WO2021223657A1 - 数据交互 - Google Patents

数据交互 Download PDF

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Publication number
WO2021223657A1
WO2021223657A1 PCT/CN2021/091053 CN2021091053W WO2021223657A1 WO 2021223657 A1 WO2021223657 A1 WO 2021223657A1 CN 2021091053 W CN2021091053 W CN 2021091053W WO 2021223657 A1 WO2021223657 A1 WO 2021223657A1
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WO
WIPO (PCT)
Prior art keywords
trial
business object
risk
result
abnormal
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PCT/CN2021/091053
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English (en)
French (fr)
Inventor
周悦
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支付宝(杭州)信息技术有限公司
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Publication of WO2021223657A1 publication Critical patent/WO2021223657A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/38—Payment protocols; Details thereof
    • G06Q20/40—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
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • 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
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis

Definitions

  • the embodiments of this specification relate to the field of computer technology, and particularly relate to data interaction methods, devices, and electronic equipment.
  • the embodiments of this specification provide a data interaction method, device, and electronic equipment to quickly identify whether a business object is at risk.
  • the technical solutions of the embodiments of this specification are as follows.
  • the first aspect of the embodiments of this specification provides a data interaction method applied to an analysis server, including: receiving a risk analysis request, where the risk analysis request includes the business object to be analyzed; determining the risk index of the business object; judging the business Whether the object hits the list of abnormal business objects; send a trial request to the trial server according to the judgment result and the determination result; receive the trial result fed back by the trial server; and feed back the trial result as the risk analysis result.
  • the second aspect of the embodiments of this specification provides a data exchange method, applied to a trial server, including: receiving a trial request, the trial request includes a judgment result and a determination result, the judgment result is determined by determining whether the business object hits abnormally The list of business objects is obtained, and the determination result is obtained by determining the risk index of the business object; if the judgment result is that the list of abnormal business objects hits and the determination result is a normal risk index, check whether the business object actually hits the abnormal business object List; if it really hits the list of abnormal business objects, the abnormality will be fed back as the result of the trial.
  • the third aspect of the embodiments of this specification provides a data exchange method, applied to a trial server, including: receiving a trial request, the trial request includes a judgment result and a determination result, the judgment result is determined by determining whether the business object hits abnormally The list of business objects is obtained, and the determination result is obtained by determining the risk indicators of the business object; if the judgment result is that the list of abnormal business objects is not hit, and the determination result is an abnormal risk indicator, check whether the business object is truly at risk; If there is a risk, take the abnormality as the trial result for feedback; or, if there is no risk, take the normal as the trial result for feedback; or, if it is uncertain, use the identification of the data to be supplemented as the trial result for feedback.
  • the fourth aspect of the embodiments of this specification provides a data exchange method, applied to a trial server, including: receiving a trial request, the trial request includes a judgment result and a determination result, the judgment result is determined by determining whether the business object hits abnormally The business object list is obtained, and the determination result is obtained by determining the risk index of the business object; if the determination result is a list of abnormal business objects hitting the list, and the determination result is an abnormal risk level, check whether the business object actually hits the list of abnormal business objects ; If it really hits the list of abnormal business objects, the abnormality will be fed back as the result of the trial.
  • the fifth aspect of the embodiments of this specification provides a data exchange device, which is applied to an analysis server, and includes: a first receiving module for receiving a risk analysis request, where the risk analysis request includes a business object to be analyzed; a determining module , Used to determine the risk indicators of the business object; the judgment module, used to judge whether the business object hits the list of abnormal business objects; the sending module, used to send the trial request to the trial server based on the judgment result and the determination result; the second receiving module, used To receive the trial result fed back by the trial server; the feedback module is used to feed back the trial result as a risk analysis result.
  • the sixth aspect of the embodiments of this specification provides a data exchange device, which is applied to a trial server, and includes: a receiving module for receiving a trial request, the trial request includes a judgment result and a determination result, and the judgment result passes the judgment Whether the business object hits the list of abnormal business objects is obtained, and the determination result is obtained by determining the risk index of the business object; the verification module is used for if the judgment result is a list of abnormal business objects hit, and the determination result is a normal risk index , To check whether the business object actually hits the abnormal business object list; the feedback module is used to feed back the abnormality as the trial result if it actually hits the abnormal business object list.
  • the seventh aspect of the embodiments of this specification provides a data exchange device, which is applied to a trial server, and includes: a receiving module for receiving a trial request, the trial request includes a judgment result and a determination result, and the judgment result passes the judgment Whether the business object hits the abnormal business object list is obtained, the determination result is obtained by determining the risk index of the business object; the investigation module is used for if the judgment result is that the abnormal business object list is not hit, and the determination result is abnormal risk Indicators to check whether the business object is truly at risk; the feedback module is used to feed back the abnormality as the trial result if there is a risk; or, if there is no risk, take normal as the trial result for feedback; or if it is uncertain, it will wait The identification of the supplementary data shall be fed back as the result of the trial.
  • the eighth aspect of the embodiments of this specification provides a data exchange device, which is applied to a trial server, and includes: a receiving module for receiving a trial request.
  • the trial request includes a judgment result and a determination result.
  • the judgment result passes the judgment. Whether the business object hits the abnormal business object list is obtained, and the determination result is obtained by determining the risk index of the business object; the trial module is configured to, if the judgment result is that the abnormal business object list is hit, and the determination result is an abnormal risk level, Check whether the business object actually hits the abnormal business object list; the feedback module is used to feed back the abnormality as the trial result if it actually hits the abnormal business object list.
  • the ninth aspect of the embodiments of this specification provides an electronic device, including at least one processor and a memory storing program instructions.
  • the program instructions are configured to be adapted to be executed by the at least one processor, and the program instructions include instructions for executing the method according to the first aspect, the second aspect, the third aspect, or the fourth aspect.
  • the analysis server after receiving the risk analysis request, can determine the risk index of the business object, and can determine whether the business object hits the abnormal business object list; it can combine the judgment result and the determination result to provide unified feedback on the risk analysis result. This is helpful to quickly determine whether the business object is at risk.
  • Fig. 1 is a schematic flow diagram of a data interaction method in an embodiment of this specification
  • FIG. 2 is a schematic flowchart of the data interaction method in the embodiment of this specification.
  • FIG. 3 is a schematic flowchart of a data interaction method in an embodiment of this specification
  • FIG. 4 is a schematic flowchart of a data interaction method in an embodiment of this specification.
  • FIG. 5 is a schematic flowchart of a data interaction method in an embodiment of the specification.
  • FIG. 6 is a schematic diagram of the structure of the data interaction device in the embodiment of this specification.
  • FIG. 7 is a schematic diagram of the structure of the data interaction device in the embodiment of the specification.
  • FIG. 8 is a schematic diagram of the structure of the data interaction device in the embodiment of the specification.
  • FIG. 9 is a schematic diagram of the structure of the data interaction device in the embodiment of the specification.
  • FIG. 10 is a schematic diagram of the structure of the electronic device in the embodiment of the specification.
  • the business object it can be judged whether the business object is a sanctioned business object; if so, the judgment result can be reviewed to verify whether the business object is indeed a sanctioned business object.
  • the risk level of the business object can be determined; if the determination result is a high risk level, the determination result can be reviewed to verify whether the business object actually has a risk.
  • the two aspects of identifying whether a business object is at risk are carried out independently and are not related to each other. In this way, the judgment result and the determination result need to be heard separately. It takes a long time to conduct two trials, which makes it less efficient to identify whether the business object is at risk.
  • two aspects of identifying whether a business object has risks are carried out independently, two types of risk identification results can be obtained. In this way, in some cases, it is possible to obtain two completely opposite risk identification results.
  • the embodiment of this specification provides a data interaction method.
  • the implementation environment of the data interaction method may include a business server, an analysis server, and a review server.
  • the service server is used to provide service data to the analysis server.
  • the analysis server is used to analyze the risk situation of the business object involved in the business data.
  • the review server is used to review the risk situation of the business object.
  • the trial may refer to the review and verification of whether the business object is truly at risk.
  • the business server, the risk server, and the trial server may all be a single server, a server cluster composed of multiple servers, or a server deployed in the cloud.
  • the data interaction method can be applied to anti-money laundering business scenarios. Of course, the data interaction method can also be applied to other business scenarios, such as anti-fraud business scenarios, anti-Internet gambling business scenarios, and so on. Please refer to Figure 1.
  • the data interaction method may include the following steps.
  • Step S102 The business server sends a risk analysis request to the analysis server.
  • the risk analysis request may include a business object to be analyzed, and the business object may include enterprise users, individual users, enterprise accounts, personal accounts, and so on.
  • the business server may send a risk analysis request to the analysis server, the risk analysis request may include the business data, and the business object to be analyzed may include the business The business objects involved in the data.
  • the service server can receive the service data input by the user in the terminal device.
  • the service server may also obtain service data in other ways, which is not specifically limited in this embodiment.
  • the business data may include transaction data, and the transaction data may include the payer, the payee, the transaction amount, the transaction type, the transaction time, the geographic location of the payer, and the delivery address.
  • the business objects involved in the transaction data may include the payer and/or the payee.
  • the business data may also include other forms of data, for example, it may also include product review data or entry data.
  • the entry data may include data submitted when the merchant enters the online platform, such as a business license, an ID card, and a collection account.
