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or a combination of software and hardware. Moreover, the present invention may employ computer usable storage media (including but not included) in one or more of the computer usable program code embodied therein. It is limited to the form of a computer program product implemented on a disk storage, a CD-ROM, an optical storage, or the like.
  • the computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture comprising the instruction device.
  • the apparatus implements the functions specified in one or more blocks of a flow or a flow and/or block diagram of the flowchart.
  • These computer program instructions can also be loaded onto a computer or other programmable data processing device such that a series of operational steps are performed on a computer or other programmable device to produce computer-implemented processing for execution on a computer or other programmable device.
  • the instructions provide steps for implementing the functions specified in one or more of the flow or in a block or blocks of a flow diagram.
  • a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
  • processors CPUs
  • input/output interfaces network interfaces
  • memory volatile and non-volatile memory
  • the memory may include non-persistent memory, random access memory (RAM), and/or non-volatile memory in a computer readable medium, such as read only memory (ROM) or flash memory.
  • RAM random access memory
  • ROM read only memory
  • Memory is an example of a computer readable medium.
  • Computer readable media includes both permanent and non-persistent, removable and non-removable media.
  • Information storage can be implemented by any method or technology.
  • the information can be computer readable instructions, data structures, modules of programs, or other data.
  • Examples of storage media for a computer include, but are not limited to, phase Variable Memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Other Types of Random Access Memory (RAM), Read Only Memory (ROM), EEPROM Memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape storage or other magnetic
