Abstract: Systems and methods provide a flexible environment for intelligently scoring transactions from data sources. An example method includes providing a user interface configured to generate a rubric by receiving selection of one or more one or more conditions, each condition identifying a tag generated by a classifier in the library of classifiers and for each identified tag, receiving selection of a value for the tag that satisfies the condition and receiving selection of an outcome attribute for the condition. The outcome attribute may be a weight for the tag or an alert condition. The method includes storing the rubric in a data store and applying the stored rubric to scoring units of a transaction. The method also includes aggregating scores for transactions occurring during a trend period and displaying the trend score. In some implementations, at least one classifier in the library is a rule-based classifier defined by a user.

Abstract: Systems and methods provide a flexible environment for intelligently scoring transactions from data sources. An example method includes providing a user interface configured to generate a rubric by receiving selection of one or more one or more conditions, each condition identifying a tag generated by a classifier in the library of classifiers and for each identified tag, receiving selection of a value for the tag that satisfies the condition and receiving selection of an outcome attribute for the condition. The outcome attribute may be a weight for the tag or an alert condition. The method includes storing the rubric in a data store and applying the stored rubric to scoring units of a transaction. The method also includes aggregating scores for transactions occurring during a trend period and displaying the trend score. In some implementations, at least one classifier in the library is a rule-based classifier defined by a user.

Abstract: Systems and methods provide a flexible environment for intelligently scoring transactions from data sources. An example method includes providing a user interface configured to generate a rubric by receiving selection of one or more one or more conditions, each condition identifying a tag generated by a classifier in the library of classifiers and for each identified tag, receiving selection of a value for the tag that satisfies the condition and receiving selection of an outcome attribute for the condition. The outcome attribute may be a weight for the tag or an alert condition. The method includes storing the rubric in a data store and applying the stored rubric to scoring units of a transaction. The method also includes aggregating scores for transactions occurring during a trend period and displaying the trend score. In some implementations, at least one classifier in the library is a rule-based classifier defined by a user.

Abstract: Systems and methods provide a flexible environment for intelligently scoring transactions from data sources. An example method includes providing a user interface configured to generate a rubric by receiving selection of one or more one or more conditions, each condition identifying a tag generated by a classifier in the library of classifiers and for each identified tag, receiving selection of a value for the tag that satisfies the condition and receiving selection of an outcome attribute for the condition. The outcome attribute may be a weight for the tag or an alert condition. The method includes storing the rubric in a data store and applying the stored rubric to scoring units of a transaction. The method also includes aggregating scores for transactions occurring during a trend period and displaying the trend score. In some implementations, at least one classifier in the library is a rule-based classifier defined by a user.