CN110782143A - Data processing method and device - Google Patents
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- CN110782143A CN110782143A CN201910979271.9A CN201910979271A CN110782143A CN 110782143 A CN110782143 A CN 110782143A CN 201910979271 A CN201910979271 A CN 201910979271A CN 110782143 A CN110782143 A CN 110782143A
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Abstract
The present disclosure provides a data processing method and apparatus. Specifically, the present disclosure provides a white list processing method, including: acquiring the transaction risk rate of each merchant in the white list in a first period, wherein the transaction risk rate of the merchant in the first period is the ratio of the number of risk transactions occurring by the merchant in the first period to the total number of transactions; determining a confidence interval of the transaction risk rate of each merchant in the white list in a first period as a first confidence interval; determining a transaction risk rate for a first merchant at the first period; and determining whether to add the first merchant to the white list according to the first confidence interval and the transaction risk rate of the first merchant.
Description
Technical Field
The present invention relates generally to the field of internet, and more particularly to a method and apparatus for managing merchant white list entry and exit.
Background
In an internet platform, in order to improve user experience of high-quality merchants, it is desirable to reduce the disturbance rate of the merchants and ensure the security of a transaction platform. Generally, a white list of merchants can be determined through credit data, and preferential services are provided for merchants in the white list, thereby realizing risk control and improving user experience. However, the existing white list management scheme mainly comprises manual entry based on expert experience and static threshold setting to manage the entry and exit of the white list, and the white list cannot be effectively managed in real time.
Therefore, an efficient solution for managing merchant white list entry and exit is needed.
Disclosure of Invention
In order to solve the above technical problem, the present invention provides a white list processing method, including:
acquiring the transaction risk rate of each merchant in the white list in a first period, wherein the transaction risk rate of the merchant in the first period is the ratio of the number of risk transactions occurring by the merchant in the first period to the total number of transactions;
determining a confidence interval of the transaction risk rate of each merchant in the white list in a first period as a first confidence interval;
determining a transaction risk rate for a first merchant at the first period; and
determining whether to add the first merchant to the white list according to the first confidence interval and a transaction risk rate of the first merchant.
Optionally, the first cycle is a current cycle or a previous cycle.
Optionally, the determining whether to add the first merchant to the whitelist comprises:
comparing the transaction risk rate of the first merchant to an upper limit of the first confidence interval; and
and if the transaction risk rate of the first merchant is less than the upper limit of the first confidence interval, determining to add the first merchant to the white list.
Optionally, the method further comprises:
in response to receiving an exit whitelist request associated with a second merchant in the whitelist:
determining the transaction risk rate of each merchant in the white list in a second period, wherein the second period is the current period or the previous period when the request for quitting the white list is received;
determining an average value of transaction risk rates of all merchants in the white list in the second period;
determining a confidence interval of the transaction risk rate of the second period as a second confidence interval;
determining a transaction risk rate of the second merchant at the second period; and
determining whether to delete the second merchant from the whitelist according to the transaction risk rate of the second merchant, the average, and the second confidence interval.
Optionally, the method further comprises:
determining not to delete the second merchant from the whitelist if the transaction risk rate of the second merchant is below the average;
determining a risk type involved in the white list exit request if the transaction risk rate of the second merchant is above the average and below an upper limit of the second confidence interval, and determining whether to delete the second merchant from the white list according to the risk type and a plurality of characteristics of the second merchant; and
and if the transaction risk rate of the second merchant is higher than the upper limit of the confidence interval, determining to delete the second merchant from the white list.
Optionally, the method further comprises:
if the transaction risk rate of the second merchant is above the average and below the upper limit of the second confidence interval:
obtaining the plurality of features of the second merchant; and
entering the plurality of features of the second merchant into an exit white list decision model corresponding to the risk type to determine whether to delete the second merchant from the white list.
Optionally, the method further comprises:
and for each exit white list judgment model corresponding to each risk type, using the merchant sample which is related to the risk type and is complained successfully as a white sample, and using the merchant sample which is related to the risk type and is complained unsuccessfully as a black sample to train the exit white list judgment model.
Optionally, the second period is a current period or a previous period when the request to exit white list is received.
Optionally, the confidence interval is a 95% confidence interval.
Optionally, the method further comprises:
in response to a third merchant in the whitelist being complained, determining whether the third merchant is at risk using a complaint model; and
if the third merchant is determined to be at risk, the third merchant is removed from the white list.
Optionally, the method further comprises:
in response to receiving the complaint request by the third merchant, determining whether the third merchant is at risk using a complaint model;
determining not to add the third merchant back to the white list if it is determined using the complaint model that the third merchant is at risk; and
determining to add the third merchant back to the white list if it is determined using the complaint model that the third merchant is not at risk.
