Generative AI in Finance and Banking

Module 1: Foundations of Generative AI

Learning Objectives

By the end of this module, participants will be able to:

• Explain in plain terms what generative AI is and how it differs from predictive AI
• Recognise the core technologies and tools driving generative AI
• Identify where generative AI already touches financial services
• Appreciate the power and limitations of large language models (LLMs)

Content

• What is generative AI? – the ‘satellite-level’ view
  • Generative AI in finance – e.g. financial report drafting, synthetic data, personalized communications, scenario stress testing, code generation, conversational financial assistants
  • Discriminative AI in finance – e.g. sentiment analysis, credit scoring, fraud detection, algorithmic trading, quantitative risk modelling, anomaly detection, portfolio optimization, data analytics, time series forecasting
• Generative AI vs. discriminative/predictive AI, using examples from credit scoring and client briefing generation
• Generative AI vs. RPA (Robotic Process Automation)
• How LLMs (Large Language Models) are trained on selected data patterns
• Core tools in use today: such as ChatGPT, DALL·E, Claude, Gemini, along with domain-specific fintech AI platforms
• Why now? The drivers of adoption in banking and finance (data availability, cloud power, regulatory pressure, competitive advantage)
• Live Demo: Summarising a market news article
• Interactive Activity: Drafting a client-facing investment note in plain English

Module 2: Generative AI Use Cases in Banking & Finance

Learning Objectives

By the end of this module, participants will be able to:

• Identify high-value applications of generative AI across banking functions
• Describe how AI can assist in client-facing, operational, and research contexts
• Explain how RAG improves reliability by grounding outputs in trusted sources
• Evaluate which use cases are most relevant to their own role or team

Content

• Client-facing applications: personalized portfolio updates, 24/7 chatbots
• Operations & risk management: regulatory report drafting, fraud detection explanations
• The importance of data-input cleanliness
• Time savings, reduced manual effort, and the cost-benefit of generative AI
• Grounded AI outputs with RAG: combining generative AI with internal knowledge bases (e.g., compliance manuals, research archives) to reduce ‘hallucinations’ and increase trust
• Research & analysis: summarizing company filings, scenario testing narratives
• Innovation & product design: product brainstorming, client persona simulation
• Interactive Activity #1: Summarising a fictional earnings call transcript
• Interactive Activity #2: Extracting compliance requirements from a mock regulatory text

Module 3: Hands-On Generative AI for Finance Tasks

Learning Objectives

• By the end of this module, participants will be able to:
• Apply prompt engineering techniques to generate effective outputs
• Adapt the style and tone of AI-generated content for different audiences
• Detect and manage hallucinations and inaccuracies
• Use generative AI to create practical outputs such as client notes and compliance drafts

Content

• Prompt engineering basics: open vs. specific prompts, role-playing prompts
• Adjusting tone: compliance-legal vs. client-friendly
• Stress testing – Red-teaming AI: testing limitations and weaknesses
• The problem with hallucinations:
  • Demo: What can go wrong?
  • Avoiding hallucinations: verifying AI outputs against data
• Demo: ChatGPT alone vs. ChatGPT-with-RAG on a regulatory document query
• Interactive Activities:
  • #1: Drafting a short client note on “The Impact of Interest Rate Changes”
  • #2: Creating a compliance reminder email for internal use
• Code Generation for Finance – Using LLMs to create small scripts for financial tasks (e.g., generating a Python snippet to pull stock data from the cloud or writing an Excel VBA macro to automate risk reporting)
• Interactive Activity #3: Use ChatGPT to write a useful Excel VBA macro

Module 4: Risks, Ethics, and the Future of Generative AI in Finance

By the end of this module, participants will be able to:

• Identify key risks of generative AI adoption in financial services
• Discuss ethical issues such as bias, transparency, and accountability
• Summarise current regulatory perspectives and requirements
• Explain how RAG pipelines and human oversight mitigate risk
• Envision future applications of generative AI in finance and fintech

Content

• Key risks: data leakage, bias, over-reliance, hallucinations
• Ethical issues: transparency, client trust, accountability
• Regulatory perspectives:
  • EU AI Act – “high-risk” classification for financial applications
  • FCA, SEC, MAS – Emerging supervisory positions
• Mitigation and risk management:
  • Human-in-the-loop oversight
  • Measuring output quality (accuracy, bias, speed, cost)
  • AI sandboxes for experimentation
  • Vendor due diligence and audit controls
• RAG pipelines for traceability and audit-friendly outputs
• Debate: “Would you trust AI to draft a client suitability report?”
• Future Trends:
  • Generative AI in algorithmic trading, surveillance, hyper-personalized advice, risk modelling
  • Fintech/startup partnerships with banks
• Critical success factors for generative AI in banking and finance
• Final activity: Each participant to work with ChatGPT to explore a future vision scenario in their area of work (e.g., wealth management, risk, compliance, markets) to enhance their uptake of generative AI

Requirements
This course will assume that participants are able to access ChatGPT during the course and will be able to download various input files required for interactive course activities via a dedicated GitHub project. For the exercise on generating Excel VBA code, it would be useful (though not essential) if participants are able to access Microsoft Excel during the course, with the ‘Developer’ option enabled.

