CN110298563A - A kind of statistical method of discriminant risk order - Google Patents
A kind of statistical method of discriminant risk order Download PDFInfo
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- CN110298563A CN110298563A CN201910517931.1A CN201910517931A CN110298563A CN 110298563 A CN110298563 A CN 110298563A CN 201910517931 A CN201910517931 A CN 201910517931A CN 110298563 A CN110298563 A CN 110298563A
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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
- 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
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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
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0609—Buyer or seller confidence or verification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0633—Lists, e.g. purchase orders, compilation or processing
- G06Q30/0635—Processing of requisition or of purchase orders
Abstract
A kind of statistical method of discriminant risk order, the described method comprises the following steps: the order information of S1, acquisition user within the recent history time；The order distribution information of S2, acquisition user within the recent history time；S3, statistics order and distribution information, set the criterion of risk order；S4, risk subscribers and risk order are identified by risk criterion；S5, risk subscribers and risk order are handled.The present invention descends single act and distribution information by user's history recently, and then identify risk order, according to longitude and latitude single under N days in user's history or the same day, order value, whether same category is bought, the timeliness of category and the distribution information of order, accumulative rule reach specified threshold, user is continued to place an order, identification risk subscribers and risk order are carried out according to the time dimension threshold value of configuration, it is high to risk subscribers and risk order accuracy of identification, the generation of risk order can be effectively prevent.
Description
Technical field
The present invention relates to Internet technical field more particularly to a kind of statistical methods of discriminant risk order.
Background technique
With the development of network technology, network server can come to visit in parallel for a large amount of by its powerful process performance
Ask that user provides information service, such as such as software download, online chat, shopping online etc..However in access network service
In the user of device, it is commonly present some malicious users that other people equity are damaged using technological means.For large-scale electric business platform, when per
Per quarter has a large amount of order to strike a bargain.In a large amount of transaction, under cover fraudulent trading of the small part from criminal.These
Fraudulent trading may be unreasonable brush single act, it may be possible to which fraudster usurps the fraud carried out after the account of legitimate user and hands over
Easily, or after usurping other people credits card fraud consumption etc. is carried out.
For above situation, large-scale electric business platform needs to monitor order safety in real time.Currently used order safety
Monitoring method is directed to order main body mostly, identifies whether lower single user and its associated payment accounts have security risk.However with
The increase of business and the raising of technical level, fraudster the anti-detecting level of risk control is being stepped up, therefore electricity
Quotient's platform needs that order risk is identified and controlled from different perspectives using various methods as far as possible.
To solve the above problems, proposing a kind of statistical method of discriminant risk order in the application.
Summary of the invention
(1) goal of the invention
To solve technical problem present in background technique, the present invention proposes a kind of statistical method of discriminant risk order,
Single act and distribution information are descended recently by user's history, it is high to risk subscribers and risk order accuracy of identification, it can effectively prevent
The generation of risk order.
(2) technical solution
To solve the above problems, the present invention provides a kind of statistical method of discriminant risk order, the method includes with
Lower step:
The order information of S1, acquisition user within the recent history time；
The order distribution information of S2, acquisition user within the recent history time；
S3, statistics order and distribution information, set the criterion of risk order；
S4, risk subscribers and risk order are identified by risk criterion；
S5, risk subscribers and risk order are handled.
Preferably, order information of the step S1 acquisition user within the recent history time includes:
S11, acquisition user browse merchandise news within the recent history time；
S12, acquisition user's purchase order information within the recent history time；
S13, acquisition user complete trade order information within the recent history time.
Preferably, S12 acquisition user's purchase order information within the recent history time include lower single longitude and latitude, under
Single amount of money, the timeliness for whether buying same category and category.
Preferably, order distribution information of the step S2 acquisition user within the recent history time includes:
S21, acquisition user's order displacement information within the recent history time；
S22, acquisition the user knight within the recent history time dispense distance and expense；
S23, acquisition user's order addressee information within the recent history time.
Preferably, the step S3 statistics order and distribution information, setting the criterion of risk order includes:
S31, low amount of money order accounting are greater than 40%；
S32, order are displaced abnormal accounting and are greater than 40%；
S33, order knight concentrate.
