Abstract: Predicting churn from a multi-product data using sequential modeling includes collecting, by a journey module, data from a plurality of platforms to build a journey defined by one or more events, and creating, by a sequential deep learning (DL) model, relationship data identifying between events in a chronological order, capturing temporal dependencies present in the data. This also includes correlating, by a churn prediction module, sequences of events to a churn event, and identifying when the churn event is going to occur based on pattern learnt to differentiate between “churn” and “non-churn” journeys.

Abstract: A cross-sectional model is provided that determines the relationship between macroeconomic factors and the odds to score relationship of a scoring model. The cross-sectional model takes economic data from various economic regions, as opposed to time periods, as input, and produces, as output, a prediction of the curve-of-best fit that relates a score to a probability (i.e., the probability of the outcome in question such as paying back a loan or filing an insurance claim, etc.). Related systems, methods and articles are also described.