Another aspect of the present disclosure provides a white list processing apparatus, including:
the method comprises the steps of obtaining transaction risk rate of each merchant in a white list in a first period, wherein the transaction risk rate of a merchant in the first period is the ratio of the number of risk transactions occurring by the merchant in the first period to the total number of transactions;
means for determining a confidence interval of a transaction risk rate of each merchant in the whitelist for a first period as a first confidence interval;
means for determining a transaction risk rate for a first merchant at the first period; and
means for determining whether to add the first merchant to the whitelist based on the first confidence interval and a transaction risk rate of the first merchant.
Optionally, the first cycle is a current cycle or a previous cycle.
Optionally, the means for determining whether to add the first merchant to the whitelist comprises:
means for comparing a transaction risk rate of the first merchant to an upper limit of the first confidence interval; and
means for determining to add the first merchant to the whitelist if the transaction risk rate of the first merchant is less than the upper limit of the first confidence interval.
Optionally, the apparatus further comprises:
means for, in response to receiving an exit whitelist request associated with a second merchant in the whitelist:
determining the transaction risk rate of each merchant in the white list in a second period, wherein the second period is the current period or the previous period when the request for quitting the white list is received;
determining an average value of transaction risk rates of all merchants in the white list in the second period;
determining a confidence interval of the transaction risk rate of the second period as a second confidence interval;
determining a transaction risk rate of the second merchant at the second period; and
determining whether to delete the second merchant from the whitelist according to the transaction risk rate of the second merchant, the average, and the second confidence interval.
Optionally, the apparatus further comprises:
means for determining not to delete the second merchant from the whitelist if the transaction risk rate of the second merchant is below the average;
means for determining a risk type involved in the white list exit request if the transaction risk rate of the second merchant is above the average and below an upper limit of the second confidence interval, and determining whether to delete the second merchant from the white list based on the risk type and a plurality of characteristics of the second merchant; and
means for determining to delete the second merchant from the whitelist if the transaction risk rate of the second merchant is above the upper limit of the confidence interval.
Optionally, the apparatus further comprises:
means for performing the following if the transaction risk rate of the second merchant is above the average and below an upper limit of the second confidence interval:
obtaining the plurality of features of the second merchant; and
entering the plurality of features of the second merchant into an exit white list decision model corresponding to the risk type to determine whether to delete the second merchant from the white list.
Optionally, the apparatus further comprises:
and for each risk type, training the exit white list judgment model by using the compliant merchant samples related to the risk type and having successful complaints as white samples and using the compliant merchant samples related to the risk type and having failed complaints as black samples.
Optionally, the second period is a current period or a previous period when the request to exit white list is received.
Optionally, the confidence interval is a 95% confidence interval.
Optionally, the apparatus further comprises:
in response to a third merchant in the whitelist being complained, determining whether the third merchant is at risk using a complaint model; and
if the third merchant is determined to be at risk, the third merchant is removed from the white list.
Optionally, the apparatus further comprises:
means for determining, in response to receiving a complaint request by the third merchant, whether the third merchant is at risk using a complaint model;
means for determining not to add the third merchant back to the white list if it is determined using the complaint model that the third merchant is at risk; and
means for determining to add the third merchant back to the white list if it is determined using the complaint model that the third merchant is not at risk.
Yet another aspect of the present disclosure provides a white list processing apparatus, including:
a processor; and
a memory arranged to store computer executable instructions that, when executed, cause the processor to:
acquiring the transaction risk rate of each merchant in the white list in a first period, wherein the transaction risk rate of the merchant in the first period is the ratio of the number of risk transactions occurring by the merchant in the first period to the total number of transactions;
determining a confidence interval of the transaction risk rate of each merchant in the white list in a first period as a first confidence interval;
determining a transaction risk rate for a first merchant at the first period; and
determining whether to add the first merchant to the white list according to the first confidence interval and a transaction risk rate of the first merchant.
Drawings
Fig. 1 is a diagram of a white list period, according to aspects of the present disclosure.
Fig. 2 is a diagram of a system for managing white list entry and exit, according to aspects of the present disclosure.
Fig. 3 is a diagram of confidence intervals, in accordance with aspects of the present disclosure.
Fig. 4 is a diagram of an apparatus that makes an exit white list determination based on risk types and merchant characteristics, according to aspects of the present disclosure.
Fig. 5 is a flow diagram of a method for determining whether to white list a merchant (first merchant) according to aspects of the present disclosure.
Fig. 6 is a flow diagram of a method of determining whether to delete a merchant (second merchant) from the whitelist, according to aspects of the present disclosure.
Fig. 7 is a flow diagram of a method for managing a whitelist according to aspects of the present disclosure.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described herein, and thus the present invention is not limited to the specific embodiments disclosed below.