Preferably, the step S4 identifies risk subscribers by risk criterion and risk order includes:
S41, risk subscribers are judged by passing order and distribution information；
S42, risk order is judged according to order placement information again；
S43, risk subscribers are drawn a circle to approve, is focused on.
Preferably, the step S5, which handle to risk subscribers and risk order, includes:
S51, blacklist management is carried out to delineation risk subscribers；
S52, cancelled for the risk order that risk subscribers are assigned.
Preferably, the S42 judges that risk order is the amount of money lower than 10 yuan according to order placement information again, and pays knight in fact
Amount received difference >=3 yuan of order.
Preferably, the S43 draws a circle to approve risk subscribers, and centralized processing includes determining risk subscribers, cancels the user
Order and first lower single pair user draw a circle to approve, and descend single pair user to be determined again and cancel this time to place an order.
Above-mentioned technical proposal of the invention has following beneficial technical effect: descend recently by user's history single act with
Distribution information, and then identify risk order, according to longitude and latitude single under N days in user's history or the same day, order value, if purchase
Same category, the timeliness of category and the distribution information of order are bought, accumulative rule reaches specified threshold, under continuing for user
It is single, identification risk subscribers and risk order are carried out according to the time dimension threshold value of configuration；Pass through low amount of money order accounting 40%
Above, order is displaced abnormal accounting and draws a circle to approve with the standard of order knight concentration to risk subscribers greater than 40%, passes through risk
The amount of money that user places an order again and the real statistics for paying knight's amount received difference, determine risk order, it is convenient and
When cancel risk order, the present invention is high to risk subscribers and risk order accuracy of identification, can effectively prevent the generation of risk order.
Detailed description of the invention
Fig. 1 is a kind of structural schematic diagram of the statistical method of discriminant risk order proposed by the present invention.
Fig. 2 is the structural schematic diagram that step S1 acquires order information of the user within the recent history time in Fig. 1.
Fig. 3 is the structural schematic diagram that step S2 acquires order distribution information of the user within the recent history time in Fig. 1.
Fig. 4 is that step S3 counts order and distribution information in Fig. 1, sets the structural schematic diagram of risk order criterion.
Fig. 5 is the structural schematic diagram that step S4 identifies risk subscribers and risk order by risk criterion in Fig. 1.
Fig. 6 is the structural schematic diagram that step S5 handles risk subscribers and risk order in Fig. 1.
Specific embodiment
In order to make the objectives, technical solutions and advantages of the present invention clearer, With reference to embodiment and join
According to attached drawing, the present invention is described in more detail.It should be understood that these descriptions are merely illustrative, and it is not intended to limit this hair
Bright range.In addition, in the following description, descriptions of well-known structures and technologies are omitted, to avoid this is unnecessarily obscured
The concept of invention.
As shown in figures 1 to 6, the statistical method of a kind of discriminant risk order proposed by the present invention, the method includes following steps
It is rapid:
The order information of S1, acquisition user within the recent history time；By acquiring user's ordering in historical time section
Single information judges user's shopping tendency and shopping frequency, is conducive to identify user's shopping environment；
The order distribution information of S2, acquisition user within the recent history time；Pass through the distribution information to user's order
Acquisition judges the practical shopping information of user, is conducive to the differentiation to risk subscribers and risk order；
S3, statistics order and distribution information, set the criterion of risk order；By statistics order and distribution information, divide
It analyses order and distribution information situation and Risk Evaluation standard is formulated according to order and distribution information related data, be conducive to to wind
The differentiation of dangerous user and risk order；
S4, risk subscribers and risk order are identified by risk criterion；According to risk assessment standard to shop correlation
User carries out risk judgment, and carries out risk identification to user's order；
S5, risk subscribers and risk order are handled, by being counted to user's order and distribution information, and according to wind
Dangerous judgment criteria judges risk subscribers and risk order, and the generation of risk order is prevented by handling in time.
In an alternative embodiment, order information packet of the step S1 acquisition user within the recent history time
It includes:
S11, acquisition user browse merchandise news within the recent history time；Judge to use by the information that user browses commodity
Family shopping tendency, is conducive to identify risk subscribers；
S12, acquisition user's purchase order information within the recent history time；Pass through the browsing information and purchase order of user
The comparison of information obtains user's shopping tendency and frequency, is identified according to shopping frequency to consumer's risk shopping, is conducive to mention
High recognition accuracy；
S13, acquisition user complete trade order information within the recent history time；What user carried out in historical time section
Purchase order includes the order and the order for retracting cancellation that transaction is completed, and the information collection by completing order to transaction judges to use
Family purchase transaction completion rate, identifies risk trade by the height of order transaction completion rate.
