Generative AI in Finance and Banking

Name: Generative AI in Finance and Banking
Availability: InStock

A practical one-day course for banking and finance professionals to understand and apply generative AI safely and effectively in client services, compliance, risk, and research

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A one-day AI in banking course presented over two half-days in a virtual class

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In-house pricing available – often more cost-effective for teams of 10+

Download: Course Outline

Module 1: Foundations of Generative AI

Learning Objectives

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, personalised 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 optimisation, 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)
AI in finance training explores how LLMs 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, and 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

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: personalised 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: summarising 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

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

Learning Objectives

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-personalised advice, and risk modelling
- Fintech/startup partnerships with banks
Critical success factors for generative AI in banking and finance
Final activity: Each participant will 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 AI in finance course assumes participants have access to ChatGPT during the course and will be able to download various input files required for interactive activities via a dedicated GitHub project. For the exercise on generating Excel VBA code, it would be useful (though not essential) if participants can access Microsoft Excel during the course, with the ‘Developer’ option enabled.

An experienced trainer and consultant with over three decades of front-line expertise at the intersection of finance, technology, and education delivers our AI in banking course.

He began his career in technology and banking in the late 1990s. He excelled as a software developer, team leader, and project manager at leading technology firms such as Sun Microsystems and Oracle Corporation. He later moved 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. His dissertation focused on artificial intelligence, an early foundation for his later work at the cutting edge of AI in finance. He is the author of two technology books published by O’Reilly Media, 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 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. He has taught at institutions such as the London Business School and Cambridge University’s Judge Business School. He has designed and delivered 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 applicable. His unique combination of deep technical knowledge and real-world financial experience helps guide banking professionals through the opportunities and challenges of generative AI.

By the end of this one-day AI for finance 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 applied in banking and finance.
Recognise 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 your role and organisation.
Gain a clear, hands-on, practical understanding of the power of generative AI.

This AI in financial services course is a must-know 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 offers practical, finance-specific insights, hands-on experience with tools like ChatGPT, and clear strategies for adopting AI in data-driven scenarios.

You should attend if you:

Work in banking or financial services, and want to understand how generative AI is changing the industry
Need to leverage 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
You want concrete, role-relevant ideas you can take back to your team immediately

This 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.

Redcliffe’s AI in finance training addresses the risks, ethical considerations, and regulatory requirements surrounding generative AI. A particular focus is 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

I thought the course would be more technical, but it was actually very practical. It gave me the tools I need to further develop my use of gen AI in my role. I will be using the knowledge gained to build my own RAG to automate one of my team's key products, which will cut down our time spent on production by half. The course also gave me the tools to speak more knowledgeably with engineers and data scientists in my organisation.

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