  • the online platforms may include Tmall, Suning.com, Pinduoduo, etc.
  • Step S104 The analysis server receives the risk analysis request.
  • Step S106 The analysis server determines the risk index of the business object and determines whether the business object hits the abnormal business object list.
  • the risk analysis request may include business data.
  • the analysis server can obtain the business object to be analyzed from the business data; can determine the risk index of the business object and obtain the determination result; and can obtain the judgment result whether the business object hits the abnormal business object list.
  • the risk indicator is used to characterize the possibility that the business object has risks, and the risks may include money laundering risks, fraud risks, online gambling risks, and the like.
  • the risk indicator may include a risk level, such as a credit risk rating (Credit Risk Rating, CRR).
  • CRR Credit Risk Rating
  • the risk level may be taken from a set of risk levels, and the set of risk levels may include at least two risk levels.
  • the set of risk levels may include a high risk level and a low risk level.
  • the risk level may include a low risk level, a medium risk level, and a high risk level.
  • the risk indicator may also include other forms, such as scores.
  • the determination result may be a normal risk indicator or an abnormal risk indicator.
  • the risk indicator may include a risk level, the risk level may be taken from a risk level set, and the risk level set may include a high risk level and a low risk level. Then, normal risk indicators may include the low risk level, and abnormal risk indicators may include the high risk level.
  • the risk indicator may include a risk level, the risk level may be taken from a risk level set, and the risk level set may include a low risk level, a medium risk level, and a high risk level. Then, normal risk indicators may include the low risk level and the medium risk level, and abnormal risk indicators may include the high risk level.
  • the risk indicator may include a score. Then, normal risk indicators may include scores less than a certain threshold, and abnormal risk indicators may include scores greater than the threshold.
  • the analysis server may use the risk prediction rules to determine the risk index of the business object, and obtain the determination result.
  • the risk prediction rule may include data processing logic constructed through expert experience.
  • the risk prediction rule may also include a model trained through machine learning.
  • the analysis server may input the business data into the risk prediction rule to determine the risk index of the business object.
  • the list of abnormal business objects may include at least one abnormal business object.
  • the abnormal business object may refer to a business object that is at risk, such as a business object that is sanctioned.
  • the business object that is sanctioned may include a business object that is suspected of money laundering, a business object that is suspected of fraud, a business object that is suspected of online gambling, and so on.
  • the judgment result may be that the list of abnormal business objects is hit or the list of abnormal business objects is not hit.
  • the list of abnormal business objects hits may mean that the list of abnormal business objects includes a list of abnormal business objects that match the business objects.
  • Failure to hit the abnormal business object list may mean that the abnormal business object list does not include the abnormal business object list that matches the business object.
  • the analysis server can match the business object in the abnormal business object list; if one or more abnormal business objects are successfully matched, it can be judged that the business object hits the abnormal business object list; if no one is matched Or multiple abnormal business objects, it can be determined that the business object does not hit the abnormal business object list.
  • the process of determining the risk indicator of the business object and the process of judging whether the business object hits the abnormal business object list may be performed sequentially.
  • the analysis server may first determine the risk index of the business object, and then determine whether the business object hits the list of abnormal business objects.
  • the analysis server may first determine whether the business object hits the abnormal business object list, and then determine whether the business object hits the abnormal business object list.
  • the process of determining the risk index of the business object and the process of judging whether the business object hits the abnormal business object list can also be executed in parallel.
  • Step S108 The analysis server feeds back normal as the risk analysis result.
  • the analysis server can determine the risk index of the business object and determine whether the business object hits the abnormal business object list. If the determination result is a normal risk indicator, and the judgment result is that the list of abnormal business objects is not hit, the analysis server may consider that the business object is not at risk; may use normal as the risk analysis result, and may report to the The business server feeds back the risk analysis result.
  • Step S110 The analysis server sends a review request to the review server.
  • the analysis server can determine the risk index of the business object and determine whether the business object hits the abnormal business object list. If the determination result is an abnormal risk indicator and the judgment result is an abnormal business object list, or if the determination result is an abnormal risk indicator and the judgment result is no abnormal business object list, or If the determination result is a normal risk indicator, and the judgment result is a list of abnormal business objects, the analysis server may consider that the business object is suspected of being at risk; it may send a trial request to the trial server so that all The review server reviews and verifies whether the business object is truly at risk.
  • the trial request may include the determination result and the judgment result.
  • the trial request may also include other content.
  • the trial request may also include the business data.
  • Step S112 The trial server receives the trial request.
  • Step S114 If the judgment result is that the abnormal business object list is hit, and the determination result is a normal risk index, the audit server checks whether the business object actually hits the abnormal business object list.
  • the analysis server determines that the business object hits the abnormal business object list, in some cases (for example, the user has the same name), the business object may actually hit the abnormal business object list, or it is possible There is no list of real business objects that hit anomalies. Therefore, after receiving the trial request, if the judgment result is that the abnormal business object list is hit, and the determination result is a normal risk indicator, the trial server can check whether the business object actually hits the abnormal business object list.
  • the review server may provide the reviewer with the business object and the list of abnormal business objects matching the business object in the list of abnormal business objects.
  • the reviewer checks whether the business object actually hits the list of abnormal business objects.
  • the review server may send the business object and the list of abnormal business objects matching the business object in the list of abnormal business objects to a specific server; may receive the verification input by the reviewer in the specific server result.
  • Step S116 If the list of abnormal business objects is actually hit, the trial server will feedback the abnormality as the trial result.
  • the trial server can check whether the business object actually hits the abnormal business object list. If the list of abnormal business objects is truly hit, the trial server may consider that the business object is at risk; the abnormality may be used as a trial result, and the trial result may be fed back to the analysis server.
  • Step S118 If there is no real hit in the list of abnormal business objects, the trial server judges whether the business object hits the list of suspected abnormal business objects.
  • the list of suspected abnormal business objects may include at least one suspected abnormal business object.
  • the suspected abnormal business object may refer to a business object that is suspected of being at risk, such as a business object that is suspected of money laundering, a business object that is suspected of fraud, and a business object that is suspected of online gambling.
  • the suspected abnormal business object may include PEP.
  • the trial server can check whether the business object actually hits the abnormal business object list. If there is no real hit in the list of abnormal business objects, the trial server may match the business object in the list of suspected abnormal business objects; if one or more abnormal business objects are successfully matched, it may be judged that the business object hits A list of suspected abnormal business objects; if one or more abnormal business objects are not matched, it can be determined that the business object does not hit the list of suspected abnormal business objects.
  • Step S120 If there is no hit on the list of suspected abnormal business objects, the trial server will respond normally as the trial result.
  • the trial server can determine whether the business object hits the list of suspected abnormal business objects. If there is no hit on the list of suspected abnormal business objects, the trial server may consider that the business object is not at risk; normal can be regarded as the trial result, and the trial result can be fed back to the analysis server.
  • Step S122 If the list of suspected abnormal business objects is hit, the trial server checks whether the business object is truly at risk.
  • the trial server can determine whether the business object hits the list of suspected abnormal business objects. If it hits the list of suspected abnormal business objects, the trial server can consider that the business object is suspected of being at risk; it can check whether the business object is truly at risk.
  • the review server may provide the business object (or business data related to the business object) to reviewers.
  • the auditors shall verify whether the business object is truly at risk through EDD (Enhanced Due Diligence).
  • the review server may send the business object to a specific server; may receive the verification result input by the reviewer in the specific server.
  • Step S124 If there is a risk, the trial server will feed back the abnormality as the trial result; or, if there is no risk, the trial server will feed back normally as the trial result; or, if it is uncertain, use the identification of the data to be supplemented as the trial result. Feedback.
  • the trial server can check whether the business object is truly at risk. If there is a risk, the trial server can use the exception as a trial result, and can feed back the trial result to the analysis server. Or, if there is no risk, the trial server may regard normal as the trial result, and may feed back the trial result to the analysis server. Or, in some cases, some data (such as ID cards, business licenses, etc.) may be missing, making the trial server unable to determine whether the business object is truly at risk. In this way, the review server can use the identifier of the data to be supplemented as the review result, and can feed back the review result to the analysis server. The identification of the data to be supplemented may include, for example, the name and serial number of the data to be supplemented.
  • Step S126 If the judgment result is that the list of abnormal business objects is not hit, and the result is an abnormal risk indicator, the trial server checks whether the business object is truly at risk.
  • the analysis server determines that the risk indicator of the business object is an abnormal risk indicator, the business object may or may not be at risk. Therefore, after receiving the trial request, if the judgment result is that the list of abnormal business objects is not hit, and the determination result is an abnormal risk indicator, the trial server can check whether the business object is truly at risk. The process of verifying whether the business object actually has a risk can refer to the previous step S122, which will not be repeated here.
  • Step S128 If the judgment result is that the abnormal business object list is hit, and the determination result is the abnormal risk level, the trial server checks whether the business object actually hits the abnormal business object list.
  • the analysis server determines that the business object hits the abnormal business object list, in some cases (for example, the user has the same name), the business object may actually hit the abnormal business object list, or it is possible There is no list of real business objects that hit anomalies. Therefore, after receiving the trial request, if the judgment result is that the abnormal business object list is hit, and the determination result is the abnormal risk level, the trial server can check whether the business object actually hits the abnormal business object list. For the process of checking whether the business object actually hits the abnormal business object list, refer to the previous step S114, which will not be repeated here.