  • PRAM phase Variable Memory
  • SRAM Static Random Access Memory
  • DRAM Dynamic Random Access Memory
  • RAM Random Access Memory
  • RAM Read Only Memory
  • flash memory or other memory technology
  • CD-ROM compact disc
  • DVD digital versatile disc
  • magnetic cassette magnetic tape storage or other magnetic
  • embodiments of the present application can be provided as a method, system, or computer program product.
  • the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment in combination of software and hardware.
  • the application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) including computer usable program code.

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Abstract

本申请公开了一种风险识别方法、风险识别装置、云风险识别装置及系统，包括：终端设备在采集到业务数据时，基于存储的风险识别规则，对产生业务数据的业务处理请求进行风险识别，并在无法确定风险识别结果时，触发由云风险识别设备对产生所述业务数据的业务处理请求进行风险识别。在本申请实施例中提出了分布式风险识别架构，有效避免了现有技术中风险服务器在对接收到的数据进行处理时耗时较长导致风险控制效率较低的问题，通过这种多层次的风险识别，减轻了风险服务器的运算压力，减少了系统资源的开销，同时在终端设备上完成风险识别，能够缩短风险识别的时间，提升用户的用户体验。

Description

风险识别方法、风险识别装置、云风险识别装置及系统 技术领域
本申请涉及互联网信息处理技术领域，尤其涉及一种风险识别方法、风险识别装置、云风险识别装置及系统。
背景技术
随着互联网技术的发展，出现了各种各样的电子商务平台。在这些电子商务平台中存在一种与金融行业相关的互联网金融业务平台。对于互联网金融业务平台，如何有效地进行风险控制是首要考虑的重要问题。
在实际应用中，随着互联网金融业务平台中业务类型、产品类型以及交易类型的越来越复杂，相继产生的风险控制难度也越来越高。目前，有效地进行风险控制的方式为：
互联网金融业务平台的前端采集各种数据，这里的各种数据包含设备数据、环境数据、行为数据等等，前端将采集到的各种数据通过网络传输给风险控制服务器，由风险控制服务器对接收到的各种数据进行处理或计算，进而对用户端的业务行为进行风险识别和处理，最后将风险识别结果返回给前端，以实现有效地风险控制。
但是，经研究发现，前端在采集各种数据之后，由于采集的数据量比较大，那么对于整个系统的网络传输、后台计算资源以及数据存储等各个方面提出很高要求，使得风险控制服务器在接收到前端发送的大量数据之后，导致数据计算的耗时较长，降低风险控制的效率。
发明内容
有鉴于此，本申请实施例提供了一种风险识别方法、风险识别装置、云风险识别装置及系统，用于解决现有技术中风险控制服务器在对接收到的数据进行处理时耗时较长导致风险控制效率较低的问题。
本申请实施例提供了一种风险识别方法，包括：
采集业务数据，所述业务数据根据业务处理请求产生；
基于存储的风险识别规则或风险识别模型，结合所述业务数据，对所述业务处理请求进行风险识别；
当无法确定风险识别结果时，向云风险识别设备发送包含所述业务数据的风险识别请求，所述风险识别请求用于请求所述云风险识别设备对产生所述业务数据的业务处理请求进行风险识别。
本申请实施例提供了一种风险识别方法，云风险识别设备中存储的风险识别规则与终端设备中存储的风险识别规则不同，所述方法包括：
所述云风险识别设备接收所述终端设备发送的风险识别请求，所述风险识别请求中包含所述业务数据；
所述云风险识别设备基于存储的风险识别规则或风险识别模型，结合所述业务数据，对产生所述业务数据的业务处理请求进行风险识别。
本申请实施例提供了一种应用于终端设备上的风险识别装置，所述风险识别装置包括：
采集单元，采集业务数据，所述业务数据根据业务处理请求产生；
风险识别单元，基于存储的风险识别规则或风险识别模型，结合所述业务数据，对所述业务处理请求进行风险识别；
发送单元，当无法确定风险识别结果时，向云风险识别设备发送包含所述业务数据的风险识别请求，所述风险识别请求用于请求所述云风险识别设备对产生所述业务数据的业务处理请求进行风险识别。
