ELECTRONIC PAYMENT RISK PROCESSING
Past payment thresholds of a payment account are received. A first data sequence is determined by applying a differential operation to the past payment thresholds. A second data sequence is determined by processing the first data sequence. A payment threshold change rule is determined based on the second data sequence. The payment threshold change rule is applied before completing a fund transaction service for the payment account to mitigate the risk of the payment account.
1 . A computer-implemented method for mitigating a risk of a payment account, the method being executed by one or more processors and comprising:
retrieving, by the one or more processors, past payment thresholds of the payment account;
determining, by the one or more processors, a first data sequence by applying a differential operation to the past payment thresholds;
determining, by the one or more processors, a second data sequence by processing the first data sequence;
determining, by the one or more processors, a payment threshold change rule based on the second data sequence; and
applying, by the one or more processors, the payment threshold change rule before completing a fund transaction service for the payment account to mitigate the risk of the payment account.
2 . The computer-implemented method of claim 1 , wherein determining the payment threshold change rule comprises performing a regression analysis on the second data sequence to obtain the payment threshold change rule.
3 . The computer-implemented method of claim 2 , wherein performing the regression analysis on the second data sequence to obtain the payment threshold change rule comprises:
establishing a regression model according to the second data sequence; and
determining the payment threshold change rule according to the regression model when a residual error obeys a normal distribution.
4 . The computer-implemented method of claim 1 , wherein determining the first data sequence comprises:
determining a data quantile according to a preset user interruption rate; and
assigning values to the historical transaction data of the user at different moments according to the data quantile to obtain the first data sequence.
5 . The computer-implemented method of claim 1 , wherein determining the second data sequence comprises: performing a log conversion on the first data sequence to obtain the second data sequence.
6 . The computer-implemented method of claim 1 , wherein the service comprises at least one of a transfer of data and a payment.
7 . The computer-implemented method of claim 1 , further comprising correcting the payment threshold change rule according to a preset condition.
8 . A non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform operations comprising:
retrieving past payment thresholds of a payment account;
determining a first data sequence by applying a differential operation to the past payment thresholds;
determining a second data sequence by processing the first data sequence;
determining a payment threshold change rule based on the second data sequence; and
applying the payment threshold change rule before completing a fund transaction service for the payment account to mitigate the risk of the payment account.
9 . The non-transitory, computer-readable medium of claim 8 , wherein determining the payment threshold change rule comprises performing a regression analysis on the second data sequence to obtain the payment threshold change rule.
10 . The non-transitory of claim 9 , wherein performing the regression analysis on the second data sequence to obtain the payment threshold change rule comprises:
establishing a regression model according to the second data sequence; and
determining the payment threshold change rule according to the regression model when a residual error obeys a normal distribution.
11 . The non-transitory, computer-readable medium of claim 8 , wherein determining the first data sequence comprises:
determining a data quantile according to a preset user interruption rate; and
assigning values to the historical transaction data of the user at different moments according to the data quantile to obtain the first data sequence.
12 . The non-transitory, computer-readable medium of claim 8 , wherein determining the second data sequence comprises: performing a log conversion on the first data sequence to obtain the second data sequence.
13 . The non-transitory, computer-readable medium of claim 8 , wherein the service comprises at least one of a transfer of data and a payment.
14 . The non-transitory, computer-readable medium of claim 8 , further comprising correcting the payment threshold change rule according to a preset condition.
15 . A computer-implemented system for secure offline payment, comprising:
one or more computers; and
one or more computer memory devices interoperably coupled with the one or more computers and having tangible, non-transitory, machine-readable media storing instructions that, when executed by the one or more computers, perform operations comprising:
retrieving past payment thresholds of a payment account;
determining a first data sequence by applying a differential operation to the past payment thresholds;
determining a second data sequence by processing the first data sequence;
determining a payment threshold change rule based on the second data sequence; and
applying the payment threshold change rule before completing a fund transaction service for the payment account to mitigate the risk of the payment account.
16 . The computer-implemented system of claim 15 , wherein determining the payment threshold change rule comprises performing a regression analysis on the second data sequence to obtain the payment threshold change rule.
17 . The computer-implemented system of claim 16 , wherein performing the regression analysis on the second data sequence to obtain the payment threshold change rule comprises:
establishing a regression model according to the second data sequence; and
determining the payment threshold change rule according to the regression model when a residual error obeys a normal distribution.
18 . The computer-implemented system of claim 15 , wherein determining the first data sequence comprises:
determining a data quantile according to a preset user interruption rate; and
assigning values to the historical transaction data of the user at different moments according to the data quantile to obtain the first data sequence.
19 . The computer-implemented system of claim 15 , wherein determining the second data sequence comprises: performing a log conversion on the first data sequence to obtain the second data sequence.
20 . The computer-implemented system of claim 15 , wherein the service comprises at least one of a transfer of data and a payment.