Large Language Model Application for Regulatory Horizon Scanning: Case Study on ESG Greenwashing Regulations
This white paper explores the application of Generative AI, specifically Large Language Models (LLMs), to enhance regulatory horizon scanning within financial services. Using the Financial Conduct Authority’s (FCA) 2024 anti-greenwashing rule as a case study, we demonstrate how LLMs can be integrated into the strategic foresight process to detect early regulatory signals, analyse stakeholder feedback, and forecast future regulatory developments.
Our framework builds upon the traditional horizon scanning model, comprising exploration, assessment, application, and continuation, and incorporates advanced text analysis techniques including semantic similarity testing with models such as BERT and RoBERTa.
The study shows that LLMs can significantly improve the efficiency, accuracy, and scalability of horizon scanning by extracting meaningful insights from large, unstructured datasets. The results highlight the potential of LLM-driven foresight to enhance regulatory preparedness, guide compliance strategies, and inform policy design in an increasingly complex and dynamic regulatory environment.
Sustainable Financial Products and UK Pension Schemes
Sustainable financial products have gained significant traction in the financial world as climate change and social responsibility concerns continue to dominate public discourse. In the UK, Environmental, Social, and Governance (ESG) and sustainability considerations have been steadily gaining attention as both financial product designs and risk management tools.
Economic trends, regulations, and soft laws have been reactive over the last decade to growing transparency and demands for accountability (Palea, 20221; Escrig-Olmedo, Muñoz-Torres, Fernandez-Izquierdo, 20132).
This paper explores the growing role of sustainable financial products in the UK’s Defined Contribution (DC) pension schemes. It highlights key challenges and opportunities, focusing on the interplay between sustainable investment products, pension dashboards, Fintech, and institutional perspectives.
Strategic Foresight in FinTech: Harnessing Scenario Planning for Future Readiness
Strategic foresight is an essential approach for anticipating and preparing for potential developments in a rapidly evolving ecosystem. This white paper explores the critical importance of future thinking and foresight methods in fintech ecosystem. It highlights scenario planning as a powerful tool for strategic foresight in fintech ecosystem. It examines the value of scenario planning for businesses, governments, and regulators, while addressing the challenges and limitations of its application.
The paper reviews specific use cases of scenario planning in government and financial institutions, offering insights into how it can further benefit these sectors. Ultimately, the paper calls on stakeholders to embrace future thinking and scenario planning as integral elements of their strategic planning processes.
Simplifying Compliance: The Role of AI and RegTech
The Financial Regulation Innovation Lab (FRIL) is dedicated to simplifying compliance through emerging technologies, with Artificial Intelligence (AI) representing the latest evolution in regulatory technology (RegTech). Building on previous research and industry engagement—including workshops, blogs, webinars, and a micro-credential course—this White Paper presents key considerations for the conceptualisation, design, and implementation of AI-driven compliance systems.
We begin by examining the nature of regulatory rules and the compliance process before exploring the complexities that challenge AI deployment. The discussion then shifts to Generative AI (GenAI) as a cutting-edge innovation, analysing its capabilities and relevance to compliance functions.
A focused use case on GenAI in robo-advisory services illustrates AI’s potential in asset management, where conventional AI is already well-established. Finally, we consider the broader organisational implications of AI adoption, emphasising the opportunity to view compliance as an embedded and adaptive function able to evolve and respond to changing stakeholder expectations and regulatory frameworks.
Shifting the Skills Conversation: Building a Lifelong Learning Culture to Foster Innovation in Scotland’s Financial Sector
Building a sustainable, productive and inclusive financial services sector in Scotland means thinking beyond the needs of today. ‘Fostering innovation’ requires investing in people’s ability to think broader than what’s right in front of them, and such capability to be dynamic in behaviour, knowledge and practical expertise is built through the process of learning – over and over again, exploring many different skills and interests, and throughout life.
It’s for this reason that in this white paper we propose shifting the skills conversation beyond plugging skills gaps and identifying which is the latest technical need in the workforce, and instead building a ‘lifelong learning’ culture to better foster innovation in Scotland’s financial sector, long-term.
Robo Advisors as a Use Case of AI Systems: Linking Responsible Business Practices, Compliance and Outcomes
In this paper, we explore the workings of robo-advisors as an example of AI-based systems.
We discuss the performance and challenges of robo-advice, as well as offer reflections on how and why robo-advice as part of the broader fintech and financial services sector intersects practices in AI systems, regulation and compliance. We draw attention to the implications for explainable AI, the role of humans in the loop, compliance and business practices.
Our approach focuses on how the AI capabilities in robo-advisors can help to build responsible business practices and compliance elements into operating models and business processes. We explore how these interactions apply to selected use cases in the UK and discuss implications for improvements in responsible business practices, regulations and consumer/client outcomes.
The Shifting Locus of Authoritative Advice for Gen-Z and Their Financial Lives: An Opportunity for the Credit Union Sector?
Gen Z are reshaping the way financial advice is sought and acted upon. Moving away from traditional sources like family, banks, and financial advisors, younger generations are turning to social media platforms like TikTok and Instagram, where financial influencers —“finfluencers”— offer accessible, though often unregulated, advice.
While this shift has democratized financial education, it has also introduced significant risks to advice-seekers, including misinformation, high-risk investment recommendations, and a lack of regulatory oversight.