本申请实施例提供了一种云风险识别装置，云风险识别装置中存储的风险识别规则与终端设备中存储的风险识别规则不同，所述云风险识别装置包括：
接收单元，接收所述终端设备发送的风险识别请求，所述风险识别请求中包含所述业务数据；
风险识别单元，基于存储的风险识别规则或风险识别模型，结合所述 业务数据，对产生所述业务数据的业务处理请求进行风险识别。
本申请实施例提供了一种风险识别系统，所述风险识别系统包括：终端设备和云风险识别设备，其中：
所述终端设备，采集业务数据，所述业务数据根据业务处理请求产生；基于存储的风险识别规则或风险识别模型，结合所述业务数据，对所述业务处理请求进行风险识别；当无法确定风险识别结果时，向所述云风险识别设备发送包含所述业务数据的风险识别请求；
所述云风险识别设备，接收所述终端设备发送的风险识别请求，并基于存储的风险识别规则和所述风险识别请求中包含的所述业务数据，对产生所述业务数据的业务处理请求进行风险识别。
本申请实施例采用的上述至少一个技术方案能够达到以下有益效果：
终端设备在采集到业务数据时，基于存储的风险识别规则或风险识别模型，对产生业务数据的业务处理请求进行风险识别，并在无法确定风险识别结果时，触发由云风险识别设备对产生所述业务数据的业务处理请求进行风险识别。在本申请实施例中提出了分布式风险识别架构，有效避免了现有技术中风险服务器在对接收到的数据进行处理时耗时较长导致风险控制效率较低的问题，通过这种多层次的风险识别，减轻了风险服务器的运算压力，减少了系统资源的开销，同时在终端设备上完成风险识别，能够缩短风险识别的时间，提升用户的用户体验。
附图说明
此处所说明的附图用来提供对本申请的进一步理解，构成本申请的一部分，本申请的示意性实施例及其说明用于解释本申请，并不构成对本申请的不当限定。在附图中：
图1为本申请实施例提供的一种风险识别方法的流程示意图；
图2为本申请实施例提供的一种风险识别方法的流程示意图；
图3为本申请实施例提供的一种风险识别方法的流程示意图；
图4为本申请实施例提供的一种风险识别方法的场景示意图；
图5为本申请实施例提供的一种应用于终端设备上的风险识别装置的结构示意图；
图6为本申请实施例提供的一种应用于终端设备上的风险识别装置的结构示意图；
图7为本申请实施例提供的一种云风险识别装置的结构示意图；
图8为本申请实施例提供的一种云风险识别装置的结构示意图；
图9为本申请实施例提供的一种风险识别系统的结构示意图；
图10为本申请实施例提供的一种智能平台设备的结构示意图。
具体实施方式
为了实现本申请的目的，本申请实施例提供了一种风险识别方法、风险识别装置、云风险识别装置及系统，终端设备在采集到业务数据时，基于存储的风险识别规则或风险识别模型，对产生业务数据的业务处理请求进行风险识别，并在无法确定风险识别结果时，触发由云风险识别设备对产生所述业务数据的业务处理请求进行风险识别。在本申请实施例中提出了分布式风险识别架构，有效避免了现有技术中风险服务器在对接收到的数据进行处理时耗时较长导致风险控制效率较低的问题，通过这种多层次的风险识别，减轻了风险服务器的运算压力，减少了系统资源的开销，同时在终端设备上完成风险识别，能够缩短风险识别的时间，提升用户的用户体验。
需要说明的是，本申请实施例中所记载的云风险识别装置可以是基于云功能的风险识别设备，也可以是服务端的风控系统，这里不做具体限定。
本申请实施例中所记载的风险识别装置可以是基于用户端的风险识别终端设备，可以集成在用户端的应用软件中，能够支持不同操作系统的要求，这里不做具体限定。
这里的风险识别装置可以承载在移动终端设备，也可以承载在PC设 备，本申请实施例中所记载的应用在终端设备上的风险识别方法也可以应用在移动终端设备中安装的App客户端，也可以是PC设备中的控件，这里不做具体限定。
需要说明的是，云风险识别设备中存储的风险识别规则与终端设备中存储的风险识别规则存在差异。
下面结合本申请具体实施例及相应的附图对本申请技术方案进行清楚、完整地描述。显然，所描述的实施例仅是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
以下结合附图，详细说明本申请各实施例提供的技术方案。
实施例1
图1为本申请实施例提供的一种风险识别方法的流程示意图。所述方法可以如下所示。本申请实施例的执行主体可以为终端设备。
步骤101：采集业务数据，所述业务数据根据业务处理请求产生。
在本申请实施例中，终端设备接收用户发送的业务处理请求，并在接收到该业务处理请求时，启动风险识别操作。这样能够保证业务处理过程中及时进行风险控制。
终端设备在启动风险识别操作时，实时或者周期地采用业务处理请求产生的业务数据。这里的业务数据包括但不限于：设备数据(例如：设备标识、设备运行时产生的数据等)、环境数据、用户执行业务过程中产生的用户行为数据、业务执行过程中产生的交易数据等等。
步骤102：基于存储的风险识别规则或风险识别模型，结合所述业务数据，对所述业务处理请求进行风险识别。
在本申请实施例中，终端设备在采集到业务数据之后，对所述业务数据进行分析；并基于存储的风险识别规则或风险识别模型，结合分析得到的分析结果，对所述业务处理请求进行风险识别。
