Fintech meets ESG: mitigate risk with machine learning, alternative data and AI

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Presented by

Elena Kozhemyakina, Fintech4Funds | Libby Bernick, Impact-Cubed | Patrick Wood Uribe, Util | Joseph Naayem, Kalmus Capital

About this talk

ESG investing has broken into the mainstream in recent years. With global awareness of the need to find solutions to climate change increasing, and the global pandemic exposing the underlying inefficiencies of conventional investment practices, demand for ESG investment solutions has never been so high. However, the ESG market lacks credibility for end investors. Since ESG ratings are based on past performance, advisors are hedging their bets on investment opportunities without sufficient insight into the risks and opportunities not identified by those data sets. As it stands, ESG ratings based on past performance are by their nature risky, as positive past performance does not always reflect future success. To drive the uptake of ESG, the industry needs to harness tech to increase access to and optimise the analysis of real-time corporate data. By leveraging technologies like Natural Language Processing (NLP,) machine learning and AI, financial players can extract and dissect non-financial corporate data to monitor and detect changes in corporate ESG risk profiles. In Episode 14 of Driving Fintech Forward, we’re taking a deep-dive into the world of ESGtech, to explore how innovations in technology are advancing the reliability of ESG reporting and driving the growth of the market. Topics for discussion include: - The importance of real-time data when assessing ESG risk - What role for technology in defining ESG performance? - Alternative data and machine learning: how non-financial data can be leveraged to deliver more reliable ESG data sets - And more Confirmed speakers: - Libby Bernick, CEO, Impact-Cubed - Patrick Wood Uribe, CEO, Util - Joseph Naayem, Managing Partner, Kalmus Capital This series is hosted by Elena Kozhemyakina, Founder and CEO, Fintech4Funds.

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