Assumption of Risk Reduction Calculator
The formula
What the assumption of risk reduction tells you
The 5×5 matrix the risk industry actually uses, for assumption of risk reduction: gross likelihood × impact, discounted by how well your controls actually work.
Calculation: gross = likelihood × impact (1–25); net = gross × (1 − controls). Enter your inputs and every substitution is shown below the result.
Reading the result
Scores rank attention; they do not price losses. Anything 15+ gets a treatment plan with a named owner.
Rules of thumb: Most organisations carry 3–5 reds; more usually means likelihood inflation, not danger.
Limitations and best practice
Control ratings drift optimistic — audit one control’s evidence and re-mark the rest against it.
This calculator gives an estimate based on standard formulas; it is not professional financial, medical, legal or engineering advice. Verify anything you plan to act on with a qualified professional and your own records.
Frequently asked questions
Why discount by controls?
Do these scores compare across teams?
Source & methodology
ISO 31000-aligned qualitative risk matrices.
Assumption of Risk Reduction: the number, the method and the caveats
You are here because you need a specific number, and you want to see how it was produced before you rely on it. There are plenty of pages about Assumption of Risk Reduction; almost none of them show the working. This one does: the formula, every input, a fully computed example, and a sensitivity analysis calculated live from this engine’s own defaults.
There is a habit that separates people who get useful answers from Personal Legal numbers: they decide what question the figure answers before they compute it. Assumption of Risk Reduction answers “Net risk score?” — and nothing else, which is precisely why the result is trustworthy.
Like most useful measures, Assumption of Risk Reduction is a summary, and summaries hide as much as they reveal. The companion outputs and the input table are here to reopen what the headline compressed.
Every figure in the prose is generated by the engine rather than written beside it — the worked example resolves to net risk score = 5.8 / 25 on the default inputs, and the sensitivity section ranks the inputs that move the result — Likelihood (1 = rare … 5 = almost certain) first, at ≈10.0 % of the headline for a ten-percent error either way in that input. Read the sections in order and you will be able to reproduce every figure by hand.
How Assumption of Risk Reduction is computed, line by line
Here is the complete model this tool runs — gross = likelihood × impact (1–25); net = gross × (1 − controls) — with each input explained below. If a competitor’s page gives you a different answer from the same inputs, this is the text that settles which of you is right.
Likelihood (1 = rare … 5 = almost certain) (example value 2) is a numeric input whose unit is fixed by the label; a wrong unit is a wrong answer wearing a plausible disguise. Replace the example with the figure from your own records — the tool recomputes instantly and the printed steps follow your numbers.
Impact (1 = negligible … 5 = severe) (example value 5) is a numeric input whose unit is fixed by the label; a wrong unit is a wrong answer wearing a plausible disguise. If you are unsure what belongs in this field, look at the printed steps: the substitution shows exactly how the engine uses it.
Control effectiveness (0–100 %) (example value 42) is a measured number in the unit named by the label; the formula does not convert for you. Check the convention first: if the source says “6.5 % per year”, type 6.5 and let the formula do the rest.
Assumption of Risk Reduction on the example inputs: the complete substitution
The tool prints its own working; this is the same arithmetic on the example values, reproduced so you can follow it by hand. Starting figures: Likelihood (1 = rare … 5 = almost certain), Impact (1 = negligible … 5 = severe), Control effectiveness (0–100 %). Run to the end, the net risk score resolves to 5.8 / 25 — Low-moderate — monitor at cadence on the bands this tool uses.
- Gross = 2 × 5 = 10
- Net = 10 × (1 − 42 %) = 5.8
Gross score — 10 / 25. Read next to the headline, this is the figure that shows the shape of the trade-off.
Control credit — 4.2 pts. If your inputs change, this is usually the first companion figure to move.
Likelihood × impact band — Mixed. Read next to the headline, this is the figure that shows the shape of the trade-off.
Keep this worked example open while you enter your own figures: matching the shape of the substitution is the fastest way to spot a unit or convention mistake.
