AI in Financial Services: Intelligence Meets Money
Kate Cooper
Financial Services CEO, Founder HW.I
Australia's specialist AI course for people who work in financial services
Build your understanding of AI in financial services, then apply it through guided practice and an AI impact project.
Understand predictive AI, generative AI and agents, and their implications for customers, products, people, trust and judgement across retail and wholesale finance.
Across four weeks, three live workshops combine cases, discussion and hands-on exercises. These build your understanding; your AI impact project shows how you use it. Develop a product or service, improve a process, assess risk, evaluate a supplier or prepare a team. Use reusable methods and templates to explain value, evidence, risks, human responsibilities and your next step.
Receive midpoint AI-supported feedback and a final assessment personally reviewed by Kate Cooper. The accompanying Intelligence Meets Money publication connects research and practice.
On successful completion, receive an HW.I certificate recording learning dates, completed hours and outcomes for your CPD record. Eligibility depends on your professional body; financial advisers should check with their AFS licensee.
Materials open 16 November. Three two-hour workshops. Allow 12–14 hours. No coding or paid AI account required.
What you’ll learn
Build your understanding of AI in financial services, apply it with sound judgement and develop your AI impact project.
Distinguish predictive AI, generative AI and agents, and understand their capabilities and limitations.
Examine financial-services cases to explore changes in products, customer experience, operations and decision-making.
Use research and future scenarios to assess what AI developments mean for your role and organisation.
Practise framing tasks, supplying useful context and refining AI outputs through hands-on financial-services exercises.
Apply “Let’s Think”: form a view, examine evidence, use AI, challenge its output and decide what to do.
Check sources, assumptions and calculations. Reflect on where AI helped, what you corrected and what remained your responsibility.
Identify a specific opportunity and compare AI with current practice and simpler alternatives.
Assess expected benefits, costs and risks, including unreliable outputs, data exposure and effects on people.
Define human responsibilities, permissions and safeguards, plus the evidence needed to proceed, change direction or stop.
Use methods and templates to develop your project and a tangible example relevant to your role.
Strengthen your project through peer challenge and midpoint AI-supported feedback before Kate Cooper’s final assessment.
Explain your recommendation, expected value, risks, responsibilities and next step to a workplace decision-maker.
Learn directly from Kate
Kate Cooper
Financial Services CEO. Former OKX Australia CEO. Leadership at NAB and Westpac.
Who this course is for
Product, commercial and client professionals in financial services who want to use AI to improve products, services and customer outcomes.
Operations, technology and people professionals in financial services who want to improve processes and prepare teams to work with AI.
Leaders and advisers in risk, legal, compliance and governance who need to evaluate AI proposals, suppliers and safeguards in finance.
What's included
Live sessions
Learn directly from Kate Cooper in a real-time, interactive format.
First access to Intelligence Meets Money
Receive Kate Cooper’s new publication on AI, people and financial services, released first to course participants. Use its research, findings and practical methods to inform your exercises and AI impact project. Access is planned for 16 November 2026.
AI impact project toolkit
Build your project with guides, templates, worked examples and an evidence record. Improve a process, design a product or service, assess risk, evaluate a supplier or prepare a team. Use your own workplace situation or a supplied fictional case, with an equivalent project option if needed.
Financial-services case files
Explore documented cases across retail and wholesale finance. Examine evidence, competing priorities and decisions, then consider clearly labelled 2027 scenarios. Revisit your initial assessment as new information emerges and compare your reasoning and confidence at the end of the course.
Your AI practice companion
Practise with adaptable prompts, worked examples and checking criteria drawn from Kate’s own AI working practices. Think first, provide context, challenge assumptions and verify outputs. Use public or fictional material with an employer-approved assistant for workplace tasks. No paid AI account is required.
