Key Takeaway
A SR&ED claim can be fully eligible and still get cut if wage allocations, year-over-year jumps or technical narratives don't hold up. Risk scoring before you file finds those weak spots while you can still fix them.
Eligible work and a defensible claim are two different things. You can qualify on every criterion and still lose 30% in review, because the reviewer isn’t asking whether the work counted. They’re asking whether you can prove it.
Risk scoring is how you find that out before you file instead of after.
This article covers the red flags reviewers look for, how to tell a low-risk signal from a high-risk one, and how to stress-test your claim while you can still change it.
What is SR&ED claim risk scoring?
It’s an assessment of how likely your claim is to be reviewed, reduced or adjusted, based on technical, financial and behavioural signals.
The distinction matters:
- Eligibility asks whether the work qualifies.
- Risk scoring asks how defensible it looks under scrutiny.
A claim can pass the first and fail the second. High wage allocations, a sharp year-over-year cost jump, or narratives that shift between filings will all raise exposure on work that genuinely qualified.
Screening has to be selective, because the program is big. More than 20,000 claimants file SR&ED claims each year, and over $3 billion in incentives goes out annually. CRA assesses risk before and after filing to decide where to spend review resources.
Why do SR&ED claims get flagged?
Because the CRA screens on patterns. It weighs three categories of signal: technical narratives, financial allocations, and filing behaviour.
Three things reliably raise the odds. Project descriptions that never name a specific uncertainty. Engineering wages allocated at 60-80% without clear linkage to experimental work. Expenditures that spike sharply against last year.
Size plays a part too. Small businesses file 64% of SR&ED claims each year. First-time filers and fast-growing claims draw closer attention, especially when the methodology or scope moves between years.
How does the CRA assess risk?
Through structured screening that looks at patterns, variances and internal consistency before anyone commits review time. Most of your exposure is visible before an auditor ever calls.
Desk screening and pre-review assessment
Every claim gets desk screening first. That’s a check on completeness, internal alignment and baseline compliance.
Claims move to pre-review risk assessment when something looks off: wage allocations out of proportion, unclear project narratives, or cost growth that diverges from prior filings.
At that point the question changes. CRA stops checking whether the claim is complete and starts estimating which parts are likely to be adjusted.
Historical filings and claim deltas
Your past filings set a baseline, and big changes against that baseline get noticed.
A 40% year-over-year expenditure increase changes your statistical profile. So does a sudden proxy shift, or a run of amendments. Patterns across years drive how review resources get prioritized.
Technical and financial lenses
Reviewers apply two lenses, and they’re independent.
- Technical risk: does the work actually meet the SR&ED criteria? Advancement, systematic investigation, evidence of experimentation.
- Financial risk: are the expenditures reasonable, eligible and substantiated by records, time tracking and cost allocations?
A claim can score clean on one and badly on the other. Test both.
What technical red flags raise risk?
The technical section usually decides whether anyone looks harder. If uncertainty and experimentation aren’t clear there, reviewers arrive at the financials already skeptical.
Vague, outcome-driven narratives
Narratives built around results are the most common problem. “Improved latency.” “Better scalability.” “Enhanced UX.” Those describe outcomes, not the technical problem you were solving.
If it reads like a release summary, reviewers will wonder whether real uncertainty ever existed. Saying API performance improved 30% means little on its own. What the narrative needs is the constraint, the approaches you tried, and why standard methods came up short.
No clearly stated technological uncertainty
Uncertainty has to be specific and bounded. “We weren’t sure if it would work” says nothing.
Name the limitation instead. Concurrency behaviour under unpredictable load. Memory fragmentation under a new runtime. Without that precision, routine engineering reads as misclassified advancement.
No evidence of systematic investigation
Systematic investigation leaves a trail: hypothesis, experiment, measurement, iteration. When there are no test logs, branch comparisons, failed prototype commits or benchmarks, credibility drops.
Data based on CRA disclosures shows 367 to 1,176 SR&ED claims are fully denied each year, with denied expenditures of roughly $29M to $76M annually. Most of those denials come down to experimentation that couldn’t be demonstrated from records.
| Signal present | Risk impact |
|---|---|
| Documented failed tests | Lower |
| Iteration without logs | Higher |
| Clear hypothesis tracking | Lower |
| Post-hoc summaries only | Higher |
Work that looks like routine development
Feature extensions, refactoring and version upgrades read as standard development. If the work mostly implemented known frameworks or followed vendor guidance, risk stays high even where some of it qualifies.
Separate the experimental work from the implementation and configuration around it. Claiming them together drags the whole project down.
