Multimodal AI for Scaling Targeted Support : Navigating the FCA Advice–Guidance Boundary

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Zhang, Hao and Bowden, James and Cummins, Mark (2026) Multimodal AI for Scaling Targeted Support : Navigating the FCA Advice–Guidance Boundary. University of Strathclyde, Glasgow. (https://doi.org/10.17868/strath.00095463)

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Abstract

The Financial Conduct Authority’s Advice Guidance Boundary Review (AGBR) seeks to address a persistent advice gap in UK financial services by enabling new forms of scalable, decision-relevant consumer support that sit between generic guidance and personalised financial advice. This challenge is particularly acute in pensions, where consumers face complex, long-term decisions but exhibit low engagement with traditional advice services. This white paper examines the potential of multimodal generative artificial intelligence to deliver targeted support in pensions while remaining within the advice–guidance boundary. Drawing on recent advances in Vision Language Models and multimodal conversational architectures, the paper develops a solution framework for speech-enabled, audio-visual digital advisors that are compliant by design. A Digital Pensions Advisor prototype is presented to demonstrate how such systems can interpret real consumer narratives, recognise and respond appropriately to vulnerability, and maintain boundary discipline when confronted with requests for personalised advice. The paper concludes by outlining a roadmap for strengthening auditability, explainability, and supervisory readiness, and by identifying future research directions, including the role of formal and informal information sources in shaping consumer understanding. Collectively, the findings suggest that multimodal AI can play a meaningful role in scaling targeted support in pensions while preserving regulatory safeguards and consumer trust.

ORCID iDs

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