Abstract: A finite state automata (FSA) may comprise of a plurality of states and a plurality of events that are triggered to transition between the plurality of states to enable the continuous delivery of one or more machine learning (ML) models. Datasets may be uploaded by one or more users to a computing device. Each dataset may include columns of attributes and/or rows of data for each attribute. Each dataset may be analyzed according to respective predefined ML model criteria. Each dataset may be automatically transformed and/or enhanced to meet the predefined ML model criteria. Data analysis may be performed on each dataset to inform the building of the ML models. An algorithm may be received for each ML model. Each ML model may be built based on the respective dataset and the respective algorithm to generate an ML model file. Logs may be stored for tracking the process performed by the FSA.

Abstract: A system of computers for reducing a policy surrender propensity comprising a business process computing engine (150) configured to generate plurality of policies in accordance with a first data set, a feedback engine (170) configured to dynamically alter a set of decisions by adopting machine learning (ML) models to determine the policy surrender propensity of the plurality of the policies from the first data set and a second data set, the second data set is external to the first data set, and a customer management computing engine (160) configured to reduce the policy surrender propensity by altering one or more data in the first data set based on the policy surrender propensity.