The 2025 AI Programme provides a practical introduction to using Large Language Models (LLMs) like ChatGPT, Microsoft Copilot, Gemini, and Claude in financial services. Suitable for all levels, including those with little or no prior experience, the series explores key applications such as automating compliance tasks, enhancing client communication, and streamlining research. Participants will learn prompt engineering techniques, how to manage AI-generated inaccuracies, and the regulatory considerations of AI adoption. Through real-world case studies and live demonstrations, attendees will gain the confidence to integrate AI tools effectively while ensuring compliance, accuracy, and efficiency in their financial operations.
This programme will comprise 5 x 1-hour webinars of 60 minutes, inclusive of Q&A, delivered by Ben Banim.
Suitable For
This programme is suitable for financial services professionals, compliance officers, risk managers, and decision-makers looking to understand the impact of AI on the industry, regulatory considerations, and practical applications for business efficiency and innovation.
Agenda
Introduction to LLMs in Financial Services: Opportunities and Use Cases
30 April 2025
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Understanding LLMs – How they work and why they matter in financial services
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Key applications – Enhancing client communications, reporting, compliance, and research
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Real-world examples – How financial firms are using AI today
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Setting realistic expectations – AI’s strengths, limitations, and where human expertise is still required
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Introduction to AI risks – Why accuracy, bias, and compliance matter—but saving deeper discussion for later sessions
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Effective Prompt Engineering: Getting the Best Out of Al Tools
14 May 2025
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Why prompt engineering is critical – How well-structured prompts lead to better AI responses
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Crafting effective prompts – Techniques for specificity, context, and iteration
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AI-driven financial tasks – Writing compliance summaries, meeting notes, and client reports
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Live demonstration – How refining prompts improves AI outputs
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Limitations of prompts – Why AI still needs verification
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Managing Al Hallucinations: Verification and Validation Techniques
11 June 2025
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What are AI hallucinations? – Why AI sometimes generates incorrect or misleading information
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Risks in financial services – The dangers of trusting unverified AI-generated data
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Validation techniques– Cross-referencing sources, verification workflows, and human oversight
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Case studies – Real-world examples of AI errors and how to prevent them
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Link to compliance – Why validated AI outputs are necessary for regulatory adherence
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Navigating Risks and Compliance when Using Al at Work
18 June 2025
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Regulatory landscape – Key financial regulations impacting AI (GDPR, data privacy, audit requirements)
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AI governance – Preventing bias, ensuring transparency, and maintaining ethical AI use
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Record-keeping & auditability – Best practices for tracking AI-generated content
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Developing responsible AI policies – Creating guidelines for your firm’s AI usage
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Balancing innovation and compliance – Encouraging AI adoption without breaching regulations
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Building an AI-Enabled Culture: Training, Tools, and Team Adoption
02 July 2025
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Choosing the right AI tools – Evaluating platforms suited for financial services
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Upskilling employees – Training teams to use AI effectively
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Integrating AI into daily workflows – Ensuring AI enhances productivity without replacing human expertise
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Fostering AI experimentation (with safeguards) – Encouraging innovation while mitigating risk
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Future trends – Where AI in financial services is heading and how to stay ahead
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