Open Account Risk Calculator
The formula
What the open account risk tells you
The 5×5 matrix the risk industry actually uses, for open account risk: 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.
Open Account Risk: what this tool answers
This page was written for one job: turning your own inputs into a defensible figure, with the working shown. There are plenty of pages about Open Account Risk; 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.
In plain terms, Open Account Risk answers the question "Net risk score?". The measurement matters because it converts a situation that feels complicated — several inputs pulling in different directions — into a single number you can act on. Within Finance & Banking, Currency & International is the context where this figure is quoted most often, and professionals treat it as a first-pass screen rather than a verdict.
The number itself is the easy part. What takes judgement is knowing which inputs are facts in your situation and which are guesses wearing a decimal point — a distinction this page makes explicit for every field above.
What makes a calculation page trustworthy is that its copy is derived from its own arithmetic: here net risk score = 9.6 / 25, recomputed every time a field changes, 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.
The Open Account Risk formula, explained
The engine implements a single stated model: gross = likelihood × impact (1–25); net = gross × (1 − controls). There is no hidden coefficient and no login-gated precision — the formula printed is the formula executed, which is also what makes the result citable.
Likelihood (1 = rare … 5 = almost certain) (example value 4) is a measured number in the unit named by the label; the formula does not convert for you. If you are unsure what belongs in this field, look at the printed steps: the substitution shows exactly how the engine uses it.
Impact (1 = negligible … 5 = severe) (example value 4) is a measured number in the unit named by the label; the formula does not convert for you. This input feeds the formula directly; a defensible figure here is worth more than false precision anywhere else.
Control effectiveness (0–100 %) (example value 40) is a quantity rather than a category — precision here is cheap and worth having. Rates are the most mis-transcribed input on any page like this; copy it from the document rather than retyping from memory.
Open Account Risk with real numbers, step by step
Rather than describe the calculation, here it is end to end — every substitution in order, starting from the inputs above. Starting figures: Likelihood (1 = rare … 5 = almost certain), Impact (1 = negligible … 5 = severe), Control effectiveness (0–100 %). The computed net risk score comes out at 9.6 / 25, which the interpretation band labels “Amber — needs an owner and a date”.
- Gross = 4 × 4 = 16
- Net = 16 × (1 − 40 %) = 9.6
Gross score — 16 / 25. Compare it against the main result of 9.6 / 25 before drawing conclusions.
Control credit — 6.4 pts. Read next to the headline, this is the figure that shows the shape of the trade-off.
Likelihood × impact band — High×High. If your inputs change, this is usually the first companion figure to move.
Overwrite any input above and every line of this example updates with it — the substitution is live, not a screenshot of somebody else’s numbers.
How far Open Account Risk can reasonably move
Single-point answers invite false confidence, so the same engine was run across a band around the input that matters most here. Each row is a full recalculation of Open Account Risk, not an interpolation.
| Likelihood (1 = rare … 5 = almost certain) setting | Net risk score |
|---|---|
| 80 % of the example value | 7.7 / 25 |
| 90 % of the example value | 8.6 / 25 |
| example inputs | 9.6 / 25 |
| 110 % of the example value | 10.6 / 25 |
| 120 % of the example value | 11.5 / 25 |
The corridor runs from 7.7 / 25 to 11.5 / 25 across that band — which is the range worth planning inside rather than the single 9.6 / 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.
Sensitivity: which inputs change Open Account Risk the most
This list was computed for this specific engine just now: each numeric input was nudged ±10 % while every other field held its example value, and the net risk score was recalculated from scratch. For Open Account Risk 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) +10 % → Open Account Risk = 10.6 / 25 · −10 % → 8.6 / 25 (the net risk score shifts ≈10.0 %)
- Nudge Impact (1 = negligible … 5 = severe) by ±10 % and Open Account Risk lands between 8.6 / 25 and 10.6 / 25 (sensitivity ≈10.0 %)
- Control effectiveness: ten percent up gives 9.0 / 25, ten percent down gives 10.2 / 25 — ≈6.7 % of the net risk score either way
Likelihood (1 = rare … 5 = almost certain) is the dominant lever here: ten percent either way moves the result by about 10.0 %, from 8.6 / 25 to 10.6 / 25. Spend your verification effort there first, and Impact (1 = negligible … 5 = severe) is effectively tied with it (≈10.0 % for the same nudge), so verify the pair together. The rest of the inputs earn only a rounding-error of attention, and that asymmetry is the practical payoff of measuring rather than asserting.
Reading the result: bands, zones and thresholds
A number on its own is trivia; a number against a range is intelligence. The verdict attached to the default calculation — “Amber — needs an owner and a date” — is one band on a ladder your own figure may sit somewhere else on. Before treating the band as fact, note its width: ten percent either way on Likelihood (1 = rare … 5 = almost certain) already spans 8.6 / 25 to 10.6 / 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.
Related measures answer related questions, and the distinctions matter: Open Account Risk is the quantity, Net risk score is this tool’s reading of it, and the decision that follows belongs to neither of them separately.
Why Open Account Risk results go wrong — and how to prevent it
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.
Reporting the headline and quietly discarding Gross score. The companions are where the shape of the trade-off survives the rounding.
Attention is also misallocated between fields. Having measured this engine, we can say where it belongs: Likelihood (1 = rare … 5 = almost certain) first, everything else second.
A final trap is presentation: reporting Open Account Risk to more decimal places than the inputs support. The tool prints what the arithmetic gives; you should quote only what your least precise field justifies.
Six things to confirm before acting on Open Account Risk
- State, in one sentence, which convention each rate, unit or period in the form is using.
- Check that every field the formula needs actually arrived from a document, not from recollection.
