公开(公告)号：US12218926B2

公开(公告)日：2025-02-04

申请号：US17399300

申请日：2021-08-11

Applicant: PayPal, Inc.

Inventor： Vishal Sood ,  Yegya Narayanan Gopala Krishnan ,  Sudhindra Murthy ,  Vidya Sagar Durga ,  Chirag Gupta

IPC: G06N20/00 ,  H04L9/40 ,  H04L29/06

Abstract: Techniques are disclosed for time constrained electronic request evaluation. A server system receives, from a computing device, a request submitted via an account, including a first set of characteristics associated with the request. The system executes a first machine-learning model to determine a first risk score for the request by inputting the first set of characteristics into the first model. The system generates an initial authentication decision for the request based on the first score and sends the decision to the device. The system executes a second, different machine-learning model to determine a second risk score for the request, by inputting the first set of characteristics and a second, different set of characteristics associated with the account into the second model. Based on the second score, the system determines a final authentication decision. The disclosed techniques may advantageously improve computer security and operations via identification of malicious electronic requests.

公开(公告)号：US20220417229A1

公开(公告)日：2022-12-29

申请号：US17399300

申请日：2021-08-11

Applicant: PayPal, Inc.

Inventor： Vishal Sood ,  Yegya Narayanan Gopala Krishnan ,  Sudhindra Murthy ,  Vidya Sagar Durga ,  Chirag Gupta

IPC: H04L29/06 ,  G06N20/00

Abstract: Techniques are disclosed for time constrained electronic request evaluation. A server system receives, from a computing device, a request submitted via an account, including a first set of characteristics associated with the request. The system executes a first machine-learning model to determine a first risk score for the request by inputting the first set of characteristics into the first model. The system generates an initial authentication decision for the request based on the first score and sends the decision to the device. The system executes a second, different machine-learning model to determine a second risk score for the request, by inputting the first set of characteristics and a second, different set of characteristics associated with the account into the second model. Based on the second score, the system determines a final authentication decision. The disclosed techniques may advantageously improve computer security and operations via identification of malicious electronic requests.