Sustainable finance

How can data science techniques be deployed to align financial institutions with the goal of global environmental sustainability?


Tackling global environmental challenges requires capital to flow into economic activity that supports sustainability and away from activity that does not. At the same time, environmental change (such as physical climate impacts) and societal responses to these changes (such as regulation and technological change) are affecting the value of assets across sectors of the global economy. The environment is now a material consideration in the performance of investments across sectors, geographies, and asset classes.

Explaining the science

Aligning the global financial system with sustainability requires environmental factors to be properly measured and for capital to be efficiently deployed into solutions to environmental challenges.

Enabling technologies, such as distributed ledgers and smart contracts, can enable financial transactions and behaviours that support the transition to sustainability. Data science and AI can help the financial system secure much more accurate, consistent, and timely data to inform decision-making, risk pricing, and capital allocation. These innovations will lead to ultra-transparency that can upend the current imbalances in information that exist between companies and their investors, and between financial institutions and their regulators. This has huge potential to help align the financial system with environmental sustainability. 


This programme challenge will bring together researchers from across several fields of study, together with practitioners, to tackle the following challenges: 

  • Deploy data science techniques to new and existing datasets, including alternative data, to support financial institutions and financial regulators in the transition to global environmental sustainability
  • Measure and track environment-related risks and impacts facing companies and investor portfolios 
  • Mainstream the use of geospatial data and analysis, particularly asset-level data, relevant to financial decision-making 
  • Analyse the performance of (un)sustainable investments in different asset classes using novel datasets
  • Harness new technologies, including distributed ledgers and smart contracts, to enable the efficient deployment of capital into sustainable investments across different asset classes, sectors, and geographies
  • Ensure greater data quality, consistency, and comparability, including through better data assurance and new data standards 



Dr Steven Reece

Senior Research Associate, Machine Learning Research Group, University of Oxford

AI for Sustainable Finance seminar series

This regular seminar series is an opportunity for researchers and practitioners in this emerging interdisciplinary field to come together and explore new methods, datasets, and research questions. It is also an opportunity to share updates with colleagues and network with your peers. To find out more, join our mailing list above.