CN113837617A - Anti-bill-swiping risk management method and device - Google Patents
Anti-bill-swiping risk management method and device Download PDFInfo
- Publication number
- CN113837617A CN113837617A CN202111130113.XA CN202111130113A CN113837617A CN 113837617 A CN113837617 A CN 113837617A CN 202111130113 A CN202111130113 A CN 202111130113A CN 113837617 A CN113837617 A CN 113837617A
- Authority
- CN
- China
- Prior art keywords
- order
- information
- commerce
- orders
- receiving address
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000007726 management method Methods 0.000 title claims abstract description 31
- 230000000875 corresponding effect Effects 0.000 claims description 9
- 238000011144 upstream manufacturing Methods 0.000 claims description 7
- 230000002596 correlated effect Effects 0.000 claims description 6
- 230000001680 brushing effect Effects 0.000 claims description 2
- 238000000034 method Methods 0.000 description 20
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000011010 flushing procedure Methods 0.000 description 3
- 238000009825 accumulation Methods 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000014509 gene expression Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000005201 scrubbing Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- 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
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2474—Sequence data queries, e.g. querying versioned data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/25—Integrating or interfacing systems involving database management systems
- G06F16/252—Integrating or interfacing systems involving database management systems between a Database Management System and a front-end application
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
-
- 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
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Databases & Information Systems (AREA)
- General Physics & Mathematics (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Finance (AREA)
- Accounting & Taxation (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Development Economics (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- Fuzzy Systems (AREA)
- Mathematical Physics (AREA)
- Probability & Statistics with Applications (AREA)
- Software Systems (AREA)
- Computational Linguistics (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Educational Administration (AREA)
- Remote Sensing (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The application discloses a risk management method and device for preventing billing, order time information, identity information and receiving address information contained in an e-commerce order are obtained by analyzing the e-commerce order, the e-commerce order is subjected to correlation matching in an ElasticSearch database by utilizing ElasticSearch, first similar orders which have the same identity information but different receiving address information within a first preset time period are indexed by taking the order time information as a reference, the correlation of the receiving address information is larger than a first preset threshold but smaller than 100%, and whether billing exists between the e-commerce order and the first similar orders or not is determined by judging the quantity of the first similar orders, so that risk marking is performed, and the problem that billing is performed through the same identity card but similar receiving addresses in the existing billing situation is solved.
Description
Technical Field
The application relates to the technical field of order analysis, in particular to an anti-order-swiping risk management method and device.
Background
With the ever-expanding e-commerce platforms, the real trading of stores becomes of particular importance. In order to improve the credibility, ranking, grading and even the information of each dimension such as the number of commodity comments of the stores, the sellers of the e-commerce adopt a fraud risk mode of bill swiping.
After the existing order information is combed, a plurality of orders with the same identity card but inconsistent receiving addresses exist, and the receiving addresses of the orders are extremely similar, for example, a building or a floor or a house number has slight difference.
Therefore, it is highly desirable for those skilled in the art to provide a method and an apparatus for risk management of anti-ticket-flushing to solve the problem of ticket-flushing through the same id card but similar receiving address in the existing ticket-flushing situation.
Disclosure of Invention
The application provides a risk management method and device for preventing bill swiping, and solves the problem that in the existing bill swiping situation, bills are swiped through the same identity card but similar delivery addresses.
In view of the above, a first aspect of the present application provides an anti-policy risk management method, including:
s101, receiving an E-commerce order sent by an upstream order system;
s102, analyzing the E-commerce order, and acquiring order time information, identity information and receiving address information in the E-commerce order;
s103, according to the order time information, the identity information and the receiving address information, searching the quantity of first similar orders which are consistent with the identity information and have the correlation degree with the receiving address information larger than a first preset threshold value but smaller than 100% in an ElasticSearch database within a first preset time period based on the order time information through ElasticSearch;
and S104, if the quantity of the first similar orders is larger than a second preset threshold value, marking the e-commerce orders and the first similar orders as risk orders.
