Abstract: Systems and methods to screen a predictive model for risks of the predictive model are provided. The method includes obtaining a predictive model and metadata of the predictive model. The method also includes determining, based on a set of criteria for screening the predictive model, a risk of one or more negative consequences associated with the predictive model. The method further includes providing an analysis of the risk of one or more negative consequences.

Abstract: An algorithmic real-time learning engine comprising an algorithmic model generator configured to process a set of system variables from a big data source using at least one of a pattern recognition algorithm and a statistical test algorithm to identify patterns, relationships between variables, and important variables; and generate at least one of: a predictive model based on the identified patterns, relationships between variables, and important variables; statistical test model about correlations, differences between variables, or patterns in time across variables; and recurring clusters model of similar observations across variables. A data preprocessor can select system variables of interest, align the selected system variables based on time, and arrange the aligned variables into rows. The selected system variables can also be aggregated based on a pre-defined aggregate.