Google Inc.

Daniel Jeng

John P Kozura

Ridhima Kedia

Sarat Chandra Tummala

Brian Wesley Goldman

Mark Damien Walick

Paul Copenhaver

Mateusz Waldemar Mach

Jett Wilson Rink

Computing systems and methods can use machine learning to improve the authorization of payments by payment systems. Specifically, contrary to existing payment authorization approaches which are static in nature, example aspects of the present disclosure are directed to machine learning systems which enable the dynamic and real-time optimization of one or more variable request parameters associated with a payment authorization request. Specifically, example computing systems described herein can employ one or more machine-learned models to assist in selection of a particular payment processor to which the authorization request is routed, optimization of one or more variable message parameters included in the authorization message (e.g., selection of values for a merchant identification, a merchandise category code, a transaction type, and/or other variable message parameters), and/or automatic generation and/or execution of an automated retry strategy that can be executed if a first authorization request is declined.

Payment authorization via machine learning

Growing community of inventors

Sunnyvale, CA, United States of America

Daniel Jeng