Business systems typically include a risk control architecture based on risk data that includes a set of business rules, including rules regarding customer complaints, merchant complaints, entry and exit of merchant whitelists, and the like.
For example, a user, after discovering that a transaction with a merchant is at risk (e.g., a theft of funds occurs), may complain about the merchant to the system, which may analyze the transaction according to a set of preset rules (e.g., a complaint analysis model), determine whether the transaction is at risk, and qualify the merchant accordingly. If it is determined that the transaction is not risky, the merchant may be qualified as white; if the transaction is determined to be at risk, the merchant may be qualified as black.
The complained merchant can put out a complaint to the system after knowing that the complained merchant is qualified as black, the system can analyze the transaction according to another set of preset rules (for example, a complaint analysis model), and if the transaction is determined to be risk-free, the complaint is successful; if the transaction is determined to be at risk, the complaint fails.
Further, the system may use the results of the analysis of the complaint and complaint processes to add or drop relevant merchants from the white list.
The present disclosure proposes a scheme for managing whitelists that determines dynamic thresholds for entering and exiting whitelists based on the current risk rate distribution (e.g., confidence interval, average, etc.) of merchants within the current whitelist. The white list is changed continuously along with the time, new high-quality merchants are added into the white list, and merchants which do not meet the conditions are deleted from the white list. The present disclosure takes into account the changes of merchants in the whitelist and the corresponding changes of merchant data (e.g., transaction risk rates), divides time into a plurality of whitelist periods (also referred to herein simply as periods), and uses data within an active whitelist period (e.g., a current period, or a previous period) to determine the relevant threshold in determining whether to add or remove a merchant from the whitelist.
The present disclosure obtains relevant parameters (e.g., distribution of transaction risk rates) based on white list periods, thereby determining dynamic thresholds for entering and exiting white lists.
Fig. 1 is a diagram of multiple white list periods, according to aspects of the present disclosure. As shown in FIG. 1, time may be divided into a plurality of white list periods P
nThe threshold for entry and exit of the whitelist may be dynamically determined based on sample data within a particular whitelist period (e.g., a current whitelist period or a last whitelist period), thereby enabling an efficient whitelist entry and exit mechanism.
In an aspect, the dynamic threshold for entering and exiting the white list may be determined based on the transaction risk rates of the merchants in the white list during the current white list period. For example, if in period P
3A time point a in the white list is determined whether a merchant is to be added to the white list, and then it can be determined that each merchant in the white list is in the period P
3Transactions already in progress (e.g. from period P)
3Start transaction to time point a), from which a threshold for entering a white list is determined for determining whether to allow a transaction or notThe merchant is allowed to be white listed. If in period P
5To determine whether a merchant is to be deleted from the white list at time point b, it may be determined that each merchant in the white list is in period P
5Transactions already in progress (e.g. from period P)
5Transaction initiated to time point b), determining a threshold for exiting the white list from the distribution for determining whether to delete the merchant from the white list.
In another aspect, the dynamic thresholds for entering and exiting the white list may be determined based on the transaction risk rates of each merchant in the white list during a previous white list period. For example, if in period P
3A time point a in the white list is determined whether to add a merchant to the white list, and then each merchant in the white list can be obtained in the previous period P
2A transaction risk rate distribution for the transactions conducted within, a white list entry threshold is determined from the distribution for determining whether to allow the merchant to be white listed. If in period P
5To determine whether a merchant is to be deleted from the white list at time point b, it may be determined that each merchant in the white list is in the previous period P
4A transaction risk rate distribution for the transactions conducted within, a threshold for exiting the white list for determining whether to delete the merchant from the white list based on the distribution.
Specific details of whitelist entry and exit are described below.
In an aspect, in determining whether to white list a merchant, a current transaction risk distribution (e.g., confidence interval) for each merchant in an existing white list may be determined, a white list threshold (e.g., upper limit of confidence interval) may be determined based on the white list current transaction risk distribution, and the merchant may then be compared to the white list threshold to determine whether to white list the merchant.
In another aspect, in determining whether to delete a merchant from the whitelist (e.g., upon receiving a complaint request associated with the merchant), a threshold (e.g., average, upper limit of confidence interval) for white-list exit may also be determined based on the current transaction risk rate distribution of the whitelist. For example, if the transaction risk rate of the merchant is lower than the average of the current transaction risk rates of the merchants in the white list, indicating that the transaction risk rate of the merchant belongs to the normal range of the transaction risk rates of the white list, it may be determined that the merchant is not to be deleted from the white list. If the transaction risk rate of the merchant is higher than the upper limit of the confidence interval, indicating that the transaction risk rate of the merchant is too high compared with the transaction risk rate of the white list, it may be determined that the merchant is deleted from the white list. If the transaction risk rate of the merchant is higher than the average value and lower than the upper limit of the confidence interval, a further white list quit judgment strategy can be adopted, for example, whether the merchant is deleted from the white list or not can be determined according to the risk type involved in the complaint (such as fund embezzlement, account embezzlement, identity information leakage and the like) and based on the characteristics of the merchant (such as the industry involved in the merchant, credit score, registration time, transaction history, complaint history information and the like).
