ARTIFICIAL INTELLIGENCE MODELING FOR ASSESSING FUTURE RECURRING TRANSACTIONS

Abstract: disclosed herein are methods and systems for using machine learning to improve the likelihood of success of recurring transactions. in one example, a suite of different machine learning models can be used together, such that a first machine learning model predicts a likelihood of success for a recurring transaction associated with a user account and the second machine learning model predicts whether a pre-authorization would help with the predicted likelihood of success. as a result, a server may pre-authorize the recurring transactions at a time earlier than the scheduled transaction time and place a hold on the user account using an amount predicted by the second machine learning model where the hold amount can be adjusted in accordance with the user account's activities. data associated with the recurring transaction itself can be ingested by the second machine learning model for re-calibration purposes.

Inventor(s): Alexander THIEMANN, Ji HUANG, Arne ROOMANN-KURRIK

CPC Classification: G06Q20/42 (Confirmation, e.g. check or permission by the legal debtor of payment)

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