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ESG Fingerprint: How Big Data and Artificial Intelligence Can Support Investors, Companies, and Stakeholders?

In: Responsible Artificial Intelligence

Author

Listed:
  • Pajam Hassan

    (intuitive.ai GmbH)

  • Frank Passing

    (intuitive.ai GmbH
    IU International University of Applied Sciences)

  • Jorge Marx Goméz

    (Carl von Ossietzky University of Oldenburg)

Abstract

Current research is investigating the extent to which measurements of corporate sustainability through environmental-social-governance (ESG) controversies have an impact on a company’s valuation. Early investors, stakeholders, and companies are already using the ESG data and ratings generated to inform their investments or strategic decisions in companies. On the other hand, it is apparent that measurements are often based on static indicators collected annually. Furthermore, analysis has shown that mainstream ESG ratings lack a consistent ESG framework. Similarly, ESG rating indicators are often predefined and do not provide users with sufficient transparency to integrate them into their daily business processes. This is where this chapter comes in and develops an ESG taxonomy based on historical ESG events from which risk patterns, the so-called ESG fingerprint, are automatically extracted. These help to reduce complexity and enable the design of artificial intelligence-based ESG information systems that map the risk management process across phases.

Suggested Citation

  • Pajam Hassan & Frank Passing & Jorge Marx Goméz, 2023. "ESG Fingerprint: How Big Data and Artificial Intelligence Can Support Investors, Companies, and Stakeholders?," CSR, Sustainability, Ethics & Governance, in: René Schmidpeter & Reinhard Altenburger (ed.), Responsible Artificial Intelligence, pages 219-234, Springer.
    • Handle: RePEc:spr:csrchp:978-3-031-09245-9_11
    DOI: 10.1007/978-3-031-09245-9_11
    as

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