The current transaction risk rate distribution of each merchant in the white list may be the transaction risk rate distribution of the merchants in the current white list period or the latest white list period.
Fig. 2 is an illustration of a system for managing merchant whitelists, in accordance with aspects of the present disclosure.
As shown in fig. 2, a system 200 for managing a merchant white list may include a plurality of terminals 201. Each terminal 201 may have a network transaction application installed thereon. The terminal 201 may include a cellular telephone (e.g., a smart phone), a laptop computer, a desktop computer, a tablet device, and so on.
The user of terminal 201 may include a merchant or a consumer. The user may use the terminal 201 to perform transactions, payments, transfers, etc. The user may be a merchant registered as an e-commerce platform or may be a consumer who is to purchase a product or service on the e-commerce platform. The merchant and the consumer may use respective terminals 201 to conduct transactions.
The server 202 may include a white list management module 203 and a memory 204. The white list management module 203 may manage entry and exit of the merchant white list. The merchant white list may include premium merchants that may be preferentially serviced.
In particular, the white list management module 203 may determine a distribution of transaction risk rates for a plurality of merchants within the white list in real time. The transaction risk rate for a merchant may be a ratio of the number of risky transactions to the total number of transactions by the merchant over a period (e.g., one week, one month, etc.). Here, the risky transaction may be a transaction that is verified as being non-compliant or illegal (e.g., a fund theft, an account theft, an identity information leak, etc.).
In general, the risk rate distribution (also referred to herein simply as white list distribution) for multiple merchants within the white list may be normally distributed. Confidence intervals for the white list distribution, e.g., 95% confidence intervals, 90% confidence intervals, etc., may be determined in real-time.
Confidence interval refers to an interval estimate calculated from the sample for the overall parameter value. In statistics, the confidence interval of a probability sample is an interval estimate for some overall parameter of the sample, which exhibits the extent to which the true value of the parameter has a certain probability (i.e. confidence level) to fall around the measurement, in other words, the confidence interval gives the confidence level of the measured value of the measured parameter.
For example, the 95% confidence interval for transaction risk rates for multiple merchants may be expressed as follows:
wherein the content of the first and second substances,
is the average of the transaction risk rates of the multiple merchants, σ is the variance of the transaction risk rates of the multiple merchants, and n is the number of the multiple merchants.
In one aspect, the disclosure may use a lower limit of the confidence interval (e.g.,
) An upper limit (e.g.,
) And average value
To determine dynamic thresholds for merchants entering and exiting the white list.
FIG. 3 shows a diagram of one example of confidence intervals for transaction risk rate distributions for multiple merchants, according to aspects of the present disclosure. As shown in fig. 3, x represents the value of the transaction risk rate of each merchant, x1 is the lower limit of the confidence interval of the transaction risk rate distribution, x2 is the upper limit of the confidence interval of the transaction risk rate distribution,
is the average of the transaction risk rate distribution.
In an aspect, in determining whether to add a merchant to the whitelist, the current transaction risk rate for the merchant may be determined, and confidence intervals (e.g., 95% confidence intervals, 90% confidence intervals, etc.) for the current transaction risk rates for n merchants within the whitelist are determined. If the merchant's current transaction risk rate is less than the upper bound x2 of the current confidence interval, it may be determined to whitelist the merchant.
In another aspect, in determining whether to delete a merchant from the whitelist (e.g., upon receiving a complaint request for the merchant or when complaints for the merchant are qualified as black), the current transaction risk rate for the merchant may be determined, and confidence intervals for the current transaction risk rates for n merchants within the whitelist are determined (e.g., determining an upper bound x2, average for confidence intervals, etc.)
). If the current transaction risk rate for the merchant is below a first threshold (e.g., average value)
) Then it may be determined not to delete the merchant from the white list. If the merchant's current transaction risk rate is above a first threshold and below a second threshold (e.g., upper limit x2 of confidence interval), the complaint may be based onThe type of risk involved (e.g., funds appropriateness, account appropriateness, identity information disclosure, etc.) and a plurality of characteristics of the merchant to determine whether to delete the merchant from the whitelist. If the transaction risk rate for the merchant is above a second threshold, the merchant may be determined to be deleted from the white list.