How far Assumption of Risk Reduction can reasonably move
Read this table as the practical error bar on the headline figure. It answers the question people actually have after seeing a single number: how wrong could this be if the main input is off by ten or twenty percent?
| Likelihood (1 = rare … 5 = almost certain) setting | Net risk score |
|---|---|
| 80 % of the example value | 4.6 / 25 |
| 90 % of the example value | 5.2 / 25 |
| example inputs | 5.8 / 25 |
| 110 % of the example value | 6.4 / 25 |
| 120 % of the example value | 7.0 / 25 |
The corridor runs from 4.6 / 25 to 7.0 / 25 across that band — which is the range worth planning inside rather than the single 5.8 / 25 point estimate. Reproduce it in the form above by editing Likelihood (1 = rare … 5 = almost certain) alone; the substitution steps will follow your figures exactly.
What actually moves your Assumption of Risk Reduction
Nobody should have to guess which input matters. The engine was re-run once per field below — ten percent up, ten percent down, everything else frozen — and the results ranked by how far they moved the net risk score. For Assumption of Risk Reduction the ranking is measured rather than guessed, and it belongs to these defaults: shift the starting point and the order can shift with it.
- Likelihood (1 = rare … 5 = almost certain): ten percent up gives 6.4 / 25, ten percent down gives 5.2 / 25 — ≈10.0 % of the net risk score either way
- Nudge Impact (1 = negligible … 5 = severe) by ±10 % and Assumption of Risk Reduction lands between 5.2 / 25 and 5.8 / 25 (sensitivity ≈10.0 %)
- Control effectiveness +10 % → Assumption of Risk Reduction = 5.4 / 25 · −10 % → 6.2 / 25 (the net risk score shifts ≈7.2 %)
Likelihood (1 = rare … 5 = almost certain) is the dominant lever here: ten percent either way moves the result by about 10.0 %, from 5.2 / 25 to 6.4 / 25. Measure that input from a document rather than from memory, and Impact (1 = negligible … 5 = severe) is effectively tied with it (≈10.0 % for the same nudge), so verify the pair together. Everything below that can stay approximate, which is a licence to stop chasing data that would not change the decision.
Is your Assumption of Risk Reduction good — reading the result bands
Nobody acts on a bare figure — they act on a figure with a comparison attached. The verdict attached to the default calculation — “Low-moderate — monitor at cadence” — is one band on a ladder your own figure may sit somewhere else on. How much of that band is luck? On Likelihood (1 = rare … 5 = almost certain) alone ten percent either way covers 5.2 / 25 to 6.4 / 25. The practical reading rules: compare against your own history before comparing against other people; treat band boundaries as zones, not walls — one point on the wrong side of a threshold rarely changes a real outcome; and when a companion output contradicts the headline, believe the pair.
Where Assumption of Risk Reduction sits next to similar measures: this tool answers “how much is the Assumption of Risk Reduction”, while the related calculators answer “what do I do about it”. Reading the result alone tends to over-weight a single snapshot; pairing it with the companion tools above turns the number into a decision.
How people get Assumption of Risk Reduction wrong
Control ratings drift optimistic — audit one control’s evidence and re-mark the rest against it. This calculator gives an estimate based on standard formulas; it is not professional financial, medical, legal or engineering advice. Verify anything you plan to act on with a qualified professional and your own records.
Chasing precision in Impact (1 = negligible … 5 = severe) while Likelihood (1 = rare … 5 = almost certain) is still a guess. The sensitivity ranking above shows exactly where the accuracy budget should go.
This model has its own particular trap: Likelihood (1 = rare … 5 = almost certain) carries ≈10.0 % of the response per ten percent of error, so effort spent elsewhere is largely decorative.
Another is comparing across contexts: an Assumption of Risk Reduction computed with one set of conventions should never be lined up against a figure computed with another. Check the inputs behind both before ranking them.
Before you trust your Assumption of Risk Reduction: a short checklist
- Compare the output against one published benchmark or a colleague’s independent estimate.
- Recompute once with the dominant input deliberately set low, and see whether the decision changes.
- Put Likelihood (1 = rare … 5 = almost certain) under a magnifier; the sensitivity table above is the reason why.
