Abstract: There are provided systems and methods for reducing latency through propensity models that predict data calls. A service provider, such as an electronic transaction processor for digital transactions, may provide computing services to users including those for electronic transaction processing. In order to provide sequence-based forecasting of computing events and processing requests for users, accounts, and/or activities associated with the service provider, the service provider may provide a machine learning model, such as a deep neural network, that predicted potential occurrences and likelihoods of computing events occurring at future times. When predicting the events, the service provider's machine learning predictive framework may further predict data calls required to be executed to retrieve data needed for processing during the events. These predicted calls may then be batched together into a batch processing job, which may be executed to retrieve the data prior to the predicted events.