Optionally, after the step S104, the method further includes:
and if the quantity of the first similar orders is smaller than a second preset threshold value, storing the identity information and the receiving address information in the E-commerce orders into an ElasticSearch database in a correlated mode.
Optionally, the step S102 specifically includes:
and analyzing the E-commerce order to obtain an order number, order time information, identity information encrypted through a first encryption algorithm, contact information encrypted through a second encryption algorithm and receiving address information in the E-commerce order.
Optionally, the step S103 specifically includes:
importing the order time information, the identity information and the receiving address information into an ElasticSearch database;
based on the order time information and the identity information, indexing historical receiving address information of historical e-commerce orders with consistent identity information in historical e-commerce orders in a first preset time period by taking the order time corresponding to the order time information as a terminal in the ElasticSearch database;
sequentially calculating the correlation degree of the historical goods receiving address information and the goods receiving address information;
if the correlation degree is larger than a first preset threshold value but smaller than 100%, marking the historical e-commerce order as a first similar order;
and counting the number of the first similar orders.
Optionally, the method further comprises:
analyzing the E-commerce order, and acquiring order time information, contact information and receiving address information in the E-commerce order;
according to the order time information, the contact information and the receiving address information, searching for the number of second similar orders which are consistent with the contact information and have correlation degree with the receiving address information larger than a third preset threshold value but smaller than 100% in an ElasticSearch database within a second preset time period based on the order time information through ElasticSearch;
and if the number of the second similar orders is larger than a fourth preset threshold value, marking the E-commerce orders and the second similar orders as risk orders.
This application second aspect provides a risk management device that electric commerce prevents brushing list, the device includes:
the receiving unit is used for receiving the E-commerce orders sent by the upstream order system;
the first analysis unit is used for analyzing the e-commerce order and acquiring order time information, identity information and receiving address information in the e-commerce order;
a first matching unit, configured to search, according to the order time information, the identity information, and the receiving address information, for a number of first similar orders in an elastic search database, which are consistent with the identity information and have a correlation with the receiving address information that is greater than a first preset threshold but less than 100%, within a first preset time period based on the order time information;
the first marking unit is used for marking the E-commerce order and the first similar order as risk orders if the quantity of the first similar order is larger than a second preset threshold value.
Optionally, the method further comprises:
and the storage unit is used for storing the identity information and the receiving address information in the E-commerce order into an ElasticSearch database in a correlated manner if the quantity of the first similar orders is smaller than a second preset threshold value.
Optionally, the first parsing unit is specifically configured to:
and analyzing the E-commerce order to obtain an order number, order time information, identity information encrypted through a first encryption algorithm, contact information encrypted through a second encryption algorithm and receiving address information in the E-commerce order.
Optionally, the first matching unit is specifically configured to:
importing the order time information, the identity information and the receiving address information into an ElasticSearch database;
based on the order time information and the identity information, indexing historical receiving address information of historical e-commerce orders with consistent identity information in historical e-commerce orders in a first preset time period by taking the order time corresponding to the order time information as a terminal in the ElasticSearch database;
sequentially calculating the correlation degree of the historical goods receiving address information and the goods receiving address information;
if the correlation degree is larger than a first preset threshold value but smaller than 100%, marking the historical e-commerce order as a first similar order;
and counting the number of the first similar orders.
Optionally, the method further comprises:
the second analysis unit is used for analyzing the E-commerce order and acquiring order time information, contact information and receiving address information in the E-commerce order;
a second matching unit, configured to search, according to the order time information, the contact information, and the receiving address information, for a number of second similar orders in an elastic search database, within a second preset time period based on the order time information, that are consistent with the contact information and have a correlation with the receiving address information that is greater than a third preset threshold but less than 100%, through an elastic search;
and the second marking unit is used for marking the E-commerce order and the second similar order as risk orders if the quantity of the second similar order is larger than a fourth preset threshold value.