具体地，终端设备根据采集到所述业务数据，确定所述业务数据对应的业务指标。本申请实施例中所记载的业务指标可以包含但不限于：所述业务数据在预定时间窗口的计数值、加和值、起始值、终止值、区别值、平均值、标准差、最大值和/或最小值。终端设备基于确定的业务指标和所述业务数据，分析所述业务操作请求出现的频率特性和/或终端设备的运行环境特性。终端设备触发规则引擎和/或模型引擎对确定的业务指标进行逻辑分析和/或概率分析，进而得到分析结果。
终端设备根据分析结果，确定所述业务处理请求的风险识别结果。
本申请实施例中所记载的终端设备中存储的风险识别规则可以是后台服务器下发的，也可以是从后台服务器请求的，这里不做限定。此外，本申请实施例所记载的风险识别方案中还可以包含一个智能平台设备，该智能平台设备中存储风险识别规则、风险控制策略等，终端设备也可以从智能平台设备中获取风险识别规则，这里不做具体限定。
需要说明的是，本申请实施例中所记载的智能平台设备与现有技术中所说的后台服务器可以是同一个设备，也可以是不同的设备，若是不同的设备，区别在于：本申请实施例中记载的智能平台设备能够集开发维护为一体的服务平台，能够用来开发、维护变量、规则以及模型等，功能相对于后台服务器更全面一些。
需要说明的是，本申请实施例中终端设备在对所述业务处理请求进行风险识别时，可以采用终端设备所支持的处理方式，具体处理方式不做具体限定。
步骤103：对步骤102的操作结果进行判断，若无法确定风险识别结果，则执行步骤104；若能够确定风险识别结果，则执行步骤105和/或步骤106。
在本申请实施例中，由于终端设备的计算能力等条件限制，或者有些风险识别规则不适宜存在终端设备、或者其他原因，这样，就意味着在步骤102中，终端设备在对所述业务处理请求进行风险识别时，存在以下两 种情况：
第一种情况：能够确定风险识别结果；第二种情况：无法确定风险识别结果。
即将业务数据或者分析结果输入规则引擎和/或模型引擎，若输出结果为明确结果的(例如：Accept或Reject)，说明能够确定风险识别结果；
若输出结果为不明确结果的(例如：Unknown或者NULL)，说明无法确定风险识别结果。
本申请实施例中所记载的明确结果可以理解为能够为下一步风险控制提供明确方向的结果。
那么针对不同的输入结果，终端设备将触发执行不同的操作。
步骤104：当无法确定风险识别结果时，向云风险识别设备发送包含所述业务数据的风险识别请求。
所述风险识别请求用于请求所述云风险识别设备对产生所述业务数据的业务处理请求进行风险识别。
在本申请实施例中，若步骤102中无法确定风险识别结果，则说明终端设备对发生的风险行为不能进行精准识别，需要云风险识别设备进行判断，此时终端设备需要向云风险识别设备发送风险识别请求，并在风险识别请求中携带采集到的业务数据。
步骤105：当能够确定风险识别结果时，将确定的所述风险识别结果以及所述业务数据同步至所述云风险识别设备。
在本申请实施例中，步骤105可以与步骤106中所记载的一种情形同时执行，也可以顺序执行，这里不做具体限定。
需要说明的是，在将确定的所述风险识别结果以及所述业务数据同步至所述云风险识别设备时，可以选择所述业务数据中的关键数据量发送给云风险识别设备，这样既能够达到同步目的，还能够保证传输数据量较小，提升数据传输速率，减少系统资源消耗。
步骤106：根据确定的风险识别结果，对所述业务处理请求进行风险 控制。
在本申请实施例中，执行步骤106的操作可以基于以下两种情况：
第一种情况：步骤102中能够确定风险识别结果，即得到明确的风险识别结果。
那么终端设备将得到的明确的风险识别结果发送给终端设备中的业务处理单元，对所述业务处理请求进行有效地风险控制。
同时，还可以触发执行步骤105。
第二种情况：步骤102中无法确定风险识别结果。
那么由于步骤104终端设备在无法确定风险识别结果时，向云风险识别设备发送风险识别请求，一旦终端设备接收到云风险识别设备发送的风险识别结果，则可以根据接收到的所述风险识别结果，对所述业务处理请求进行风险控制。
具体地，终端设备将接收到的风险识别结果发送给终端设备中的业务处理单元，对所述业务处理请求进行有效地风险控制。
通过本申请实施例提供的技术方案，终端设备在采集到业务数据时，基于存储的风险识别或风险识别模型，对产生业务数据的业务处理请求进行风险识别，并在无法确定风险识别结果时，触发由云风险识别设备对产生所述业务数据的业务处理请求进行风险识别。在本申请实施例中提出了分布式风险识别架构，有效避免了现有技术中风险服务器在对接收到的数据进行处理时耗时较长导致风险控制效率较低的问题，通过这种多层次的风险识别，减轻了风险服务器的运算压力，减少了系统资源的开销，同时在终端设备上完成风险识别，能够缩短风险识别的时间，提升用户的用户体验。
实施例2
基于同一个发明构思，图2为本申请实施例提供的一种风险识别方法的流程示意图。本申请实施例的执行主体可以为云风险识别设备。
步骤201：所述云风险识别设备接收所述终端设备发送的风险识别请 求，所述风险识别请求中包含所述业务数据。
