Agentic AI for Scaling Targeted Support : A Governance Framework for the FCA Advice–Guidance Boundary

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Cummins, Mark and Bowden, James and Zhang, Hao and Jain, Kushagra (2026) Agentic AI for Scaling Targeted Support : A Governance Framework for the FCA Advice–Guidance Boundary. University of Strathclyde, Glasgow. (https://doi.org/10.17868/strath.00095854)

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Abstract

The Advice–Guidance Boundary Review (AGBR) introduces targeted support as a new regulated activity intended to address the persistent financial advice gap in the UK. While generative AI technologies offer the potential to scale accessible financial support, doing so within the advice–guidance boundary introduces significant governance challenges. This white paper proposes an agentic AI governance framework that embeds these regulatory functions within the architecture of AI-enabled financial support systems. The framework distributes responsibility across specialised agents responsible for consumer segmentation logic, boundary monitoring, vulnerability detection, knowledge management, and supervisory audit. By embedding compliance functions as interacting agents surrounding a consumer-facing multimodal generative AI-based financial advisor, the proposed architecture transforms regulatory compliance from a behavioural expectation into a structural property of the system. The framework provides a conceptual foundation for scaling targeted pensions support safely and transparently under the FCA’s AGBR while supporting responsible innovation in AI-enabled financial services.

ORCID iDs

Cummins, Mark ORCID logoORCID: https://orcid.org/0000-0002-3539-8843, Bowden, James ORCID logoORCID: https://orcid.org/0000-0002-0419-1882, Zhang, Hao ORCID logoORCID: https://orcid.org/0009-0002-1021-7476 and Jain, Kushagra ORCID logoORCID: https://orcid.org/0009-0001-8435-8346;
CORE (COnnecting REpositories)