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 governance (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.

负面筛选是一种避免与不适当实体互动的方法。例如，金融机构保留有环境，社会和治理（ESG）问题的不适当公司的投资排除列表。他们通过收集来自各种新闻来源的信息来创建投资排除列表，以保持其投资组合的盈利和绿色。国际组织还维护精明的制裁名单，该名单被用来禁止与参与非法活动的实体进行贸易。在本文中，我们专注于金融领域投资排除清单的预测。我们构建了一个庞大的异构信息网络，涵盖了每个公司周围的必要信息，它是由七个专业策划的数据集和两个开放的数据集组成的，总共约有5000万个节点和4亿条边。利用这些庞大的数据集，并以专业调查员和记者进行日常调查为动机，我们提出了一个模型，该模型可以学习预测在不久的将来更有可能加入投资排除列表的公司。我们使用2012年1月至2018年5月全球35,000多家公司的负面新闻投资排除列表数据对我们的方法进行了测试。与使用和不使用网络的最新方法进行比较，我们得出的预测准确性为当使用存储在异构信息网络中的大量信息时，性能得到了显着改善。