Fair Isaac Corporation

Christopher Allan Ralph

Gerald Fahner

Helen Geraldine E Rosario

Liang Meng

Michael S Sossi

Gary J Sullivan

Stefan Kuzminski

Yaxin Liu

John Duchnowski

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.

Enhanced data privacy through structure-preserving autoencoder with latent space augmentation

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.

Interpretable feature discovery with grammar-based bayesian optimization

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.

Adaptive random trees integer non-linear programming

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 na&#xef;ve 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.

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Christopher Allan Ralph