  • Step S130 If there is no list of business objects that actually hit the abnormal business object, the trial server checks whether the business object is truly at risk.
  • the trial server can check whether the business object actually hits the abnormal business object list. If there is no real hit list of abnormal business objects, the trial server can check whether the business objects are truly at risk. The process of verifying whether the business object actually has a risk can refer to the previous step S122, which will not be repeated here.
  • the trial server can directly check whether the business object is truly at risk. Or, if there is no actual hit on the list of abnormal business objects, the trial server can also determine whether the business object hits the list of suspected abnormal business objects; if there is no hit on the list of suspected abnormal business objects, it can check whether the business object is truly at risk; if the hit is suspected to be abnormal
  • the list of business objects can be used to check whether the business objects are truly at risk. For the specific process, refer to the previous step S118, which will not be repeated here.
  • Step S132 If the list of abnormal business objects is truly hit, the trial server will feedback the abnormality as the trial result.
  • the trial server can check whether the business object actually hits the abnormal business object list. If the list of abnormal business objects is truly hit, the trial server may consider that the business object is at risk; the abnormality may be used as a trial result, and the trial result may be fed back to the analysis server.
  • Step S134 The analysis server feeds back the review result as the risk analysis result.
  • the review server may feed back the review result to the analysis server.
  • the analysis server can receive the trial result; the trial result can be used as the risk analysis result, and the risk analysis result can be fed back to the business server.
  • Step S136 The service server receives the risk analysis result.
  • the analysis server may feed back the risk analysis result to the business server.
  • the service server may receive the risk analysis result.
  • the service server may perform corresponding operations according to the received risk analysis result.
  • the business data may include transaction data. If the risk analysis result is normal, the business server can execute the transaction normally according to the transaction data. If the risk analysis result is abnormal, the service server may refuse to execute the transaction; it may send a prompt message indicating that the transaction is refused to be executed to the user's terminal device.
  • the business server can send a risk analysis request to the analysis server.
  • the analysis server can receive the risk analysis request; it can determine the risk index of the business object; it can combine whether the business object hits the abnormal business object list; it can uniformly feedback the risk analysis result to the business server according to the judgment result and the determination result. In this way, the business server can quickly obtain the risk analysis results. This facilitates the business server to quickly determine whether the business object is at risk. In addition, by uniformly feeding back the risk analysis results to the business server, it can also prevent the business server from obtaining two completely opposite risk analysis results.
  • the data interaction method can be implemented by the analysis server in the embodiment corresponding to FIG. 1. Please refer to FIG. 2, the data interaction method may include the following steps.
  • Step S202 Receive a risk analysis request.
  • the risk analysis request may include the business object to be analyzed.
  • the business server may send a risk analysis request to the analysis server.
  • the analysis server may receive the risk analysis request.
  • the risk analysis request may include the business data
  • the business object to be analyzed may include the business object involved in the business data.
  • Step S204 Determine the risk index of the business object.
  • Step S206 Determine whether the business object hits the abnormal business object list.
  • Step S208 Send a trial request to the trial server according to the judgment result and the determination result.
  • the analysis server may consider that the business object is suspected of being at risk; it may report to the trial server Send a trial request so that the trial server can review and verify whether the business object is truly at risk.
  • the trial request may include the determination result and the judgment result.
  • the trial request may also include other content.
  • the trial request may also include the business data.
  • Step S210 Receive the trial result fed back by the trial server.
  • the trial server can receive a trial request; can process the trial request to obtain a trial result; and can feed back the trial result to the analysis server.
  • the analysis server may receive the trial result.
  • For the process of obtaining the trial result refer to the embodiment corresponding to FIG. 1.
  • Step S212 feedback the trial result as the risk analysis result.
  • the analysis server can receive the trial result; the trial result can be used as the risk analysis result, and the risk analysis result can be fed back to the business server.
  • the analysis server may feed back normal as the risk analysis result.
  • the business server can send a risk analysis request to the analysis server.
  • the analysis server can receive the risk analysis request; it can determine the risk index of the business object; it can determine whether the business object hits the abnormal business object list; it can combine the judgment result and the determination result to uniformly feed back the risk analysis result to the business server.
  • the business server can quickly obtain the risk analysis results. This is beneficial to quickly determine whether the business object is at risk.
  • by uniformly feeding back the risk analysis results to the business server it can also prevent the business server from obtaining two completely opposite risk analysis results.
  • the data interaction method can be implemented by the trial server in the embodiment corresponding to FIG. 1.
  • the data interaction method may include the following steps.
  • Step S302 Receive a trial request.
  • the analysis server may send a trial request to the trial server.
  • the trial server may receive the trial request.
  • the trial request may include a judgment result and a determination result.
  • the judgment result can be obtained by judging whether the business object hits the abnormal business object list.
  • the determination result can be obtained by determining the risk index of the business object. For the process of obtaining the judgment result and the determination result, refer to the previous step S106.
  • Step S304 If the judgment result is that the abnormal business object list is hit, and the determination result is a normal risk indicator, check whether the business object actually hits the abnormal business object list.
  • Step S306 If it really hits the list of abnormal business objects, feedback the abnormality as a result of the trial.
  • the trial server can determine whether the business object hits the list of suspected abnormal business objects; if there is no hit in the list of suspected abnormal business objects, it can feed back normal as the trial result; if By hitting the list of suspected abnormal business objects, you can check whether the business objects are truly at risk. If there is a risk, the trial server can feed back the abnormality as the trial result. Or, if there is no risk, the trial server can feed back normal as the trial result. Or, if unsure, the trial server can feed back the identification of the data to be supplemented as the trial result.
  • the analysis server can send a review request to the review server.
  • the trial server can receive the trial request; and can uniformly feed back the trial result to the analysis server according to the determination result and the judgment result in the trial request. This is helpful to quickly determine whether the business object is at risk.
  • the data interaction method can be implemented by the trial server in the embodiment corresponding to FIG. 1.
  • the data interaction method may include the following steps.
  • Step S402 Receive a trial request.
  • the analysis server may send a trial request to the trial server.
  • the trial server may receive the trial request.
  • the trial request may include a judgment result and a determination result.
  • the judgment result can be obtained by judging whether the business object hits the abnormal business object list.
  • the determination result can be obtained by determining the risk index of the business object. For the process of obtaining the judgment result and the determination result, refer to the previous step S106.
  • Step S404 If the judgment result is that the list of abnormal business objects is not hit, and the determination result is an abnormal risk index, check whether the business object is truly at risk.
  • Step S406 If there is a risk, feed back the abnormality as the trial result; or if there is no risk, feed back the normal as the trial result; or, if it is uncertain, feed back the identification of the data to be supplemented as the trial result.
  • the analysis server can send a review request to the review server.
  • the trial server can receive the trial request; and can uniformly feed back the trial result to the analysis server according to the determination result and the judgment result in the trial request. This is helpful to quickly determine whether the business object is at risk.
  • the data interaction method can be implemented by the trial server in the embodiment corresponding to FIG. 1.
  • the data interaction method may include the following steps.
  • Step S502 Receive a request for trial.
  • the analysis server may send a trial request to the trial server.
  • the trial server may receive the trial request.
  • the trial request may include a judgment result and a determination result.
  • the judgment result can be obtained by judging whether the business object hits the abnormal business object list.
  • the determination result can be obtained by determining the risk index of the business object. For the process of obtaining the judgment result and the determination result, refer to the previous step S106.
  • Step S504 If the judgment result is that the abnormal business object list is hit, and the determination result is the abnormal risk level, check whether the business object actually hits the abnormal business object list.
  • Step S506 If it really hits the list of abnormal business objects, feedback the abnormality as the trial result.
  • the trial server can check whether the business object is truly at risk. If there is a risk, the trial server can feed back the abnormality as the trial result. Or, if there is no risk, the trial server can feed back normal as the trial result. Or, if unsure, the trial server can feed back the identification of the data to be supplemented as the trial result.
  • the analysis server can send a review request to the review server.
  • the trial server can receive the trial request; and can uniformly feed back the trial result to the analysis server according to the determination result and the judgment result in the trial request. This is helpful to quickly determine whether the business object is at risk.
  • the data interaction device can be applied to an analysis server, and specifically can include the following module units.
  • the first receiving module 602 is configured to receive a risk analysis request, where the risk analysis request includes the business object to be analyzed;
  • the determining module 604 is used to determine the risk index of the business object
  • the judging module 606 is used to judge whether the business object hits the abnormal business object list
  • the sending module 608 is used to send a trial request to the trial server according to the judgment result and the determination result;
  • the second receiving module 610 is configured to receive the trial result fed back by the trial server
  • the feedback module 612 is configured to feed back the trial result as a risk analysis result.
  • the data interaction device may be applied to a trial server, and may specifically include the following module units.
  • the receiving module 702 is configured to receive a trial request, the trial request including a judgment result and a determination result, the judgment result is obtained by judging whether the business object hits the abnormal business object list, and the determination result is obtained by determining the risk index of the business object;
  • the verification module 704 is configured to check whether the business object actually hits the abnormal business object list if the judgment result is a list of abnormal business objects and the determination result is a normal risk indicator;
  • the feedback module 706 is configured to feed back the abnormality as a trial result if it actually hits the list of abnormal business objects.