The above-mentioned current transaction risk ratio of the merchant and the confidence interval of the current transaction risk ratio of each merchant in the white list (also referred to as the current confidence interval of the white list) may be determined according to the current white list period or the latest white list period, as described above.
In an aspect, the current transaction risk rate for the merchant may be determined according to the same period as the period used to determine the current confidence interval for the whitelist. For example, if the current confidence interval of the white list is according to the period P as in FIG. 2
6The current transaction risk rate of the merchant can also be determined according to the period P
6Determined from transaction data of the merchant.
Each risk type may have a corresponding exit white list decision model. If the transaction risk rate of the merchant is higher than the first threshold and lower than the second threshold, the characteristics of the merchant (e.g., the industry involved, the credit score, the registration duration, the complaint history information, and the complaint history information) may be input into the white list exit determination model corresponding to the risk type involved in the complaint, so as to predict the risk level of the merchant, and further determine whether to delete the merchant from the white list.
For example, if the transaction risk rate of a merchant is above a first threshold and below a second threshold, and the complaint for the merchant is a theft of funds, the characteristics of the merchant may be input into an exit white list determination model of the theft of funds to determine whether the merchant is to be deleted from the white list.
Fig. 4 is a diagram of an apparatus 400 that makes an exit white list determination based on risk types and merchant characteristics, according to aspects of the present disclosure.
As shown in FIG. 4, apparatus 400 may include a selector 402, a first risk type model 404-1, a second risk type model 404-2, and an … … Nth risk type model 404-N. The risk types and various characteristics of the merchant to which the complaint relates may be input into the selector 402. The selector 402 may select a corresponding model 404-i according to the risk type, and input a plurality of characteristics of the merchant into the selected model to determine whether to delete the merchant from the whitelist.
Each risk type model 404-i may be an exit white list decision model for merchants associated with the risk type (e.g., merchants complained for the risk type). The corresponding model may be trained using white and black samples associated with each risk type, where a white sample may be a sample for a merchant for which the risk type was complained but the complaint was successful, and a black sample may be a sample for a merchant for which the risk type was complained and the complaint failed.
Fig. 5 is a flow diagram of determining whether to white list a merchant (first merchant) according to aspects of the present disclosure.
At step 502, the transaction risk rate of each merchant in the white list for the first period may be obtained.
The transaction risk rate for a merchant during a first period is the ratio of the number of risky transactions that the merchant has occurred during the first period to the total number of transactions.
The first period may be the current white list period or the most recent white list period, as described above.
At step 504, a confidence interval for the transaction risk rate for each merchant in the whitelist at the first period may be determined as the first confidence interval.
The confidence interval may be determined by the distribution of the transaction risk rates of each merchant in the whitelist. The confidence interval may be selected according to specific traffic needs, for example, a 95% confidence interval.
At step 506, a transaction risk rate for the first merchant at a first period may be determined.
At step 508, it may be determined whether to whitelist the first merchant based on the first confidence interval and the transaction risk rate of the first merchant.
In particular, the transaction risk rate of the first merchant may be compared to an upper limit of the first confidence interval. And if the transaction risk rate of the first merchant is smaller than the upper limit of the first confidence interval, which indicates that the transaction risk rate of the first merchant is within the transaction risk rate range of most merchants in all merchants of the current white list, determining to add the first merchant into the white list.
Fig. 6 is a flow diagram of a method of determining whether to delete a merchant (second merchant) from the whitelist, according to aspects of the present disclosure.
At 602, a whitelist exit request associated with a second merchant in the whitelist may be received.
For example, upon receiving a complaint request for a second merchant in the whitelist or when complaints for the second merchant are qualified as black, the system may send an exit whitelist request for the second merchant to the whitelist management module 203.
At 604, a transaction risk rate for each merchant in the white list at a second period may be determined.
The second period may be a current white list period or a last white list period when the request to exit the white list is received.
At 606, an average of the transaction risk rates of the merchants in the white list for the second period may be determined, and a confidence interval of the transaction risk rates may be determined as the second confidence interval.
At 608, a transaction risk rate for the second merchant at the second period may be determined.
At 610, it may be determined whether to delete the second merchant from the whitelist based on the transaction risk rate of the second merchant, the average, and the second confidence interval.
Specifically, if the transaction risk rate of the second merchant is below the average, it is determined not to delete the second merchant from the white list.
And if the transaction risk rate of the second merchant is higher than the upper limit of the confidence interval, which indicates that the transaction risk rate of the second merchant exceeds the transaction risk rate of most merchants in the merchants of the current white list, determining to delete the second merchant from the white list.
If the transaction risk rate of the second merchant is above the average and below an upper limit of a second confidence interval, determining a risk type involved in the request to exit the white list, and determining whether to delete the second merchant from the white list based on the risk type and a plurality of characteristics of the second merchant.
