CN111161050B - Artificial intelligence big data processing method applied to bank wind control department - Google Patents
Artificial intelligence big data processing method applied to bank wind control department Download PDFInfo
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- CN111161050B CN111161050B CN201911412195.XA CN201911412195A CN111161050B CN 111161050 B CN111161050 B CN 111161050B CN 201911412195 A CN201911412195 A CN 201911412195A CN 111161050 B CN111161050 B CN 111161050B
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/02—Banking, e.g. interest calculation or account maintenance
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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
Abstract
The artificial intelligence big data processing method applied to the bank wind control department comprises the steps of defining a first wind control grade of a user according to stored first payment data of the user; responding to second payment data acquired in real time, and defining a second wind control level of the second payment data; judging whether the first wind control level is higher than or equal to the second wind control level, if so, skipping to a1 to execute second payment data; if not, jumping to b1 to execute a first wind control verification, if the first wind control verification is successful, jumping to a1, if the first wind control verification is failed, jumping to c1 to execute a second wind control verification, if the second wind control verification is successful, jumping to a1, if the second wind control verification is failed, executing a third wind control verification d1, if the third wind control verification is successful, jumping to a1, and if the third wind control verification is failed, jumping to d2 to send an alarm signal. The invention provides convenience for users and ensures the financial wind control safety of the bank wind control part.
Description
Technical Field
The invention relates to a wind control method of a bank wind control department, in particular to a wind control processing method of a bank wind control part for processing big data.
Background
The banking wind control department aims to reduce the double risks of the user and the bank, including the risk of payment. In the progress of science and technology and society, people prefer the noninductive payment, namely, the fewer the artificial steps are, the more users prefer, the higher the viscosity of the users is, and the lower the popularization cost is. Too many non-inductive payments easily cause great wind control risks, especially financial risks and payment risks.
Therefore, at present, the bank wind control department needs an artificial intelligence big data processing method which can consider convenience and safety at the same time.
Disclosure of Invention
The invention aims to provide an artificial intelligence big data processing method which can simultaneously consider convenience and safety.
The invention relates to an artificial intelligence big data processing method applied to a bank wind control department, which comprises the following steps
Acquiring first payment data after transaction;
storing the first payment data;
defining a first wind control grade of the user according to the stored first payment data of the user;
responding to second payment data acquired in real time, and defining a second wind control level of the second payment data;
judging whether the first wind control level is higher than or equal to the second wind control level, if so, skipping to a1 to execute second payment data; if not, jumping to b1 to execute a first wind control verification, jumping to a1 if the first wind control verification is successful, jumping to c1 to execute a second wind control verification if the first wind control verification is failed, jumping to a1 if the second wind control verification is successful, executing a third wind control verification d1 if the second wind control verification is failed, jumping to a1 if the third wind control verification is successful, and jumping to d2 to send an alarm signal if the third wind control verification is failed;
and sending the signal for executing the second payment data or an alarm signal to a server.
The invention is applied to an artificial intelligence big data processing method of a bank wind control department, wherein after the step of executing second payment data by jumping to a1, the method also comprises the following steps:
defining a first level of wind control for the user based on the second payment data;
and converting the second payment data into first payment data and storing the first payment data.
The invention is applied to the artificial intelligence big data processing method of the bank wind control department, wherein according to the first payment data of the user stored, define the step of the first wind control grade of the user, also include the following step:
configuring the time data of each stored first payment data and the time data within a first preset threshold range nearby the time data as the time data of a first wind control level;
the step of defining a second wind control level of the second payment data in response to the second payment data acquired in real time further includes the following steps:
configuring time data of the second payment data as time data of second wind control data;
the step of judging whether the first wind control level is higher than or equal to the second wind control level comprises the following steps:
comparing whether the time data of the second wind control data is within the time data range of the first wind control data, and if so, judging that the first wind control grade is higher than or equal to the second wind control grade; and if not, judging that the first wind control level is lower than the second wind control level.