  • the data interaction device may be applied to a trial server, and may specifically include the following module units.
  • the receiving module 802 is configured to receive a trial request, the trial request including a judgment result and a determination result, the judgment result is obtained by judging whether the business object hits the abnormal business object list, and the determination result is obtained by determining the risk index of the business object;
  • the investigation module 804 is configured to, if the judgment result is that the list of abnormal business objects is not hit, and the determination result is an abnormal risk index, check whether the business object is truly at risk;
  • the feedback module 806 is used to feed back the abnormality as the trial result if there is a risk; or, if there is no risk, use the normal as the trial result for feedback; or, if it is uncertain, use the identification of the data to be supplemented as the trial result for feedback .
  • the data interaction device can be applied to a trial server, and specifically can include the following module units.
  • the receiving module 902 is configured to receive a trial request, the trial request including a judgment result and a determination result, the judgment result is obtained by judging whether the business object hits the abnormal business object list, and the determination result is obtained by determining the risk index of the business object;
  • the trial module 904 is configured to check whether the business object actually hits the abnormal business object list if the judgment result is an abnormal business object list and the determination result is an abnormal risk level;
  • the feedback module 906 is used to feed back the abnormality as a trial result if it actually hits the list of abnormal business objects.
  • FIG. 10 is a schematic diagram of the hardware structure of the electronic device in this embodiment.
  • the electronic device may include one or more (only one is shown in the figure) processor, memory, and transmission module.
  • processor any electronic device that can be included in the electronic device.
  • memory any type of memory
  • transmission module any suitable transmission module.
  • the hardware structure shown in FIG. 10 is only for illustration, and it does not limit the hardware structure of the above electronic device.
  • the electronic device may also include more or fewer component units than shown in FIG. 10; or, it may have a configuration different from that shown in FIG. 10.
  • the memory may include a high-speed random access memory; or, may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory.
  • the storage may also include a remotely set network storage.
  • the remotely set network storage can be connected to the electronic device through a network such as the Internet, an intranet, a local area network, a mobile communication network, and the like.
  • the memory may be used to store program instructions or modules of application software, for example, the program instructions or modules of the embodiments corresponding to FIGS. 2 to 5 in this specification.
  • the processor can be implemented in any suitable way.
  • the processor may take the form of, for example, a microprocessor or a processor and a computer-readable medium storing computer-readable program codes (for example, software or firmware) executable by the (micro)processor, logic gates, switches, special-purpose integrated Circuit (Application Specific Integrated Circuit, ASIC), programmable logic controller and embedded microcontroller form, etc.
  • the processor can read and execute program instructions or modules in the memory.
  • the transmission module may be used for data transmission via a network, for example, data transmission via a network such as the Internet, an enterprise intranet, a local area network, a mobile communication network, and the like.
  • a network such as the Internet, an enterprise intranet, a local area network, a mobile communication network, and the like.
  • the computer storage medium includes but is not limited to random access memory (Random Access Memory, RAM), read-only memory (Read-Only Memory, ROM), cache (Cache), hard disk (Hard Disk Drive, HDD), memory card ( Memory Card) and so on.
  • the computer storage medium stores computer program instructions. It is realized when the computer program instructions are executed: the program instructions or modules of the embodiments corresponding to FIGS. 2 to 5 in this specification.
  • the improvement of a technology can be clearly distinguished between hardware improvements (for example, improvements in circuit structures such as diodes, transistors, switches, etc.) or software improvements (improvements in method flow).
