Inputs
Enter your best estimates. Use annual values where possible.
Example Data Table
| Scenario | Revenue | Employees | Industry | Claims (3y) | Avg Claim | Score | Coverage Range |
|---|---|---|---|---|---|---|---|
| Retail storefront | $2,500,000 | 18 | High | 1 | $22,000 | 58 | $2.4M – $4.1M |
| B2B services firm | $1,200,000 | 10 | Medium | 0 | $0 | 34 | $1.1M – $1.9M |
| Manufacturer | $8,000,000 | 95 | Very High | 3 | $75,000 | 82 | $10.0M – $17.0M |
Examples are illustrative. Your results depend on your inputs.
Business Liability Insights
Liability exposure concentrates around frequency and severity
Claims history is treated as two signals: how often incidents occur and how expensive they become. A business with 2 claims in three years and a $25,000 average cost can look worse than one $50,000 event because repeats suggest process gaps. The calculator blends frequency and severity and caps the combined factor.
Revenue and contracts shape the realistic loss ceiling
Revenue acts as a capacity proxy for settlements, defense, and disruption. Contract exposure adds pressure: 40 contracts per year at $60,000 each implies $2.4M in commitments, raising dispute and indemnity risk. The suggested range starts at the larger of $250,000 or 50% of revenue, then adds limited bumps for claims and contracts.
Operational touchpoints drive third‑party injury probability
Visitor volume helps approximate premises risk. For example, 3,000 visitors per month increases exposure versus a back‑office firm. Employee count also matters: more staff means more interactions, more driving, and more supervision risk. These drivers are weighted but kept below claims and contracts to reduce noise.
Controls and compliance modify risk rather than replace it
Industry, regulatory exposure, and cyber controls apply multipliers to the base score. A base score of 50 becomes 62.5 in a high‑risk industry (×1.25). Strong controls reduce the cyber penalty, but they do not erase claims history. Use the multiplier table to explain differences between similar firms.
Use the coverage gap to prioritize practical next steps
If your current limit is $1M and the suggested range is $2.0M to $3.4M, the gap signals under‑protection for plausible scenarios. Consider tightening contracts, reducing visitor hazards, and improving incident documentation before buying more limit. Re‑run the calculator after changes to measure improvement. Document assumptions for audit trails.
Formula Used
This calculator builds a 0–100 risk score using weighted drivers, then scales the score using multipliers.
RiskScore = clamp(BaseScore × Industry × Regulatory × Products × Professional, 0, 100)
RecommendedCoverage = (max(250k, 0.50·Revenue) + HistoryBump + ContractBump) × RangeMultiplier
- Rev is log-scaled revenue, capped from 0 to 1.
- Claims blends claim frequency and severity, capped from 0 to 1.
- CyberPenalty increases as controls weaken.
- RangeMultiplier depends on the final risk band.
How to Use This Calculator
- Enter annual revenue and employee count for your current year.
- Add contracts and average contract value to reflect obligations.
- Include visitor volume if you operate public-facing locations.
- Record claims from the last three years and average costs.
- Select risk settings for industry, regulation, cyber, and exposure types.
- Submit to view your score, suggested range, and coverage gap.
- Download CSV for spreadsheets or PDF for internal reporting.
FAQs
What does the risk score represent?
It summarizes relative liability exposure on a 0–100 scale using revenue, claims, contracts, visitors, staffing, and controls. It is a planning signal for comparing scenarios, not a legal or underwriting decision.
How should I choose an industry risk level?
Pick the level that best matches your main revenue activity. If you operate across lines, choose the highest-risk segment that could drive large claims or strict compliance requirements.
Why does contract exposure increase recommended coverage?
Large contract volume and value raise dispute probability, indemnity obligations, and defense costs. The model treats contract exposure as a bounded add-on so it influences the range without overpowering claims history.
Is the premium estimate an actual quote?
No. It is an illustrative calculation that scales a baseline rate by your score and deductible. Real premiums depend on coverage form, limits, location, loss runs, and carrier appetite.
How can I lower the score without cutting revenue?
Reduce claim frequency with safety procedures, training, and documentation. Strengthen cyber controls, review contracts for unfair indemnities, and improve incident response. Then re-run scenarios to validate the impact.
What inputs matter most if I have limited data?
Start with revenue, claims count, and average claim cost. Add contracts and average value next. Use reasonable defaults for visitors and controls, then refine as you gather better operational numbers.