According to the technical scheme, the embodiment of the application has the following advantages:
the method comprises the steps of analyzing an e-commerce order to obtain order time information, identity information and receiving address information contained in the e-commerce order, utilizing an ElasticSearch to match the e-commerce order in an ElasticSearch database for correlation, indexing a first similar order which has the same identity information but different receiving address information in a first preset time period by taking the order time information as a reference, wherein the correlation of the receiving address information is larger than a first preset threshold but smaller than 100%, and determining whether the e-commerce order and the first similar order have a condition of swiping the order by judging the quantity of the first similar order, so that risk marking is performed, and the problem that the order is swiped through the same identity card but similar receiving address in the existing condition of swiping the order is solved.
Drawings
FIG. 1 is a flowchart illustrating a method for risk management of anti-scrub in an embodiment of the present application;
fig. 2 is a schematic structural diagram of a risk management device for preventing a bill from being swiped in an embodiment of the present application.
Detailed Description
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The application designs a risk management method and device for preventing bill swiping, and solves the problem that in the existing bill swiping situation, bills are swiped through the same identity card but similar delivery addresses.
For easy understanding, please refer to fig. 1, where fig. 1 is a flowchart of a method for risk management of anti-scrub list in an embodiment of the present application, and as shown in fig. 1, the method specifically includes:
s101, receiving an E-commerce order sent by an upstream order system;
it should be noted that the upstream order system needs to go through the risk management check of the anti-refresh order after creating a new e-commerce order.
S102, analyzing an E-commerce order, and acquiring order time information, identity information and receiving address information in the E-commerce order;
the method specifically comprises the following steps:
and analyzing the E-commerce order, and acquiring an order number, order time information, identity information encrypted through a first encryption algorithm, contact information encrypted through a second encryption algorithm and receiving address information in the E-commerce order.
It should be noted that, usually, an e-commerce order carries various information, but in the risk management of anti-billing, the most important obtained is order time information, identity information and receiving address information in the e-commerce order, the order time information may be time for creating the e-commerce order, or may be payment time for the e-commerce order, and the like, the identity information is usually identity card information of an ordering person of the e-commerce order, and the receiving address information is a receiving address corresponding to the e-commerce order. The identity information and the contact information are encrypted through an encryption algorithm, and the encryption algorithm can be a uniform encryption algorithm or different encryption algorithms can be selected for encryption.
S103, according to the order time information, the identity information and the receiving address information, searching the quantity of first similar orders which are consistent with the identity information and have the correlation degree with the receiving address information larger than a first preset threshold value but smaller than 100% in a first preset time period based on the order time information in an ElasticSearch database through ElasticSearch;
the method specifically comprises the following steps:
importing order time information, identity information and receiving address information into an ElasticSearch database;
based on the order time information and the identity information, indexing historical receiving address information of the historical e-commerce orders with consistent identity information in the historical e-commerce orders in a first preset time period by taking the order time corresponding to the order time information as a terminal in an ElasticSearch database;
sequentially calculating the correlation degree of the historical goods receiving address information and the goods receiving address information;
if the correlation degree is larger than a first preset threshold value but smaller than 100%, marking the historical e-commerce order as a first similar order;
and counting the number of the first similar orders.
It should be noted that after the order time information, the identity information and the receiving address information are obtained by analyzing the e-commerce order, a historical e-commerce order submitted by the same identity information in the elastosearch database is searched through the elastosearch in the elastosearch database based on the order time information, and further, a correlation between the e-commerce order and the receiving address information of the historical e-commerce order is calculated based on the receiving address information of the e-commerce order, where the correlation is greater than a first preset threshold value, which indicates that the receiving address information of the historical e-commerce order is similar to the height, but the correlation does not reach 100%, indicates that the historical e-commerce order and the current e-commerce order are not the same, and there may be a risk of scrubbing the e-commerce order.