The invention is applied to an artificial intelligence big data processing method of a bank wind control department, wherein after the step of executing second payment data by jumping to a1, the method also comprises the following steps:
updating the first preset threshold value Y according to the following formula according to the number P of the steps of jumping to a1 to execute the second payment data:
after the step of sending the alarm signal by jumping to d2, the method also comprises the following steps:
and restoring the original updated first preset threshold value Y to the most initial first preset threshold value Y.
The invention relates to an artificial intelligence big data processing method applied to a bank wind control department, wherein in the step of defining a first wind control grade of a user according to stored first payment data of the user, the method further comprises the following steps:
configuring the position data of each stored first payment data and the position data within a second preset threshold range nearby the position data as position data of a first wind control level;
the step of defining a second wind control level of the second payment data in response to the second payment data acquired in real time further includes the following steps:
configuring the location data of the second payment data as location data of second wind control data;
the step of judging whether the first wind control level is higher than or equal to the second wind control level comprises the following steps:
comparing whether the position data of the second wind control data is within the range of the position data of the first wind control data, and if so, judging that the first wind control grade is higher than or equal to the second wind control grade; and if not, judging that the first wind control level is lower than the second wind control level.
The invention is applied to an artificial intelligence big data processing method of a bank wind control department, wherein after the step of executing second payment data by jumping to a1, the method also comprises the following steps:
updating a second preset threshold Q according to the number P of said steps of jumping to a1 to execute the second payment data as follows:
after the step of sending the alarm signal by jumping to d2, the method also comprises the following steps:
and restoring the original updated second preset threshold Q to the most initial second preset threshold Q.
The invention is applied to the artificial intelligence big data processing method of the bank wind control department, wherein after the step of sending an alarm signal by skipping to d2, the method also comprises the following steps:
deleting the following data in the stored first payment data: the time data is the first payment data within a third preset threshold around the time of the second payment data;
the first wind control level is redefined with the currently stored first payment data.
The invention is applied to the artificial intelligence big data processing method of the bank wind control department, wherein after the step of sending an alarm signal by skipping to d2, the method also comprises the following steps:
deleting the following data in the stored first payment data: the location data is first payment data within a second preset threshold around the location data of the second payment data;
redefining the first wind control level by using the currently stored first payment data;
the artificial intelligence big data processing method applied to the bank wind control department is different from the prior art in that the artificial intelligence big data processing method applied to the bank wind control department adds the wind control grades one by one and verifies various information of the user layer by layer, so that the hidden danger and trouble of the safety grade caused by the user executing the payment instruction can be greatly reduced. The user does not need to pay the ordinary money and to check the identity with great effort to reduce the risk, and the safety hazards of the user and the bank are not increased because the user can pay the important money freely. The financial wind control system provides convenience for users and ensures the financial wind control safety of the bank wind control part.
The artificial intelligence big data processing method applied to the bank wind control department of the invention is further explained with the attached drawings.
Drawings
FIG. 1 is a front view of an artificial intelligence big data processing method applied to a bank wind control department.
Detailed Description
As shown in FIG. 1, the artificial intelligence big data processing method applied to the bank wind control department of the invention comprises the following steps
Acquiring first payment data after transaction;
storing the first payment data;
defining a first wind control grade of the user according to the stored first payment data of the user;
responding to second payment data acquired in real time, and defining a second wind control level of the second payment data;
judging whether the first wind control level is higher than or equal to the second wind control level, if so, skipping to a1 to execute second payment data; if not, jumping to b1 to execute a first wind control verification, jumping to a1 if the first wind control verification is successful, jumping to c1 to execute a second wind control verification if the first wind control verification is failed, jumping to a1 if the second wind control verification is successful, executing a third wind control verification d1 if the second wind control verification is failed, jumping to a1 if the third wind control verification is successful, and jumping to d2 to send an alarm signal if the third wind control verification is failed;
and sending the signal for executing the second payment data or an alarm signal to a server.