  • hardware improvements for example, improvements in circuit structures such as diodes, transistors, switches, etc.
  • software improvements improvements in method flow.
  • the improvement of many methods and processes of today can be regarded as a direct improvement of the hardware circuit structure.
  • Designers almost always get the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that the improvement of a method flow cannot be realized by the hardware entity module.
  • a programmable logic device for example, a Field Programmable Gate Array (Field Programmable Gate Array, FPGA)
  • PLD Programmable Logic Device
  • FPGA Field Programmable Gate Array
  • HDL Hardware Description Language
  • ABEL Advanced Boolean Expression Language
  • AHDL Altera Hardware Description Language
  • HDCal JHDL
  • Lava Lava
  • Lola MyHDL
  • PALASM RHDL
  • VHDL Very-High-Speed Integrated Circuit Hardware Description Language
  • Verilog2 Verilog2
  • a typical implementation device is a computer.
  • the computer may be, for example, a personal computer, a laptop computer, a cell phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or Any combination of these devices.
  • This manual can be used in many general-purpose or special-purpose computer system environments or configurations.
  • program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types.
  • This specification can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network.
  • program modules can be located in local and remote computer storage media including storage devices.

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Abstract

一种数据交互方法、装置和电子设备。所述方法包括：接收风险分析请求（S202），所述风险分析请求包括待分析的业务对象；确定业务对象的风险指标（S204）；判断业务对象是否命中异常业务对象名单（S206）；根据判断结果和确定结果，向审理服务器发送审理请求（S208）；接收审理服务器反馈的审理结果（S210）；将所述审理结果作为风险分析结果进行反馈（S212）。所述方法可以快速地识别业务对象是否存在风险。

Description

数据交互 技术领域
本说明书实施例涉及计算机技术领域，特别涉及数据交互方法、装置和电子设备。
背景技术
随着互联网的发展，需要识别业务对象是否存在风险。如何快速地识别业务对象是否存在风险，是当前亟需解决的技术问题。
发明内容
本说明书实施例提供一种数据交互方法、装置和电子设备，以快速地识别业务对象是否存在风险。本说明书实施例的技术方案如下。
本说明书实施例的第一方面，提供了一种数据交互方法，应用于分析服务器，包括：接收风险分析请求，所述风险分析请求包括待分析的业务对象；确定业务对象的风险指标；判断业务对象是否命中异常业务对象名单；根据判断结果和确定结果，向审理服务器发送审理请求；接收审理服务器反馈的审理结果；将所述审理结果作为风险分析结果进行反馈。
本说明书实施例的第二方面，提供了一种数据交互方法，应用于审理服务器，包括：接收审理请求，所述审理请求包括判断结果和确定结果，所述判断结果通过判断业务对象是否命中异常业务对象名单得到，所述确定结果通过确定业务对象的风险指标得到；若所述判断结果为命中异常业务对象名单、并且所述确定结果为正常的风险指标，核查业务对象是否真实命中异常业务对象名单；若真实命中异常业务对象名单，将异常作为审理结果进行反馈。
本说明书实施例的第三方面，提供了一种数据交互方法，应用于审理服务器，包括：接收审理请求，所述审理请求包括判断结果和确定结果，所述判断结果通过判断业务对象是否命中异常业务对象名单得到，所述确定结果通过确定业务对象的风险指标得到；若所述判断结果为没有命中异常业务对象名单、并且所述确定结果为异常的风险指标，核查业务对象是否真实存在风险；若存在风险，将异常作为审理结果进行反馈；或者，若不存在风险，将正常作为审理结果进行反馈；或者，若不确定，将待补充数据的标识 作为审理结果进行反馈。
本说明书实施例的第四方面，提供了一种数据交互方法，应用于审理服务器，包括：接收审理请求，所述审理请求包括判断结果和确定结果，所述判断结果通过判断业务对象是否命中异常业务对象名单得到，所述确定结果通过确定业务对象的风险指标得到；若所述判断结果为命中异常业务对象名单、并且所述确定结果为异常险级别，核查业务对象是否真实命中异常业务对象名单；若真实命中异常业务对象名单，将异常作为审理结果进行反馈。
本说明书实施例的第五方面，提供了一种数据交互装置，应用于分析服务器，包括：第一接收模块，用于接收风险分析请求，所述风险分析请求包括待分析的业务对象；确定模块，用于确定业务对象的风险指标；判断模块，用于判断业务对象是否命中异常业务对象名单；发送模块，用于根据判断结果和确定结果，向审理服务器发送审理请求；第二接收模块，用于接收审理服务器反馈的审理结果；反馈模块，用于将所述审理结果作为风险分析结果进行反馈。
本说明书实施例的第六方面，提供了一种数据交互装置，应用于审理服务器，包括：接收模块，用于接收审理请求，所述审理请求包括判断结果和确定结果，所述判断结果通过判断业务对象是否命中异常业务对象名单得到，所述确定结果通过确定业务对象的风险指标得到；核查模块，用于若所述判断结果为命中异常业务对象名单、并且所述确定结果为正常的风险指标，核查业务对象是否真实命中异常业务对象名单；反馈模块，用于若真实命中异常业务对象名单，将异常作为审理结果进行反馈。
本说明书实施例的第七方面，提供了一种数据交互装置，应用于审理服务器，包括：接收模块，用于接收审理请求，所述审理请求包括判断结果和确定结果，所述判断结果通过判断业务对象是否命中异常业务对象名单得到，所述确定结果通过确定业务对象的风险指标得到；调查模块，用于若所述判断结果为没有命中异常业务对象名单、并且所述确定结果为异常的风险指标，核查业务对象是否真实存在风险；反馈模块，用于若存在风险，将异常作为审理结果进行反馈；或者，若不存在风险，将正常作为审理结果进行反馈；或者，若不确定，将待补充数据的标识作为审理结果进行反馈。
本说明书实施例的第八方面，提供了一种数据交互装置，应用于审理服务器，包括：接收模块，用于接收审理请求，所述审理请求包括判断结果和确定结果，所述判断结果通过判断业务对象是否命中异常业务对象名单得到，所述确定结果通过确定业务对象的风险指标得到；审理模块，用于若所述判断结果为命中异常业务对象名单、并且所述确 定结果为异常险级别，核查业务对象是否真实命中异常业务对象名单；反馈模块，用于若真实命中异常业务对象名单，将异常作为审理结果进行反馈。
本说明书实施例的第九方面，提供了一种电子设备，包括至少一个处理器和存储有程序指令的存储器。其中，所述程序指令被配置为适于由所述至少一个处理器执行，所述程序指令包括用于执行如第一方面、第二方面、第三方面或第四方面所述方法的指令。
本说明书实施例提供的技术方案，在接收到风险分析请求以后，分析服务器可以确定业务对象的风险指标，可以判断业务对象是否命中异常业务对象名单；可结合判断结果和确定结果，统一反馈风险分析结果。这样有利于快速地确定业务对象是否存在风险。
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，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为本说明书实施例中数据交互方法的流程示意图；
图2为本说明书实施例中数据交互方法的流程示意图；
图3为本说明书实施例中数据交互方法的流程示意图；
图4为本说明书实施例中数据交互方法的流程示意图；
图5为本说明书实施例中数据交互方法的流程示意图；
图6为本说明书实施例中数据交互装置的结构示意图；
图7为本说明书实施例中数据交互装置的结构示意图；
图8为本说明书实施例中数据交互装置的结构示意图；
图9为本说明书实施例中数据交互装置的结构示意图；
图10为本说明书实施例中电子设备的结构示意图。
具体实施方式
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。
在相关技术中，一方面，可以判断业务对象是否为受到制裁的业务对象；若是，可以对所述判断结果进行审理，以便核实所述业务对象是否确实为受到制裁的业务对象。另一方面，可以确定业务对象的风险等级；若确定结果为高风险等级，可以对所述确定结果进行审理，以便核实所述业务对象是否真实存在风险。在上述相关技术中，识别业务对象是否存在风险的两个方面是分别独立进行的，相互之间没有关联性。这样需要分别对判断结果和确定结果进行审理。进行行两次审理所花费的时间较长，使得识别业务对象是否存在风险的效率较低。此外，由于识别业务对象是否存在风险的两个方面是分别独立进行的，因而可以获得两种风险识别结果。这样在一些情况下，有可能获得两种完全相反的风险识别结果。
本说明书实施例提供一种数据交互方法。所述数据交互方法的实施环境可以包括业务服务器、分析服务器和审理服务器。所述业务服务器用于向所述分析服务器提供业务数据。所述分析服务器用于对业务数据涉及的业务对象的风险情况进行分析。所述审理服务器用于对业务对象的风险情况进行审理。所述审理可以指审查核实业务对象是否真实存在风险。所述业务服务器、所述风险服务器和所述审理服务器均可以为单个服务器、由多个服务器构成的服务器集群、或者部署在云端的服务器。所述数据交互方法可以应用于反洗钱的业务场景中。当然，所述数据交互方法还可以应用于其它的业务场景，例如反诈骗的业务场景、反网络赌博的业务场景等。请参阅图1。所述数据交互方法可以包括以下步骤。