And S104, if the quantity of the first similar orders is larger than a second preset threshold value, marking the e-commerce orders and the first similar orders as risk orders.
It should be noted that, in order to further determine whether there is a risk of refreshing the electronic commerce orders, the quantity of the first similar orders needs to be counted, and if the quantity accumulation of the first similar orders exceeds a second preset threshold, for example, the quantity of the first similar orders is 4 orders, and the second preset threshold is 3 orders, it can be determined that the identity information is utilized to refresh the orders if the quantity accumulation of the first similar orders exceeds the second preset threshold.
It can be understood that the anti-billing risk management method provided by the application only takes the identity information, the order time information and the receiving address information of the ordering person as the dimension of risk judgment in various information dimensions of the e-commerce order without considering the commodities purchased by the e-commerce order, so that the requirement of risk judgment is reduced, meanwhile, the risk management efficiency is improved, and the data capacity in the ElasticSearch database is reduced.
Further, step S104 is followed by:
and if the quantity of the first similar orders is smaller than a second preset threshold value, storing the identity information and the receiving address information in the E-commerce orders into an ElasticSearch database in a correlated mode.
Further, still include:
analyzing the E-commerce order, and acquiring order time information, contact information and receiving address information in the E-commerce order;
according to the order time information, the contact information and the receiving address information, searching the quantity of second similar orders which are consistent with the contact information and have the correlation degree with the receiving address information larger than a third preset threshold value but smaller than 100% in a second preset time period based on the order time information in an ElasticSearch database through ElasticSearch;
and if the number of the second similar orders is larger than a fourth preset threshold value, marking the E-business orders and the second similar orders as risk orders.
It should be noted that, in addition to considering the similar receipt condition of the same identity information and the similar receiving address, the similar receipt risk management can be performed based on the same contact information and the similar receiving address, so as to avoid the similar receipt condition.
The anti-bill-swiping risk management method further comprises the following steps:
analyzing an e-commerce order, and acquiring order time information, IP address information and receiving address information in the e-commerce order;
according to the order time information, the IP address information and the receiving address information, searching the quantity of third similar orders which are consistent with the IP address information and have the correlation degree with the receiving address information larger than a fifth preset threshold value but smaller than 100% in a third preset time period based on the order time information in an ElasticSearch database through ElasticSearch;
and if the third similar order number is larger than a sixth preset threshold value, marking the e-commerce order and the second similar order as risk orders.
It should be noted that, in addition to considering the similar receipt condition of the same identity information and the similar receiving address, the similar receipt risk management can be performed by using the same IP address information and the similar receiving address as the reference, so as to avoid the similar receipt condition.
Referring to fig. 2, fig. 2 is a schematic structural diagram of a risk management device for preventing a scrub-down list in an embodiment of the present application, as shown in fig. 2, specifically including:
the receiving unit 201 is configured to receive an e-commerce order sent by an upstream order system;
the first analysis unit 202 is configured to analyze an e-commerce order, and obtain order time information, identity information, and receiving address information in the e-commerce order;
the first matching unit 203 is used for searching the quantity of first similar orders which are consistent with the identity information and have the correlation degree with the receiving address information larger than a first preset threshold value but smaller than 100% in an ElasticSearch database in a first preset time period based on the order time information according to the order time information, the identity information and the receiving address information;
the first marking unit 204 is configured to mark both the e-commerce order and the first similar order as risk orders if the number of the first similar orders is greater than a second preset threshold.
Further, still include:
and the storage unit is used for storing the identity information and the receiving address information in the e-commerce order into an ElasticSearch database in a correlated manner if the quantity of the first similar orders is smaller than a second preset threshold value.
Further, the first parsing unit 202 is specifically configured to:
and analyzing the E-commerce order, and acquiring an order number, order time information, identity information encrypted through a first encryption algorithm, contact information encrypted through a second encryption algorithm and receiving address information in the E-commerce order.
