Abstract: A method, a system, and a computer program product for generating an interpretable set of features. One or more search parameters and one or more constraints on one or more search parameters for searching data received from one or more data sources are defined. The data received from one or more data sources is searched using the defined search parameters and constraints. One or more first features are extracted from the searched data. The first features are associated with one or more predictive score values. The searching is repeated in response to receiving a feedback data responsive to the extracted first features. One or more second features resulting from the repeated searching are generated.

Abstract: A method, a system, and a computer program product for generating an interpretable set of features. One or more search parameters and one or more constraints on one or more search parameters for searching data received from one or more data sources are defined. The data received from one or more data sources is searched using the defined search parameters and constraints. One or more first features are extracted from the searched data. The first features are associated with one or more predictive score values. The searching is repeated in response to receiving a feedback data responsive to the extracted first features. One or more second features resulting from the repeated searching are generated.

Abstract: A method, a system, and a computer program product for generating a refined synthetic data from one or more sources of data. One or more source data are received from one or more data sources. One or more encoded source data are generated from the one or more source data. A synthetic data is generated by decoding one or more encoded source data. One or more variables in the synthetic data are selected and one or more predetermined identifiability values and one or more predetermined anonymity values are associated with them. The generated synthetic data including the selected variables is decoded using associated one or more predetermined identifiability values and one or more predetermined anonymity values. The decoded synthetic data is outputted.

Abstract: A method, a system, and a computer program product for generating an interpretable set of features. One or more search parameters and one or more constraints on one or more search parameters for searching data received from one or more data sources are defined. The data received from one or more data sources is searched using the defined search parameters and constraints. One or more first features are extracted from the searched data. The first features are associated with one or more predictive score values. The searching is repeated in response to receiving a feedback data responsive to the extracted first features. One or more second features resulting from the repeated searching are generated.

Abstract: A method, a system, and a computer program product for calibrating synthetic data. A synthetic data is generated based on one or more source data using one or more generative models. The generative models are used to generate a latent space based on one or more source data. One or more latent space vectors associated with the generated latent space are determined in accordance with one or more data profiles associated with the one or more source data. The latent space vectors associated with the generated latent space are sampled. Based on the sampling, an optimized synthetic data is generated by comparing the sampled latent space vectors with one or more baseline data associated with one or more data profiles.

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 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.