In an alternative embodiment, S12 acquisition user's purchase order information within the recent history time includes
Lower list longitude and latitude, order value, the timeliness for whether buying same category and category.Lower list longitude and latitude may determine that user normally purchases
Tendency and shopping area are bought, the amount of money to place an order of doing shopping judges that user normally does shopping cost, if purchase is the same as category product and same product
The Use Limitation of class product may determine that the used demand to such product, can be more accurate by the acquisition of many-sided information
Judge whether user is risk subscribers, to reduce the generation of risk order.
In an alternative embodiment, order distribution information of the step S2 acquisition user within the recent history time
Include:
S21, acquisition user's order displacement information within the recent history time；It needs to buy by express delivery after user's shopping
Object is transported in user hand, by judging user's shopping transportation route to order article express delivery displacement information, is facilitated deciding on
It whether is normally to do shopping, such as abnormal order displacement information occurs repeatedly, and displacement information is different, then marks the user
Remember that emphasis is judged；
S22, acquisition the user knight within the recent history time dispense distance and expense；The order of certain brush single users exists
The dispatching of no object is dispensed apart from extremely short, and dispatching expense is not inconsistent with practical order, by sentencing to the information for dispensing distance and expense
Disconnected risk order is conducive to improve risk order discrimination precision；
S23, acquisition user's order addressee information within the recent history time, by user's order addressee information
Statistics judges the case where user is with the presence or absence of occurrence risk order, to judge consumer's risk.
In an alternative embodiment, the step S3 statistics order and distribution information, setting risk order determine mark
Standard includes:
S31, low amount of money order accounting are greater than 40%；Risk order is mostly that never improper order or brush are single etc, this
Order preliminary to the progress of risk order can be sentenced mostly based on low trading by counting low amount of money quantity on order
It is disconnected；
S32, order are displaced abnormal accounting and are greater than 40%；For risk order or improper order, usual order purchase
Part will not finally be sent to user, therefore can have the generation of order misalignment, abnormal by statistics order misalignment
Quantity further judges risk order；
S33, order knight concentrate, and transport because risk order usually has improper order, therefore will appear order knight
Centralization, the performance of order knight's centralization includes but are not limited to lower single user or consignee is that knight's order accounting is greater than
60%, risk order and user are judged by these data.
In an alternative embodiment, the step S4 identifies risk subscribers and risk order by risk criterion
Include:
S41, risk subscribers are judged by passing order and distribution information；Pass through the system to passing order and distribution information
Meter, judges risk subscribers according to risk discrimination standard；
S42, risk order is judged according to order placement information again；According to risk discrimination standard, order placement information is judged,
Judge whether order placement information meets the standard of risk differentiation, order information and lower single user are judged, identification risk is used
Family；
S43, risk subscribers are drawn a circle to approve, focus on, by risk discrimination standard to risk subscribers drawn a circle to approve with
And risk order is positioned, and is focused on for risk subscribers and order, and the generation of risk order is effectively avoided.
In an alternative embodiment, the step S5, which handle to risk subscribers and risk order, includes:
S51, blacklist management is carried out to delineation risk subscribers；It is statisticallyd analyze by volume of data and to risk judgment
Comparison of standards, the user there are risk is drawn a circle to approve, blacklist it middle access of the limitation to retail shop, thus effectively
Risk subscribers are avoided to generate risk order；
S52, cancelled for the risk order that risk subscribers are assigned, the wind that the risk subscribers tentatively drawn a circle to approve are assigned
Dangerous order, avoids order from generating in time by way of cancelling an order, and prevents risk order from having adverse effect on to retail shop.
In an alternative embodiment, the S42 judges that risk order is the amount of money lower than 10 according to order placement information again
Member, and it is real pay knight's amount received difference >=3 yuan of order, since risk order belongs to the low amount of money and order knight is different more
It often concentrates, therefore the information by taking in difference to user's order amount of money and knight judges risk subscribers, and to the risk subscribers
Order cancelled, prevent the generation of risk order.
