Uncovering hidden signals for sustainable investing using Big Data: Artificial intelligence, machine learning and natural language processing

Risk managers and investors have increasingly been seeking high-quality environment, social and governance (ESG) data in order to assess nonfinancial risks as well as allocate capital towards companies that manage themselves in a ‘socially responsible’ way and adhere to their contract with society. The problem is that due to the lack of agreed-upon standards for companies to use for reporting on sustainability issues, there is a paucity of high-quality firm-level data to serve as key inputs in assessing a company’s risks and adherence to ESG criteria. Big Data, developed through cutting-edge statistical models, artificial intelligence (AI) and natural language processing (NLP) covering dozens of languages, provides the solution for ESG rankings and ratings and can help combat self-reported bias and ‘greenwashing’ and provide high-quality data. The ‘next generation’ measures of firms ‘doing good’ are the UN sustainable development goals (SDGs), which are this decade’s benchmarks against which millennials and many investors are beginning to assess companies. The SDGs go beyond the more narrowly focused set of sustainability issues embedded in ESGs, and quality data to measure performance against the SDGs are even more sparse. Using Big Data, Global AI Corporation uncovers data measuring companies’ and counties’ performance on all 17 SDGs, which can enable the integration of SDG factors into investment, risk management and national policy decision-making processes. Big Data is providing statistical indicators and performance metrics data to national governments and the United Nations to benchmark progress towards achieving the SDGs. It is also producing the SDG footprint of the private sector at the regional and global levels for policy purposes as shown in the United Nations Conference on Trade and Development’s (UNCTAD) SDG Pulse publication. Using Big Data, Global AI Corporation eliminates self-reporting biases and uncovers hidden data, which results in negative as well as positive ESG/SDG scores, while the self-reporting data only produces positive scores.