步骤S102：业务服务器向分析服务器发送风险分析请求。
在一些实施例中，所述风险分析请求可以包括待分析的业务对象，所述业务对象可以包括企业用户、个人用户、企业账户、个人账户等。在实际应用中，在获得业务数据以后，所述业务服务器可以向所述分析服务器发送风险分析请求，所述风险分析请求可以包括所述业务数据，所述待分析的业务对象可以包括所述业务数据涉及的业务对象。所述业务服务器可以接收用户在终端设备输入的业务数据。当然，所述业务服务器还可以采用其它的方式获得业务数据，本实施例不做具体限定。
例如，所述业务数据可以包括交易数据，所述交易数据可以包括付款方、收款方、交易金额、交易类型、交易时间、付款方的地理位置和收货地址等。所述交易数据涉及 的业务对象可以包括付款方和/或收款方。当然所述业务数据还可以包括其它形式的数据，例如还可以包括商品评论数据或者入驻数据。所述入驻数据可以包括商家入驻线上平台时提交的数据，例如营业执照、身份证、收款账户等。所述线上平台可以包括天猫、苏宁易购、拼多多等。
步骤S104：分析服务器接收风险分析请求。
步骤S106：分析服务器确定业务对象的风险指标、以及判断业务对象是否命中异常业务对象名单。
在一些实施例中，所述风险分析请求可以包括业务数据。所述分析服务器可以从所述业务数据中获取待分析的业务对象；可以确定所述业务对象的风险指标，得到确定结果；可以所述业务对象是否命中异常业务对象名单，得到判断结果。
在一些实施例中，所述风险指标用于表征所述业务对象存在风险的可能性，所述风险可以包括洗钱风险、诈骗风险、网络赌博风险等。所述风险指标可以包括风险等级，例如信用风险等级(Credit Risk Rating，CRR)。所述风险等级可以取自风险等级集合，所述风险等级集合可以包括至少两个风险等级。例如，所述风险等级集合可以包括高风险等级和低风险等级。另举一例，所述风险等级可以包括低风险等级、中风险等级和高风险等级。当然，所述风险指标还可以包括其它的形式，例如分值。
所述确定结果可以为正常的风险指标或异常的风险指标。根据风险指标形式的不同，正常的风险指标和异常的风险指标也可以不同。例如，所述风险指标可以包括风险等级，所述风险等级可以取自风险等级集合，所述风险等级集合可以包括高风险等级和低风险等级。那么，正常的风险指标可以包括所述低风险等级，异常的风险指标可以包括所述高风险等级。另举一例，所述风险指标可以包括风险等级，所述风险等级可以取自风险等级集合，所述风险等级集合可以包括低风险等级、中风险等级和高风险等级。那么，正常的风险指标可以包括所述低风险等级和所述中风险等级，异常的风险指标可以包括所述高风险等级。另举一例，所述风险指标可以包括分值。那么，正常的风险指标可以包括小于某一阈值的分值，异常的风险指标可以包括大于该阈值的分值。
所述分析服务器可以利用风险预测规则，确定业务对象的风险指标，得到确定结果。所述风险预测规则可以包括通过专家经验构建的数据处理逻辑。或者，所述风险预测规则还可以包括通过机器学习方式训练得到的模型。例如，所述分析服务器可以将所述业务数据输入至所述风险预测规则，以确定所述业务对象的风险指标。
在一些实施例中，所述异常业务对象名单可以包括至少一个异常业务对象。所述异常业务对象可以指存在风险的业务对象，例如受到制裁的业务对象，受到制裁的业务对象可以包括涉嫌洗钱的业务对象、涉嫌诈骗的业务对象、涉嫌网络赌博的业务对象等。
所述判断结果可以为命中异常业务对象名单或没有命中异常业务对象名单。命中异常业务对象名单可以指所述异常业务对象名单命包括与所述业务对象相匹配的异常业务对象名单。没有命中异常业务对象名单可以指所述异常业务对象名单命不包括与所述业务对象相匹配的异常业务对象名单。
所述分析服务器可以将所述业务对象在所述异常业务对象名单中进行匹配；若成功匹配到一个或多个异常业务对象，可以判断所述业务对象命中异常业务对象名单；若没有匹配到一个或多个异常业务对象，可以判断所述业务对象没有命中异常业务对象名单。
在一些实施例中，确定业务对象风险指标的过程与判断业务对象是否命中异常业务对象名单的过程可以顺序执行。例如，所述分析服务器可以先确定业务对象的风险指标，后判断业务对象是否命中异常业务对象名单。或者，所述分析服务器还可以先判断业务对象是否命中异常业务对象名单，后判断业务对象是否命中异常业务对象名单。当然，确定业务对象风险指标的过程与判断业务对象是否命中异常业务对象名单的过程也可以并行执行。
步骤S108：分析服务器将正常作为风险分析结果进行反馈。
在一些实施例中，通过步骤S106，分析服务器可以确定业务对象的风险指标、以及判断业务对象是否命中异常业务对象名单。若所述确定结果为正常的风险指标、并且所述判断结果为没有命中异常业务对象名单，所述分析服务器可以认为所述业务对象不存在风险；可以将正常作为风险分析结果，可以向所述业务服务器反馈风险分析结果。
步骤S110：分析服务器向审理服务器发送审理请求。
在一些实施例中，通过步骤S106，分析服务器可以确定业务对象的风险指标、以及判断业务对象是否命中异常业务对象名单。若所述确定结果为异常的风险指标、并且所述判断结果为命中异常业务对象名单，或者，若所述确定结果为异常的风险指标、并且所述判断结果为没有命中异常业务对象名单，或者，若所述确定结果为正常的风险指标、并且所述判断结果为命中异常业务对象名单，所述分析服务器可以认为所述业务对象疑似存在风险；可以向所述审理服务器发送审理请求，以便所述审理服务器审查核实所述业务对象是否真实存在风险。所述审理请求可以包括所述确定结果和所述判断结果。当 然，所述审理请求还可以包括其它的内容。例如，所述审理请求还可以包括所述业务数据。
步骤S112：审理服务器接收审理请求。
步骤S114：若所述判断结果为命中异常业务对象名单、并且所述确定结果为正常的风险指标，审理服务器核查业务对象是否真实命中异常业务对象名单。
在一些实施例中，考虑到尽管分析服务器判断所述业务对象命中异常业务对象名单，但在一些情况(例如用户重名的情况)下所述业务对象有可能真实命中异常业务对象名单，也有可能没有真实命中异常业务对象名单。因而在接收到审理请求以后，若所述判断结果为命中异常业务对象名单、并且所述确定结果为正常的风险指标，所述审理服务器可以核查业务对象是否真实命中异常业务对象名单。
在实际应用中，核查业务对象是否真实命中异常业务对象名单可以有多种实现方式。例如，所述审理服务器可以将所述业务对象、以及异常业务对象名单中与所述业务对象相匹配的异常业务对象名单提供给审理人员。由审理人员核查业务对象是否真实命中异常业务对象名单。具体地，例如，所述审理服务器可以将所述业务对象、以及异常业务对象名单中与所述业务对象相匹配的异常业务对象名单发送至特定服务器；可以接收审理人员在特定服务器中输入的核查结果。
步骤S116：若真实命中异常业务对象名单，审理服务器将异常作为审理结果进行反馈。
在一些实施例中，通过步骤S114，审理服务器可以核查业务对象是否真实命中异常业务对象名单。若真实命中异常业务对象名单，所述审理服务器可以认为所述业务对象存在风险；可以将异常作为审理结果，可以向所述分析服务器反馈审理结果。
步骤S118：若没有真实命中异常业务对象名单，审理服务器判断业务对象是否命中疑似异常业务对象名单。
在一些实施例中，所述疑似异常业务对象名单可以包括至少一个疑似异常业务对象。所述疑似异常业务对象可以指疑似存在风险的业务对象，例如疑似涉嫌洗钱的业务对象、疑似涉嫌诈骗的业务对象、疑似涉嫌网络赌博的业务对象。具体地，例如，考虑到PEP(Politically Exposed Persons，政治公众人物)属于洗钱高风险人员，所述疑似异常业务对象可以包括PEP。
在一些实施例中，通过步骤S114，审理服务器可以核查业务对象是否真实命中异常 业务对象名单。若没有真实命中异常业务对象名单，所述审理服务器可以将所述业务对象在所述疑似异常业务对象名单中进行匹配；若成功匹配到一个或多个异常业务对象，可以判断所述业务对象命中疑似异常业务对象名单；若没有匹配到一个或多个异常业务对象，可以判断所述业务对象没有命中疑似异常业务对象名单。
步骤S120：若没有命中疑似异常业务对象名单，审理服务器将正常作为审理结果进行反馈。
在一些实施例中，通过步骤S118，审理服务器可以判断业务对象是否命中疑似异常业务对象名单。若没有命中疑似异常业务对象名单，所述审理服务器可以认为所述业务对象不存在风险；可以将正常作为审理结果，可以向所述分析服务器反馈审理结果。
步骤S122：若命中疑似异常业务对象名单，审理服务器核查业务对象是否真实存在风险。
在一些实施例中，通过步骤S118，审理服务器可以判断业务对象是否命中疑似异常业务对象名单。若命中疑似异常业务对象名单，所述审理服务器可以认为所述业务对象疑似存在风险；可以核查所述业务对象是否真实存在风险。
在实际应用中，核查业务对象是否真实存在风险可以有多种实现方式。例如，所述审理服务器可以将所述业务对象(或者涉及到所述业务对象的业务数据)提供给审理人员。由审理人员通过EDD(Enhanced Due Diligence，增强尽职调查)的方式核查业务对象是否真实存在风险。具体地，例如，所述审理服务器可以将所述业务对象发送至特定服务器；可以接收审理人员在特定服务器中输入的核查结果。
步骤S124：若存在风险，审理服务器将异常作为审理结果进行反馈；或者，若不存在风险，审理服务器将正常作为审理结果进行反馈；或者，若不确定，将待补充数据的标识作为审理结果进行反馈。
在一些实施例中，通过步骤S122，或者，通过步骤S126，或者，通过步骤S130，审理服务器可以核查业务对象是否真实存在风险。若存在风险，所述审理服务器可以将异常作为审理结果，可以向所述分析服务器反馈审理结果。或者，若不存在风险，所述审理服务器可以将正常作为审理结果，可以向所述分析服务器反馈审理结果。或者，在一些情况下有可能缺失了部分数据(例如身份证、营业执照等)，使得审理服务器无法确定业务对象是否真实存在风险。这样所述审理服务器可以将待补充数据的标识作为审理结果，可以向所述分析服务器反馈审理结果。待补充数据的标识例如可以包括待补充 数据的名称、编号等。
步骤S126：若判断结果为没有命中异常业务对象名单、并且确定结果为异常的风险指标，审理服务器核查业务对象是否真实存在风险。
在一些实施例中，考虑到尽管分析服务器确定所述业务对象的风险指标为异常的风险指标，但所述业务对象有可能存在风险，也有可能不存在风险。因而在接收到审理请求以后，若所述判断结果为没有命中异常业务对象名单、并且所述确定结果为异常的风险指标，所述审理服务器可以核查业务对象是否真实存在风险。核查业务对象是否真实存在风险的过程可以参见前面的步骤S122，此处不再赘述。
步骤S128：若判断结果为命中异常业务对象名单、并且确定结果为异常险级别，审理服务器核查业务对象是否真实命中异常业务对象名单。
在一些实施例中，考虑到尽管分析服务器判断所述业务对象命中异常业务对象名单，但在一些情况(例如用户重名的情况)下所述业务对象有可能真实命中异常业务对象名单，也有可能没有真实命中异常业务对象名单。因而在接收到审理请求以后，若判断结果为命中异常业务对象名单、并且确定结果为异常险级别，所述审理服务器可以核查业务对象是否真实命中异常业务对象名单。核查业务对象是否真实命中异常业务对象名单的过程可以参见前面的步骤S114，此处不再赘述。