According to the invention, the wind control level is added gradually and various information of the user is verified layer by layer, so that the hidden danger and trouble of the safety level caused by the user executing the payment instruction can be greatly reduced. The user does not need to pay the ordinary money and to check the identity with great effort to reduce the risk, and the safety hazards of the user and the bank are not increased because the user can pay the important money freely. The financial wind control system provides convenience for users and ensures the financial wind control safety of the bank wind control part.
In the step of defining the second pneumatic control level of the second payment data in response to the second payment data acquired in real time according to the first payment data of the user, sub-options of the first pneumatic control level can be defined according to upper limits of categories, geographical positions, total prices and quantity of users who often shop, for example, a good supermarket of a city in the Fuqian, the upper limits of which are 3 6 yuan of oranges, 1 yuan of strawberries and 2 yuan of pineapples and 40 yuan of pineapples, and a good supermarket of a pomegranate huqin in the city in the west purchases 10 yuan of 5 yuan of pencils, 1 yuan of rubbers and 2 yuan of rulers and 4 yuan; in the second payment data, for example, 1 orange and 2 yuan are purchased in a home supermarket of a rich street in the east city area, and it is determined that the first wind control level is higher than the second wind control level. For another example, a supermarket for home and beauty who presents a rich street in the east city area purchases 1 rubber and 2 rubber, and the data is not presented in the first payment data, so that the first wind control level is lower than the second wind control level. At this time, multiple wind control checks are needed to define the identity information of the user, and the first wind control level can be updated in real time according to the success or failure of the checks.
According to the invention, for the events with different degrees, the users with different degrees are graded, and then different treatments are carried out according to different levels.
As a further explanation of the present invention, after jumping to the step of a1 executing the second payment data, the following steps are included:
defining a first level of wind control for the user based on the second payment data;
and converting the second payment data into first payment data and storing the first payment data.
After the identity authentication is passed, the second payment data after the authentication is successfully verified can be converted into the trusted first payment data, and the first wind control level is stored and updated, so that a more intelligent and learnable database is constructed to ensure the intelligence and flexibility of the first wind control level.
As a further explanation of the present invention, the step of defining the first wind-control level of the user according to the stored first payment data of the user further comprises the steps of:
configuring the time data of each stored first payment data and the time data within a first preset threshold range nearby the time data as the time data of a first wind control level;
the step of defining a second wind control level of the second payment data in response to the second payment data acquired in real time further includes the following steps:
configuring time data of the second payment data as time data of second wind control data;
the step of judging whether the first wind control level is higher than or equal to the second wind control level comprises the following steps:
comparing whether the time data of the second wind control data is within the time data range of the first wind control data, and if so, judging that the first wind control grade is higher than or equal to the second wind control grade; and if not, judging that the first wind control level is lower than the second wind control level.
According to the invention, the comparison relation between the first wind control level and the second wind control level is configured in the manner, so that whether the first wind control level is within the range of the preset first preset threshold value or not can be clearly known, and the comparison of the second wind control level and the second wind control level is convenient for the second payment data of the payment in the common time or not, thereby reducing the financial risk and ensuring the safety of the user.
For example, the time data of the first payment data may be eight pm of the first day of each week/day/month, and the first preset threshold is 2 hours, that is, if the time data of the second payment data occurs at 6 pm to 10 pm of the first day of each week/day/month, it is determined that the first wind control level is higher than or equal to the second wind control level.
Wherein, the first preset threshold value can be (1min, 2 hours), preferably 30 min.
As a further explanation of the present invention, after jumping to the step of a1 executing the second payment data, the following steps are included:
updating the first preset threshold value Y according to the following formula according to the number P of the steps of jumping to a1 to execute the second payment data:
after the step of sending the alarm signal by jumping to d2, the method also comprises the following steps:
and restoring the original updated first preset threshold value Y to the most initial first preset threshold value Y.
The present invention can utilize the slope of Ln function as smaller and smaller in the above way, and increase the confidence level for the user after jumping to a1 to execute the step of second payment data in the initial stage (i.e. when P is smaller), and influence the decision effect in order to reduce the first preset threshold Y as large as possible in the later stage (i.e. when P is larger) so as to make more second payment data pass directly; and when the wind control verification fails for a plurality of times and an alarm signal is sent out, the original first preset threshold value Y is reduced to be in an initial state, so that the safety of a user is protected.