This course is led by a highly experienced trainer and consultant with over three decades of front-line expertise at the intersection of finance, technology, and education.

He began his career in technology and banking in the late 1990s, excelling as a software developer, team leader, and project manager at leading technology firms such as Sun Microsystems and Oracle Corporation, before later moving over to UBS and J.P. Morgan, where he played a central role in the redesign and relaunch of J.P. Morgan’s flagship ‘Pyramid’ derivatives trading platform. He went on to earn the Certificate in Quantitative Finance (CQF) and worked alongside Dr. Paul Wilmott, delivering advanced derivatives training to financial professionals worldwide.

He holds a First-Class Honours degree in Cognitive Psychology from Sheffield University, where his dissertation focused on artificial intelligence — an early foundation for his later work at the cutting edge of AI in finance. He is also the author of two technology books published by O’Reilly media, further underscoring his ability to translate complex technical ideas into practical tools and insights.

As a specialist in Python, machine learning, and fintech innovation, he has developed systems for trading, legal analysis, valuation, and predictive analytics, including recent work on AI-driven profit growth forecasting and retrieval-augmented AI solutions for tax and law applications. He has also consulted for micro hedge funds on AI-based crypto trading via cointegrated pairs identification, along with other tools for price prediction.

Over the past 20 years, the trainer has built a global reputation as a world-class educator, teaching at institutions such as London Business School and Cambridge University’s Judge Business School and designing and delivering training programs for major financial institutions, including Goldman Sachs and J.P. Morgan.

His passion is making complex concepts in finance and artificial intelligence accessible, practical, and immediately useful. His unique combination of deep technical knowledge and real-world financial experience makes him ideally placed to guide banking professionals through the opportunities and challenges of generative AI.

By the end of this one-day course, participants will:

• Understand the fundamentals of generative AI and how it differs from traditional AI and machine learning
• Know where generative AI is already being applied in banking and finance
• Recognize real-world opportunities and the risks of generative AI in financial services
• Explore practical applications in client services, research, compliance, and risk management
• Understand how Retrieval-Augmented Generation (RAG) increases the trustworthiness and reliability of AI outputs
• Develop concrete ideas for safe and effective adoption within their own roles and organizations
• Gain a clear, hands-on, practical understanding of the power of generative AI

Generative AI is transforming the financial industry. From personalised client communication to regulatory compliance support, banks and financial institutions are rapidly exploring how these tools can create competitive advantage whilst managing risk.

This one-day course will provide a clear, practical introduction to generative AI tailored to the banking and finance sector. Participants will gain a grounded understanding of the technology, explore real-world use cases, and work with hands-on tools such as ChatGPT. The course balances innovation with responsibility, ensuring participants understand both the opportunities and the risks of generative AI.

This course is suited for banking and finance professionals (both business and technical) interested in practical applications of generative AI within financial services.

Generative AI is no longer a distant innovation — it’s here, and it’s reshaping how banks and financial institutions operate, compete, and serve their clients. Yet with opportunity comes risk: issues of accuracy, bias, and regulation demand careful handling. This course is designed for professionals who want more than buzzwords — it offers practical, finance-specific insights, hands-on experience with tools like ChatGPT, and clear strategies for adopting AI responsibly.

You should attend if you:

• Work in banking or financial services and want to understand how generative AI is changing the industry
• Need to apply AI responsibly in client-facing, compliance, research, or risk management contexts
• Want hands-on experience using tools such as ChatGPT for real finance-related tasks
• Are concerned about issues such as bias, hallucinations, and regulatory compliance in AI adoption
• Need to understand Retrieval-Augmented Generation (RAG) and other strategies for making AI outputs reliable and auditable
• Want to explore practical, near-term applications of AI as well as long-term future trends
• Are looking for concrete, role-relevant ideas you can take back to your team immediately

This one-day course provides a practical introduction to Generative AI in Banking and Finance, designed for professionals who want to understand and apply this fast-evolving technology in their daily work. Participants will learn what generative AI is, how it differs from discriminative and predictive AI, and why it is becoming a critical capability in financial services.

Through a combination of expert-led sessions, live demonstrations, and interactive exercises with ChatGPT and Excel, the course explores real-world applications across client services, compliance, risk management, and research. Participants will gain hands-on experience generating client communications, drafting compliance notes, generating useful code snippets, and summarising financial information using AI tools.

The course also addresses the risks, ethical considerations, and regulatory requirements surrounding generative AI, with a particular focus on strategies such as Retrieval-Augmented Generation (RAG) to ensure reliability and auditability. The course concludes with forward-looking discussions on emerging trends and an individual exercise in which each participant develops a practical vision for how AI could support their own role or team.

By the end of the course, participants will leave with:

• A clear understanding of generative AI fundamentals
• Practical skills for using AI tools responsibly in financial contexts
• Awareness of the risks and regulatory expectations surrounding AI
• Actionable ideas for applying generative AI to their own professional challenges