For example, a plurality of features of the second merchant may be obtained, and the plurality of features of the second merchant may be input into an exit whitelist decision model corresponding to the risk type to determine whether to delete the second merchant from the whitelist.
Further, for the exit white list determination model corresponding to each risk type, the exit white list determination model may be trained by using the merchant sample related to the risk type and complaint successfully as a white sample and using the merchant sample related to the risk type and complaint unsuccessfully as a black sample.
In the prior art, a white list validity period is generally set for merchants in a white list, and when the validity period expires, whether the merchant stays in the white list or leaves the white list is determined. The scheme lacks dynamic management on the white list, and effective white list quitting measures cannot be triggered if merchants in the white list generate risk behaviors due to various reasons.
According to the scheme, the risk behaviors of the merchants in the white list can be monitored in real time, and the dynamic threshold is set based on the transaction risk rate of each merchant in the white list, so that the safety and the real-time performance of merchant white list management are improved.
Fig. 7 is a flow diagram of a method for managing a whitelist according to aspects of the present disclosure.
After a user conducts a transaction with a merchant, if the transaction is found to be at risk, a complaint request can be sent to the system to complaint the merchant, and the complaint request can include the type of risk involved in the transaction (e.g., fund theft, account theft, identity information disclosure, etc.) and related data.
As shown in FIG. 7, after receiving a complaint request about a merchant, the system may conduct merchant qualitative operations at block 702. In particular, the system may analyze data of the merchant (e.g., data of transactions involved in complaints) to determine whether the merchant is at risk. For example, the system may analyze sample data for the transaction (e.g., evidence submitted by the user of a risk for the transaction) according to a set of preset rules (e.g., a complaint analysis model) to qualify the transaction. If it is determined that the transaction is not risky, the merchant may be qualified as white; if the transaction is determined to be at risk, the merchant may be qualified as black.
If the merchant is qualified as white, flow may proceed to block 704 where the current transaction risk distribution for each merchant in the white list may be determined. For example, an average of the current transaction risk rates for these merchants may be determined. Further, a current transaction risk rate for the complaint merchant may be determined. The current transaction risk rate for the complaint merchant is the transaction risk rate for the current cycle or the previous cycle at the time the complaint request was received. The current transaction risk rate distribution for each merchant in the white list may be the transaction risk rate distribution for the current cycle or the previous cycle at the time the complaint request was received.
The current transaction risk rates for the complained merchants may then be compared to an average of the current transaction risk rates for merchants within the whitelist.
If the current transaction risk rate for the complaint merchant is below the average, flow proceeds to block 706 where a determination is made not to delete the merchant from the white list (i.e., the merchant does not exit the white list).
If the current transaction risk rate for the complaint merchant is greater than or equal to the average, flow proceeds to block 708 where it is determined whether the merchant's current transaction risk rate is less than the upper limit of the confidence interval.
If the merchant's current transaction risk rate is less than the upper limit of the confidence interval, flow may proceed to block 712 to determine whether to delete the merchant from the whitelist based on the risk type involved in the complaint. For example, a white list exit determination model may be set for each risk type, and each relevant characteristic of the merchant (e.g., related industry, credit score, registration duration, complaint history information, and complaint history information) may be input into the determination model corresponding to the risk type related to the complaint, so as to predict the risk level of the merchant, and further determine whether to delete the merchant from the white list.
If the merchant's current transaction risk rate is greater than or equal to the upper limit of the confidence interval, flow may proceed to block 710 where a determination is made to delete the merchant from the whitelist.
Returning to block 702, if the merchant is qualified as black, flow may proceed to block 714 where a determination is made to delete the merchant from the white list.
The merchant, after learning that the merchant is qualified as dark, may issue a complaint request to the system, which may include complaint material data provided by the merchant.
At block 716, the system may analyze the complaint transaction, e.g., complaint material data provided by the merchant, according to a set of preset rules (e.g., complaint analysis models). If the transaction is determined to be risk free, the complaint is successful. If the transaction is determined to be at risk, the complaint fails.
If the complaint is successful, flow may proceed to block 718 to add the merchant back to the white list. If the complaint fails, flow may proceed to block 720 where a determination is made not to add the merchant back to the white list.
The illustrations set forth herein in connection with the figures describe example configurations and are not intended to represent all examples that may be implemented or fall within the scope of the claims. The term "exemplary" as used herein means "serving as an example, instance, or illustration," and does not mean "preferred" or "advantageous over other examples. The detailed description includes specific details to provide an understanding of the described technology. However, the techniques may be practiced without these specific details. In some instances, well-known structures and devices are shown in block diagram form in order to avoid obscuring the concepts of the described examples.