在本申请实施例中，所述云风险识别设备接收到的所述终端设备发送的风险识别请求是在终端设备无法确定风险识别结果时发送的。
步骤202：所述云风险识别设备基于存储的风险识别规则或风险识别模型，结合所述业务数据，对产生所述业务数据的业务处理请求进行风险识别。
云风险识别设备中存储的风险识别规则与终端设备中存储的风险识别规则存在差异。
在本申请实施例中，由于云风险识别设备能够支持复杂风险行为的风险识别，那么在终端设备无法对风险行为进行识别时，对于接收到的终端设备发送的风险识别请求，提取该风险识别请求中包含的业务数据，并对所述业务数据进行分析；进而基于存储的风险识别规则或风险识别模型，结合分析得到的分析结果，对产生所述业务数据的业务处理请求进行风险识别。
步骤203：所述云风险识别设备向所述终端设备发送风险识别结果，使所述终端设备根据所述风险识别结果，对所述业务处理请求进行风险控制。
可选地，在本申请实施例中，假设终端设备能够确定风险识别结果，那么本申请实施例中所记载的云风险识别设备还可以接收终端设备发送的风险识别结果，并存储风险识别结果和得到该风险识别结果的业务数据，这样后续云风险识别设备可以将存储的风险识别结果和业务数据作为训练样本，优化风险识别规则，进而提升风险识别的精度。
通过本申请实施例所记载的风险识别方案，云风险识别设备在终端设备无法确定某一业务处理请求的风险识别结果时，针对接收到的业务数据和存储的风险识别规则或风险识别模型，对该业务处理请求进行风险识别，这样在本发明实施例提供的风险识别方案中，采用“端+云”的模式，一方面减轻了云风险识别设备的数据计算压力，另一方面借助于云风险识 别设备强大的计算能力，对复杂的风险行为进行风险识别，保证了风险识别的精度。
实施例3
基于同一个发明构思，图3为本申请实施例提供的一种风险识别方法的流程示意图。
步骤301：终端设备和云风险识别设备从智能平台设备中获取风险识别策略数据包。
在本申请实施例中智能平台设备实时或者定期更新存储的风险识别策略数据包，并采用推(Push)或者拉(Pull)方式将风险识别策略数据包传输给终端设备和云风险识别设备，使得终端设备和云风险识别设备同步更新本地存储的风险识别策略数据包。
步骤302：终端设备接收用户发送的业务处理请求，并采集该业务处理请求在执行过程中产生的业务数据。
步骤303：终端设备基于存储的风险识别规则或风险识别模型，结合所述业务数据，对业务处理请求进行风险识别。
步骤304：终端设备确定是否能够得到风险识别结果，若是，则执行步骤305；若否，则执行步骤307。
步骤305：终端设备将风险识别结果和业务数据发送至云风险识别设备。
步骤306：终端设备根据风险识别结果，对该业务处理请求进行风险控制。
步骤307：终端设备向云风险识别设备发送风险识别请求，所述风险识别请求中包含业务数据。
步骤308：云风险识别接收所述风险识别请求，并基于存储的风险识别策略数据包和所述业务数据，对产生所述业务数据的业务处理请求进行风险识别。
步骤309：云风险识别将得到的风险识别结果发送给终端设备，跳转 执行步骤306。
图4为本申请实施例提供的一种风险识别方法的场景示意图。
从图4中可以看出，终端设备在采集到业务数据时，基于存储的风险识别规则或风险识别模型，对产生业务数据的业务处理请求进行风险识别，并在无法确定风险识别结果时，触发由云风险识别设备对产生所述业务数据的业务处理请求进行风险识别。在本申请实施例中提出了分布式风险识别架构，有效避免了现有技术中风险服务器在对接收到的数据进行处理时耗时较长导致风险控制效率较低的问题，通过这种多层次的风险识别，减轻了风险服务器的运算压力，减少了系统资源的开销，同时在终端设备上完成风险识别，能够缩短风险识别的时间，提升用户的用户体验。
实施例4
图5为本申请实施例提供的一种应用在终端设备的风险识别装置的结构示意图。所述风险识别装置包括：采集单元51、风险识别单元52和发送单元53，其中：
采集单元51，采集业务数据，所述业务数据根据业务处理请求产生；
风险识别单元52，基于存储的风险识别规则或风险识别模型，结合所述业务数据，对所述业务处理请求进行风险识别；
发送单元53，当无法确定风险识别结果时，向云风险识别设备发送包含所述业务数据的风险识别请求，所述风险识别请求用于请求所述云风险识别设备对产生所述业务数据的业务处理请求进行风险识别。
在本申请的另一个实施例中，所述风险识别装置还包括：控制单元54，其中：
所述控制单元54，根据所述风险识别单元确定的风险识别结果，对所述业务处理请求进行风险控制。
在本申请的另一个实施例中，所述风险识别装置还包括：同步单元55，其中：
所述同步单元55，当所述风险识别单元能够确定风险识别结果时，将 确定的所述风险识别结果以及所述业务数据同步至所述云风险识别设备。
在本申请的另一个实施例中，所述控制单元54根据确定的风险识别结果，对所述业务处理请求进行风险控制，包括：
接收所述云风险识别设备发送的风险识别结果；
根据接收到的所述风险识别结果，对所述业务处理请求进行风险控制。
在本申请的另一个实施例中，所述风险识别单元52基于存储的风险识别规则或风险识别模型，结合所述业务数据，对所述业务处理请求进行风险识别，包括：