In an alternative embodiment, the S43 draws a circle to approve risk subscribers, and centralized processing includes determining that risk is used
Family, cancels user's order and lower single pair user draws a circle to approve for the first time, descends single pair user to be determined again and cancels under this time
It is single.
It should be understood that above-mentioned specific embodiment of the invention is used only for exemplary illustration or explains of the invention
Principle, but not to limit the present invention.Therefore, that is done without departing from the spirit and scope of the present invention is any
Modification, equivalent replacement, improvement etc., should all be included in the protection scope of the present invention.In addition, appended claims purport of the present invention
Covering the whole variations fallen into attached claim scope and boundary or this range and the equivalent form on boundary and is repairing
Change example.
Claims (9)
1. a kind of statistical method of discriminant risk order, which is characterized in that the described method comprises the following steps:
The order information of S1, acquisition user within the recent history time；
The order distribution information of S2, acquisition user within the recent history time；
S3, statistics order and distribution information, set the criterion of risk order；
S4, risk subscribers and risk order are identified by risk criterion；
S5, risk subscribers and risk order are handled.
2. a kind of statistical method of discriminant risk order according to claim 1, which is characterized in that the step S1 acquisition
Order information of the user within the recent history time include:
S11, acquisition user browse merchandise news within the recent history time；
S12, acquisition user's purchase order information within the recent history time；
S13, acquisition user complete trade order information within the recent history time.
3. a kind of statistical method of discriminant risk order according to claim 2, which is characterized in that the S12 acquisition is used
Family purchase order information within the recent history time includes lower single longitude and latitude, order value, whether buys same category and category
Timeliness.
4. a kind of statistical method of discriminant risk order according to claim 1, which is characterized in that the step S2 acquisition
Order distribution information of the user within the recent history time include:
S21, acquisition user's order displacement information within the recent history time；
S22, acquisition the user knight within the recent history time dispense distance and expense；
S23, acquisition user's order addressee information within the recent history time.
5. a kind of statistical method of discriminant risk order according to claim 1, which is characterized in that the step S3 statistics
Order and distribution information, setting risk order criterion include:
S31, low amount of money order accounting are greater than 40%；
S32, order are displaced abnormal accounting and are greater than 40%；
S33, order knight concentrate.
6. a kind of statistical method of discriminant risk order according to claim 1, which is characterized in that the step S4 passes through
Risk criterion identification risk subscribers and risk order include:
S41, risk subscribers are judged by passing order and distribution information；
S42, risk order is judged according to order placement information again；
S43, risk subscribers are drawn a circle to approve, is focused on.
7. a kind of statistical method of discriminant risk order according to claim 1, which is characterized in that the step S5 is to wind
Dangerous user and risk order carry out processing
S51, blacklist management is carried out to delineation risk subscribers；
S52, cancelled for the risk order that risk subscribers are assigned.
8. a kind of statistical method of discriminant risk order according to claim 6, which is characterized in that the S42 is according to again
Secondary order placement information judges that risk order is the amount of money lower than 10 yuan, and real pays knight's amount received difference >=3 yuan of order.
9. a kind of statistical method of discriminant risk order according to claim 6, which is characterized in that the S43 is to risk
User draws a circle to approve, and centralized processing includes determining risk subscribers, cancels user's order and lower single pair user draws a circle to approve for the first time,
It descends single pair user to be determined again and cancels this time to place an order.
