15. A non-transitory computer program product storing instructions, which when executed by at least one data processor of at least one computing system, implement a method, the method comprising: accessing, using at least one data processor and from at least one database, data from a plurality of disparate data sources; automatically building, by a model building engine using at least one data processor and the data obtained from the accessed data sources, a plurality of test models, each test model having predetermined predictive variables and each test model built from one or more of the plurality of disparate data sources; determining, by a variable selector and using at least one data processor, a final set of predictive variables from the predetermined predictive variables in the plurality of test models by comparing the predictive power of the predictive variables across the plurality of test models, the final set of predictive variables being the most predictive of the predetermined predictive variables; generating, using at least one data processor, a master dataset comprising data selected from the disparate data sources and corresponding to the determined final set of predictive variables; and building, using at least one data processor and from the master dataset, a master model that combines the final set of predictive variables from the plurality of disparate data sources, the master model characterizing a quantitative estimate of the probability that an entity will display a defined behavior.
15. A non-transitory computer program product storing instructions, which when executed by at least one data processor of at least one computing system, implement a method, the method comprising: accessing, using at least one data processor and from at least one database, data from a plurality of disparate data sources; automatically building, by a model building engine using at least one data processor and the data obtained from the accessed data sources, a plurality of test models, each test model having predetermined predictive variables and each test model built from one or more of the plurality of disparate data sources; determining, by a variable selector and using at least one data processor, a final set of predictive variables from the predetermined predictive variables in the plurality of test models by comparing the predictive power of the predictive variables across the plurality of test models, the final set of predictive variables being the most predictive of the predetermined predictive variables; generating, using at least one data processor, a master dataset comprising data selected from the disparate data sources and corresponding to the determined final set of predictive variables; and building, using at least one data processor and from the master dataset, a master model that combines the final set of predictive variables from the plurality of disparate data sources, the master model characterizing a quantitative estimate of the probability that an entity will display a defined behavior. 20. The computer program product of claim 15 , the method further comprising automatic down sampling of one or more disparate data sources when a number of records in the disparate data source exceeds a predetermined value.