Abstract: Systems and methods for secure authentication information retrieval. In some aspects, the system receives a request for accessing authentication information for an authentication token. The system retrieves a user profile for the user comprising token identifiers for authentication tokens associated with the user and authentication information for each authentication token. The system receives an indication that a near field communication (NFC) interaction has been initiated between the user device and the authentication token. The system determines whether the authentication token is associated with the user and renders the authentication information into a graphical overlay. The system may additionally transmit the graphical overlay for displaying the authentication information over an image of the authentication token captured via an imaging sensor included in the user device.

Abstract: Disclosed embodiments pertain to determining and communicating purchasing power through a visualization. A trained machine learning model can be invoked on a credit card without a preset limit to predict a dynamic spending limit based on credit card data or credit score data. Purchasing power can be computed as the difference between the dynamic spending limit and a current balance, and a purchase power class can be determined based on the purchasing power. Further, a graphic representation of a purchasing power class can be determined. Subsequently, presentation of the graphic representation can be triggered in a manner that overlays the graphic representation on or around the physical card.

Abstract: Disclosed embodiments pertain to determining and communicating purchasing power through a visualization. A trained machine learning model can be invoked on a credit card without a preset limit to predict a dynamic spending limit based on credit card data or credit score data. Purchasing power can be computed as the difference between the dynamic spending limit and a current balance, and a purchase power class can be determined based on the purchasing power. Further, a graphic representation of a purchasing power class can be determined. Subsequently, presentation of the graphic representation can be triggered in a manner that overlays the graphic representation on or around the physical card.

Abstract: Disclosed embodiments pertain to determining and communicating purchasing power through a visualization. A trained machine learning model can be invoked on a credit card without a preset limit to predict a dynamic spending limit based on credit card data or credit score data. Purchasing power can be computed as the difference between the dynamic spending limit and a current balance, and a purchase power class can be determined based on the purchasing power. Further, a graphic representation of a purchasing power class can be determined. Subsequently, presentation of the graphic representation can be triggered in a manner that overlays the graphic representation on or around the physical card.