Abstract: Some aspects of the present disclosure relate to systems, methods, and computer-readable media for configuring a computer system to detect violation conditions in a target dataset. In one example implementation, a computer implemented method includes: receiving data associated with an electronic communication; labelling the received data; creating a machine learning model based on the received data; creating a lexicon, where the lexicon represents one or more terms or regular expressions; creating a scenario using the machine learning models and the lexicon, where the scenario represents a violation condition; and configuring a computer system to detect violation conditions in a target dataset using the scenario, where the target dataset represents electronic communications.

Abstract: Some aspects of the present disclosure relate to systems, methods, and computer-readable media for configuring a conduct surveillance system. In one example implementation, a computer implemented method includes: receiving at least one alert from a conduct surveillance system, where the at least one alert represents a potential violation of a predetermined policy, where the predetermined policy includes a scenario, a target population, and a workflow; determining whether each of the at least one alert represents an actual violation of the predetermined policy; calculating a metric based on the actual violations and the potential violations where the metric includes a number of false positives associated with the at least one alert or the number of false negatives associated with the at least one alert; and changing at least one of the scenario, the target population, or the workflow based on the calculated metric.

Abstract: In some aspects, systems and methods for rapidly building, managing, and sharing machine learning models are provided. Managing the lifecycle of machine learning models can include: receiving a set of unannotated data; requesting annotations of samples of the unannotated data to produce an annotated set of data; building a machine learning model based on the annotated set of data; deploying the machine learning model to a client system, wherein production annotations are generated; collecting the generated production annotations and generating a new machine learning model incorporating the production annotations; and selecting one of the machine learning model built based on the annotated set of data or the new machine learning model.