The maximum value of the updated first preset threshold value Y can be limited to be 1.5 times of the most original Y, so that the first preset threshold value is prevented from being too large in floating and losing the meaning of limiting the range of the first preset threshold value Y, and the first preset threshold value is immediately restored to the initial state when an alarm signal occurs.
As a further explanation of the present invention, the step of defining the first wind-control level of the user according to the stored first payment data of the user further comprises the steps of:
configuring the position data of each stored first payment data and the position data within a second preset threshold range nearby the position data as position data of a first wind control level;
the step of defining a second wind control level of the second payment data in response to the second payment data acquired in real time further includes the following steps:
configuring the location data of the second payment data as location data of second wind control data;
the step of judging whether the first wind control level is higher than or equal to the second wind control level comprises the following steps:
comparing whether the position data of the second wind control data is within the range of the position data of the first wind control data, and if so, judging that the first wind control grade is higher than or equal to the second wind control grade; and if not, judging that the first wind control level is lower than the second wind control level.
According to the invention, the comparison relation between the first wind control level and the second wind control level is configured in the manner, so that whether the first wind control level is within the range of the preset second preset threshold value can be clearly known, and the comparison of the second wind control level and the second wind control level is convenient for the user to determine whether the second wind control level is the second payment data paid by the common position, thereby reducing the financial risk and ensuring the safety of the user.
For example, the location data of the first payment data may be the B port of the wang fu subway station, the second preset threshold is 500 meters, that is, the square circle of the B port of the wang fu subway station is 500 meters, or the navigation walking distance is 500 meters, or the navigation driving distance is within 500 meters, and if the location data of the second payment data occurs, it is determined that the first wind control level is higher than or equal to the second wind control level.
Wherein the second preset threshold may be (10 meters, 10 kilometers), preferably 300 meters.
As a further explanation of the present invention, after jumping to the step of a1 executing the second payment data, the following steps are included:
according to the aboveJumping to the number P of times that the step of a1 executes the second payment data, the second preset threshold Q is updated as follows:
after the step of sending the alarm signal by jumping to d2, the method also comprises the following steps:
and restoring the original updated second preset threshold Q to the most initial second preset threshold Q.
The present invention can utilize the slope of Ln function as smaller and smaller in the above way, and increase the confidence level for the user after jumping to a1 to execute the step of second payment data in the initial stage (i.e. when P is smaller), and influence the decision effect in order to reduce the second preset threshold Y as large as possible in the later stage (i.e. when P is larger) so as to make more second payment data pass directly; and when the wind control verification fails for a plurality of times and an alarm signal is sent out, the original second preset threshold value Q is reduced to be in an initial state, so that the safety of a user is protected.
The maximum value of the updated second preset threshold Q can be limited to 1.5 times of the most original Y, so that the second preset threshold is prevented from being too large to lose the meaning of limiting the range of the second preset threshold Q, and the alarm signal is immediately restored to the initial state.
As a further explanation of the present invention, after the step of jumping to d2 to send out the alarm signal, the following steps are included:
deleting the following data in the stored first payment data: the time data is the first payment data within a third preset threshold around the time of the second payment data;
redefining the first wind control level by using the currently stored first payment data;
the invention continuously updates the first payment data stored in the dangerous time and the time nearby the dangerous time, particularly the executed first payment data, can be convenient for taking the nearby transaction record as a dangerous record when finding out one time of distrust, the dangerous record can be the dangerous record generated by the factors of forcing, stealing, password stealing and the like, and the alarm signal can be sent to the user terminal of the user and the user terminal of the police.
In the above step, when the step of sending the alarm signal is skipped to the step of d2, the time data is the first payment data within the third preset threshold around the time of the second payment data, for example: and if the payment time of the second payment data sending the alarm signal is 4 pm on 1 st 2019 and the third preset threshold is 2 hours, the first payment data with the time data of 2-6 pm on 1 st 2019 in the stored first payment data are deleted, and the first wind control level is redefined according to the stored first payment data.
