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AI in corporate finance functions
AI in finance
AI in finance, as used on this page, means AI inside an organisation's own finance function: month-end close, financial planning and analysis, accounts payable and receivable, cash and treasury, and forecasting. It is not about retail banking or investing.
It complements AI for accountants, which is written mainly for practices. If you want your team trained on these workflows, go straight to AI training for finance teams.
See where AI genuinely helps an in-house finance team, what controls each use needs, which regulator material is relevant, and how to build the team's skills.
GenAI can help at four stages of period-end close, including high-volume reconciliations and trial balance checks; the close always requires professional oversight. ICAEW: How GenAI can save you time during the accounting cycle ↗
ICAEW guide covering accounting use cases, risks and limitations, prompt engineering, ethics, legal considerations and cyber security. ICAEW: Generative AI guide ↗
New to the topic? Read the background on artificial intelligence.
AI in corporate finance functions
where does AI in finance help an in-house team?
In the high-volume, rules-heavy work around close and payables, and in drafting the narrative that explains the numbers.
The clearest uses are matching and reconciliation, invoice capture and coding in accounts payable, cash application in accounts receivable, variance commentary, and first drafts of management reporting.
ICAEW describes generative AI helping at four stages of period-end close, including high-volume reconciliations that highlight discrepancies and checking whether an uploaded trial balance balances and which accounts look anomalous, while stating that the close will always require professional oversight (ICAEW).
Forecasting and FP&A benefit differently. Machine learning models can produce a statistical baseline, while generative AI helps an analyst explore scenarios, write the narrative and answer questions about the drivers. The judgement about assumptions stays with the team.
Treasury use tends to be narrower: cash positioning, short-term cash forecasting and spotting unusual payments. Anything that moves money needs controls that do not depend on an AI tool behaving.
Close and reconciliations.
AP capture, AR cash application.
Commentary, FP&A scenarios, cash forecasting.
AI in corporate finance functions
what controls does AI in finance need?
The same internal controls as the process it touches, plus clarity on what the AI produced and who approved it.
Segregation of duties does not change because a tool drafted a journal. The preparer and approver must still be different people, and an AI-proposed journal is a proposal.
Payments are the highest-risk area. Supplier bank detail changes and payment approvals should keep independent verification, whatever an AP tool suggests.
For forecasting and reporting, keep a record of which figures came from a model, which assumptions were changed by people, and which narrative was AI-drafted. Boards and auditors will ask.
Data handling matters as much as accuracy. Payroll, supplier and customer data include personal data; read the ICO guidance on AI and data protection and check your vendor's data use terms before connecting ledgers to a general-purpose assistant.
AI in corporate finance functions
do the FCA and Bank of England rules on AI in finance apply to us?
Only if you are a regulated financial services firm; otherwise their material is useful context, not your rulebook.
The FCA says its approach is principles-based and that it does not plan to introduce extra regulations for AI, relying instead on existing frameworks such as the Consumer Duty and accountability rules for senior managers. Those rules bind regulated firms, not a typical company's finance function.
The joint Bank of England and FCA 2024 survey of AI in UK financial services found that optimisation of internal processes was the most common area of AI use among respondents, at 41%, followed by cybersecurity and fraud detection. That is the closest regulated-sector parallel to in-house finance work.
The same survey reported that 84% of firms had an accountable person for their AI framework. The 2026 follow-up survey reports wider use of generative AI, AI-enabled agents and third-party AI, and frameworks such as the NIST AI Risk Management Framework being cited by respondents.
For an unregulated company, the useful takeaway is governance shape: one accountable owner, a register of AI uses, and controls proportionate to risk.
| Check | What to establish |
|---|---|
| 1 | Regulated firms: FCA rules apply. |
| 2 | Others: use the governance pattern. |
| 3 | Name one accountable owner. |
AI in corporate finance functions
how should a finance team start with AI in finance?
Pick one painful, measurable process, record a baseline, and pilot with review built in.
Close is usually the best starting point because it repeats monthly, has clear outputs and a natural review step. Pick one reconciliation or one commentary pack, not the whole close.
Record how long it takes now and where errors occur. Without a baseline you cannot tell whether the pilot helped, and any saving claimed later is a guess.
Run the AI-assisted version in parallel for at least one cycle. Compare outputs line by line and write down where the tool was wrong, not just where it was fast.
Then decide: adopt with a written procedure, adjust, or stop. Stopping is a legitimate result and costs less than a tool nobody trusts.
AI in corporate finance functions
what skills does a finance team need for AI in finance?
Prompting for accuracy, checking output against source data, and knowing what not to paste into a tool.
Most finance staff already have the hardest skill, which is spotting a number that looks wrong. Training should build on that: how to get a tool to show its workings, how to reconcile its output back to the ledger, and when to distrust a fluent answer.
ICAEW's generative AI guide covers use cases, risks and limitations, prompt engineering, ethics and cyber security for accountants, and is a solid foundation for professionally qualified staff.
Spreadsheet work deserves specific attention. Generative tools can write formulas and summarise models, but a plausible formula with the wrong range is still wrong, so test outputs against known answers.
Policy literacy matters too: staff should know which tools are approved, what data classes they may use, and how to record AI use in a reporting pack.
Show workings, then reconcile.
Test formulas against known answers.
Know the approved tools and data rules.
AI in corporate finance functions
how does predeploy help with AI in finance?
By training your finance team on its own workflows and tools, and by helping design and embed the controls.
Our AI training for finance teams uses your close, FP&A and AP/AR processes as the training material, in workshops or a short programme with follow-up. Practices should see AI training for accountants.
Where a team needs more than training, we can help scope a pilot, write the procedure and work alongside staff during the first cycles. That is an embedded delivery engagement, not permanent recruitment.
We train people, not models on your data. We do not provide accreditation, investment or regulatory advice, and we do not promise a saving before measuring your baseline.
If audit readiness is your concern, read AI in audit; for the bookkeeping layer, read AI bookkeeping; and for tool selection, AI tools for accountants.
Training on your real workflows.
Embedded help for pilots.
No guaranteed savings or accreditation.
Make the decision concrete
What does the current task cost in time?
Choose a task connected to ai in finance 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 in finance.
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.
Continue with the right question
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.
GenAI can help at four stages of period-end close, including high-volume reconciliations and trial balance checks; the close always requires professional oversight. ICAEW: How GenAI can save you time during the accounting cycle ↗
ICAEW guide covering accounting use cases, risks and limitations, prompt engineering, ethics, legal considerations and cyber security. ICAEW: Generative AI guide ↗
ICO guidance covers AI accountability, transparency and fairness; the guidance is currently under review. ICO: AI and data protection guidance ↗
FCA approach is principles-based; it does not plan extra AI regulations and relies on existing frameworks such as the Consumer Duty and senior manager accountability. FCA: AI and the FCA: our approach ↗
Optimisation of internal processes is the most common AI use (41% of respondents); 84% of firms reported an accountable person for their AI framework. Bank of England and FCA: AI in UK financial services 2024 ↗
2026 survey reports wider use of generative AI, AI-enabled agents and third-party AI; respondents cite frameworks including the NIST AI RMF. Bank of England and FCA: AI in UK financial services 2026 ↗
Background reading on the broader field of artificial intelligence. Wikipedia: artificial intelligence ↗
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