Finance & AI
Finance Expertise Meets AI Capability.
Explore how finance professionals can apply AI thoughtfully, verify outputs carefully and strengthen their work without surrendering professional judgement.
The changing finance landscape
Capability is expanding, accountability is not.
Finance professionals are working across financial expertise, data literacy and increasingly capable digital tools. The opportunity is not to replace judgement, but to make room for better questions, clearer analysis and more deliberate review.
AI can help organise information, draft a first pass or surface patterns for investigation. It cannot independently establish whether the data is complete, the context is appropriate or the conclusion is defensible.
Janet's proposed learning framework
The F.A.C.T. Model.
A practical sequence for connecting financial knowledge with AI application and professional accountability. This is not a certified standard.
Financial Foundations
Understand the objective, principles, data and business context.
AI Application
Choose an appropriate tool and structure the task and inputs.
Critical Verification
Reconcile outputs, test calculations and challenge assumptions.
Trusted Judgement
Interpret verified information and retain accountability.
Practical applications
Four illustrative workflows.
Budget variance analysis
Problem: explain a movement. Input: budget, actuals and period context. AI may draft possible drivers. Verify by reconciling source records and speaking with the budget owner.
Illustrative example · fictional data
Financial report drafting
Problem: create a first commentary. Input: verified figures and approved narrative. AI may structure a draft. Review every claim, period and assumption before circulation.
Illustrative example · fictional data
Spreadsheet interpretation
Problem: find patterns across a table. Input: clean, non-confidential data. AI may suggest questions. Test calculations and confirm the relevant population.
Illustrative example · fictional data
Management commentary and decision support
Problem: prepare decision questions. Input: verified analysis and business context. AI may organise alternatives. A human owns the recommendation and its limitations.
Illustrative example · fictional data
Responsible practice
Trust is built into the workflow.
The F.A.C.T. Model can also inform finance education through practical cases where learners define the task, critique an AI-assisted draft, reconcile it with source material and explain their reasoning.
FREE PROFESSIONAL RESOURCE
The Future-Ready Finance Professional's AI Toolkit
A practical starter kit for identifying appropriate AI use cases, structuring useful prompts and checking outputs without compromising financial accuracy or confidentiality.
Get the Free Toolkit