Abstract: According to some embodiments, a ML framework for an enterprise may include an analytics data store containing electronic records that include record characteristics to be processed by a data as a service layer to create data virtualization. A ML feature pipeline may perform feature engineering based on the data virtualization to create a ML model, and a consumer layer may process operational system information utilizing a business rule engine. In addition, a real-time inference platform may expose an API endpoint associated with deployment of the ML model using the data received from the consumer layer. A CI/CD platform may then automatically provide feedback information to the ML feature pipeline regarding performance of the deployed ML model.

Abstract: According to some embodiments, a data store may contain electronic data records each representing an event associated with an existing risk association between an enterprise and an entity. Each electronic data record may be associated with a record identifier and a set of record characteristic values. Moreover, at least some of the electronic data records may be linked to supplemental electronic note data messages. A back-end application computer server, coupled to the data store, may automatically queue new supplemental electronic note data messages in accordance with at least one trigger rule. The computer server may also automatically transmit batches of information about the queued new supplemental electronic note data messages in near real-time. According to some embodiments, information may be exchanged with a plurality of remote management system terminals, via a distributed communication network, to support an interactive user interface display including the supplemental electronic note data messages.