FRS 102: What good looks like

Part 3 of 3. This is the final post in a three-part series on the amended FRS 102 Section 20. Part 1 covers what changes and why it matters. Part 2 walks through the step-by-step transition guide.

If Part 1 explained what is changing under the amended FRS 102 Section 20, and Part 2 gave you a step-by-step transition plan, this post covers what a well-run FRS 102 lease process actually looks like in practice. Which judgement calls matter most, where the work is automated, and what auditors look for when the evidence is in good order.

The principle: automate the calculation, surface the judgement

Dedicated lease accounting software reduces the manual burden and makes the process easier to evidence. But the real value is not just calculation. It is moving the hard work to the right point in the process.

LeaseAccounting.app is built around that idea. The system does the work that can be automated, then directs the user to the parts that require business judgement.

The calculation engine handles the math: discounting cash flows, building the amortisation schedule, generating journal entries, producing movement tables. AI assistance handles structured extraction: reading the contract PDF and pre-filling the lease setup form.

The finance team’s role is the judgement layer:

• whether an extension or break clause changes the lease term
• whether the payment schedule has been amended by indexation or rent review
• whether the borrowing profile has changed materially since the last constructed rate
• whether a service component should be unbundled from the lease

These are the calls a system cannot make on its own. They depend on facts only the business knows. Software is most useful when it removes the busywork around them, so the time spent on judgement is time that actually matters.

Where AI extraction earns its keep

AI lease extraction reads the lease agreement and pre-fills the lease setup form. It extracts key dates, payment terms, lease components, indexation clauses, rent reviews, options and other relevant terms from the document. The user reviews and confirms the extracted data instead of manually entering the lease from scratch.

This is more than basic OCR. OCR turns a document into text. AI lease extraction turns a lease agreement into structured accounting inputs, then highlights the areas where the user needs to make or confirm a judgement.

The practical effect is that the finance team spends almost no time on data entry, and most of their time on the few clauses that affect the accounting. For property leases, that usually means rent review mechanics, extension options, and break clauses. For vehicle leases, it usually means residual value guarantees, mileage limits, and end-of-term options.

The discount rate, documented from day one

The Discount Rate Advisor proposes and documents the discount rate, with a memo the finance team reviews and approves. After the initial setup, the methodology can be applied consistently to future leases and lease changes.

The benefit is consistency rather than just speed. A documented methodology applied across the portfolio gives auditors something they can review once, then verify is being applied as designed. Compare that with a spreadsheet world where each lease has a slightly different rate, sourced from a different bank quote, with documentation built retrospectively for the audit. The first is reviewable. The second is fragile.

Journal entries, traceable from contract through to GL

Journal entries are generated directly from the lease calculation. The system produces the recurring entries for depreciation, interest, lease payments and liability unwind, so the finance team does not need to maintain a separate spreadsheet model to support monthly or quarterly accounting.

For audit purposes, the value is the chain of evidence: the lease contract uploaded into the system, the extracted terms confirmed by the user, the discount rate constructed using the documented methodology, the schedule generated by the calculation engine, and the journal entries produced from that schedule. Every step is traceable, with no detached spreadsheet in the middle.

What the auditor actually receives

In a traditional process, auditors often receive a folder of lease agreements, spreadsheets and PDF schedules, then spend time working out what is missing, what changed and which assumptions need support. That creates back-and-forth, slows the audit and drains finance team time.

The alternative is not a cleverer folder. It is that the schedules, journals and disclosure tables all derive from the same lease data, so what the auditor receives already reconciles. Lease schedules, journal entries, movement tables and the maturity analysis export to Excel and PDF for any period and any subset of the portfolio.

For teams preparing for the 31 December 2026 year end, the benefit is consistency. The same lease data, rate methodology and calculation logic can be applied across the portfolio, with a clear trail from contract through to journal entries and disclosure schedules.

What good looks like, in one paragraph

A well-run FRS 102 lease process has a complete lease register, a documented discount rate methodology, lease terms confirmed against the contract, journal entries generated from the same data that produces the schedules, and disclosure tables that reconcile to the ledger without a manual bridge. The finance team spends its time on judgement calls (extension options, modifications, indexation, covenants) rather than on rebuilding spreadsheets every quarter. The auditor reviews structured output, not assembled artefacts.

The philosophy is simple: automate the work that software can do, surface the judgement calls that only the business can make, and get the documentation in order before the audit begins.

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Back to Part 1: What changes and why it matters · Part 2: The transition guide.

Book a demo and see the full FRS 102 workflow: the discount rate memo, lease schedules, journal entries and disclosure support.