For any finance team dealing with lease portfolios, IFRS 16 has been a persistent source of friction since its effective date. The standard requires lessees to recognize nearly all leases on the balance sheet, which sounds straightforward until you face the reality: extracting lease data from fragmented contracts, determining incremental borrowing rates, calculating right-of-use (ROU) assets and lease liabilities, and then tracking modifications, reassessments, and termination events across hundreds or thousands of leases. Many controllers still rely on Excel-heavy workarounds or expensive third-party software that requires manual data entry. The result is a process prone to errors, audit scrutiny, and wasted hours that could be spent on strategic analysis.
AI, specifically large language models like Claude, changes this calculus entirely. By ingesting lease contracts, amendments, and financial data, an AI tool can parse complex lease terms, apply IFRS 16 logic, and produce journal entries, disclosure schedules, and amortization tables in minutes. The key is not just automation—it’s the ability to handle nuance. Variable lease payments, residual value guarantees, purchase options, and lease-versus-service determinations all require judgment. A well-prompted AI can replicate that judgment consistently, reducing the risk of restatements and freeing your team to focus on portfolio optimization.
This post provides two ready-to-use prompts that turn Claude into an IFRS 16 lease accounting engine. Use them to accelerate month-end close, prepare for audits, or run what-if scenarios on lease modifications. Each prompt follows the structured “Anatomy of a Prompt” format, ensuring you get reliable, auditable outputs without the guesswork.
Why Most Lease Accounting Automation Fails
Before diving into the prompts, it’s worth understanding why many automation attempts fall short. Traditional software solutions require you to map every data field manually—lease start date, lease term, payment schedule, discount rate—and then run a calculation engine. If your lease agreements include non-standard clauses like CPI-based rent escalations or early termination options, the software often cannot interpret them without human override. AI solves this by reading the contract language directly and extracting the relevant financial terms, including implicit rates when available, or guiding you to determine the incremental borrowing rate based on credit profile and lease term. The prompts below are designed to capture this full workflow.
First, read these files completely before responding:
[lease_contracts.pdf] — Contains 50 lease agreements with varying terms, rent escalation clauses, and renewal options
[company_credit_rating.md] — Details our incremental borrowing rate curve by lease term (1-20 years)
[ifrs16_standard_excerpts.md] — Key paragraphs from IFRS 16 on initial measurement, subsequent measurement, and disclosure requirements
Here is a reference for what I want to achieve:
I have attached a sample Excel file showing the correct amortization table format for a single lease, including columns for period, opening liability, interest expense, lease payment, closing liability, and ROU asset depreciation.
Here’s what makes this reference work:
– The table uses a consistent 12-month fiscal year aligned with our reporting calendar
– Interest is calculated using the effective interest method with monthly compounding
– Depreciation is straight-line over the lease term, not the asset’s useful life
– All figures are rounded to two decimal places
– The table includes a separate section for variable lease payments that are not included in the liability
Here’s what I need for my version / SUCCESS BRIEF:
Type of output + length: A single consolidated Excel file with one sheet per lease showing the amortization table for the full lease term, plus a summary sheet with journal entry totals by month for the current fiscal year
Recipient’s reaction: The controller should be able to post the journal entries directly from this output without manual adjustments
Does NOT sound like: A generic template — each lease must reflect its specific payment schedule, escalation terms, and renewal periods as stated in the contract
Success means: The totals on the summary sheet match my manual calculation for three sample leases within 0.01% tolerance
My context file contains my standards, constraints, audience. Read it fully before starting.
DO NOT start executing yet. Ask clarifying questions first.
Give me your execution plan (5 steps max) before you begin.
This first prompt is designed for initial recognition and measurement of a lease portfolio. Notice how it specifies the input files, the reference format, and the success criteria with measurable tolerance. When you run this prompt with Claude, it will first ask clarifying questions—for example, whether you want to include initial direct costs or whether any leases have residual value guarantees. Answer those questions, and Claude will produce a structured output that your team can verify against manual calculations for a subset of leases. The key advantage is that the AI handles the 47 remaining leases simultaneously, applying the same logic consistently.
