Systems and methods for predictive analysis of electronic transaction representment data using machine learning

Abstract

Systems and methods are disclosed for generating a prediction on chargeback representment based on probability data and/or results from a plurality of machine learning models. The method includes receiving data associated with at least one disputed transaction for at least one user, wherein the received data includes user-specific information and/or merchant-specific information. The received data is processed to calculate a probability of success in a chargeback representment for the at least one disputed transaction. A prediction is calculated based, at least in part, on the probability of success, one or more results from a plurality of machine learning models, or a combination thereof. A presentation is generated of at least one recommendation on the chargeback representment based, at least in part, on the prediction in a user interface of at least one device associated with the at least one user.