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.

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.