Capital One Services, LLC

Erik Virbitsky

Marisa Lee

Jeremy Gerstle

Michael Walters

Jonathan Boroumand

Stephen L Jordan

Daniel Jumper

Vaibhav Rastogi

Brad Giska

Sean Easter

Olufemi Anthony

James Ricks

Aniver Bosede

Keith A Kates

Aspects provided may allow for a user to block charges from an entity without having to contact the entity. After detecting that the user has enrolled in a trial or subscription of an entity offering, the user may be provided with the option to block charges from the entity. Using incoming data, charges from the blocked entity may be identified and prevented from being applied to the user. Further aspects may provide for more discrete charge blocking, such as blocking charges for one activity while allowing other charges for different activities from the same entity to go through, and training an identification model that isolates the entity identifier from the incoming data and matches the entity identifier to known entities.

One click cancel

A system may identify, using a machine learning model, a series of recurring events associated with an account and may generate, using the machine learning model, a prediction of a future date on which a predicted event, associated with the series of recurring events, is to occur. The system may determine that a condition associated with the account is satisfied and may determine that a current date is within a threshold number of days of the future date based on the prediction of the future date. The system may transmit, to a user device, a notification based on determining that the current date is within the threshold number of days of the future date and that the condition associated with the account is satisfied, wherein the notification includes information for presentation of an input element that enables an action to be performed in connection with the account.

Transmitting proactive notifications based on machine learning model predictions

Systems and methods associated with providing user interfaces and back-end processing features for transferring recurring charges from a first account to a second account are disclosed. In one embodiment, an exemplary method may comprise receiving a request from a user to transfer recurring charges from a first account to a second account, generating a first user interface that enables the user to provide account information comprised of a plurality of statements and/or account credentials associated with the first account, determining at least one recurring charge of the first account using the account information, generating a second user interface to display the at least one recurring charge and prompt the user to submit merchant-related credentials associated with the at least one recurring charge, transferring the merchant-related credentials, the at least one recurring charge from the first account to the second account.

Computer-based systems with tools designed for real-time reconfiguring a plurality of distinct databases and methods of use thereof

A method is disclosed, comprising: aggregating a plurality of sets of transactions, each set of transactions comprising transactions related to an account-merchant pairing; determining variables characterizing a recurrence period based on transaction dates of the transactions in the each set; predicting the recurrence period for a transaction related to the account-merchant pairing for each customer of the plurality of customers; aggregating another set of transactions between the plurality of customers and the merchant; evaluating a distribution of the recurrence period for each customer within range of a distant point; and based on the evaluation of the distribution of the recurrence period for each customer, generating a probability of the merchant having a recurrent transaction with the customer. The account-merchant pairing may comprise a customer account and a merchant of a plurality of customers and merchants. Another set of transactions may comprise transactions in the plurality of sets of transactions.

Technique to aggregate merchant level information for use in a supervised learning model to detect recurring trends in consumer transactions

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Washington, DC, United States of America

Erik Virbitsky