  Negative screening is one method to avoid interactions with inappropriate
entities. For example, financial institutions keep investment exclusion lists
of inappropriate firms that have environmental, social, and government (ESG)
problems. They create their investment exclusion lists by gathering information
from various news sources to keep their portfolios profitable as well as green.
International organizations also maintain smart sanctions lists that are used
to prohibit trade with entities that are involved in illegal activities. In the
present paper, we focus on the prediction of investment exclusion lists in the
finance domain. We construct a vast heterogeneous information network that
covers the necessary information surrounding each firm, which is assembled
using seven professionally curated datasets and two open datasets, which
results in approximately 50 million nodes and 400 million edges in total.
Exploiting these vast datasets and motivated by how professional investigators
and journalists undertake their daily investigations, we propose a model that
can learn to predict firms that are more likely to be added to an investment
exclusion list in the near future. Our approach is tested using the negative
news investment exclusion list data of more than 35,000 firms worldwide from
January 2012 to May 2018. Comparing with the state-of-the-art methods with and
without using the network, we show that the predictive accuracy is
substantially improved when using the vast information stored in the
heterogeneous information network. This work suggests new ways to consolidate
the diffuse information contained in big data to monitor dominant firms on a
global scale for better risk management and more socially responsible
investment.
