A Framework for Institutional Risk Identification using Knowledge Graphs
  and Automated News Profiling
  Organizations around the world face an array of risks impacting their
operations globally. It is imperative to have a robust risk identification
process to detect and evaluate the impact of potential risks before they
materialize. Given the nature of the task and the current requirements of deep
subject matter expertise, most organizations utilize a heavily manual process.
In our work, we develop an automated system that (a) continuously monitors
global news, (b) is able to autonomously identify and characterize risks, (c)
is able to determine the proximity of reaching triggers to determine the
distance from the manifestation of the risk impact and (d) identifies
organization's operational areas that may be most impacted by the risk. Other
contributions also include: (a) a knowledge graph representation of risks and
(b) relevant news matching to risks identified by the organization utilizing a
neural embedding model to match the textual description of a given risk with
multi-lingual news.