- Record the date and the inputs alongside the number so future-you knows what was assumed.
- State, in one sentence, which convention each rate, unit or period in the form is using.
- Run the ±10 % stress on the top two levers and check that your conclusion survives both.
Five steps from raw inputs to a figure you can defend
The tool does the arithmetic; the routine below is what makes the arithmetic trustworthy. Five steps, a minute total — and afterwards you will know not just what the number is, but which assumption produced it.
Step 1 — collect, then type. Everything this tool asks for is listed above (likelihood (1 = rare … 5 = almost certain), impact (1 = negligible … 5 = severe), control effectiveness (0–100 %)). Pull each figure from something you can point at — paperwork, a dashboard, a label — before touching a field. Guesswork at the input stage cannot be repaired at the interpretation stage. Write the assumptions in the same file as the result, and future arguments take minutes instead of meetings.
Step 2 — enter the conservative case first. Resist the flattering scenario. Type the inputs you would defend in front of a sceptical reader, read the Net risk score (5.8 / 25 for the example values), and note the verdict band. When in doubt, prefer the boring input: the interesting one is usually the one you misread.
Step 3 — change one input at a time. Nudge a single field by ten percent, watch the headline and its companions move, then undo it. Two or three fields will dominate — the sensitivity table above already ranks them for this engine. Half of a good calculation is knowing which input you would hate to be wrong about.
Step 4 — compute a corridor, not a point. Three runs — worst, likely, best — turn Assumption of Risk Reduction from a snapshot into a range. Plan against the pessimistic end and treat the optimistic end as the upside story. Re-running a number costs seconds; retracting a decision costs considerably more.
Step 5 — record the context, not just the number. Note today’s date and the inputs alongside the 5.8 / 25 output. Six months from now the context is what turns this figure into a trend. Re-running a number costs seconds; retracting a decision costs considerably more.
From calculation to decision: where Assumption of Risk Reduction earns its keep
Most Personal Legal disagreements are arithmetic disagreements in disguise. When Net risk score of 5.8 / 25 sits on the table with its working shown, the conversation stops being about who sounds more confident and starts being about which input is wrong. When two sources disagree, it is almost never the arithmetic that differs.
The companion outputs — Gross score and Control credit — exist for the second stage of that screen: the headline sizes the prize, while the companions describe the trade-off you are accepting to get it. Repeating a calculation with the same numbers proves only that the arithmetic is reliable — which is worth knowing.
Nothing here is stored or transmitted, so the habit of checking is free: open the page, re-enter current figures, compare with the number you saved last time. If a result feels obviously right, that is the moment to check the inputs hardest.
People also ask about Assumption of Risk Reduction
What affects Assumption of Risk Reduction the most?
Measured on this engine, Likelihood (1 = rare … 5 = almost certain) is the dominant lever: moving it ±10 % shifts the net risk score across 5.2 / 25 to 6.4 / 25 — about 10.0 % of the headline, with Impact (1 = negligible … 5 = severe) next at ≈10.0 %. The ranked list in the sensitivity section is computed live, not asserted from a template.
Do impact (1 = negligible … 5 = severe) and likelihood (1 = rare … 5 = almost certain) matter equally?
Assumption of Risk Reduction here is co-driven: Likelihood (1 = rare … 5 = almost certain) and Impact (1 = negligible … 5 = severe) produced ≈10.0 % and ≈10.0 % responses, which is a tie for practical purposes. Everything below those two is a rounding error by comparison.
What is a typical Assumption of Risk Reduction?
With the example inputs on this page the result is 5.8 / 25, labelled “Low-moderate — monitor at cadence”. “Typical” varies with situation, which is why every result here ships with its verdict band and its sensitivity list: they turn one figure into a range you can locate yourself inside.
How do I calculate Assumption of Risk Reduction by hand?
Use the stated model — gross = likelihood × impact (1–25); net = gross × (1 − controls) — and substitute in the order the tool prints (it starts from Gross = 2 × 5 = 10). On the example inputs the hand route lands on 5.8 / 25, exactly what the calculator shows. A spreadsheet reproduces it in two or three cells; doing it once by hand is what makes the automated answer trustworthy.