Further, the first matching unit 203 is specifically configured to:
importing order time information, identity information and receiving address information into an ElasticSearch database;
based on the order time information and the identity information, indexing historical receiving address information of the historical e-commerce orders with consistent identity information in the historical e-commerce orders in a first preset time period by taking the order time corresponding to the order time information as a terminal in an ElasticSearch database;
sequentially calculating the correlation degree of the historical goods receiving address information and the goods receiving address information;
if the correlation degree is larger than a first preset threshold value but smaller than 100%, marking the historical e-commerce order as a first similar order;
and counting the number of the first similar orders.
Further, still include:
the second analysis unit is used for analyzing the e-commerce order and acquiring order time information, contact information and receiving address information in the e-commerce order;
the second matching unit is used for searching the number of second similar orders which are consistent with the contact information and have correlation degree with the receiving address information larger than a third preset threshold value but smaller than 100% in a second preset time period based on the order time information in an ElasticSearch database according to the order time information, the contact information and the receiving address information;
and the second marking unit is used for marking the e-commerce order and the second similar order as the risk order if the quantity of the second similar order is larger than a fourth preset threshold value.
In the embodiment of the application, an anti-billing risk management method and device are provided, an e-commerce order is analyzed to obtain order time information, identity information and receiving address information contained in the e-commerce order, the e-commerce order is subjected to correlation matching in an ElasticSearch database by using ElasticSearch, first similar orders which have the same identity information but different receiving address information in a first preset time period are indexed by taking the order time information as a reference, the correlation of the receiving address information is larger than a first preset threshold but smaller than 100%, and whether billing is performed between the e-commerce order and the first similar orders is determined by judging the quantity of the first similar orders, so that risk marking is performed, and the problem that billing is performed through the same identity card but similar receiving addresses in the existing billing situation is solved.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The terms "first," "second," "third," "fourth," and the like in the description of the application and the above-described figures, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in the present application, "at least one" means one or more, "a plurality" means two or more. "and/or" for describing an association relationship of associated objects, indicating that there may be three relationships, e.g., "a and/or B" may indicate: only A, only B and both A and B are present, wherein A and B may be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of single item(s) or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.
Claims (10)
1. A risk management method for preventing E-commerce from swiping a bill is characterized by comprising the following steps:
s101, receiving an E-commerce order sent by an upstream order system;
s102, analyzing the E-commerce order, and acquiring order time information, identity information and receiving address information in the E-commerce order;
s103, according to the order time information, the identity information and the receiving address information, searching the quantity of first similar orders which are consistent with the identity information and have the correlation degree with the receiving address information larger than a first preset threshold value but smaller than 100% in an ElasticSearch database within a first preset time period based on the order time information through ElasticSearch;
and S104, if the quantity of the first similar orders is larger than a second preset threshold value, marking the e-commerce orders and the first similar orders as risk orders.
2. The e-commerce anti-billing risk management method according to claim 1, wherein the step S104 is followed by further comprising:
and if the quantity of the first similar orders is smaller than a second preset threshold value, storing the identity information and the receiving address information in the E-commerce orders into an ElasticSearch database in a correlated mode.
3. The e-commerce anti-billing risk management method according to claim 1, wherein the step S102 specifically comprises:
and analyzing the E-commerce order to obtain an order number, order time information, identity information encrypted through a first encryption algorithm, contact information encrypted through a second encryption algorithm and receiving address information in the E-commerce order.
4. The e-commerce anti-billing risk management method according to claim 1, wherein the step S103 specifically comprises:
importing the order time information, the identity information and the receiving address information into an ElasticSearch database;
based on the order time information and the identity information, indexing historical receiving address information of historical e-commerce orders with consistent identity information in historical e-commerce orders in a first preset time period by taking the order time corresponding to the order time information as a terminal in the ElasticSearch database;
sequentially calculating the correlation degree of the historical goods receiving address information and the goods receiving address information;
if the correlation degree is larger than a first preset threshold value but smaller than 100%, marking the historical e-commerce order as a first similar order;
and counting the number of the first similar orders.
