Provision Matrix
Define aging buckets, enter gross carrying amounts and historical loss rates. Per IFRS 9.B5.5.35.
Forward-Looking Adjustment
Required by IFRS 9.5.5.17. Purely historical rates are not IFRS 9 compliant.
Advanced Features
Optional: probability-weighted scenarios, movement schedule, specific assessment, and entity details.
IFRS 9 ECL Audit Working Paper Template — free PDF
Practical audit guide covering the simplified approach provision matrix methodology, forward-looking adjustment documentation template, probability-weighted scenario framework, IFRS 7.35H movement schedule template, common ISA 540 findings on ECL estimates, and industry benchmark loss rates for 12 sectors.
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IFRS 9 Expected Credit Losses for Technology
Technology companies present a distinctive IFRS 9 ECL profile that varies dramatically depending on the business model. SaaS (Software-as-a-Service) companies with subscription-based revenue have highly predictable, recurring receivables with relatively low default rates. Enterprise software and services companies with project-based billing may have concentrated, high-value receivables with payment terms of 60–90 days. Technology companies selling to startup customers face elevated credit risk because early-stage companies have high failure rates. The key challenge for technology entity ECL estimates is appropriately segmenting the receivable portfolio to reflect these different risk profiles — a blanket loss rate applied to all technology receivables will not accurately capture the risk differential between a subscription receivable from a Fortune 500 customer and a project receivable from a pre-revenue startup.
Receivable Characteristics — Technology
SaaS subscription receivables are the lowest-risk category within technology — they are typically small, recurring, and subject to automated payment processing with dunning procedures that quickly identify non-payers. Enterprise project receivables represent higher individual values with longer payment cycles and are subject to acceptance testing, change orders, and milestone disputes. Channel partner and reseller receivables carry intermediary credit risk. Technology companies with significant hardware components may have receivables related to equipment sales with different risk profiles from software receivables. Deferred revenue adjustments and contract asset balances under IFRS 15 interact with the receivable analysis and must be carefully classified to determine IFRS 9 scope.
Forward-Looking Factors
The technology sector is uniquely sensitive to venture capital funding cycles. When VC funding contracts, startup customers face cash flow pressure that directly increases default risk on technology vendor receivables. Enterprise IT spending forecasts from analysts (Gartner, IDC) provide sector-specific forward-looking data. Interest rate environments affect the discount rate applied to future subscription revenues and indirectly affect customer payment behaviour. Technology-specific indicators include SaaS churn rates (a leading indicator of payment default for subscription receivables) and technology sector layoff announcements (signalling client budget cuts).
Key forward-looking indicators for technology:
- Technology sector VC funding trends
- Enterprise IT spending forecasts
- Startup failure rates by stage
- SaaS churn rate benchmarks
- Technology sector employment data
Regulatory and Audit Context
Auditors of technology companies should evaluate the appropriateness of receivable segmentation and challenge whether a single provision matrix adequately captures the risk differential across customer types. For technology companies with significant revenue from IFRS 15 contracts, the interaction between contract assets and trade receivables is an audit focus area — contract assets require ECL assessment under IFRS 9 if the entity has an unconditional right to consideration. Common findings include: failure to separately assess startup customer receivables (which may have loss rates 3–5× higher than enterprise customers), inadequate historical data for new product lines or market segments, and insufficient forward-looking adjustment during technology sector downturns.
Technology companies with IPO or funding round receivables (amounts due from investors) should classify these separately from trade receivables and assess credit risk based on the investor's financial position and commitment terms.
Worked Example — CloudTech Solutions B.V.
CloudTech Solutions B.V. provides enterprise SaaS and professional services with €3.2M in trade receivables. The customer base comprises 380 SaaS subscription customers (€2.1M, 66%) and 25 enterprise project customers (€1.1M, 34%). Startup customers (15% of total) carry higher credit risk but are included in the collective matrix given the diversified base. Neutral FL factor reflects stable enterprise IT spending.
| Bucket | Amount | Rate | ECL |
|---|---|---|---|
| Not yet due | €2.100.000 | 0.20% | €4.200 |
| 1–30 days | €560.000 | 0.50% | €2.800 |
| 31–60 days | €280.000 | 1.50% | €4.200 |
| 61–90 days | €140.000 | 5.00% | €7.000 |
| 91–180 days | €80.000 | 12.00% | €9.600 |
| 180+ days | €40.000 | 35.00% | €14.000 |
| Total | €3.200.000 | €41.800 |
Forward-looking adjustment factor: 1× applied to all buckets. Rates shown above are adjusted rates (historical × FL factor).