Feedback and personal assessment by Kate Cooper
Receive AI-supported midpoint feedback to improve your proposal, followed by a final assessment personally reviewed by Kate Cooper and returned by 15 December 2026. Kate checks the AI-drafted feedback against your evidence and context and makes the assessment decision. You see AI’s contribution and her reasoning.
Structured peer learning and challenge
Learn from other financial-services professionals and test your project with a peer in Workshop 3. Question the problem, evidence, assumptions, risks and next step, then use the challenge to improve your work. A guided self-check is available if you miss the session. Sharing in the project gallery is optional.
Certificate of completion and CPD evidence
On successful completion of the course and final assessment, receive an HW.I certificate recording learning dates, completed hours by session and outcomes. Use it to support your CPD record, subject to your professional body’s requirements. Financial advisers should confirm qualifying activities and hours with their AFS licensee.
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Course syllabus
3 live sessions • 2 lessons
Week 1
Nov
20
AI, customers and the future of financial services
Week 2
Nov
27
AI, people and the changing financial workplace
Schedule
Live sessions
6 hrs
Three two-hour workshops: 20 November, 27 November and 4 December 2026, 11 am to 1 pm Sydney (AEDT). Materials open 16 November; publication access planned that day. Midpoint proposal due 3 December; AI-supported feedback before Workshop 3 on 4 December. Peer challenge on 4 December. Final proposal due 11 December; Kate's assessment returned by 15 December.
Fri, Nov 20
12:00 AM—2:00 AM (UTC)
Fri, Nov 27
12:00 AM—2:00 AM (UTC)
Fri, Dec 4
12:00 AM—2:00 AM (UTC)
Projects
4-6 hrs
Approximate total for developing your artefact and proposal, peer challenge and final revision. Four milestones keep the work manageable. Allow extra time for a complex workplace problem; use a supplied fictional case for a bounded route.
Preparation and reading
2 hrs
Approximately two hours across the course for short case preparation, terminology, reflection and the transfer task. Deeper whitepaper reading is optional reference work. Together with six live hours and four to six project/feedback hours, plan for 12–14 hours overall.
Workshop 1: AI, customers and the future of financial services
Understand
Understand the differences between predictive AI, generative AI and agents, their capabilities and limitations. Explore how they could change customer experiences, institutional relationships, products and business models across retail and wholesale finance.
Apply
Examine a financial-services case and complete a bounded AI exercise. Check the evidence, compare your judgement with the AI output and identify an opportunity worth investigating.
Workshop 2: AI, people and the changing financial workplace
Understand
Understand how AI changes tasks, roles and workflows, and why faster tasks do not always produce better outcomes. Explore information quality, permissions, human review and responsibility as AI moves from generating content to taking action.
Apply
Develop a practical example for your project. Explain what AI would do, what people would do and where checks are needed. Strengthen the evidence and assumptions behind your midpoint proposal.
Workshop 3: AI, trust and judgement: making informed decisions for 2027
Understand
Understand how trust in AI is earned through evidence, safeguards and accountability. Explore unsupported outputs, automation bias and delegated authority, and how these affect decisions in financial services. Consider clearly labelled 2027 scenarios and where human judgement remains essential.
Apply
Challenge your project with a peer, testing its evidence, assumptions, risks and alternatives. Make a reasoned recommendation: proceed to a bounded test, change the approach or decide against using AI. A guided self-check is available if you miss the session or no peer is available.
Independent project and final revision
Consolidate your understanding through a new-case exercise and revisit your initial judgement and confidence. Use feedback to refine your proposal and explain what you changed and why. Submit your final proposal and supporting evidence by 11 December. Kate’s personal assessment is returned by 15 December. Sharing in the project gallery is optional.
Frequently asked questions
Your AI impact project
Turn your learning into a practical opportunity to make a difference at work.
Choose a challenge that matters to you and explore how AI could help. Throughout the course, test your assumptions, assess the benefits and risks, and develop a clear next step you can take back to your workplace.
Leave with a clear account of the opportunity, its potential value, the risks and your recommended next step.
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