For Credit Unions, this transformation presents challenges and opportunities. Younger audiences often see traditional financial institutions such as banks as outdated, inaccessible, and misaligned with their values. However, Credit Unions, with their ethical foundations and community focus, are well- positioned to fill the trust gap created by the shortcomings of both traditional institutions and finfluencers.
By engaging with young people where they seek advice – on social media – Credit Unions can offer relatable, trustworthy, and sound financial guidance that aligns with their mission to promote financial literacy and inclusivity.
This white paper explores ways in which Credit Unions can respond to this shift in advice-seeking behaviour to revitalise and grow their memberships. Discussions with UK-based Credit Unions reveal cautious optimism about engaging in the finfluencer space, with several recognizing the potential to use social media platforms to amplify messages of fairness, community, and responsible financial management. However, barriers such as limited digital innovation capacity, regulatory concerns, and a general lack of awareness about the finfluencer phenomenon remain.
To address these challenges, we propose a coordinated approach for Credit Unions to build capacity and credibility in the digital advice ecosystem. This includes developing sectoral guidelines for engaging responsibly with finfluencers, pooling resources to experiment with digital campaigns, creating a practical playbook for social media engagement, and modernizing product offerings to align with Gen Z’s preferences for fast, convenient, and values-driven services.
By strategically entering the online advice ecosystem, Credit Unions can not only mitigate the risks of misinformation but also position themselves as a trusted alternative to both traditional institutions and unregulated finfluencers. This approach offers a pathway for Credit Unions to expand their membership, strengthen their community impact, and secure their relevance in an increasingly digital world.
Authorised Push Payment Fraud Mitigation: The Role of Data and Information Sharing
Authorised Push Payment (APP) fraud has been increasingly steadily, with many of the common types originating on social media and the internet. Combatting and mitigating APP fraud will require cooperation across financial institutions and tech and telecoms companies, with data and information sharing playing a key role. Recent UK legislation aims to facilitate data and information sharing to combat fraud and privacy enhancing technologies (PETs) provide technical solutions to enable better understanding and widespread sharing of fraud intelligence that enable data protection and privacy.
Mapping ESRS Disclosure Datapoints to Relevant Datasets
The integration of geospatial data into sustainability reporting frameworks addresses challenges related to inconsistent and outdated Environmental, Social, and Governance (ESG) information. This third white paper from the Financial Regulation Innovation Laboratory (FRIL) explores the application of geospatial data in enhancing the European Sustainability Reporting Standards (ESRS). By aligning geospatial datasets with specific ESRS disclosure requirements, the study provides a foundation for corporations conducting double materiality assessments, auditors validating disclosures, and third parties—such as financial institutions and environmental organisations—performing due diligence.
Geospatial data can be applied at the asset level (e.g., factories) or aggregated using a bottom-up approach linked to financial ownership, improving transparency and comparability across companies, sectors, and regions. However, the study finds that only 7% of ESRS datapoints can be externally validated due to the dependence on proprietary company information. Despite this limitation, different stakeholders benefit from distinct datapoints: investors may prioritise datapoints linked to external risks such as flooding or greenhouse gas emissions, while water-focused non-governmental organisations may emphasise hydrological indicators.
The EU Omnibus package (February 2025) introduces significant changes to ESRS and corporate sustainability reporting. These include a reduction in in-scope companies (80% fewer under the Corporate Sustainability Reporting Directive), limited value chain coverage, and fewer required datapoints, which may lead to a data gap and reduced transparency. However, the shift towards quantitative over qualitative datapoints presents a critical opportunity for geospatial data to bridge this gap, offering independent, real-time, and scalable insights for ESG reporting.
Furthermore, the revision of assurance requirements under the Omnibus package raises concerns about data verification and reporting accuracy. Given these regulatory shifts, integrating satellite- derived data into sustainability reporting frameworks could enhance objectivity, comparability, and reliability. Future regulations should embed geospatial data as a core element to strengthen the integrity and effectiveness of sustainability disclosures in the EU and beyond.
Generative AI for Simplified ESG Reporting in Financial Services
We demonstrate the potential for Generative AI to simplify Environmental, Social, and Governance (ESG) reporting in financial services. Banking and financial institutions are required to comply with ever more stringent and demanding ESG related compliance requirements. A lack of mandatory, universally enforceable sustainable finance standards and guidelines makes effective ESG reporting across industries and countries difficult for financial institutions.
Vast amounts of data processing are required, spanning structured quantitative numerical data and unstructured qualitative textual data. Generative AI has the potential to deliver an innovative solution to this ESG reporting challenge through identifiable capabilities in decision support, including document summarisation; data visualisation; individual and multiple company analytics; and customised report generation. Furthermore, several technical features allow organisations to customise Generative AI systems to meet bespoke business requirements and information technology constraints.
These technical features include response speed and agility; multiple version choice and algorithmic support; user friendly interfaces; scalability and upgradability. In the use case demonstration, we show how a Large Language Model (LLM) can be used to generate responses to a set of common analyst questions pertaining to ESG using single and multiple annual report sources.
This use case brings to life the potential for Generative AI in simplifying compliance in respect of ESG reporting. We then bring together LLM and cutting-edge large Vision Model (LVM) capability to move from text-based prompting to verbal-based prompting for the ESG reporting exercise. We show that this integrated language-vision approach leads to enhancements in performance compared to a sole LLM approach. Indeed, we demonstrate that placing emphasis on key words within the verbal prompts generates more targeted responses from the LLM.