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Experimenting with Green AI to reduce processing time and contributes to creating a low-carbon economy

  • Host institution: University of Naples Federico II, Italy (UNA)

  • Starting month: M9

  • Duration: 36 months

  • Pillar 1: Sustainable finance (University of Naples Federico II, 4 ECTs), Work Package 5

  • Work Packages: WP5, WP6, WP7, WP8

Objectives

Green AI supports the use of resources more efficiently and conserves them for future generations. Multiple applications have been presented in different areas, however, there are no studies exploring the impact that the use of green AI concepts can have in the Financial industry. This research objective focuses on experimenting with green AI concepts in multiple applications in finance, analysing economical and practical impact of its deployment in industry. It facilitates the exchange of innovative ideas and cooperation opportunities in the field of Environmental, Social, and Governance (ESG), Sustainable Finance, and ESG Technology.

Expected Results

The project aims at providing reports about pricing and risk management of green financial instruments across all asset classes, with a focus on new products development, model validation, model risk management, funding and counterparty risk, fair and prudent valuation, applications. It aims at focusing on financial inclusion and inequality. The WP will also have a strong focus on discussion and disseminating of the main results with the aim of spreading the culture of green AI and creating a table for the discussion of new proposals and rules. 1) publications in open access journals, 2) presentations at prestigious conferences and 3) knowledge exchange with stakeholders and project partners 4) General outreach (Media, Open Science Day).

Planned Secondments

  • Swedbank (SWE). Prof. Dr. Tadas Gudaitis, M21, 18 months, policies for asset, sustainable fund management

  • Athena Research and Innovation Center (ARC). Prof. Dr. Ioannis Emiris, M39, 6 months, applied industry-research, using large-scale computing infrastructure to implement the theory

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Planned Timetable

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