对所述业务数据进行分析；
基于存储的风险识别规则或风险识别模型，结合分析得到的分析结果，对所述业务处理请求进行风险识别。
需要说明的是，本申请实施例提供的风险识别装置可以通过硬件方式实现，也可以通过软件方式实现，这里不做具体限定。该风险识别装置可以承载在终端设备上，终端设备在采集到业务数据时，基于存储的风险识别或风险识别模型，对产生业务数据的业务处理请求进行风险识别，并在无法确定风险识别结果时，触发由云风险识别设备对产生所述业务数据的业务处理请求进行风险识别。基于提出的分布式风险识别架构，有效避免了现有技术中风险服务器在对接收到的数据进行处理时耗时较长导致风险控制效率较低的问题，通过这种多层次的风险识别，减轻了风险服务器的运算压力，减少了系统资源的开销，同时在终端设备上完成风险识别，能够缩短风险识别的时间，提升用户的用户体验。
图6为本申请实施例提供的一种应用在终端设备的风险识别装置的结构示意图。假设将图5中所记载的功能集成在风险识别装置的风险控制模块61中，那么在图6中所示的风险识别装置除了包括风险控制模块61之外，还包括：安全模块62、数据模块63和通信模块64，其中：
数据模块63，支持采集设备数据、环境数据、行为数据、交易数据等， 支持采集Socket数据，服务调用信息等；支持对异常环境进行识别，比如模拟器识别等；支持对数据进行安全存储和查询；支持数据处理和数据运算，比如算术运算、逻辑运算、Velocity计算等。
风险控制模块61，支持终端设备上的规则引擎、模型引擎，并基于规则引擎、模型引擎，对采集到的业务处理请求对应的业务数据进行分析和计算，进而对该业务处理请求进行风险识别。
安全模块62，支持反破解、反注入、反调整，并且能为被部署到终端设备上的算法、变量、规则、模型等提供虚拟安全环境(Virtual Secure Environment)。
通信模块64，支持接收由服务器推送到端上的数据、算法、规则、模型等；支持对“云”风控服务的同步调用；支持对“云”风控服务的异步调用；支付与服务器端进行其他形式的通讯，比如状态监测等。
例如：向云风险识别设备发送风险识别请求；接收云风险识别设备发送的风险识别结果等等。
本申请实施例中记载的终端设备又可以称之为Edge端风控设备，可以是一种基于移动设备的风控系统，支持Andriod和ISO操作平台，被部署在用户端运行。这样，终端设备在采集到业务数据时，能够做到“随采随用”，并及时对业务数据对应的业务处理请求的风险进行识别，有效避免了将数据传输至后台服务器所造成的风险识别延时，同时减轻了后台服务器的资源消耗，提升了整个风险识别系统的工作效率。
实施例5
图7为本申请实施例提供的一种云风险识别装置的结构示意图。所述云风险识别装置包括：接收单元71和风险识别单元72，其中：
接收单元71，接收所述终端设备发送的风险识别请求，所述风险识别请求中包含所述业务数据；
风险识别单元72，基于存储的风险识别规则或风险识别模型，结合所述业务数据，对产生所述业务数据的业务处理请求进行风险识别。
在本申请的另一个实施例中，所述云风险识别装置还包括：发送单元73，其中：
所述发送单元73，向所述终端设备发送风险识别结果，使所述终端设备根据所述风险识别结果，对所述业务处理请求进行风险控制。
在本申请的另一个实施例中，所述风险识别单元72基于存储的风险识别规则或风险识别模型，结合所述业务数据，对产生所述业务数据的业务处理请求进行风险识别，包括：
对所述业务数据进行分析；
基于存储的风险识别规则或风险识别模型，结合分析得到的分析结果，对产生所述业务数据的业务处理请求进行风险识别。
在本申请的另一实施例中，云风险识别设备中存储的风险识别规则与终端设备中存储的风险识别规则存在差异。
需要说明的是，本申请实施例所提供的云风险识别装置可以通过软件方式实现，也可以通过硬件方式实现，这里不做具体限定。本申请实施例中所记载的云风险识别装置在终端设备无法确定某一业务处理请求的风险识别结果时，针对接收到的业务数据和存储的风险识别规则或风险识别模型，对该业务处理请求进行风险识别，这样在本发明实施例提供的风险识别方案中，采用“端+云”的模式，一方面减轻了云风险识别设备的数据计算压力，另一方面借助于云风险识别设备强大的计算能力，对复杂的风险行为进行风险识别，保证了风险识别的精度。
基于同一个发明构思，图8为本申请实施例提供的一种云风险识别装置的结构示意图。假设将7中所记载的功能集成在云风险识别设备的风险控制模块81中，那么图8中所示的云风险识别装置除了包含风险控制模块81之外，还包括：数据模块82和通信模块83，其中：
风险控制模块81，支持后台服务器上的规则引擎、模型引擎，基于存储的风险识别规则和接收到的风险识别请求中包含的业务数据，对产生该业务数据的业务处理请求进行风险识别。
数据模块82，支持接收各种类型数据，存储并查询各种类型数据，以及进行数据处理和数据运算。
通信模块83，接收终端设备发送的数据(包含业务数据、请求消息等等)，接收智能平台设备发送的风险识别策略数据包、风险识别规则、模型等，支持与终端设备之间的同步或者异步数据调用。
