Abstract: Techniques to use machine learning for risk management are described. Some techniques build and train a machine learning model with features of which at least one feature corresponds to risk indicia and at least one other feature corresponds to a data source. These features, in general, provide data (e.g., values) indicating a degree of relevance between a particular record and a risk assessment of that particular record's subject matter. User refinement in the form of user selections and other interactions with the particular record and other records provide insights into proper risk management. The techniques leverage such user refinement to improve upon an accuracy of the machine learning model. Driven by such insights, these techniques enhance the machine learning model with accurate feature values and feature weights to perform risk assessment. Other embodiments are described and claimed.

Abstract: Various embodiments are generally directed to techniques to build and train a machine learning model with features of which at least one feature corresponds to risk indicia and at least one other feature corresponds to a data source. These features, in general, provide data (e.g., values) indicating a degree of relevance between a particular record and a risk assessment of that particular record's subject matter. User refinement in the form of user selections and other interactions with the particular record and other records provide insights into proper risk management.