Chime Financial, Inc.

Tilo Sloboda

Michael Ducker

Taylor Lentz

Ryan Alexander King

Zachary Vaughn Smith

A real-time activity recommendation system receives an input from a user device regarding a targeted financial goal, such as a target credit score. Using machine learning models to evaluate patterns of user activity that contribute positively towards the goal, and to evaluate the limitations and opportunities of the user's financial circumstances and profile, the recommendation system makes an assessment in real time to determine user actions that can be taken to improve credit health based on a user's profile and activity data. A user-specific recommendation regarding an activity that should be performed to reach the goal is generated and transmitted to the user. User and third party activity is later monitored as the user's financial status changes over time, and the recommendations are updated accordingly.

Generating credit building recommendations through machine learning analysis of user activity-based feedback

A transaction approval system makes an assessment in real time whether to approve a purchase transaction and to select an account of the customer to fund the purchase transaction. The transaction approval system can use a machine learning model to select the appropriate account of the customer to fund a purchase transaction that satisfies one or more preferences or goals established by the customer or the system. The machine learning model can receive, as inputs, information on one or more of the customer's accounts, user preference information, information regarding the transaction and/or information regarding prior transactions by the customer and use the inputs and machine learning model to make a selection of a customer account that satisfies the preferences or goals established by the customer or system.

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San Jose, CA, United States of America

Tilo Sloboda