Abstract: A machine learning method for performing an efficiency analysis on a decision to accept or reject a data transaction. A machine learning classifier receives a decision analysis for data transactions, the decision analysis determining if each of the data transactions was accepted or rejected. The machine learning classifier performs an overall result analysis of a result that would occur if all true negatives and all false positives were accepted. The machine learning classifier performs an impact analysis of the false negatives on the true negatives that were properly accepted. The machine learning classifier performing an efficiency analysis by finding a ratio of the impact of the false negatives on the true negatives that were properly accepted to the result that would occur if all true negatives and all false positives were accepted.

Abstract: Self-learning and adaptive modeling is employed with respect to predictive analytics. A hierarchical model structure can be employed comprising a set of predictive models automatically built from accumulated data and distributed across multiple levels. For a given input type, a set of candidate models can be identified across varying levels of granularity, and a best model selected based on a comparison of performance metrics of the models. The best model can then be activated for use in making predictions. Of course, the best model can change based on most recent training performance results, since as more data becomes available more specific models can be developed.

Abstract: A two stage model in which the first stage of the model applies a different weighting schema to different types of transactions in a transaction-based system is described. The first stage of the model focuses on capturing currently known patterns that indicate bad transactions. The second stage of the model focuses on rejecting transactions that are approved by the current model, with the objective of maximizing a measurable goal. Business knowledge is used to lower the cost of finding the optimal solution of the model by estimating parameters provided to the first and second stages of the model. Evaluation of the model accounts for retry transactions and for churn. Parameters associated with the model that maximizes the goal can be selected for a future model.

Abstract: Self-learning and adaptive modeling is employed with respect to predictive analytics. A hierarchical model structure can be employed comprising a set of predictive models automatically built from accumulated data and distributed across multiple levels. For a given input type, a set of candidate models can be identified across varying levels of granularity, and a best model selected based on a comparison of performance metrics of the models. The best model can then be activated for use in making predictions. Of course, the best model can change based on most recent training performance results, since as more data becomes available more specific models can be developed.

Abstract: Methods, systems, and computer-readable media are disclosed for generating a score for a check cashing transaction. A method includes receiving from a cashing station, having cashing station data, a transaction request including check data and identity data associated with a user, and determining whether the transaction request represents a first transaction request from the user at that cashing station. If so, an enrollment validation process and identification authentication process are performed; if not, a repeat validation process and check casher identification process are performed. The method includes determining whether there is a match between at least one of check data or identity data and data in a negative file or a knowledge file.