Scope that shifts between years
Reviewers notice when a project description changes. If Year 1 describes platform architecture research and Year 2 reframes similar work as algorithm development with no clear continuity, that inconsistency invites a look. Technical evolution that reads as continuous is much easier to defend.
What documentation red flags matter?
Reviewers judge credibility on timestamps, linkage and internal consistency. Technically valid work still gets reduced when the records behind it are weak.
Badly reconstructed timesheets
Reconstruction doesn’t automatically kill a claim. Bad reconstruction does: approximate hour estimates, spreadsheets with no timestamps, allocations nobody can verify.
CRA guidance favours contemporaneous documentation that’s dated, signed and specific to the work. Courts have noted that without detailed contemporaneous experiment records, a claimant is in a “precarious position.”
The real test is whether the documentation looks reliable. Logs rebuilt months later with no system metadata and no manager sign-off invite questions.
- Strong reconstruction: version history, payroll cross-reference, supervisory approval.
- Weak reconstruction: retroactive spreadsheets, rounded estimates with no source, missing metadata, allocations that can’t be verified independently.
| Record type | Risk signal |
|---|---|
| Timestamped tool exports | Lower |
| Rounded weekly estimates | Higher |
| Manager-approved allocations | Lower |
| Unverified spreadsheets | Higher |
No link between work and claimed activity
Traceability is the whole game. If payroll shows 70% allocation to SR&ED but the Jira tickets don’t map to experimental tasks, the allocation looks unreasonable no matter how true it is.
Every wage claim should tie to a project. Every project should tie to identifiable experimental steps.
Generic supporting artifacts
A ticket titled “improve performance” and a commit titled “refactor code” prove nothing. Records need to show hypotheses, configuration changes, failed test results and measurements. Without those, routine engineering and claimed experimentation look identical.
Documentation created at filing time
Assemble everything in March and the gaps show. Records cluster around the submission deadline instead of spreading across the development timeline, and reviewers read that pattern easily.
Documentation built as you go reflects how the work actually unfolded. That’s the difference between evidence and justification.
What financial red flags matter?
A technically sound claim still gets tested on proportionality, consistency and traceability across payroll, overhead and scope.
Sudden year-over-year spikes
Growth without an operational explanation raises questions. A claim going from $900K to $1.6M while headcount and project count hold steady stands out immediately in screening.
Historical reviews indicate nearly 60% of site-reviewed claims are reduced. Not all of that is financial, but avoidable allocation problems are a bad reason to end up in that group.
The increase may be entirely justified: new hires, expanded experimentation, an infrastructure shift. Document the driver and it’s defensible. Leave it unexplained and it looks unsupported.
| Scenario | Risk signal |
|---|---|
| Growth aligned with new R&D teams | Lower |
| Large increase driven by higher allocations only | Higher |
| Expanded experimentation scope documented | Lower |
| No change in project volume | Higher |
Weak linkage between wages and SR&ED work
Allocation methods have to be supportable. If 75% of an engineering team’s wages go to SR&ED while ticket activity shows a mix of maintenance and release work, the allocation won’t hold.
Map allocations to specific experimental efforts, not to general product development. That’s what connects the dollars to the work.
Aggressive overhead or proxy allocations
Proxy and traditional method calculations have to reflect how the business actually runs. Switching methodology without showing that overhead exceeded proxy assumptions draws questions, and high overhead percentages with no cost records behind them get reduced.
Financial scale that doesn’t match technical depth
The size of the claim should match the size of the experimentation. A $2M wage claim attached to limited hypothesis testing looks disproportionate, and disproportionate claims are hard to defend.
Check the ratio before you file. If it looks odd to you, it will look odd to a reviewer.
What filing-pattern red flags matter?
CRA evaluates patterns, so consistency is itself a risk control. Volatility attracts attention.
Late filings and repeated amendments
A claim prepared at the last minute tends to look like one. If you consistently file close to statutory deadlines or revise months later, reviewers question whether the internal process is reliable.
Individual amendments are fine and often legitimate. A run of them reads as instability. Raising wage allocations after an internal review, without changing the time-tracking method underneath, looks like a correction rather than a process.
Methodology that changes year to year
Changes need explanations. Moving from proxy to traditional overhead, changing wage allocation logic, or switching time-tracking methods all invite scrutiny when nothing documents why.
Year 1 on estimated allocation and Year 2 on detailed tracking is a good change. Say so in writing and it strengthens the claim instead of weakening it.
Narratives rebuilt from memory
Narratives written long after the work lose credibility fast, especially when the technical language drifts toward eligibility wording over time.
