Shoyemi, Olatokunbo, Ahammed, Amit, Akinrinsola, Akintayo, Adisa, Olalekan ORCID: https://orcid.org/0000-0002-8006-7736 and Okubote, Idowu (2025) Artificial intelligence, corporate governance, and sustainability in the insurance business: a systematic literature review and future research agenda. International Journal of Management, Social Sciences, Peace and Conflict Studies (IJMSSPCS), 8 (4). pp. 153-171.

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Abstract

The rapid integration of artificial intelligence into insurance operations has intensified debates around corporate governance, accountability, and sustainability within the financial services sector. While existing studies examine specific applications of artificial intelligence in underwriting, claims management, and fraud detection, the governance mechanisms through which these technologies shape sustainable insurance practices remain fragmented. This systematic literature review synthesises peer reviewed research published between 2015 and 2025, drawing on evidence from Scopus, Web of Science Core Collection, and EBSCO Business Source Premier. Using a structured screening and thematic synthesis approach, the review analyses how artificial intelligence influences corporate governance structures, risk oversight, and sustainability outcomes in insurance firms. The findings reveal that artificial intelligence functions not only as an operational tool but also as a governance enabler that reshapes board oversight, transparency, and stakeholder trust. However, governance gaps persist, particularly in relation to ethical accountability, regulatory alignment, and long-term sustainability objectives. The review contributes by developing a unified analytical framework that explicates how artificial intelligence influences sustainability and ESG outcomes in the insurance business through corporate governance mechanisms. Practical implications are outlined for insurers, regulators, and policymakers seeking to harness artificial intelligence responsibly while safeguarding governance integrity and sustainable value creation.

Item Type:	Article
Status:	Published
School/Department:	London Campus
URI:	https://ray.yorksj.ac.uk/id/eprint/15240

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