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Invite colleaguesUncovering hidden signals for sustainable investing using Big Data: Artificial intelligence, machine learning and natural language processing
Abstract
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.
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Author's Biography
Dr. Madelyn Antoncic is a Senior Fellow, Development Research Institute (DRI), New York University. She is a Member of the Board of Directors and Risk Committee of ACWA Power, Saudi Arabia and S&P Global Ratings. Dr. Antoncic most recently was Senior Advisor to UNCTAD on ESG and SDG Reporting; was CEO of Global Algorithmic Institute, an early-stage start-up NGO using Big Data to track corporate SDG reporting; and CEO of SASB. She is a former Vice-President and Treasurer of the World Bank. Her experience spans global financial markets across all asset classes in large, complex, global financial institutions in both the private and public sector. She began her career as an Economist at the Federal Reserve Bank of New York, followed by various senior market roles at Goldman Sachs, Barclays Capital, Lehman Brothers and Principal Global Investors. She has published widely on ESG issues and has served as a judge of Corporate Sustainability Report (CSR’s) competitions for both UNCTAD-ISAR’s and the Asia Sustainability Reporting Summit, respectively. She is known for her leadership in financial innovation including implementation of national climate-related disaster risk reduction and mitigation structures to help emerging and developing economies transfer climate-related disaster and other risks to the markets to enable them to be more fiscally resilient. Dr. Antoncic is an internationally recognized industry leader and expert on risk management, ESG and governance. She is the subject of many risk management and governance graduate school case studies concerning the Great Financial Crisis. She is a frequent speaker and key note including at high-level fora such as at the UN General Assembly (UNGA), SDG Business Forum Plenary; the Ministry Economy, Trade and Industry (METI) Tokyo, First Annual TCFD Summit; UNCTAD-ISARs; G20 Greening the Financial System: Financing for Sustainable Growth and Development; World Bank and IMF High-level Global Infrastructure Forum on Mechanisms to Increase Infrastructure Finance; the UN Climate Change Summit; The Sandai Dialogue, Mainstreaming Disaster Risk Management in National Development Planning. Her awards include First Prize Honoree of the International UNCTAD-ISAR Honours 2020, with GAI for Harmonizing Corporate Sustainability Reporting; the Fulbright Scholars Award for International Cooperation; National Partnership for Women and Families award for Leadership sharing the stage with Senator Christopher J. Dodd; Risk Manager of the Year; was named among the 100 Most Influential People in Finance; listed among top thought leaders helping shape (and reshape) accounting in 2020 and beyond; received the Women in Business, Distinguished Alumna award; and the Girl Scouts Annual Tribute award. She is a member of the Board of Overseers of Weill Cornell Medicine of Cornell University and the Editorial Boards of the Journal of Risk Management in Financial Institutions; and the Journal of A.I., Robotics & Workplace Automation. She is a former Board member of One-to-World supporting Fulbright students and scholars; the World Bank Group Pension Finance Committee, as Vice-Chairman; the Public Sector Issuers Forum, as Co-chair; Barclay’s Capital Board and member of the Executive Committee of the Board; and the Board of Directors of the Girl Scout Council of Greater New York. Dr. Antoncic holds a Ph.D. in Economics, a minor in Finance from NYU; was an Alfred P Sloan Foundation Doctoral Fellow and a member of the adjunct faculty at NYU Stern’s graduate and undergraduate schools.
Citation
Antoncic, Madelyn (2020, March 1). Uncovering hidden signals for sustainable investing using Big Data: Artificial intelligence, machine learning and natural language processing. In the Journal of Risk Management in Financial Institutions, Volume 13, Issue 2. https://doi.org/10.69554/CIKJ7477.Publications LLP