步骤S130：若没有真实命中异常业务对象名单，审理服务器核查业务对象是否真实存在风险。
在一些实施例中，通过步骤S128，审理服务器可以核查业务对象是否真实命中异常业务对象名单。若没有真实命中异常业务对象名单，所述审理服务器可以核查业务对象是否真实存在风险。核查业务对象是否真实存在风险的过程可以参见前面的步骤S122，此处不再赘述。
值得说明的是，若没有真实命中异常业务对象名单，所述审理服务器可以直接核查业务对象是否真实存在风险。或者，若没有真实命中异常业务对象名单，所述审理服务器还可以判断业务对象是否命中疑似异常业务对象名单；若没有命中疑似异常业务对象名单，可以核查业务对象是否真实存在风险；若命中疑似异常业务对象名单，可以核查业务对象是否真实存在风险。具体过程可以参见前面的步骤S118，此处不再赘述。
步骤S132：若真实命中异常业务对象名单，审理服务器将异常作为审理结果进行反馈。
在一些实施例中，通过步骤S128，审理服务器可以核查业务对象是否真实命中异常业务对象名单。若真实命中异常业务对象名单，所述审理服务器可以认为所述业务对象存在风险；可以将异常作为审理结果，可以向所述分析服务器反馈审理结果。
步骤S134：分析服务器将审理结果作为风险分析结果进行反馈。
在一些实施例中，通过步骤S116，或者，通过步骤S120，或者，通过步骤S124，或者，通过步骤S132，审理服务器可以向分析服务器反馈审理结果。所述分析服务器可以接收审理结果；可以将审理结果作为风险分析结果，可以向业务服务器反馈风险分析结果。
步骤S136：业务服务器接收风险分析结果。
在一些实施例中，通过步骤S108，或者，通过步骤S134，分析服务器可以向业务服务器反馈风险分析结果。所述业务服务器可以接收风险分析结果。所述业务服务器可以根据接收的风险分析结果执行相应的操作。例如，所述业务数据可以包括交易数据。若风险分析结果为正常，所述业务服务器可以根据交易数据正常执行交易。若风险分析结果为异常，所述业务服务器可以拒绝执行交易；可以向用户的终端设备发送拒绝执行交易的提示信息。
本说明书实施例的数据交互方法，业务服务器可以向分析服务器发送风险分析请求。分析服务器可以接收风险分析请求；可以确定业务对象的风险指标；可以结合业务对象是否命中异常业务对象名单；可以根据判断结果和确定结果，向业务服务器统一反馈风险分析结果。这样业务服务器可以快速地获得风险分析结果。从而有利于业务服务器快速地确定业务对象是否存在风险。另外，通过向业务服务器统一反馈风险分析结果，还可以避免业务服务器获得两种完全相反的风险分析结果。
本说明书还提供数据交互方法的另一个实施例。所述数据交互方法可以由图1所对应的实施例中的分析服务器实施。请参阅图2，所述数据交互方法可以包括以下步骤。
步骤S202：接收风险分析请求。
在一些实施例中，所述风险分析请求可以包括待分析的业务对象。具体地，在获得业务数据以后，业务服务器可以向所述分析服务器发送风险分析请求。所述分析服务器可以接收所述风险分析请求。其中，所述风险分析请求可以包括所述业务数据，所述待分析的业务对象可以包括所述业务数据涉及的业务对象。
步骤S204：确定业务对象的风险指标。
确定业务对象风险指标的过程可以参见前面的步骤S106。
步骤S206：判断业务对象是否命中异常业务对象名单。
判断业务对象是否命中异常业务对象名单的过程可以参见前面的步骤S106。
步骤S208：根据判断结果和确定结果，向审理服务器发送审理请求。
在一些实施例中，若所述确定结果为异常的风险指标、并且所述判断结果为命中异常业务对象名单，或者，若所述确定结果为异常的风险指标、并且所述判断结果为没有命中异常业务对象名单，或者，若所述确定结果为正常的风险指标、并且所述判断结果为命中异常业务对象名单，所述分析服务器可以认为所述业务对象疑似存在风险；可以向所述审理服务器发送审理请求，以便所述审理服务器审查核实所述业务对象是否真实存在风险。所述审理请求可以包括所述确定结果和所述判断结果。当然，所述审理请求还可以包括其它的内容。例如，所述审理请求还可以包括所述业务数据。
步骤S210：接收审理服务器反馈的审理结果。
在一些实施例中，审理服务器可以接收审理请求；可以处理所述审理请求，得到审理结果；可以向所述分析服务器反馈所述审理结果。所述分析服务器可以接收所述审理结果。其中，所述审理结果的获得过程可以参见图1所对应的实施例。
步骤S212：将所述审理结果作为风险分析结果进行反馈。
在一些实施例中，所述分析服务器可以接收审理结果；可以将审理结果作为风险分析结果，可以向业务服务器反馈风险分析结果。
在一些实施例中，若所述确定结果为正常的风险指标、并且所述判断结果为没有命中异常业务对象名单，所述分析服务器可以将正常作为风险分析结果进行反馈。
本说明书实施例的数据交互方法，业务服务器可以向分析服务器发送风险分析请求。分析服务器可以接收风险分析请求；可以确定业务对象的风险指标；可以判断业务对象是否命中异常业务对象名单；可以结合判断结果和确定结果，向业务服务器统一反馈风险分析结果。这样业务服务器可以快速地获得风险分析结果。从而有利于快速地确定业务对象是否存在风险。另外，通过向业务服务器统一反馈风险分析结果，还可以避免业务服务器获得两种完全相反的风险分析结果。
本说明书还提供数据交互方法的另一个实施例。所述数据交互方法可以由图1所对应的实施例中的审理服务器实施。请参阅图3，所述数据交互方法可以包括以下步骤。
步骤S302：接收审理请求。
在一些实施例中，分析服务器可以向所述审理服务器发送审理请求。所述审理服务器可以接收所述审理请求。所述审理请求可以包括判断结果和确定结果。所述判断结果可以通过判断业务对象是否命中异常业务对象名单得到。所述确定结果可以通过确定业务对象的风险指标得到。所述判断结果和所述确定结果的获得过程可以参见前面的步骤S106。
步骤S304：若所述判断结果为命中异常业务对象名单、并且所述确定结果为正常的风险指标，核查业务对象是否真实命中异常业务对象名单。
核查业务对象是否真实命中异常业务对象名单的过程可以参见前面的步骤S114。
步骤S306：若真实命中异常业务对象名单，将异常作为审理结果进行反馈。
在一些实施例中，若没有真实命中异常业务对象名单，所述审理服务器可以判断业务对象是否命中疑似异常业务对象名单；若没有命中疑似异常业务对象名单，可以将正常作为审理结果进行反馈；若命中疑似异常业务对象名单，可以核查业务对象是否真实存在风险。若存在风险，审理服务器可以将异常作为审理结果进行反馈。或者，若不存在风险，审理服务器可以将正常作为审理结果进行反馈。或者，若不确定，审理服务器可以将待补充数据的标识作为审理结果进行反馈。
本说明书实施例的数据交互方法，分析服务器可以向审理服务器发送审理请求。审理服务器可以接收审理请求；可以根据所述审理请求中的确定结果和判断结果，向分析服务器统一反馈审理结果。这样有利于快速地确定业务对象是否存在风险。
本说明书还提供数据交互方法的另一个实施例。所述数据交互方法可以由图1所对应的实施例中的审理服务器实施。请参阅图4，所述数据交互方法可以包括以下步骤。
步骤S402：接收审理请求。
在一些实施例中，分析服务器可以向所述审理服务器发送审理请求。所述审理服务器可以接收所述审理请求。所述审理请求可以包括判断结果和确定结果。所述判断结果可以通过判断业务对象是否命中异常业务对象名单得到。所述确定结果可以通过确定业务对象的风险指标得到。所述判断结果和所述确定结果的获得过程可以参见前面的步骤S106。
步骤S404：若所述判断结果为没有命中异常业务对象名单、并且所述确定结果 为异常的风险指标，核查业务对象是否真实存在风险。
核查业务对象是否真实存在风险的过程可以参见前面的步骤S122。
步骤S406：若存在风险，将异常作为审理结果进行反馈；或者，若不存在风险，将正常作为审理结果进行反馈；或者，若不确定，将待补充数据的标识作为审理结果进行反馈。
本说明书实施例的数据交互方法，分析服务器可以向审理服务器发送审理请求。审理服务器可以接收审理请求；可以根据所述审理请求中的确定结果和判断结果，向分析服务器统一反馈审理结果。这样有利于快速地确定业务对象是否存在风险。
本说明书还提供数据交互方法的另一个实施例。所述数据交互方法可以由图1所对应的实施例中的审理服务器实施。请参阅图5，所述数据交互方法可包括以下步骤。
步骤S502：接收审理请求。
在一些实施例中，分析服务器可以向所述审理服务器发送审理请求。所述审理服务器可以接收所述审理请求。所述审理请求可以包括判断结果和确定结果。所述判断结果可以通过判断业务对象是否命中异常业务对象名单得到。所述确定结果可以通过确定业务对象的风险指标得到。所述判断结果和所述确定结果的获得过程可以参见前面的步骤S106。
步骤S504：若所述判断结果为命中异常业务对象名单、并且所述确定结果为异常险级别，核查业务对象是否真实命中异常业务对象名单。
核查业务对象是否真实命中异常业务对象名单的过程可以参见前面的步骤S114。
步骤S506：若真实命中异常业务对象名单，将异常作为审理结果进行反馈。
在一些实施例中，若没有真实命中异常业务对象名单，所述审理服务器可以核查业务对象是否真实存在风险。若存在风险，审理服务器可以将异常作为审理结果进行反馈。或者，若不存在风险，审理服务器可以将正常作为审理结果进行反馈。或者，若不确定，审理服务器可以将待补充数据的标识作为审理结果进行反馈。
本说明书实施例的数据交互方法，分析服务器可以向审理服务器发送审理请求。审理服务器可以接收审理请求；可以根据所述审理请求中的确定结果和判断结果，向分析服务器统一反馈审理结果。这样有利于快速地确定业务对象是否存在风险。
请参阅图6。本说明书还提供数据交互装置的一个实施例。所述数据交互装置可 以应用于分析服务器，具体可以包括以下模块单元。
第一接收模块602，用于接收风险分析请求，所述风险分析请求包括待分析的业务对象；
确定模块604，用于确定业务对象的风险指标；
判断模块606，用于判断业务对象是否命中异常业务对象名单；
发送模块608，用于根据判断结果和确定结果，向审理服务器发送审理请求；
第二接收模块610，用于接收审理服务器反馈的审理结果；
反馈模块612，用于将所述审理结果作为风险分析结果进行反馈。
请参阅图7。本说明书还提供数据交互装置的一个实施例。所述数据交互装置可以应用于审理服务器，具体可以包括以下模块单元。
接收模块702，用于接收审理请求，所述审理请求包括判断结果和确定结果，所述判断结果通过判断业务对象是否命中异常业务对象名单得到，所述确定结果通过确定业务对象的风险指标得到；
核查模块704，用于若所述判断结果为命中异常业务对象名单、并且所述确定结果为正常的风险指标，核查业务对象是否真实命中异常业务对象名单；
反馈模块706，用于若真实命中异常业务对象名单，将异常作为审理结果进行反馈。
请参阅图8。本说明书还提供数据交互装置的一个实施例。所述数据交互装置可以应用于审理服务器，具体可以包括以下模块单元。
接收模块802，用于接收审理请求，所述审理请求包括判断结果和确定结果，所述判断结果通过判断业务对象是否命中异常业务对象名单得到，所述确定结果通过确定业务对象的风险指标得到；
调查模块804，用于若所述判断结果为没有命中异常业务对象名单、并且所述确定结果为异常的风险指标，核查业务对象是否真实存在风险；
