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Data Science and Artificial Intelligence

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.

This research theme seeks to deploy data science and AI to support the theory and practice of sustainable finance.

Research topics include:

  • 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
  • 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

To find out more about our work on data science and AI, contact Dr Nataliya Tkachenko.

Recent publications:

  • Tkachenko, N., Procter, R. and Jarvis, S. (2017) How do eyewitness social media reports reflect socio-economic effects of natural hazards? In, Ciampaglia, G., Mashhahi, A. and Yasseri, T. (eds) Social Informatics. SocInfo 2017. Lecture Notes in Computer Science, vol 10540. Springer, Cham.
  • Tkachenko, N., Procter, R. and Jarvis, S. (2017) Predicting floods with Flickr tags. PLoS ONE, 12(2): e0172870.
  • Tkachenko, N., Procter, R. and Jarvis, S. (2016) Predicting the impact of urban flooding using open data. Royal Society Open Science, 3(5).
  • Caldecott, B. (2019) Next Gen Carbon Markets, BusinessGreen, October 2019.