In a globalized world we live in today consequences of catastrophic events easily transgress national borders. Whether it’s a natural disaster, a war or an economic crisis it’s likely to spread out and affect all of us. We propose a framework to model global risks that is not bound to any specific model and is a hybrid of human and machine intelligence. The core of this approach is in using Bayesian Nets of causalities constructed by an analyst equipped with text mining and a map of economic, political and business interconnections.

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The development of computational data science techniques in natural language processing (NLP) and machine learning (ML) algorithms to analyze large and complex textual information opens new avenues to study intricate processes, such as government regulation of financial markets, at a scale unimaginable even a few years ago. This project develops scalable NLP and ML algorithms (classification, clustering and ranking methods) that automatically classify laws into various codes/labels, rank feature sets based on use case, and induce best structured representation of sentences for various types of computational analysis.

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Understand interconnected nature of global multi-national companies via their supply chain, product and services competition, co-investments and co-ownerships as well as other dependencies between operations and revenue streams. We would like to consider the way news on any company specifically propagate down the connection graph and impact other businesses that are related in a way that is not necessarily explicit.

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