Abstract: Optimizing the presentation of data is described. A payment service computing platform may apply a trained artificial intelligence (AI) model to user data associated with users of a payment service, wherein the trained AI model associates a value with each of the users, and wherein the value represents a predicted engagement metric indicating a change in a level of engagement if an individual user of the users is offered an incentive having a particular attribute. The payment service computing platform may generate the incentive having the particular attribute, identify a subset of the users based at least in part on the value, and cause the incentive to be presented via a user interface of a payment application associated with the payment service and executing on a device of a user in the subset.

Abstract: A payment service system receives contextual information regarding an interaction between the payment service and a user device associated with a user. A propensity metric for the user is determined based at least in part on inputting the contextual information into a machine learning (ML) model. Based on the propensity metric, a user interface is dynamically configured to comprise user interface elements arranged in a layout personalized for the user, where a user interface element represents content particular to a service offered by the payment service. Based on receiving an interaction with the user interface element, a booklet is launched corresponding to the service with which the user interface element is associated.