5. The e-commerce anti-billing risk management method of claim 3, further comprising:
analyzing the E-commerce order, and acquiring order time information, contact information and receiving address information in the E-commerce order;
according to the order time information, the contact information and the receiving address information, searching for the number of second similar orders which are consistent with the contact information and have correlation degree with the receiving address information larger than a third preset threshold value but smaller than 100% in an ElasticSearch database within a second preset time period based on the order time information through ElasticSearch;
and if the number of the second similar orders is larger than a fourth preset threshold value, marking the E-commerce orders and the second similar orders as risk orders.
6. A risk management device that electric commerce prevents brushing list, its characterized in that includes:
the receiving unit is used for receiving the E-commerce orders sent by the upstream order system;
the first analysis unit is used for analyzing the e-commerce order and acquiring order time information, identity information and receiving address information in the e-commerce order;
a first matching unit, configured to search, according to the order time information, the identity information, and the receiving address information, for a number of first similar orders in an elastic search database, which are consistent with the identity information and have a correlation with the receiving address information that is greater than a first preset threshold but less than 100%, within a first preset time period based on the order time information;
the first marking unit is used for marking the E-commerce order and the first similar order as risk orders if the quantity of the first similar order is larger than a second preset threshold value.
7. The anti-scrub risk management device of claim 6, further comprising:
and the storage unit is used for storing the identity information and the receiving address information in the E-commerce order into an ElasticSearch database in a correlated manner if the quantity of the first similar orders is smaller than a second preset threshold value.
8. The anti-billing risk management device according to claim 6, wherein the first parsing unit is specifically configured to:
and analyzing the E-commerce order to obtain an order number, order time information, identity information encrypted through a first encryption algorithm, contact information encrypted through a second encryption algorithm and receiving address information in the E-commerce order.
9. The anti-scrub risk management device of claim 6, wherein the first matching unit is specifically configured to:
importing the order time information, the identity information and the receiving address information into an ElasticSearch database;
based on the order time information and the identity information, indexing historical receiving address information of historical e-commerce orders with consistent identity information in historical e-commerce orders in a first preset time period by taking the order time corresponding to the order time information as a terminal in the ElasticSearch database;
sequentially calculating the correlation degree of the historical goods receiving address information and the goods receiving address information;
if the correlation degree is larger than a first preset threshold value but smaller than 100%, marking the historical e-commerce order as a first similar order;
and counting the number of the first similar orders.
10. The anti-scrub risk management device of claim 8, further comprising:
the second analysis unit is used for analyzing the E-commerce order and acquiring order time information, contact information and receiving address information in the E-commerce order;
a second matching unit, configured to search, according to the order time information, the contact information, and the receiving address information, for a number of second similar orders in an elastic search database, within a second preset time period based on the order time information, that are consistent with the contact information and have a correlation with the receiving address information that is greater than a third preset threshold but less than 100%, through an elastic search;
and the second marking unit is used for marking the E-commerce order and the second similar order as risk orders if the quantity of the second similar order is larger than a fourth preset threshold value.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111130113.XA CN113837617A (en) | 2021-09-26 | 2021-09-26 | Anti-bill-swiping risk management method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111130113.XA CN113837617A (en) | 2021-09-26 | 2021-09-26 | Anti-bill-swiping risk management method and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113837617A true CN113837617A (en) | 2021-12-24 |