Wherein, the third preset threshold value can be (1min, 2 hours), preferably 30 min.
As a further explanation of the present invention, after the step of jumping to d2 to send out the alarm signal, the following steps are included:
deleting the following data in the stored first payment data: the location data is first payment data within a second preset threshold around the location data of the second payment data;
redefining the first wind control level by using the currently stored first payment data;
the invention continuously updates the first payment data stored in the dangerous time and the time nearby the dangerous time, particularly the executed first payment data, can be convenient for taking the nearby transaction record as a dangerous record when finding out one time of distrust, the dangerous record can be the dangerous record generated by the factors of forcing, stealing, password stealing and the like, and the alarm signal can be sent to the user terminal of the user and the user terminal of the police.
After the step of jumping to d2 to send out the alarm signal, the position data is the first payment data within a fourth preset threshold around the position data of the second payment data; in (e), for example: and if the position data of the second payment data for sending the alarm signal is the A port of the east station of the subway Tianan door and the fourth preset threshold value is 500 meters, the first payment data which is stored in the first payment data and has the position data of 500 meters of the square circle of the A port of the east station of the Tianan door or the navigation walking distance of 500 meters or the navigation vehicle walking distance of 500 meters is deleted, and the first pneumatic control grade is redefined according to the stored first payment data.
The location data may be data of a location from which the second payment data originated.
The time data may be data of a time at which the second payment data is initiated.
Wherein, the fourth preset threshold value may be (10 meters, 10 kilometers), preferably 300 meters.
The first wind control verification, the second wind control verification and the third wind control verification can be face identification, fingerprint identification, password identification, U shield identification, short message verification and code scanning verification; more specifically, the first wind control verification may be face recognition, the second wind control verification may be fingerprint recognition or password recognition, and the third wind control verification may be U shield recognition.
Executing the second payment data;
that is, when the first wind control level is higher than or equal to the second wind control level, the second payment data is executed; when the first wind control level is lower than the second wind control level and the difference is smaller than a first preset threshold value, executing first wind control verification, and if the first wind control verification is successful, executing second payment data; if the first wind control verification fails, executing second wind control verification, and if the second wind control verification succeeds (and the first wind control level of the user is improved/reduced), executing the second payment data; if the second wind control verification fails, executing third wind control verification, and if the third wind control verification succeeds, executing second payment data; if the third wind control verification fails, an alarm signal is sent out;
the above-mentioned embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements of the technical solution of the present invention by those skilled in the art should fall within the protection scope defined by the claims of the present invention without departing from the spirit of the present invention.
Claims (1)
1. An artificial intelligence big data processing method applied to a bank wind control department is characterized in that: comprises that
Acquiring first payment data after transaction;
storing the first payment data;
defining a first wind control grade of the user according to the stored first payment data of the user;
responding to second payment data acquired in real time, and defining a second wind control level of the second payment data;
judging whether the first wind control level is higher than or equal to the second wind control level, if so, skipping to a1 to execute second payment data; if not, jumping to b1 to execute a first wind control verification, jumping to a1 if the first wind control verification is successful, jumping to c1 to execute a second wind control verification if the first wind control verification is failed, jumping to a1 if the second wind control verification is successful, executing a third wind control verification d1 if the second wind control verification is failed, jumping to a1 if the third wind control verification is successful, and jumping to d2 to send an alarm signal if the third wind control verification is failed;
sending the signal for executing the second payment data or an alarm signal to a server;
after jumping to the step of a1 executing the second payment data, the method further comprises the following steps:
defining a first wind control level of the user according to the second payment data;
converting the second payment data into first payment data and storing the first payment data;