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| CN201910517931.1A CN110298563A (en) | 2019-06-14 | 2019-06-14 | A kind of statistical method of discriminant risk order |
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110992135A (en) * | 2019-11-25 | 2020-04-10 | 拉扎斯网络科技（上海）有限公司 | Risk identification method and device, electronic equipment and storage medium |
| CN111340508A (en) * | 2020-05-22 | 2020-06-26 | 支付宝(杭州)信息技术有限公司 | Payment operation control method and system |
| CN112132649A (en) * | 2020-08-28 | 2020-12-25 | 绿瘦健康产业集团有限公司 | Order verification processing method, device, medium and terminal equipment |
| CN112907263A (en) * | 2021-03-22 | 2021-06-04 | 北京太火红鸟科技有限公司 | Abnormal order quantity detection method, device, equipment and storage medium |
| CN113222306A (en) * | 2020-01-21 | 2021-08-06 | 北京三快在线科技有限公司 | Activity event risk identification method and device, readable storage medium and electronic equipment |
| CN113743697A (en) * | 2020-08-21 | 2021-12-03 | 西安京迅递供应链科技有限公司 | Risk alarm method and device |
| CN116843346A (en) * | 2023-09-01 | 2023-10-03 | 北京三五通联科技发展有限公司 | Abnormal order monitoring and early warning method and system based on cloud platform |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105205567A (en) * | 2015-10-14 | 2015-12-30 | 山东德州烟草有限公司 | Tobacco ordering abnormity tracking system and method |
| CA2942224A1 (en) * | 2015-09-17 | 2017-03-17 | Viesoft, Inc. | Systems and methods for managing compliance with one or more minimum advertised pricing policies via interconnected data structures |
| CN107133833A (en) * | 2016-02-26 | 2017-09-05 | 阿里巴巴集团控股有限公司 | abnormal transaction identification method and device |
| CN108197956A (en) * | 2017-12-29 | 2018-06-22 | 上海携程商务有限公司 | The risk control method and system of the product exception of electric business platform |
| CN108564448A (en) * | 2018-04-23 | 2018-09-21 | 广东奥园奥买家电子商务有限公司 | A kind of implementation method of the anti-brush of order |
| CN107464169B (en) * | 2017-08-10 | 2020-11-10 | 北京星选科技有限公司 | Information output method and device |
-
2019
- 2019-06-14 CN CN201910517931.1A patent/CN110298563A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2942224A1 (en) * | 2015-09-17 | 2017-03-17 | Viesoft, Inc. | Systems and methods for managing compliance with one or more minimum advertised pricing policies via interconnected data structures |
| CN105205567A (en) * | 2015-10-14 | 2015-12-30 | 山东德州烟草有限公司 | Tobacco ordering abnormity tracking system and method |
| CN107133833A (en) * | 2016-02-26 | 2017-09-05 | 阿里巴巴集团控股有限公司 | abnormal transaction identification method and device |
| CN107464169B (en) * | 2017-08-10 | 2020-11-10 | 北京星选科技有限公司 | Information output method and device |
| CN108197956A (en) * | 2017-12-29 | 2018-06-22 | 上海携程商务有限公司 | The risk control method and system of the product exception of electric business platform |
| CN108564448A (en) * | 2018-04-23 | 2018-09-21 | 广东奥园奥买家电子商务有限公司 | A kind of implementation method of the anti-brush of order |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110992135A (en) * | 2019-11-25 | 2020-04-10 | 拉扎斯网络科技（上海）有限公司 | Risk identification method and device, electronic equipment and storage medium |
| CN110992135B (en) * | 2019-11-25 | 2024-01-23 | 拉扎斯网络科技（上海）有限公司 | Risk identification method and device, electronic equipment and storage medium |
| CN113222306A (en) * | 2020-01-21 | 2021-08-06 | 北京三快在线科技有限公司 | Activity event risk identification method and device, readable storage medium and electronic equipment |
| CN111340508A (en) * | 2020-05-22 | 2020-06-26 | 支付宝(杭州)信息技术有限公司 | Payment operation control method and system |
| CN113743697A (en) * | 2020-08-21 | 2021-12-03 | 西安京迅递供应链科技有限公司 | Risk alarm method and device |
| CN112132649A (en) * | 2020-08-28 | 2020-12-25 | 绿瘦健康产业集团有限公司 | Order verification processing method, device, medium and terminal equipment |
| CN112907263A (en) * | 2021-03-22 | 2021-06-04 | 北京太火红鸟科技有限公司 | Abnormal order quantity detection method, device, equipment and storage medium |
| CN116843346A (en) * | 2023-09-01 | 2023-10-03 | 北京三五通联科技发展有限公司 | Abnormal order monitoring and early warning method and system based on cloud platform |
| CN116843346B (en) * | 2023-09-01 | 2023-11-17 | 北京三五通联科技发展有限公司 | Abnormal order monitoring and early warning method and system based on cloud platform |
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Application publication date: 20191001 |