1. A computer-implemented method of developing a propensity model for an event, comprising: establishing a plurality of biased samples of data representative of a previous event, wherein each of the plurality of biased samples of data representative of a previous event is biased differently; establishing at least one propensity model for each biased sample of data; processing each of the at least one propensity model with a genetic algorithm; checking fitness of at least one propensity model for each biased sample against a validation sample of data, wherein the validation sample of data is unbiased; and identifying a first propensity model for a first biased sample of data for cross-breeding with a second propensity model for a second biased sample based on fitness of the first propensity model to the validation sample of data.