本申请实施例中所记载的云风险识别装置可以是一个基于服务器的风控系统，提供RESTFUL和SOAP的Web service接口。具备强大的计算能力以及大容量的存储空间，安全性能比较高。
实施例6
图9为本申请实施例提供的一种风险识别系统的结构示意图。所述风险识别系统包括：终端设备91和云风险识别设备92，其中：
所述终端设备91，采集业务数据，所述业务数据根据业务处理请求产生；基于存储的风险识别规则或风险识别模型，结合所述业务数据，对所述业务处理请求进行风险识别；当无法确定风险识别结果时，向所述云风险识别设备发送包含所述业务数据的风险识别请求；
所述云风险识别设备92，接收所述终端设备发送的风险识别请求，并基于存储的风险识别规则或风险识别模型，结合所述业务数据，对产生所述业务数据的业务处理请求进行风险识别。
在本申请的另一个实施例中，所述风险识别系统还包括：智能平台设备93，其中：
所述智能平台设备93，监控所述终端设备和所述云风险识别设备的运行状态，并分别向所述终端设备和所述云风险识别设备推送风险识别规则或风险识别模型。
本申请实施例中所记载的终端设备和云风险控制设备具备上述实施例中所记载的功能，这里不在详细赘述。
图10为本申请实施例提供的一种智能平台设备的结构示意图。本申请实施例中风险识别系统中包含的智能平台设备可以包含以下模块：配置 模块1001、监控模块1002和通信模块1003，其中：
配置模块1001，配置各种变量、规则和模型等；
监控模块1002，支持监控所述终端设备和所述云风险识别设备的运行状态。
通信模块1003，支持分别向所述终端设备和所述云风险识别设备推送风险识别规则。
本申请实施例中所记载的智能平台设备可以是一个集开发维护为一体的服务平台，能够用来开发、维护变量、规则以及模型等，实现本申请实施例中所记载的风险识别系统所使用的风险识别策略的动态更新。
本申请实施例提供的风险识别方案所带来的有益效果：
1、“端+云”风控解决方案，充分利用终端设备实现数据的“随采随用”，避免了将大量数据传回后台服务器，从而减少网络开销和后台服务器资源；另外本申请实施例中将关键数据和涉及复杂风险行为的数据传回后台服务器，利用“云”后台强大的计算资源来对需要进行大量计算的风险识别请求进行处理，从而保障了风控识别的精度。
2、由于目前大量的业务(例如：大部分正常交易和特别明显的问题交易)可以在“端”上完成风险识别，对于这部分的风控识别将不再产生网络时延和后台运算时间，用户体验将得到明显提升，另一方面也将缓解后台服务拥塞的情况。
3、由于一部分风控计算任务被前置到“端”上，从而降低了“云”后台的计算压力，也降低了后台的成本；另外随着“端”风控的加入，一部分风控请求将不再依赖中心化的后台，因此这种分布式的设计也提升了风控服务的可用性和容灾能力。
本领域内的技术人员应明白，本发明的实施例可提供为方法、系统、或计算机程序产品。因此，本发明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，本发明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。
本发明是参照根据本发明实施例的方法、设备(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。
还需要说明的是，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、商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、商品或者设备所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括所述要素的过程、方法、商品或者设备中还存在另外的相同要素。
本领域技术人员应明白，本申请的实施例可提供为方法、系统或计算机程序产品。因此，本申请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，本申请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。
以上所述仅为本申请的实施例而已，并不用于限制本申请。对于本领域技术人员来说，本申请可以有各种更改和变化。凡在本申请的精神和原理之内所作的任何修改、等同替换、改进等，均应包含在本申请的权利要求范围之内。

Claims (16)

  1. 一种风险识别方法，其特征在于，该方法应用于终端设备上，包括：
    采集业务数据，所述业务数据根据业务处理请求产生；
    基于存储的风险识别规则或风险识别模型，结合所述业务数据，对所述业务处理请求进行风险识别；
    当无法确定风险识别结果时，向云风险识别设备发送包含所述业务数据的风险识别请求，所述风险识别请求用于请求所述云风险识别设备对所述业务处理请求进行风险识别。
  2. 根据权利要求1所述的风险识别方法，其特征在于，所述方法还包括：
    当能够确定风险识别结果时，将确定的所述风险识别结果以及所述业务数据同步至所述云风险识别设备。
  3. 根据权利要求1所述的风险识别方法，其特征在于，所述方法还包括：