the step of defining the first wind control level of the user according to the stored first payment data of the user further comprises the following steps:
configuring the time data of each stored first payment data and the time data within a first preset threshold range nearby the time data as the time data of a first wind control level;
the step of defining a second wind control level of the second payment data in response to the second payment data acquired in real time further includes the following steps:
configuring time data of the second payment data as time data of second wind control data;
the step of judging whether the first wind control level is higher than or equal to the second wind control level comprises the following steps:
comparing whether the time data of the second wind control data is within the time data range of the first wind control data, and if so, judging that the first wind control grade is higher than or equal to the second wind control grade; if not, judging that the first wind control level is lower than the second wind control level;
after jumping to the step of a1 executing the second payment data, the method further comprises the following steps:
updating the first preset threshold value Y according to the following formula according to the number P of the steps of jumping to a1 to execute the second payment data:
after the step of sending the alarm signal by jumping to d2, the method also comprises the following steps:
restoring the original updated first preset threshold value Y to the most initial first preset threshold value Y;
the step of defining the first wind control level of the user according to the stored first payment data of the user further comprises the following steps:
configuring the position data of each stored first payment data and the position data within a second preset threshold range nearby the position data as position data of a first wind control level;
the step of defining a second wind control level of the second payment data in response to the second payment data acquired in real time further includes the following steps:
configuring the location data of the second payment data as location data of second wind control data;
the step of judging whether the first wind control level is higher than or equal to the second wind control level comprises the following steps:
comparing whether the position data of the second wind control data is within the range of the position data of the first wind control data, and if so, judging that the first wind control grade is higher than or equal to the second wind control grade; if not, judging that the first wind control level is lower than the second wind control level;
after jumping to the step of a1 executing the second payment data, the method further comprises the following steps:
updating a second preset threshold Q according to the number P of said steps of jumping to a1 to execute the second payment data as follows:
after the step of sending the alarm signal by jumping to d2, the method also comprises the following steps:
restoring the original updated second preset threshold Q to the most initial second preset threshold Q;
after the step of sending the alarm signal by jumping to d2, the method also comprises the following steps:
deleting the following data in the stored first payment data: the time data is the first payment data within a third preset threshold around the time of the second payment data;
redefining the first wind control level by using the currently stored first payment data;
after the step of sending the alarm signal by jumping to d2, the method also comprises the following steps:
deleting the following data in the stored first payment data: the location data is first payment data within a second preset threshold around the location data of the second payment data;
the first wind control level is redefined with the currently stored first payment data.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106327196A (en) * | 2015-06-19 | 2017-01-11 | 阿里巴巴集团控股有限公司 | Payment threshold acquisition method and device |
| CN108960839A (en) * | 2018-06-20 | 2018-12-07 | 阿里巴巴集团控股有限公司 | A kind of method of payment and device |
| CN109003075A (en) * | 2017-06-07 | 2018-12-14 | 阿里巴巴集团控股有限公司 | A kind of Risk Identification Method and device |
| CN109242495A (en) * | 2018-09-10 | 2019-01-18 | 湖北众锐智能技术股份有限公司 | A kind of risk control method based on big data analysis |
| CN110378695A (en) * | 2019-06-19 | 2019-10-25 | 深圳壹账通智能科技有限公司 | Bank card payment method, device, equipment and computer storage medium |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9552578B2 (en) * | 2015-02-25 | 2017-01-24 | Mastercard International Incorporated | Method and system for authentication of payment card transactions |
-
2019
- 2019-12-31 CN CN201911412195.XA patent/CN111161050B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106327196A (en) * | 2015-06-19 | 2017-01-11 | 阿里巴巴集团控股有限公司 | Payment threshold acquisition method and device |
| CN109003075A (en) * | 2017-06-07 | 2018-12-14 | 阿里巴巴集团控股有限公司 | A kind of Risk Identification Method and device |
| CN108960839A (en) * | 2018-06-20 | 2018-12-07 | 阿里巴巴集团控股有限公司 | A kind of method of payment and device |
| CN109242495A (en) * | 2018-09-10 | 2019-01-18 | 湖北众锐智能技术股份有限公司 | A kind of risk control method based on big data analysis |
| CN110378695A (en) * | 2019-06-19 | 2019-10-25 | 深圳壹账通智能科技有限公司 | Bank card payment method, device, equipment and computer storage medium |
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