Towards Real-Time Probabilistic Risk Assessment by Sensing Disruptive Events from Streamed News Feeds

Risk management has become an important concern over recent years and understanding how risk models could be developed based on the availability of real time (streaming) data has become a challenge. As the volume and velocity of event data (from news media, for instance) continues to grow, we investigate how such data can be used to inform the development of dynamic risk models. A Bayesian Belief Network based approach is adopted in this work, which is able to make use of priors derived from a variety of different news sources (based on data available in RSS feeds).