反馈模块806，用于若存在风险，将异常作为审理结果进行反馈；或者，若不存在风险，将正常作为审理结果进行反馈；或者，若不确定，将待补充数据的标识作为审理结果进行反馈。
请参阅图9。本说明书还提供数据交互装置的一个实施例。所述数据交互装置可 以应用于审理服务器，具体可以包括以下模块单元。
接收模块902，用于接收审理请求，所述审理请求包括判断结果和确定结果，所述判断结果通过判断业务对象是否命中异常业务对象名单得到，所述确定结果通过确定业务对象的风险指标得到；
审理模块904，用于若所述判断结果为命中异常业务对象名单、并且所述确定结果为异常险级别，核查业务对象是否真实命中异常业务对象名单；
反馈模块906，用于若真实命中异常业务对象名单，将异常作为审理结果进行反馈。
下面介绍本说明书电子设备的一个实施例。图10是该实施例中电子设备的硬件结构示意图。如图10所示，该电子设备可以包括一个或多个(图中仅示出一个)处理器、存储器和传输模块。当然，本领域普通技术人员可以理解，图10所示的硬件结构仅为示意，其并不对上述电子设备的硬件结构造成限定。在实际中该电子设备还可以包括比图10所示更多或者更少的组件单元；或者，具有与图10所示不同的配置。
所述存储器可以包括高速随机存储器；或者，还可以包括非易失性存储器，例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。当然，所述存储器还可以包括远程设置的网络存储器。所述远程设置的网络存储器可以通过诸如互联网、企业内部网、局域网、移动通信网等网络连接至所述电子设备。所述存储器可以用于存储应用软件的程序指令或模块，例如本说明书图2-图5所对应实施例的程序指令或模块。
所述处理器可以按任何适当的方式实现。例如，所述处理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等等。所述处理器可以读取并执行所述存储器中的程序指令或模块。
所述传输模块可以用于经由网络进行数据传输，例如经由诸如互联网、企业内部网、局域网、移动通信网等网络进行数据传输。
本说明书还提供计算机存储介质的一个实施例。所述计算机存储介质包括但不限于随机存取存储器(Random Access Memory,RAM)、只读存储器(Read-Only Memory,ROM)、缓存(Cache)、硬盘(Hard Disk Drive,HDD)、存储卡(Memory Card)等等。所述计算机存储介质存储有计算机程序指令。在所述计算机程序指令被执行时实现：本说明书图2-图5所对应实施例的程序指令或模块。
需要说明的是，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或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它实施例的不同之处。尤其，对于装置实施例、电子设备实施例、以及计算机存储介质实施例而言，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。另外，可以理解的是，本领域技术人员在阅读本说明书文件之后，可以无需创造性劳动想到将本说明书列举的部分或全部实施例进行任意组合，这些组合也在本说明书公开和保护的范围内。
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Verilog2。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。
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、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。
通过以上的实施方式的描述可知，本领域的技术人员可以清楚地了解到本说明书可借助软件加必需的通用硬件平台的方式来实现。基于这样的理解，本说明书的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品可以存储在存储介质中，如ROM/RAM、磁碟、光盘等，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本说明书各个实施例或者实施例的某些部分所述的方法。
本说明书可用于众多通用或专用的计算机系统环境或配置中。例如：个人计算机、服务器计算机、手持设备或便携式设备、平板型设备、多处理器系统、基于微处理器的系统、置顶盒、可编程的消费电子设备、网络PC、小型计算机、大型计算机、包括以上任何系统或设备的分布式计算环境等等。
本说明书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说明书，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。
虽然通过实施例描绘了本说明书，本领域普通技术人员知道，本说明书有许多变形和变化而不脱离本说明书的精神，希望所附的权利要求包括这些变形和变化而不脱离本说明书的精神。

Claims (15)

  1. 一种数据交互方法，应用于分析服务器，包括：
    接收风险分析请求，所述风险分析请求包括待分析的业务对象；
    确定业务对象的风险指标；
    判断业务对象是否命中异常业务对象名单；
    根据判断结果和确定结果，向审理服务器发送审理请求；
    接收审理服务器反馈的审理结果；
    将所述审理结果作为风险分析结果进行反馈。
  2. 如权利要求1所述的方法，所述风险分析请求包括业务数据，所述待分析的业务对象包括所述业务数据涉及的业务对象。
  3. 如权利要求1所述的方法，所述向审理服务器发送审理请求，包括：
    若所述确定结果为异常的风险指标、并且所述判断结果为命中异常业务对象名单，向审理服务器发送审理请求；或者，若所述确定结果为异常的风险指标、并且所述判断结果为没有命中异常业务对象名单，向审理服务器发送审理请求；或者，若所述确定结果为正常的风险指标、并且所述判断结果为命中异常业务对象名单，向审理服务器发送审理请求。
  4. 如权利要求1所述的方法，还包括：
    若所述确定结果为正常的风险指标、并且所述判断结果为没有命中异常业务对象名单，将正常作为风险分析结果进行反馈。
  5. 一种数据交互方法，应用于审理服务器，包括：
    接收审理请求，所述审理请求包括判断结果和确定结果，所述判断结果通过判断业务对象是否命中异常业务对象名单得到，所述确定结果通过确定业务对象的风险指标得到；
    若所述判断结果为命中异常业务对象名单、并且所述确定结果为正常的风险指标，核查业务对象是否真实命中异常业务对象名单；
    若真实命中异常业务对象名单，将异常作为审理结果进行反馈。
  6. 如权利要求5所述的方法，还包括：
    若没有真实命中异常业务对象名单，判断业务对象是否命中疑似异常业务对象名单；
    若没有命中疑似异常业务对象名单，将正常作为审理结果进行反馈。
  7. 如权利要求5所述的方法，还包括：
    若没有真实命中异常业务对象名单，判断业务对象是否命中疑似异常业务对象名单；
    若命中疑似异常业务对象名单，核查业务对象是否真实存在风险；
    若存在风险，将异常作为审理结果进行反馈；或者，若不存在风险，将正常作为审理结果进行反馈；或者，若不确定，将待补充数据的标识作为审理结果进行反馈。
  8. 一种数据交互方法，应用于审理服务器，包括：
    接收审理请求，所述审理请求包括判断结果和确定结果，所述判断结果通过判断业务对象是否命中异常业务对象名单得到，所述确定结果通过确定业务对象的风险指标得到；
    若所述判断结果为没有命中异常业务对象名单、并且所述确定结果为异常的风险指标，核查业务对象是否真实存在风险；
    若存在风险，将异常作为审理结果进行反馈；或者，若不存在风险，将正常作为审理结果进行反馈；或者，若不确定，将待补充数据的标识作为审理结果进行反馈。
  9. 一种数据交互方法，应用于审理服务器，包括：
    接收审理请求，所述审理请求包括判断结果和确定结果，所述判断结果通过判断业务对象是否命中异常业务对象名单得到，所述确定结果通过确定业务对象的风险指标得到；
    若所述判断结果为命中异常业务对象名单、并且所述确定结果为异常险级别，核查业务对象是否真实命中异常业务对象名单；
    若真实命中异常业务对象名单，将异常作为审理结果进行反馈。
  10. 如权利要求9所述的方法，还包括：
    若没有真实命中异常业务对象名单，核查业务对象是否真实存在风险；
    若存在风险，将异常作为审理结果进行反馈；或者，若不存在风险，将正常作为审理结果进行反馈；或者，若不确定，将待补充数据的标识作为审理结果进行反馈。
  11. 一种数据交互装置，应用于分析服务器，包括：
    第一接收模块，用于接收风险分析请求，所述风险分析请求包括待分析的业务对象；
    确定模块，用于确定业务对象的风险指标；
    判断模块，用于判断业务对象是否命中异常业务对象名单；
    发送模块，用于根据判断结果和确定结果，向审理服务器发送审理请求；
    第二接收模块，用于接收审理服务器反馈的审理结果；
    反馈模块，用于将所述审理结果作为风险分析结果进行反馈。
  12. 一种数据交互装置，应用于审理服务器，包括：
    接收模块，用于接收审理请求，所述审理请求包括判断结果和确定结果，所述判断 结果通过判断业务对象是否命中异常业务对象名单得到，所述确定结果通过确定业务对象的风险指标得到；
    核查模块，用于若所述判断结果为命中异常业务对象名单、并且所述确定结果为正常的风险指标，核查业务对象是否真实命中异常业务对象名单；
    反馈模块，用于若真实命中异常业务对象名单，将异常作为审理结果进行反馈。
  13. 一种数据交互装置，应用于审理服务器，包括：
    接收模块，用于接收审理请求，所述审理请求包括判断结果和确定结果，所述判断结果通过判断业务对象是否命中异常业务对象名单得到，所述确定结果通过确定业务对象的风险指标得到；
    调查模块，用于若所述判断结果为没有命中异常业务对象名单、并且所述确定结果为异常的风险指标，核查业务对象是否真实存在风险；
    反馈模块，用于若存在风险，将异常作为审理结果进行反馈；或者，若不存在风险，将正常作为审理结果进行反馈；或者，若不确定，将待补充数据的标识作为审理结果进行反馈。
  14. 一种数据交互装置，应用于审理服务器，包括：
    接收模块，用于接收审理请求，所述审理请求包括判断结果和确定结果，所述判断结果通过判断业务对象是否命中异常业务对象名单得到，所述确定结果通过确定业务对象的风险指标得到；
    审理模块，用于若所述判断结果为命中异常业务对象名单、并且所述确定结果为异常险级别，核查业务对象是否真实命中异常业务对象名单；
    反馈模块，用于若真实命中异常业务对象名单，将异常作为审理结果进行反馈。
  15. 一种电子设备，包括：
    至少一个处理器；
    存储有程序指令的存储器，其中，所述程序指令被配置为适于由所述至少一个处理器执行，所述程序指令包括用于执行如权利要求1-10中任一项所述方法的指令。
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