Abstract: A method for providing action-based modeling to facilitate predictive analytics is disclosed. The method includes aggregating raw data from various sources, the raw data including application metadata; structuring the raw data to generate a primary data set; partitioning the primary data set to generate an action data set for predetermined actions, the action data set including a status label for each data point; generating a model for each of the predetermined actions; training the model based on the corresponding action data set; and determining an explanation for each of the predetermined actions based on the corresponding trained model, the explanation including a rule-based description in a natural language format.

Abstract: Methods and systems for generating a model to be used for evaluating credit and loan applications are provided. The method includes: training a first model by using all features included in a universe of candidate features; measuring a first metric that relates to an accuracy of the first model and a second metric that relates to a disparity of the first model; constructing a graph based on pairwise correlations of the features; clustering the features into feature sets; estimating a respective disparity contribution associated with each feature set; selecting feature sets to be included in a second model; training, the second model; measuring the first metric and the second metric with respect to the second model; and determining whether the second model satisfies a predetermined accuracy level and a predetermined disparity reduction with respect to the first model.

Abstract: A method and a system for solving transactions reconciliation tasks by integrating clustering and optimization are provided. The method includes: receiving first information that relates to a plurality of transactions; extracting, from the first information, a set of transaction-specific features and a set of transaction-specific textual information; generating at least one meta-transaction by clustering a subset of the plurality of transactions based on the extracted set of transaction-specific features and the extracted set of transaction-specific textual information; and matching transactions from within the clustered subset based on the amounts of the transactions.