Finance & Reporting
Regulatory return preparation support
Structure and draft routine regulatory returns — VAT, statistical, Companies House — from financial data. Finance team reviews, validates, and submits. Reduces preparation time for high-frequency filings.
40–60%
reduction in routine filing preparation time
Opportunity assessment
Negligible commercial impact. Saves time at the margins but won't move the needle on revenue or profit.
Relatively straightforward. Off-the-shelf tools handle the core task. Implementation is well-understood with good examples to follow.
Light data requirements. Uses straightforward inputs — documents, product descriptions, customer records — that are usually accessible with minimal prep.
High risk. Significant external exposure or regulatory implications. Requires robust oversight and clear accountability.
Light people impact. A new tool slots into an existing workflow. Minimal training needed. Most people will adapt quickly.
Tooling required
Things to consider
Regulatory submissions have legal consequences for errors — the AI prepares the draft, but a qualified finance professional reviews and submits. This is not an area to automate end-to-end.
The AI needs to be grounded in current regulations — HMRC guidance, for example, changes periodically. Ensure the tool references current requirements, not training data that may be out of date.
Start with the most routine, structured return types (quarterly VAT returns with straightforward accounting) before applying to more complex filings.
Tax law is jurisdiction-specific and nuanced. An LLM that 'confidently' produces a VAT return calculation may be wrong in ways that are difficult to detect without tax expertise. Use AI for the formatting and narrative, not for the underlying tax calculation.
Experiment starter: For the next VAT return, have the AI draft the preparation workings from structured transaction data alongside the finance team's own preparation. Compare the two before submission. Identify any discrepancies and assess whether they reflect AI errors or data issues. A clean comparison justifies using AI for the initial draft on subsequent returns.
Go deeper in the playbook
Section — Opportunity Identification, including the AI Opportunity Assessment ScorecardSection — Risk and GovernanceThis opportunity scores low on risk exposure — read the risk and governance approach before you start.More in Finance & Reporting
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Invoice exception identification
Flag invoices with missing PO references, duplicate submissions, price variances, or unusual payment terms before they reach approval.
Contract data extraction
Extract key commercial terms from supplier and customer contracts — payment terms, renewal dates, liability caps, exclusivity clauses — into structured records.
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