Abstract: Systems and methods for model evaluation. A model is evaluated by performing a decomposition process for a model output, relative to a baseline input data set.

Abstract: Systems and methods for model evaluation. A protected class model that satisfies an accuracy threshold is built by using: data sets for use by a modeling system being evaluated, and protected class membership information for each data set. A target for the protected class model is a protected class membership variable indicating membership in a protected class. Each predictor of the protected class model is a predictor of an evaluated model used by the modeling system. A target of the evaluated model is different from the target of the protected class model. Each predictor is a set of one or more variables of the data sets. For each predictor of the protected class model, a protected class model impact ranking value and a modeling system impact ranking value are determined.

Abstract: Systems and methods for model evaluation. A protected class model that satisfies an accuracy threshold is built by using: data sets for use by a modeling system being evaluated, and protected class membership information for each data set. A target for the protected class model is a protected class membership variable indicating membership in a protected class. Each predictor of the protected class model is a predictor of an evaluated model used by the modeling system. A target of the evaluated model is different from the target of the protected class model. Each predictor is a set of one or more variables of the data sets. For each predictor of the protected class model, a protected class model impact ranking value and a modeling system impact ranking value are determined.

Abstract: Systems and methods for generating and processing modeling workflows are disclosed. In some examples, a reference distribution of scores generated by a model is determined. The reference distribution of scores is recorded in a structured database. One or more unexpected scores are detected during execution of the model. To detect the one or more unexpected scores, a production distribution of scores is compared with the reference distribution of scores recorded in the structured database. The production distribution of scores is generated by the model for a production input data set. An alert is then provided to an external system, when an alert condition is determined to be satisfied based on the comparison. The alert indicates detection of the one or more unexpected scores.

Abstract: A disclosed method includes mapping at least one local application module to respective message type data, and storing the message type data in association with the local application module in a local database accessible to a local server. The method includes mapping gateway account data for internetwork access to at least one remote database, to respective message type data. The first method further includes storing the message type data in association with respective gateway account data in the local database, mapping remote attribute data to local attribute data, and storing the remote attribute data in association with the local attribute data in the local database. Similar steps can be used to prepare the remote server and database for operation. Using the local client device a user can generate message type data transmitted from the local client device to the local server that determines whether the received message type data is associated with a local application module.

Abstract: A disclosed method includes mapping data identifying at least one first application module to respective message type data, and storing the message type data in association with the data identifying the first application module in a first database accessible to a first server. The method includes mapping universal resource locator for internetwork access to at least one second server, to respective message type data. The first method further includes storing the message type data in association with a respective universal resource locator in the first database, mapping second attribute data to first attribute data, and storing the second attribute data in association with the first attribute data in the first database. Similar steps can be used to prepare the second server and database for operation.