Method and system for predicting user intentions within a digital banking application

Abstract

Methods for using and training a classifier to predicting user intentions as the user navigates in a digital banking application. A pathway dataset, excluding financial information, is acquired, capturing a sequence of navigation events during a user session. These navigation events, representing user interactions with the application interface, are converted into a sequence of latent space representations and input into a classifier. The classifier is trained using a historical dataset of navigation events annotated with intention labels as ground truth. Upon identifying a predicted user intention, the method suggests a corresponding service via the graphical user interface (GUI). This method ensures accurate intention prediction without accessing sensitive financial information, so that privacy and usability in digital banking environments are enhanced.