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AI for finance training
AI training for finance teams
AI training for finance teams is practical training for in-house finance functions, covering FP&A, accounts payable and receivable, and month-end close, with controls and an audit trail built in from the start.
It is for finance teams inside a business. Accountancy practices serving clients have different confidentiality and professional standards questions; see AI training for accountants for that. This is about training people, not training models on your ledger.
Leave with agreed finance use cases, the control and review step for each, and clear rules on what data goes where.
ACCA says finance professionals need to understand the capabilities, limitations and potential applications of AI, and have a crucial role in steering ethical and responsible adoption. ACCA: Digital Horizons ↗
On 26 June 2025 the FRC published its first guidance on AI in audit, including principles for proportionate documentation of AI tools; it is not prescriptive and introduces no new regulatory requirements. FRC: guidance on the uses of AI in audit ↗
New to the topic? Read the background on artificial intelligence.
AI for finance training
what does AI training for finance teams cover?
FP&A narrative, AP/AR communication, month-end support and spreadsheet work, each with a review step.
FP&A teams use assistants for first drafts of variance commentary, board-pack narrative and scenario write-ups that an analyst then checks against the numbers.
AP and AR teams draft supplier and customer correspondence, summarise disputes and prepare process notes from facts the team supplies.
At month-end, assistants can help draft journal descriptions, reconciliation notes and checklists, but posting and approval stay in the finance system under existing controls.
AI training for finance teams also covers spreadsheet formulas and macros, where the reviewer tests the result rather than trusting the explanation.
Draft commentary, check the numbers.
Correspondence from supplied facts.
Posting stays under existing controls.
AI for finance training
how is in-house finance different from an accountancy practice?
In-house teams handle their employer's data and internal controls; practices handle client data under professional standards.
An in-house team answers to the CFO, internal controls and the external auditor. A practice answers to clients and to its professional body's standards.
That changes the use cases and the risks. For practice-side guidance see AI for accountants; this page is for the in-house function.
Many in-house staff are ICAEW or ACCA qualified. ACCA highlights the crucial role of finance professionals in steering ethical and responsible AI adoption.
AI training for finance teams uses your own close calendar, reporting pack and systems as the material.
AI for finance training
what controls and audit trail should finance teams keep?
Record where AI was used, who reviewed it and which source it was checked against.
Finance outputs feed decisions and audited figures, so the review step must be visible. A simple log noting the task, the tool and the reviewer is often enough to start.
The FRC's guidance on AI in audit is written for auditors, but it signals that auditors will expect proportionate, appropriate documentation of tools that use AI.
Segregation of duties still applies: the person who drafts with AI should not be the only person who approves the output.
We help teams fit these steps into existing controls rather than invent a parallel process.
| Check | What to establish |
|---|---|
| 1 | Log task, tool and reviewer. |
| 2 | Keep segregation of duties. |
| 3 | Expect auditors to ask about AI use. |
AI for finance training
what data handling rules apply?
Use approved enterprise accounts and treat financial and personal data as confidential.
Finance data is often commercially sensitive and sometimes includes personal data, such as payroll or customer details.
Check what your licence does with prompts. For example, Microsoft states that prompts, responses and data accessed through Microsoft Graph are not used to train foundation models in Microsoft Copilot.
The ICO's AI and data protection guidance applies wherever personal data is involved; the ICO notes it is under review.
AI training for finance teams covers what not to paste, which tool to use for which data, and how to anonymise a sample.
AI for finance training
how do formats work for finance teams?
A workshop around the close calendar, a short programme and follow-up clinics.
We schedule around month-end. A first workshop covers tools, verification, data and policy using your own reporting examples.
A short programme follows, with each sub-team testing one use case and bringing results to review.
Clinics after quarter-end catch issues that appear under real deadline pressure. See AI training for teams for the general structure.
Free pre-work is available: ICAEW offers its members up to eight hours of free e-learning on generative AI fundamentals in finance.
Schedule around close.
One use case per sub-team.
Clinics after quarter-end.
AI for finance training
what does predeploy not do in AI training for finance teams?
We do not certify staff, give audit or tax advice, or promise faster close.
predeploy does not issue accredited qualifications, does not promise time savings or exam outcomes, and does not train AI models on your data; the training is for people.
We do not replace your auditor, controls owner or tax adviser, and we do not claim any reduction in close days.
Where a team wants a workflow built rather than skills, that is consulting or embedded delivery, not training. See AI consultancy.
For a wider programme see AI training for employees, or contact us.
No accreditation.
No audit or tax advice.
No promised close-time reduction.
Make the decision concrete
What does the current task cost in time?
Choose a task connected to ai training for finance teams and enter its current workload. This gives you a baseline to discuss before buying training or commissioning a build.
A baseline is useful even if you decide to keep the process manual. Measure the work that matters, then compare quality and review effort as well as time.
Assess the workflow before changing it, or plan business AI training if the gap is how people use existing tools.
Your current workload
Estimate how much time the existing task takes before discussing ai training for finance teams.
16 hours per week
People × minutes × weekly tasks ÷ 60. A workload baseline from your inputs, not a prediction of AI time savings. Include review and support time when comparing a proposed workflow.
A useful AI investment leaves your team able to do the work and judge the result.
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Where should you go next?
Use the overview for context, then follow the pages that match your delivery decision.
See our ai training service or read the ai for accountants overview.
FAQ
Questions, answered
Evidence and further reading
Which sources support the guidance?
Source links support the specific background described here. The practical checklists and illustrative examples are predeploy’s guidance.
ACCA says finance professionals need to understand the capabilities, limitations and potential applications of AI, and have a crucial role in steering ethical and responsible adoption. ACCA: Digital Horizons ↗
On 26 June 2025 the FRC published its first guidance on AI in audit, including principles for proportionate documentation of AI tools; it is not prescriptive and introduces no new regulatory requirements. FRC: guidance on the uses of AI in audit ↗
ICAEW members can access up to eight hours of free e-learning giving a practical overview of generative AI and its application in finance. ICAEW: accountants' GenAI skills to get boost with ICAEW training ↗
Microsoft states that prompts, responses and Graph data are not used to train the foundation models. Microsoft Learn: Copilot data, privacy and security ↗
ICO guidance covers AI accountability, transparency and fairness; the guidance is currently under review. ICO: AI and data protection guidance ↗
Background reading on the broader field of artificial intelligence. Wikipedia: artificial intelligence ↗
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