Handling Lease Modifications and Reassessments
One of the most time-consuming aspects of IFRS 16 compliance is tracking modifications. A lease modification occurs when the scope or consideration changes—for example, extending the term, adding additional space, or renegotiating rent. Under IFRS 16, a modification must be accounted for as a separate lease if it grants an additional right-of-use at a price commensurate with the stand-alone price. Otherwise, it is a remeasurement of the existing lease liability, adjusted by the revised lease payments discounted at the revised discount rate. AI can handle this logic by comparing the original lease terms with the amendment language and determining the correct accounting treatment.
First, read these files completely before responding:
[original_lease_data.csv] — Contains original measurement details for all 50 leases (lease ID, commencement date, original term, discount rate, initial liability, initial ROU asset)
[modification_amendments.pdf] — 12 amendment letters with revised terms, effective dates, and any changes to rent or term
[ifrs16_modification_guidance.md] — Excerpts from IFRS 16 paragraphs 44-46 on modification accounting, including the distinction between separate lease and remeasurement
Here is a reference for what I want to achieve:
I have attached a sample journal entry template showing the correct debit/credit structure for a remeasurement where the lease term increased by 2 years and rent decreased by 5%. The template shows the adjustment to the lease liability, ROU asset, and any gain or loss recognized in profit or loss.
Here’s what makes this reference work:
– The journal entry clearly separates the change in liability from the change in ROU asset
– The gain or loss is calculated as the difference between the adjustment to the liability and the adjustment to the ROU asset
– The revised discount rate is applied from the modification effective date, not the original commencement date
– All amounts are shown with a clear explanation of the calculation methodology
Here’s what I need for my version / SUCCESS BRIEF:
Type of output + length: A single table with 12 rows (one per modification) showing: lease ID, modification type (separate lease vs. remeasurement), effective date, original liability, revised liability, adjustment amount, original ROU asset, revised ROU asset, gain/loss recognized, and a short explanation
Recipient’s reaction: The auditor should be able to trace each adjustment back to the amendment letter and the original data
Does NOT sound like: A generic adjustment — each modification must reflect the specific terms of the amendment, including any changes to the discount rate
Success means: The total adjustment amount matches the sum of individual remeasurements within 0.5% of a manual review
My context file contains my standards, constraints, audience. Read it fully before starting.
DO NOT start executing yet. Ask clarifying questions first.
Give me your execution plan (5 steps max) before you begin.
This second prompt addresses the ongoing maintenance of lease accounting under IFRS 16. Many finance teams struggle with modifications because they require re-running calculations from the modification date forward, not from the beginning of the lease. The prompt structures this by asking Claude to first classify each modification (separate lease or remeasurement), then calculate the revised liability using the updated terms and discount rate, and finally produce the journal entry. The success criterion of auditor traceability is deliberate—when an AI tool can produce outputs that an external auditor can verify against source documents, you have achieved true automation.
For practical use, start with the first prompt on a small portfolio of 5 to 10 leases before scaling to 50 or 100. Verify the outputs manually for the first few leases, then use the AI’s consistency to trust the remainder. For modifications, maintain a log of all amendments in a structured format (CSV or spreadsheet) so that the AI can reference original data without re-reading entire contracts. Over time, you can build a library of prompts for different scenarios: initial recognition, modifications, impairment testing, sublease accounting, and sale-and-leaseback transactions. The investment in prompt engineering pays dividends every reporting period.
Try running the first prompt this week with a sample of your lease portfolio. Even if you only have three leases to test, the exercise will reveal gaps in your data extraction process and help you refine the prompt for your specific lease types. Once you have a working prompt, schedule a monthly run to keep your IFRS 16 calculations current without manual effort.
Published on 30 May 2026 on growwithgpt.com