Abstract: Systems and methods for creating predictor variables from unstructured data for prediction models are provided. A variable creation application receives unstructured data and processing the unstructured data to generate processed data. Based on the processed data, the variable creation application generates an attribute pool that contains multiple predictor variables generated by applying natural language processing (NLP) procedures on the processed data. The variable creation application further executes a prediction model on at least the predictor variables in the attribute pool to generate a prediction result. Based on the prediction result, the variable creation application evaluates the predictive power of each of the predictor variables and retains predictor variables that are predictive as input predictor variables for the prediction model.

Abstract: Various aspects involve time-delay neural networks for risk assessment or other outcome predictions. For instance, a risk assessment computing system accesses time-series data of predictor variables associated with a target entity and determines a risk indicator for the target entity by inputting the time-series data of the predictor variables into a time-delay neural network. The time-delay neural network includes a set of attribute networks each corresponding to a predictor variable and a decision network configured to generate the risk indicator from outputs of the set of attribute networks. The risk assessment computing system further transmits, to a remote computing device, a responsive message including the risk indicator for use in controlling access to one or more interactive computing environments by the target entity.