Predictive model development system applied to organization management

A predictive model development system comprising:

• one or more computers;
• and one or more data storage devices having instructions stored thereon that, when executed by the computers, cause the computers to perform operations comprising:
• training each of a plurality of different types of predictive models using training data, wherein the predictive models include a plurality of each type of predictive model that are trained with different combinations of features of the training data;
• generating, for each of the plurality of trained predictive models, a measure that represents an estimation of an effectiveness of the respective trained predictive models;
• selecting two or more of the plurality of trained predictive models based on the respective measures of the trained predictive models;
• obtaining a respective predictive output from each of the selected predictive models in the two or more trained predictive models;
• combining the predictive outputs to generate a result where the trained predictive models comprise at least one causal predictive model.

A system comprising:

• one or more computers;
• and one or more data storage devices having instructions stored thereon that, when executed by the computers, cause the computers to perform operations comprising:
• prepare data representative of an organization that physically exists from a plurality of management systems and a plurality of external databases for processing;
• use a predictive model development system to develop an organization value model that quantifies a contribution of each of one or more elements of value and each of one or more external factors to a value of each of one or more segments of value of the organization;
• quantify a plurality of risks for the organization as a whole and for the one or more of the segments of value, the one or more elements of value and the one or more external factors under one or more scenarios using said organization value model where the one or more scenarios are developed using automated learning, and
• develop and output a custom risk transfer program for the organization for each scenario based on said quantified risks where the one or more segments of value comprise a current operation and one or more segments of value selected from the group consisting of derivatives, market sentiment, investments and real options.