US 12,265,966 B2
Systems and methods for failed payment recovery systems
Vijay Menon, Berkeley, CA (US); and Kevin Perko, San Francisco, CA (US)
Assigned to Butter Payments, Inc., San Francisco, CA (US)
Filed by Butter Payments, Inc., San Francisco, CA (US)
Filed on Jul. 20, 2022, as Appl. No. 17/813,884.
Claims priority of provisional application 63/224,341, filed on Jul. 21, 2021.
Prior Publication US 2023/0029024 A1, Jan. 26, 2023
Int. Cl. G06Q 20/40 (2012.01)
CPC G06Q 20/4016 (2013.01) 17 Claims
OG exemplary drawing
 
1. A method for payment recovery, the method comprising:
receiving, at a computing device, a set of transaction information;
predicting, using a set of processors in the computing device, a set of one or more authorization field values based on the received set of transaction information, wherein the set of one or more authorization field values is predicted by using a set of one or more machine learning models to determine a subset of the set of one or more authorization field values to be included in an authorization message to produce a highest likelihood of successful authorization,
wherein predicting the set of one or more authorization field values to be included in an authorization message to produce a highest likelihood of successful authorization comprises filtering out one or more authorization field values that decrease the likelihood of successful authorization,
wherein predicting the set of one or more authorization field values comprises modifying at least one authorization field value of the set of one or more authorization field values;
predicting, using the set of processors in the computing device, a set of one or more optimal send times based on the received set of transaction information, wherein the set of one or more optimal send times is predicted by using the set of one or more machine learning models to determine a set of one or more optimal send times at which to send an authorization message to produce a highest likelihood of successful authorization,
wherein the set of one or more optimal send times is predicted based on an expected number of transactions at a given credit card issuer for an authorization message,
wherein predicting the set of one or more optimal send times comprises using a reinforcement learning approach; and
transmitting, from the computing device to the given credit card issuer, a set of one or more authorization messages based on the predicted set of one or more authorization field values and the predicted set of one or more optimal send times.