Abstract: There are provided systems and methods for scenario-based decisioning by machine learning models for data processing retry success. A service provider, such as an electronic transaction processor for digital transactions, may detect a failure of data processing for a transaction or other request when processed with a data processing system. In order to minimize cost and wasted resources for retrying transactions that are likely to further fail, machine learning models may be implemented that generates a predictive score for whether a failed transaction is likely to be successful if retried with the data processing system. This may be done when the transactions are received or detected, which may occur prior to failures, for different scenario-based decisioning. Scenarios for failures may be processed by the models with different retry strategies and a predictive score may be used to predict a probability of success of the retry.

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.