ESG stands for Environmental, Social, and Governance. Asset managers, asset owners, banks, insurance companies, regulators, and central banks are increasingly applying ESG criteria as part of their analysis process to identify material risks and growth opportunities.
Artificial Intelligence – sometimes referred to as New Electricity – has tremendous potential to accelerate and industrialize evidence-based, data-driven sustainable finance, ESG-related decision making, and climate risk management. Natural language processing (NLP) and spatial AI have become leading technologies in this area, as have simulations, digital twins, virtual worlds, and omniverse.
AI models themselves are also under ESG scrutiny: accountability, unbiasedness, explainability, and trustworthiness of AI-driven applications are components of AI governance and are subject to new AI regulations linked to the EU AI Act.
There is a global trend and need for responsible investing: with the advent of climate change, health crises, and global pollution, the United Nations’ Sustainable Development Goals (SDGs) have become an inevitable new paradigm. Herein, it is increasingly recognized that SDG-compliant investing and policy design have the potential to reshape entire industries and positively impact the development of corporations, economies, and societies towards more sustainability. To actively guide policy and decision-making in this regard, alternative data can serve as a widely available, yet objective and reliable, data source.
High-quality ESG data has become very valuable over the last years, as the financial regulators in the EU and US have imposed strict reporting rules on asset managers. Timely, reliable, and objective data clearly provides a competitive edge vis-à-vis competitors and minimizes the risk of “greenwashing”. We observe rising demand for financial products and funds with an evidence-based “ESG label” and complying with EU Regulation (Art. 6 to 9), while non-compliant funds lose their rank and are removed from the recommendation lists.
Some decision-makers in finance are not aware of the capabilities of AI4ESG and some AI model builders are not aware of the meaning of ESG for society, economy, and our planet. The aim of this Research Topic is, therefore, to improve know-how transfer between both camps and to moderate a dialogue. It facilitates the exchange of innovative ideas and cooperation opportunities in the field of AI4ESG, Sustainable Finance, and ESG Technology as well as Climate Intelligence.
ESG stands for Environmental, Social, and Governance. Asset managers, asset owners, banks, insurance companies, regulators, and central banks are increasingly applying ESG criteria as part of their analysis process to identify material risks and growth opportunities.
Artificial Intelligence – sometimes referred to as New Electricity – has tremendous potential to accelerate and industrialize evidence-based, data-driven sustainable finance, ESG-related decision making, and climate risk management. Natural language processing (NLP) and spatial AI have become leading technologies in this area, as have simulations, digital twins, virtual worlds, and omniverse.
AI models themselves are also under ESG scrutiny: accountability, unbiasedness, explainability, and trustworthiness of AI-driven applications are components of AI governance and are subject to new AI regulations linked to the EU AI Act.
There is a global trend and need for responsible investing: with the advent of climate change, health crises, and global pollution, the United Nations’ Sustainable Development Goals (SDGs) have become an inevitable new paradigm. Herein, it is increasingly recognized that SDG-compliant investing and policy design have the potential to reshape entire industries and positively impact the development of corporations, economies, and societies towards more sustainability. To actively guide policy and decision-making in this regard, alternative data can serve as a widely available, yet objective and reliable, data source.
High-quality ESG data has become very valuable over the last years, as the financial regulators in the EU and US have imposed strict reporting rules on asset managers. Timely, reliable, and objective data clearly provides a competitive edge vis-à-vis competitors and minimizes the risk of “greenwashing”. We observe rising demand for financial products and funds with an evidence-based “ESG label” and complying with EU Regulation (Art. 6 to 9), while non-compliant funds lose their rank and are removed from the recommendation lists.
Some decision-makers in finance are not aware of the capabilities of AI4ESG and some AI model builders are not aware of the meaning of ESG for society, economy, and our planet. The aim of this Research Topic is, therefore, to improve know-how transfer between both camps and to moderate a dialogue. It facilitates the exchange of innovative ideas and cooperation opportunities in the field of AI4ESG, Sustainable Finance, and ESG Technology as well as Climate Intelligence.
