1. A method of building predictive models on transactional data, comprising: providing an aggregation module for each transactional record source; setting output values of each aggregation module to 0; inputting a first transactional record from each source into said corresponding aggregation module; calculating a first iteration of said output values for each aggregation module as: f k 1 (1)&equals;F(&phgr;(&Sgr;t pg w 1 m ),0), where: &phgr; is a neural network element function; F is a blending function that controls how fast a previous transactional record become obsolete; and w 1 m are weights of the neural network; inputting a next transactional record from each source into said corresponding aggregation module; updating said outputs values of each aggregation module as: f k 1 ( r&plus; 1)&equals; F (&Sgr;t pq w 1 m ), f k t ( r )); repeating the two prior steps until all transactional records are processed; and obtaining scalar values f k t as scalar inputs for traditional modeling.