Abstract: A system and method for building segmented scorecards for a population is presented. A model of the population is built using a model builder computer, and one or more variables used by the model builder to build the model is stored in a repository. A scorecard is generated for each segment of the population based on the model and using an adaptive random tree computer program. Next, the scorecard for each segment is enhanced using a integer non-linear programming computer program to determine optimal score weights associated with the variables used by the model builder to build the model, and to generate an enhanced segmented scorecard for the population.

Abstract: Data spiders, provide an automated system that can take a file or file store of historic transaction data and create the best set of variables from that data, where “best” means highly predictive. Genetic algorithms are used to parameterized transactions to form groups, which are subjected naïve Bayes score ranking. Variable groups are generated and ranked accord to the score. Data spiders span the full information available, are uncorrelated with previous methods, and are easily interpretable.

Abstract: A system and method for building segmented scorecards for a population is presented. A model of the population is built using a model builder computer, and one or more variables used by the model builder to build the model is stored in a repository. A scorecard is generated for each segment of the population based on the model and using an adaptive random tree computer program. Next, the scorecard for each segment is enhanced using a integer non-linear programming computer program to determine optimal score weights associated with the variables used by the model builder to build the model, and to generate an enhanced segmented scorecard for the population.

Abstract: A system and method for identifying homogeneous risk pools used in the calculation of minimum capital requirements for a number of segments of a population of portfolios is presented. An F-ratio objective function representing a probability of a risk event across all of the number of segments of the population is calculated using an F-ratio objective function engine. An input dataset that defines a decision tree structure for the population is received. The F-ratio objective function of the risk event is maximized using a generic algorithm-based search engine to optimize the decision tree structure to group the number of segments according to one or more of the homogeneous risk pools, and a score for each homogeneous risk pool is then generated.

Abstract: Data spiders, provide an automated system that can take a file or file store of historic transaction data and create the best set of variables from that data, where “best” means highly predictive. Genetic algorithms are used to parameterized transactions to form groups, which are subjected naïve Bayes score ranking. Variable groups are generated and ranked accord to the score. Data spiders span the full information available, are uncorrelated with previous methods, and are easily interpretable.