Historical data shows 90% of SR&ED claims are accepted as filed, 6% accepted after modification, and 4% denied. In our experience the split tracks documentation practice:
- Narratives leaning on recollection are likelier to be modified.
- Documentation prepared during the year is likelier to be accepted as filed.
Prior adjustments with no process change
Past adjustments follow you. If an earlier review cut wage allocations or challenged your documentation and nothing changed internally, expect the same issues to get attention again.
Fixing the process is also evidence. Adopting detailed time tracking, or aligning technical and financial reporting, shows the controls improved. Without that, the pattern repeats.
Low-risk versus high-risk claims
Both of these claims might qualify. Only one is easy to defend.
| Dimension | Low-risk claim | High-risk claim |
|---|---|---|
| Technical narrative | Specific uncertainty and documented experiments | Outcome-focused summaries |
| Wage allocation | Traceable to project tasks and logs | Broad percentage estimates |
| Year-over-year trend | Gradual growth aligned with hiring or scope | Sudden cost spikes without operational change |
| Documentation timing | Created during development cycles | Compiled at filing time |
| Methodology | Consistent year to year | Frequent shifts without rationale |
| Prior reviews | Adjustments followed by process changes | Repeated issues without remediation |
The difference isn’t eligibility. It’s whether the claim can be defended line by line.
How do you reduce risk before filing?
Four things, and they’re all process rather than paperwork.
Build documentation continuously
Records made during development beat records assembled later, every time. Meeting notes, test results, branch histories and allocation logs should exist because the work happened, not because the deadline arrived.
Scattered information is its own risk. Research found 47% of digital workers struggle to find documents they need, and 36% miss important information because of data overload. When artifacts live in five systems, connecting payroll to project tracking to technical reporting becomes a project of its own.
Align technical and financial narratives early
Get engineering and finance in the same room before filing, not after.
Technical leads describe the uncertainty and the experiments. Finance checks the allocation logic against that. If 65% of engineering wages go to SR&ED, ticket volume and experiment logs should show roughly 65% experimental effort. Reconcile that early and you avoid the disproportionate claim entirely.
Validate eligibility as you go
Scope creeps. Teams gradually widen what they consider eligible without meaning to.
Run internal checkpoints during the year to confirm claimed activities still look like systematic investigation rather than routine implementation. Check hypotheses, results and artifacts against current CRA guidance, and write down the decisions. Those notes are what defend you later.
Stress-test the claim
Read your own claim as a reviewer would. Look at the year-over-year cost change, any methodology shift, the wage allocation percentages and whether the narrative follows on from last year’s.
Then estimate the downside. If allocations were trimmed 20%, what happens to your cash flow? Companies that treat SR&ED as tax optimization get surprised by that number. Companies that treat it as an internal control process don’t.
How Chrono R&D lowers your risk profile
Good risk assessment needs documentation that reflects how the work actually happened. Records generated from engineering systems are grounded in verifiable activity, which is exactly what a reviewer is looking for.
Chrono R&D builds SR&ED documentation from commits, tickets and sprint data. Technical narratives and wage allocations connect to timestamped development records, so there’s no gap between what you reported and what the evidence shows.
Lucius runs inside your existing workflow. It analyzes engineering activity across the year and organizes it into structured documentation aligned to CRA requirements, updating as projects move.
That lowers risk in four specific ways:
- Allocations reflect actual experimental work, because they’re derived from it
- Weak or ambiguous eligibility areas surface before submission
- Documentation gaps stay visible instead of appearing in March
- Technical descriptions and wage claims stay aligned by construction
Screening looks at patterns across years. System-generated records make those patterns consistent, which is the easiest kind of claim to defend.
Want to see your own risk profile before you file? Try Chrono R&D.
FAQs
What makes a SR&ED claim high risk?
Misalignment between the numbers, the technical story and the filing history. Large cost increases with no business change behind them, high wage allocations that don’t match documented engineering activity, and documentation assembled at year-end. Any of those makes review and reduction more likely.
How does the CRA decide which claims to audit?
By pattern. It looks at cost changes, allocation levels, methodology shifts and filing history. Sudden changes, repeated amendments, or prior reductions with no process improvement afterward all increase the odds of detailed review.
Can an eligible claim still be denied?
Yes. Qualifying work isn’t enough if you can’t show how it was carried out and tracked. Without clear uncertainty, visible experimentation steps and cost linkage, a claim can be reduced or denied on technically sound work.
How do I assess my own risk before filing?
Audit it yourself. Do the wage allocations match actual engineering activity? Do cost increases line up with hiring or scope? Is documentation spread across the year or bunched at filing? Would someone outside your team understand the experimentation without you explaining it? Anywhere the answer is unclear, fix it before you submit.