Systems and methods for graphical user interface machine learning-based fraud detection

A non-transitory computer readable medium storing machine interpretable instructions, which when executed by a processor, cause the processor to perform a computer implemented method for controlling operation of a graphical user interface using a machine learning based fraud detection engine, the computer implemented method comprising:

• receiving, as an intermediate webpage input on an intermediate graphical user interface screen, a string identifier of a target counterparty for a transaction, the intermediate webpage input being provided in an interactive graphical user interface control element provided before a user provides a confirmation input on a final graphical user interface screen;
• in a duration of time while the user is traversing is traversing the intermediate graphical user interface screen and subsequent graphical user interface screens before the user provides the confirmation input, providing the string identifier for processing using one or more trained machine learning model data architectures operating in an inference mode to generate one or more classification logit output values indicative of a potential fraud or error classification, the one or more trained machine learning model data architectures configured to incrementally refine the one or more classification logit output values during the duration of time;
• prior to processing the confirmation input, if the one or more classification logit output values are greater than a threshold, automatically blocking the transaction to the target counterparty.