US 12,462,239 B2
Systems and methods for conducting transactions using hybrid credit/charge financial instruments
Rasik Goyal, New York, NY (US); Mike Janesch, New York, NY (US); Jeff Hofmann, New York, NY (US); Ryan Bouchard, Kennett Square, PA (US); Megan Chandler, New York, NY (US); Josh Berger, New York, NY (US); Steven Scarpato, New York, NY (US); Linda Barnett, New York, NY (US); Haritha Ravilla, New York, NY (US); and Santosh Bejjamshety, New York, NY (US)
Assigned to JCMORGAN CHASE BANK, N.A., New York, NY (US)
Filed by JPMORGAN CHASE BANK, N.A., New York, NY (US)
Filed on Jun. 23, 2022, as Appl. No. 17/808,444.
Claims priority of provisional application 63/214,722, filed on Jun. 24, 2021.
Prior Publication US 2022/0414633 A1, Dec. 29, 2022
Int. Cl. G06Q 30/00 (2023.01); G06Q 20/24 (2012.01)
CPC G06Q 20/24 (2013.01) 20 Claims
OG exemplary drawing
 
1. A method for conducting transactions using hybrid credit/charge financial instruments, comprising:
establishing, by a transaction processing computer program executed by an electronic device, a hybrid account comprising a pay-in-full portion and a revolving balance portion, the pay-in-full portion having a pay-in-full portion limit, and the revolving balance portion having a revolving balance portion limit;
receiving, by the transaction processing computer program and from a merchant point of transaction device, a transaction for a transaction amount from a customer involving the hybrid account from a point of transaction device;
determining, by a first machine learning engine of the transaction, a revolving balance amount of the transaction amount for the revolving balance portion based on customer data of the customer, spend behavior of the customer, payment rates of the customer, and business revenue trends of the customer;
applying, by a second machine learning engine of the transaction processing computer program, one or more rules to the transaction, the one or more rules being identified based on a classification to identify a pay-in-full amount of the transaction amount for the pay-in-full portion and the revolving balance amount of the transaction amount for the revolving balance portion, the classification being predicted by the machine learning engine based on a comparison customer similar to the customer and a prior classification based on an amount, a type of product, a merchant, and a purchaser;
debiting, by the transaction processing computer program, the pay-in-full portion for pay-in-full amount and the revolving balance portion for the revolving balance amount;
monitoring, by the transaction processing computer program, the pay-in-full portion and the revolving balance portion for balances and payments; and
adjusting, by the transaction processing computer program, the pay-in-full portion limit and the revolving balance portion limit based on the monitoring.