“Learning from your neighbours”: Prudential provisions of the EU AI Act for the UK insurance supervisory regime.
DOI:
https://doi.org/10.7190/fintaf.v3i1.496Abstract
This paper focuses on the prudential regulation and supervision of UK re-insurance undertakings, in relation to Artificial Intelligence (AI) considerations. Specifically, it presents a critical analysis of the prudential provisions of the European Artificial Intelligence (AI) Act which could be adjusted and adopted in the UK regulatory and supervisory regime, in line with the Prudential Regulation Authority (PRA)’s approach to insurance supervision. Building on the gaps identified regarding the supervisory approach to AI applications within the insurance value chain, it presents proposed developments based on the EU AI Act. The purpose of this paper is to present a critique on the learnings from the EU AI Act in relation to risk management systems and risk management for UK financial regulators regarding the prudential supervision of re-insurers. These are linked to the assessment performed by the European Insurance and Occupational Pensions Authority (EIOPA) in relation to the governance and risk management of AI to ensure the appropriate regulation and supervision of the risks linked to re-insurance activities. Effectively capturing how this approach towards the prudent AI governance and risk management framework could be adopted by the PRA, and ultimately how prudential supervision should be adjusted to monitor AI applications and uses. Beyond the EU AI Act, the principles from the International Association of Insurance Supervisors (IAIS) in relation to risk management systems from a prudential angle are also discussed to complement the recommendations for UK regulators, in relation to risk management practices and the prudential regulatory expectations based on the PRA’s Rulebook, in combination with the Lloyd’s of London Principles for the London market. The focus is placed on the AI considerations within the Own Risk and Solvency Assessment, model risk management and stress testing, all interlinked core prudential components of Solvency II and Delegated Acts. This doctrinal legal research adopts a socio-legal methodology combined with economic theory in analysing the prudential regulatory frameworks underpinning AI. The economic analysis of law and regulation constitutes the methodological approach adopted to critically examine the prudential provisions of the EU AI Act applicable to re-insurers. The contribution of this paper is twofold, providing insights for advances to the (a) regulation and (b) supervision of AI applications within the insurance sector for the UK, based on the EU AI Act and EIOPA’s approach. Regulating and supervising AI applications within the UK insurance industry is of high importance, linked to AI uses and the inherent purpose of insurance. In particular referring to the growth and capacity of the insurance market, with wider applications of AI, and the insurability of risks, with the case of under-insurance and protection gap, towards affordability via increased accuracy of risks and improved underwriting, both outcomes of prudential activities. Overall, this research adds to the growing literature about regulatory implications from AI, using the UK insurance industry as a case study, commenting on the EU regulatory regime, from a prudential lens, on how this could be utilised to shape UK practice and policy.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Stavros Pantos

This work is licensed under a Creative Commons Attribution 4.0 International License.
- It is the responsibility of authors to ensure that permissions to reproduce any kind of third party material are obtained from copyright holders prior to the article being submitted for publication.
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under the CC-BY licence .
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.