In the drawings, similar components or features may have the same reference numerals. Further, various components of the same type may be distinguished by following the reference label by a dash and a second label that distinguishes among the similar components. If only the first reference label is used in the specification, the description is applicable to any one of the similar components having the same first reference label irrespective of the second reference label.
The various illustrative blocks and modules described in connection with the disclosure herein may be implemented or performed with a general purpose processor, a DSP, an ASIC, an FPGA or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices (e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration).
The functions described herein may be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Other examples and implementations are within the scope of the disclosure and the following claims. For example, due to the nature of software, the functions described above may be implemented using software executed by a processor, hardware, firmware, hard-wired, or any combination thereof. Features that implement functions may also be physically located at various locations, including being distributed such that portions of functions are implemented at different physical locations. In addition, as used herein, including in the claims, "or" as used in a list of items (e.g., a list of items accompanied by a phrase such as "at least one of" or "one or more of") indicates an inclusive list, such that, for example, a list of at least one of A, B or C means a or B or C or AB or AC or BC or ABC (i.e., a and B and C). Also, as used herein, the phrase "based on" should not be read as referring to a closed condition set. For example, an exemplary step described as "based on condition a" may be based on both condition a and condition B without departing from the scope of the present disclosure. In other words, the phrase "based on," as used herein, should be interpreted in the same manner as the phrase "based, at least in part, on.
Computer-readable media includes both non-transitory computer storage media and communication media, including any medium that facilitates transfer of a computer program from one place to another. Non-transitory storage media may be any available media that can be accessed by a general purpose or special purpose computer. By way of example, and not limitation, non-transitory computer-readable media can comprise RAM, ROM, electrically erasable programmable read-only memory (EEPROM), Compact Disk (CD) ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other non-transitory medium that can be used to carry or store desired program code means in the form of instructions or data structures and that can be accessed by a general-purpose or special-purpose computer, or a general-purpose or special-purpose processor. Any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a web site, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL), or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. Disk (disk) and disc (disc), as used herein, includes CD, laser disc, optical disc, Digital Versatile Disc (DVD), floppy disk and blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above are also included within the scope of computer-readable media.
The description herein is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to the disclosure will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other variations without departing from the scope of the disclosure. Thus, the disclosure is not intended to be limited to the examples and designs described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (23)
1. A white list processing method, comprising:
acquiring the transaction risk rate of each merchant in the white list in a first period, wherein the transaction risk rate of the merchant in the first period is the ratio of the number of risk transactions occurring by the merchant in the first period to the total number of transactions;
determining a confidence interval of the transaction risk rate of each merchant in the white list in a first period as a first confidence interval;
determining a transaction risk rate for a first merchant at the first period; and
determining whether to add the first merchant to the white list according to the first confidence interval and a transaction risk rate of the first merchant.
2. The method of claim 1, wherein the first cycle is a current cycle or a previous cycle.
3. The method of claim 1, wherein the determining whether to add the first merchant to the whitelist comprises:
comparing the transaction risk rate of the first merchant to an upper limit of the first confidence interval; and
and if the transaction risk rate of the first merchant is less than the upper limit of the first confidence interval, determining to add the first merchant to the white list.
4. The method of claim 1, further comprising:
in response to receiving an exit whitelist request associated with a second merchant in the whitelist:
determining the transaction risk rate of each merchant in the white list in a second period, wherein the second period is the current period or the previous period when the request for quitting the white list is received;
determining an average value of transaction risk rates of all merchants in the white list in the second period;
determining a confidence interval of the transaction risk rate of the second period as a second confidence interval;
determining a transaction risk rate of the second merchant at the second period; and
determining whether to delete the second merchant from the whitelist according to the transaction risk rate of the second merchant, the average, and the second confidence interval.
5. The method of claim 4, further comprising:
determining not to delete the second merchant from the whitelist if the transaction risk rate of the second merchant is below the average;
determining a risk type involved in the white list exit request if the transaction risk rate of the second merchant is above the average and below an upper limit of the second confidence interval, and determining whether to delete the second merchant from the white list according to the risk type and a plurality of characteristics of the second merchant; and
and if the transaction risk rate of the second merchant is higher than the upper limit of the confidence interval, determining to delete the second merchant from the white list.
6. The method of claim 5, further comprising:
if the transaction risk rate of the second merchant is above the average and below the upper limit of the second confidence interval:
obtaining the plurality of features of the second merchant; and
entering the plurality of features of the second merchant into an exit white list decision model corresponding to the risk type to determine whether to delete the second merchant from the white list.
7. The method of claim 6, further comprising:
and for each exit white list judgment model corresponding to each risk type, using the merchant sample which is related to the risk type and is complained successfully as a white sample, and using the merchant sample which is related to the risk type and is complained unsuccessfully as a black sample to train the exit white list judgment model.