Family
ID=78970510
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111130113.XA Pending CN113837617A (en) | 2021-09-26 | 2021-09-26 | Anti-bill-swiping risk management method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113837617A (en) |
Cited By (3)
| 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 |
| CN116051130A (en) * | 2023-03-28 | 2023-05-02 | 北京龙驹易行科技有限公司 | Method, device, equipment and storage medium for identifying cutting behavior of multi-tenant platform |
| CN117035945A (en) * | 2023-09-08 | 2023-11-10 | 广东海洋大学 | Electronic information management method based on deep learning and related equipment |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107203522A (en) * | 2016-03-16 | 2017-09-26 | 北京京东尚科信息技术有限公司 | A kind of decision method and device of malice Order Address |
| CN107492021A (en) * | 2017-08-28 | 2017-12-19 | 武汉奇米网络科技有限公司 | Order source analysis method and device |
| 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 |
| CN113112323A (en) * | 2021-03-16 | 2021-07-13 | 中国平安财产保险股份有限公司 | Abnormal order identification method, device, equipment and medium based on data analysis |
-
2021
- 2021-09-26 CN CN202111130113.XA patent/CN113837617A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107203522A (en) * | 2016-03-16 | 2017-09-26 | 北京京东尚科信息技术有限公司 | A kind of decision method and device of malice Order Address |
| CN107492021A (en) * | 2017-08-28 | 2017-12-19 | 武汉奇米网络科技有限公司 | Order source analysis method and device |
| 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 |
| CN113112323A (en) * | 2021-03-16 | 2021-07-13 | 中国平安财产保险股份有限公司 | Abnormal order identification method, device, equipment and medium based on data analysis |
Cited By (5)
| 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 |
| CN116051130A (en) * | 2023-03-28 | 2023-05-02 | 北京龙驹易行科技有限公司 | Method, device, equipment and storage medium for identifying cutting behavior of multi-tenant platform |
| CN117035945A (en) * | 2023-09-08 | 2023-11-10 | 广东海洋大学 | Electronic information management method based on deep learning and related equipment |
| CN117035945B (en) * | 2023-09-08 | 2024-05-14 | 广东海洋大学 | Electronic information management method based on deep learning and related equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113837617A (en) | Anti-bill-swiping risk management method and device | |
| US11030622B2 (en) | Card systems and methods | |
| US8170998B2 (en) | Methods, systems, and computer program products for estimating accuracy of linking of customer relationships | |
| CN108780479A (en) | For to the abnormal system and method for being detected and scoring | |
| KR20160114724A (en) | Purchase information utilization system, purchase information utilization method, and program | |
| CN111143415A (en) | Data processing method and device and computer readable storage medium | |
| CN106600341A (en) | Commodity sales volume statistical method | |
| EP3543943A1 (en) | Purchase information utilization system, purchase information utilization method, and program | |
| CN114116802A (en) | Data processing method, device, equipment and storage medium of Flink computing framework | |
| CN112819476A (en) | Risk identification method and device, nonvolatile storage medium and processor | |
| JP7274572B2 (en) | Warehousing fee dynamic pricing system, method and program | |
| US10540682B1 (en) | Systems and methods for determining impact of high-affinity baskets | |
| CN111445297A (en) | Method and device for determining purchase quantity, storage medium and electronic equipment | |
| CN109450963B (en) | Message pushing method and terminal equipment | |
| CN113095939B (en) | Block chain intelligent contract recommendation method and device | |
| CN115049411A (en) | Method, device and equipment for detecting single-swiping user and storage medium | |
| CN104484357A (en) | Data processing method and device and access frequency information processing method and device | |
| CN114493673A (en) | Commodity price changing method, system, device and storage medium based on user behavior | |
| CN114493361A (en) | Effectiveness evaluation method and device for commodity recommendation algorithm | |
| CN112150225A (en) | Article information feedback method, device, equipment and computer readable storage medium | |
| CN113298550A (en) | Commodity price adjusting method and device and server | |
| CN113052673A (en) | Reconciliation clearing method and device, computer equipment and storage medium | |
| CN112734121A (en) | Information output method and device based on payment data | |
| CN111159506A (en) | Data validity identification method, device and equipment and readable storage medium | |
| CN111222905A (en) | Information processing device, information processing method, and information processing program |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20211224 |