What happens to Assumption of Risk Reduction if likelihood (1 = rare … 5 = almost certain) changes by 10 %?
On these inputs a ±10 % change in likelihood (1 = rare … 5 = almost certain) takes Assumption of Risk Reduction to 6.4 / 25 against the 5.8 / 25 example — around 10.0 % of the headline. The same test on every other field is in the sensitivity section.
Is the Assumption of Risk Reduction result of 5.8 / 25 good or bad?
Neither, on its own — the label “Low-moderate — monitor at cadence” is the band this figure falls in for the example inputs, and your own inputs will place you elsewhere on the same ladder. Judge the number against your history and your alternatives, and remember that a ten-percent error in Likelihood (1 = rare … 5 = almost certain) alone moves it by ≈10.0 %.
How do I calculate Assumption of Risk Reduction manually?
Write down the inputs, then follow the printed steps in order — gross = likelihood × impact (1–25); net = gross × (1 − controls). A spreadsheet replicates it with two or three cells. The manual route is worth doing once to build intuition; after that the calculator saves time and eliminates transcription slips.
Is Assumption of Risk Reduction accurate?
The arithmetic is exact: gross = likelihood × impact (1–25); net = gross × (1 − controls) is evaluated with full floating-point precision and every substitution is printed in the worked example. What limits accuracy is input quality and the model’s assumptions, so treat the 5.8 / 25 result as the correct answer to the question you typed — and check the assumptions section before acting on it.
Why discount by controls?
A 20-score hazard half-mitigated is a 10, and 10s behave like a different risk class.
Do these scores compare across teams?
Only if the likelihood and impact scales are calibrated to the same definitions — the matrix is shared, the calibration is organisational.
Does this Assumption of Risk Reduction tool store my numbers?
No. The entire calculation runs in your browser session; nothing entered above is transmitted or saved, which is the correct behaviour for sensitive Legal & Law figures and the reason you can re-run this page as often as you like.
The vocabulary of Assumption of Risk Reduction
- Gross score
- 10 / 25 for the Assumption of Risk Reduction worked case; it sits next to the main result because it carries context the headline leaves out.
- Control credit
- 4.2 pts for the Assumption of Risk Reduction worked case; it sits next to the main result because it carries context the headline leaves out.
- Likelihood × impact band
- Mixed in the default Assumption of Risk Reduction calculation — one of the secondary figures an experienced reader checks before trusting the primary number.
- Net risk score
- The primary output of this tool: 5.8 / 25 for the default inputs, computed exactly from gross = likelihood × impact (1–25); net = gross × (1 − controls).
- Worked example
- The calculation written out in full so that nothing on this page has to be taken on trust.
- Verdict band
- The plain-language label — here “Low-moderate — monitor at cadence” — mapping the computed figure onto experience-based ranges for Legal & Law.
- Sensitivity (±10 %)
- How far the Assumption of Risk Reduction moves when a single input is nudged ten percent either way; the ranked list on this page is computed live from this engine.
Method, sources and what this tool cannot tell you
The model is published arithmetic implemented exactly as written: gross = likelihood × impact (1–25); net = gross × (1 − controls). Source of record: ISO 31000-aligned qualitative risk matrices. Specialist software layers extra coefficients on top of the same algebra; none are hidden here, and that transparency is what makes a disagreement traceable to a specific input instead of a black box.
Treat the output as a precise answer to the question you typed: exact arithmetic, transparent formula, published source, no hidden rounding. What that buys you is reproducibility — two people, two browsers, the same arithmetic, and a printed trail from inputs to result that either of them can audit.
Turning this Assumption of Risk Reduction into a decision
Re-run the calculation with your own figures, stress the two most sensitive inputs by ±10 %, and only then act on the result.
This page was written for people who need one trustworthy number before their next decision, and the promise is that nothing above required trust. Bookmark the tool, re-run it whenever an input moves by more than a few percent, and keep the date with the number.