8. The method of claim 4, wherein the second period is a current period or a previous period when the request to exit white list is received.
9. The method of claim 1, wherein the confidence interval is a 95% confidence interval.
10. The method of claim 1, further comprising:
in response to a third merchant in the whitelist being complained, determining whether the third merchant is at risk using a complaint model; and
if the third merchant is determined to be at risk, the third merchant is removed from the white list.
11. The method of claim 10, further comprising:
in response to receiving the complaint request by the third merchant, determining whether the third merchant is at risk using a complaint model;
determining not to add the third merchant back to the white list if it is determined using the complaint model that the third merchant is at risk; and
determining to add the third merchant back to the white list if it is determined using the complaint model that the third merchant is not at risk.
12. A white list processing apparatus comprising:
the method comprises the steps of obtaining transaction risk rate of each merchant in a white list in a first period, wherein the transaction risk rate of a merchant in the first period is the ratio of the number of risk transactions occurring by the merchant in the first period to the total number of transactions;
means for determining a confidence interval of a transaction risk rate of each merchant in the whitelist for a first period as a first confidence interval;
means for determining a transaction risk rate for a first merchant at the first period; and
means for determining whether to add the first merchant to the whitelist based on the first confidence interval and a transaction risk rate of the first merchant.
13. The apparatus of claim 12, wherein the first cycle is a current cycle or a previous cycle.
14. The apparatus of claim 12, wherein the means for determining whether to add the first merchant to the whitelist comprises:
means for comparing a transaction risk rate of the first merchant to an upper limit of the first confidence interval; and
means for determining to add the first merchant to the whitelist if the transaction risk rate of the first merchant is less than the upper limit of the first confidence interval.
15. The apparatus of claim 12, further comprising:
means for, in response to receiving an exit whitelist request associated with a second merchant in the whitelist:
determining the transaction risk rate of each merchant in the white list in a second period, wherein the second period is the current period or the previous period when the request for quitting the white list is received;
determining an average value of transaction risk rates of all merchants in the white list in the second period;
determining a confidence interval of the transaction risk rate of the second period as a second confidence interval;
determining a transaction risk rate of the second merchant at the second period; and
determining whether to delete the second merchant from the whitelist according to the transaction risk rate of the second merchant, the average, and the second confidence interval.
16. The apparatus of claim 15, further comprising:
means for determining not to delete the second merchant from the whitelist if the transaction risk rate of the second merchant is below the average;
means for determining a risk type involved in the white list exit request if the transaction risk rate of the second merchant is above the average and below an upper limit of the second confidence interval, and determining whether to delete the second merchant from the white list based on the risk type and a plurality of characteristics of the second merchant; and
means for determining to delete the second merchant from the whitelist if the transaction risk rate of the second merchant is above the upper limit of the confidence interval.
17. The apparatus of claim 16, further comprising:
means for performing the following if the transaction risk rate of the second merchant is above the average and below an upper limit of the second confidence interval:
obtaining the plurality of features of the second merchant; and
entering the plurality of features of the second merchant into an exit white list decision model corresponding to the risk type to determine whether to delete the second merchant from the white list.
18. The apparatus of claim 17, further comprising:
and for each risk type, training the exit white list judgment model by using the compliant merchant samples related to the risk type and having successful complaints as white samples and using the compliant merchant samples related to the risk type and having failed complaints as black samples.
19. The apparatus of claim 15, wherein the second period is a current period or a previous period when the request to exit white list is received.
20. The apparatus of claim 12, wherein the confidence interval is a 95% confidence interval.
21. The apparatus of claim 12, further comprising:
in response to a third merchant in the whitelist being complained, determining whether the third merchant is at risk using a complaint model; and
if the third merchant is determined to be at risk, the third merchant is removed from the white list.
22. The apparatus of claim 21, further comprising:
means for determining, in response to receiving a complaint request by the third merchant, whether the third merchant is at risk using a complaint model;
means for determining not to add the third merchant back to the white list if it is determined using the complaint model that the third merchant is at risk; and
means for determining to add the third merchant back to the white list if it is determined using the complaint model that the third merchant is not at risk.
23. A white list processing apparatus comprising:
a processor; and
a memory arranged to store computer executable instructions that, when executed, cause the processor to:
acquiring the transaction risk rate of each merchant in the white list in a first period, wherein the transaction risk rate of the merchant in the first period is the ratio of the number of risk transactions occurring by the merchant in the first period to the total number of transactions;
determining a confidence interval of the transaction risk rate of each merchant in the white list in a first period as a first confidence interval;
determining a transaction risk rate for a first merchant at the first period; and
determining whether to add the first merchant to the white list according to the first confidence interval and a transaction risk rate of the first merchant.
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