Sustainable Finance

Practical example and current challenges

  • The financial system plays a key role in the transition to a low-emission, resource-efficient economy. All actors in the financial system should direct capital flows more strongly towards sustainable investments, that are in line with the goals of the Paris Climate Convention and the sustainable development goals of the United Nations (UN SDGs).
  • In addition, climate change and other negative impacts of human activity ("sustainability risks") dampen the economic performance and financial assets of an economy.
  • In this context, the Financial Stability Board (FSB) has convened a Task Force on Climate-Related Financial Disclosures (TCFD), which in 2017 published recommendations on the disclosure of physical and transitory climate risks (which are linked to the increasing regulation and pricing of climate-damaging economic and consumption patterns). At the EU level, the European Commission adopted in 2018 the Action Plan on "Financing Sustainable Growth", which defines the main objectives for the design of sustainable finance. The EU "Sustainable Finance" taxonomy, as a central building block of the strategy, makes a first comprehensive attempt to map the necessary ambition levels for the transformation towards a sustainable and climate-friendly society for important (so far mainly climate-relevant) economic activities. It thus represents an important reference framework for the transition to a sustainable financial sector.
  • The aim of the "Sustainable Finance" use case is to explore possible applications of artificial intelligence (AI) and machine learning (ML) methods for analysing the effects of sustainability risks in the context outlined above (financial market, regulation) and to derive relevant methods, models and database structures for sustainability, climate risk and impact analyses. In particular, corresponding methods for closing important, decision-relevant data and knowledge gaps are to be (further) developed and tested in exemplary fashion. A further goal is the development of a prototypical ESG data platform to bring together the relevant data.
  • In addition, the use case seeks to explore and derive novel or improved approaches to the analysis and management of sustainability risks, so-called ESG risks (from the environment, social, governance), on financial market and regulatory decisions (including those relating to financial instruments and system stability) by applying AI methods. The application is designed to support investors and financial market players in dealing with ESG risks (identification, measurement and management) and to facilitate corresponding decision-making and evaluation processes. In addition, it is intended to support government institutions (especially regulators) in the development and implementation of financial market regulations such as the EU taxonomy and the TCFD framework or to improve the sustainability data basis for decisions regarding public investments. Another central area of application is the public availability and improvement of the quality of ESG data.
Infografik: Sustainable Finance

What added value does the "GAIA-X project" offer?

  • GAIA-X facilitates the creation of a compatible data infrastructure, which includes the development of a reference data model and a security architecture. The development of a prototypical ESG data platform will enable financial market players to better understand and manage ESG risks and the sustainability impact of financial products, as well as to make informed financing and investment decisions.
  • By developing AI and ML methods in concrete policy and financial market-relevant use cases and by establishing an ESG data structure, sustainability risks for financial market and policy decisions can be better assessed and made available to the actors in an applicable, user-friendly form.
  • GAIA-X enables potentials to be identified through the use of AI methods for recording sustainability risks, to identify gaps in data availability and the linking of different data types/sources, and to concretise the relationship between climate risks and economicand financial indicators.

Use Case Team

  • Ingmar Jürgens, Christina Anselm, Lara Hensel, Ulf Moslener, Gregory Wheeler - Frankfurt School of Finance and Management