公开(公告)号：US20210224812A1

公开(公告)日：2021-07-22

申请号：US17221482

申请日：2021-04-02

Applicant: Advanced New Technologies Co., Ltd.

Inventor： Longfei Li

IPC: G06Q20/40 ,  G06K9/62 ,  G06N3/04 ,  G06N3/08 ,  G06Q20/02 ,  G06Q20/38 ,  G06Q20/08

Abstract: Techniques for identifying fraudulent transactions are described. In one example method, an operation sequence and time difference information associated with a transaction are identified by a server. A probability that the transaction is a fraudulent transaction is predicted based on a result provided by a deep learning network, where the deep learning network is trained to predict fraudulent transactions based on operation sequences and time differences associated with a plurality of transaction samples, and where the deep learning network provides the result in response to input including the operation sequence and the time difference information associated with the transaction.

公开(公告)号：US11003739B2

公开(公告)日：2021-05-11

申请号：US16722946

申请日：2019-12-20

Applicant: Advanced New Technologies Co., Ltd.

Inventor： Longfei Li

IPC: G06F17/18 ,  G06F16/215 ,  G06K9/62

Abstract: This specification describes techniques for detecting abnormal data in a data set. One example method includes obtaining, by a data processing platform, a to-be-validated data group including to-be-validated data corresponding to a predetermined feature; obtaining, by the data processing platform, a comparison data group including historical data associated with the to-be-validated data group, wherein the historical and the to-be-validated data are from a same data source; performing, by the data processing platform, a two-group significance test on the to-be-validated data group and the comparison data group to generate a test result; and determining, by the data processing platform, whether there is abnormal data in the to-be-validated data group based on the test result.

公开(公告)号：US10860453B2

公开(公告)日：2020-12-08

申请号：US16749772

申请日：2020-01-22

Applicant: Advanced New Technologies Co. Ltd.

Inventor： Longfei Li

IPC: G06F11/00 ,  G06F11/34 ,  G06F7/556 ,  G06F17/18 ,  G06N7/00

Abstract: An index anomaly detection method includes: acquiring data of each of monitoring points, contained in a period of time, of a monitored index; extracting a mean value and a variance of the data of the monitoring points using a Gaussian model; calculating, according to the mean value and the variance of the data of the monitoring points, probabilities of occurrence of the data of the monitoring points, respectively; calculating, according to the respectively calculated probabilities, joint probabilities of occurrence of the data of the monitoring points contained in respective windows divided from the period of time; and detecting, according to the joint probabilities corresponding to the respective windows, whether the monitored index is abnormal.