- Check Likelihood (1 = rare … 5 = almost certain) before anything else: it is the measured dominant input, ≈10.0 % of Open Account Risk per ten percent.
- Confirm rate conventions (APR vs effective, day-count) before comparing two offers.
- Confirm the example values are plausible for your own situation before replacing them.
- Confirm the answer against one independent method or published benchmark for Open Account Risk.
A routine for decision-grade Open Account Risk
Using a calculator well sounds trivial, but the difference between a decision-grade figure and a throwaway one lives entirely in the routine. The routine for Open Account Risk takes a minute to learn and applies to every input above.
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 — compute the version you would not be embarrassed by. If a stranger audited your inputs, which set would you hand over? Enter that set, read the result, and treat everything else as upside. The point of transparency is that a reader can disagree with a specific line rather than with you.
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. Write the figure down before moving on; memory flatters whichever number you saw last.
Step 4 — stress before you commit. A decision that survives a ten-percent squeeze on its two biggest inputs needs no luck to be sound. One that does not survive is a decision about hope, not about arithmetic. Half of a good calculation is knowing which input you would hate to be wrong about.
Step 5 — save the story with the figure. A number without its inputs and its date is unusable within a quarter. Record 9.6 / 25, the assumptions behind it, and today’s date; your future self will be auditing a stranger’s arithmetic. If a result feels obviously right, that is the moment to check the inputs hardest.
Where Open Account Risk matters in practice
In Currency & International the figure computed here is usually the first one somebody asks to see. Having it — with the formula behind it — changes your position in the conversation from participant to reference. If the answer changes your behaviour, it deserves a second run with different assumptions.
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. Compare the calculation to the rough guess you arrived with, and ask why they differ.
Re-running this page costs nothing and catches the slow drifts that never announce themselves. In Finance & Banking the expensive mistakes are rarely sudden; they are small changes in Likelihood (1 = rare … 5 = almost certain) that nobody recalculated for a year. Half of a good calculation is knowing which input you would hate to be wrong about.
People also ask about Open Account Risk
What affects Open Account Risk the most?
Open Account Risk is driven above all by Likelihood (1 = rare … 5 = almost certain). Perturbing only that field by ten percent either way produced 10.6 / 25 against the 9.6 / 25 baseline — a ≈10.0 % response, while Impact (1 = negligible … 5 = severe) manages ≈10.0 %.
Do impact (1 = negligible … 5 = severe) and likelihood (1 = rare … 5 = almost certain) matter equally?
Open Account Risk 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 Open Account Risk?
There is no honest universal average for Open Account Risk, but there is a reference point: the defaults on this page produce 9.6 / 25 (Amber — needs an owner and a date). Replace the inputs with your own and compare — that comparison is the useful meaning of “typical”.
How do I calculate Open Account Risk by hand?
gross = likelihood × impact (1–25); net = gross × (1 − controls) is the entire method. Work the inputs in that order — Gross = 4 × 4 = 16 — and you will reach 9.6 / 25 on the default figures, the same number this tool prints.
What happens to Open Account Risk if likelihood (1 = rare … 5 = almost certain) changes by 10 %?
Try it in the form above and the printed substitution will follow you; the pre-computed answer for the defaults is 8.6 / 25 to 10.6 / 25 across the ten-percent probe, i.e. ≈10.0 % sensitivity on the net risk score.
Is the Open Account Risk result of 9.6 / 25 good or bad?
The tool answers “what”, not “whether”: 9.6 / 25 carries the verdict “Amber — needs an owner and a date” for the default case, which is a starting interpretation rather than a verdict on your situation. Re-run with your own figures and compare the bands.
Is Open Account Risk the same as what my bank quotes?
Often close but not identical: institutions quote rates on their own day-count and fee assumptions. Recompute with the actual dollar figures from a statement — if the gap is large, the fine print is doing work you should understand before signing.
Can Open Account Risk be negative or empty?
Yes, and that is informative rather than broken: a negative Open Account Risk usually means the inputs encode an outflow or a shortfall, and the verdict band above labels exactly that situation instead of hiding 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.
Can this tool see the numbers I type?
It cannot. There is no submission step: the calculation happens in your browser and the result disappears when you close the page. Treat the fields above as a scratchpad rather than a form.
Key terms on this page
- Gross score
- 16 / 25 in the default Open Account Risk calculation — one of the secondary figures an experienced reader checks before trusting the primary number.
- Control credit
- 6.4 pts on the example inputs — a companion figure printed beside the headline of Open Account Risk so that no result is read in isolation.
- Likelihood × impact band
- High×High for the Open Account Risk worked case; it sits next to the main result because it carries context the headline leaves out.
- Net risk score
- The primary output of this tool: 9.6 / 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 “Amber — needs an owner and a date” — mapping the computed figure onto experience-based ranges for Finance & Banking.
- Sensitivity (±10 %)
- How far the Open Account Risk moves when a single input is nudged ten percent either way; the ranked list on this page is computed live from this engine.
Where the Open Account Risk model comes from
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. Where a professional version of this calculation differs, it differs in inputs and jurisdiction-specific coefficients, not in the algebra — which is why re-checking your inputs beats hunting for a second calculator.
Treat the output as a precise answer to the question you typed: exact arithmetic, transparent formula, published source, no hidden rounding. The sensitivity table above is part of that promise rather than a bonus: it lets you see how much of the result depends on how carefully you measured.
Next steps after the calculation
Save the number together with its inputs and today’s date; an Open Account Risk figure without context is unusable the moment an input changes.
The audience here is people who need one trustworthy number before their next decision. Everything you need to defend the result — formula, substitution, sensitivity, source — is printed above, so the only remaining variable is the honesty of your inputs.