    接收所述云风险识别设备发送的风险识别结果；
    根据接收到的所述风险识别结果，对所述业务处理请求进行风险控制。
  4. 根据权利要求1至3任一项所述的风险识别方法，其特征在于，基于存储的风险识别规则或风险识别模型，结合所述业务数据，对所述业务处理请求进行风险识别，包括：
    对所述业务数据进行分析；
    基于存储的风险识别规则和分析得到的分析结果，对所述业务处理请求进行风险识别。
  5. 一种风险识别方法，其特征在于，云风险识别设备中存储的风险 识别规则与终端设备中存储的风险识别规则不同，所述方法包括：
    所述云风险识别设备接收所述终端设备发送的风险识别请求，所述风险识别请求中包含所述业务数据；
    所述云风险识别设备基于存储的风险识别规则或风险识别模型，结合所述业务数据，对产生所述业务数据的业务处理请求进行风险识别。
  6. 根据权利要求5所述的风险识别方法，其特征在于，所述方法还包括：
    所述云风险识别设备向所述终端设备发送风险识别结果，使所述终端设备根据所述风险识别结果，对所述业务处理请求进行风险控制。
  7. 根据权利要求5或6所述的风险识别方法，其特征在于，所述云风险识别设备基于存储的风险识别规则或风险识别模型，结合所述业务数据，对产生所述业务数据的业务处理请求进行风险识别，包括：
    所述云风险识别设备对所述业务数据进行分析；
    所述云风险识别设备基于存储的风险识别规则或风险识别模型，结合分析得到的分析结果，对产生所述业务数据的业务处理请求进行风险识别。
  8. 一种应用于终端设备上的风险识别装置，其特征在于，该装置包括：
    采集单元，采集业务数据，所述业务数据根据业务处理请求产生；
    风险识别单元，基于存储的风险识别规则或风险识别模型，结合所述业务数据，对所述业务处理请求进行风险识别；
    发送单元，当无法确定风险识别结果时，向云风险识别设备发送包含所述业务数据的风险识别请求，所述风险识别请求用于请求所述云风险识别设备对所述业务处理请求进行风险识别。
  9. 根据权利要求8所述的风险识别装置，其特征在于，所述装置还 包括：同步单元，其中：
    所述同步单元，当所述风险识别单元能够确定风险识别结果时，将确定的所述风险识别结果以及所述业务数据同步至所述云风险识别设备。
  10. 根据权利要求8所述的风险识别装置，其特征在于，所述装置还包括：控制单元，其中：
    所述控制单元，接收所述云风险识别设备发送的风险识别结果；
    根据接收到的所述风险识别结果，对所述业务处理请求进行风险控制。
  11. 根据权利要求8至10任一项所述的风险识别装置，其特征在于，所述风险识别单元基于存储的风险识别规则或风险识别模型，结合所述业务数据，对所述业务处理请求进行风险识别，包括：
    对所述业务数据进行分析；
    基于存储的风险识别规则或风险识别模型，结合分析得到的分析结果，对所述业务处理请求进行风险识别。
  12. 一种云风险识别装置，其特征在于，云风险识别装置中存储的风险识别规则与终端设备中存储的风险识别规则不同，所述云风险识别装置包括：
    接收单元，接收所述终端设备发送的风险识别请求，所述风险识别请求中包含所述业务数据；
    风险识别单元，基于存储的风险识别规则或风险识别模型，结合所述业务数据，对产生所述业务数据的业务处理请求进行风险识别。
  13. 根据权利要求12所述的云风险识别装置，其特征在于，所述云风险识别装置还包括：发送单元，其中：
    所述发送单元，向所述终端设备发送风险识别结果，使所述终端设备根据所述风险识别结果，对所述业务处理请求进行风险控制。
  14. 根据权利要求12或13所述的云风险识别装置，其特征在于，所述风险识别单元基于存储的风险识别规则或风险识别模型，结合所述业务数据，对产生所述业务数据的业务处理请求进行风险识别，包括：
    对所述业务数据进行分析；
    基于存储的风险识别规则或风险识别模型，结合分析得到的分析结果，对产生所述业务数据的业务处理请求进行风险识别。
  15. 一种风险识别系统，其特征在于，所述风险识别系统包括：终端设备和云风险识别设备，其中：
    所述终端设备，采集业务数据，所述业务数据根据业务处理请求产生；基于存储的风险识别规则或风险识别模型，结合所述业务数据，对所述业务处理请求进行风险识别；当无法确定风险识别结果时，向所述云风险识别设备发送包含所述业务数据的风险识别请求；
    所述云风险识别设备，接收所述终端设备发送的风险识别请求，并基于存储的风险识别规则或风险识别模型，结合所述业务数据，对产生所述业务数据的业务处理请求进行风险识别。
  16. 根据权利要求15所述的风险识别系统，其特征在于，所述风险识别系统还包括：智能平台设备，其中：
    所述智能平台设备，监控所述终端设备和所述云风险识别设备的运行状态，并分别向所述终端设备和所述云风险识别设备推送风险识别规则。
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