Experience Mod Calculator

Turn claim history into a confident modifier. Adjust premiums with smart splits and weights today. Know the impact before quoting and renewing policies accurately.

Inputs
Add class payroll, expected loss rates, and claims. Then calculate the modifier.
Tip: Use the “Estimate parameters” mode for a quick model.
Primary portion per claim = min(claim, split point).
Used to estimate premium impact after the mod.
Choose estimated or manual weighting/ballast.
Used to estimate weighting and ballast.
How much excess losses influence the mod.
Stability amount added to numerator and denominator.

Class Detail
Expected losses = (Payroll / 100) × ELR. Expected primary = expected losses × D-ratio.
Class Payroll ELR D-ratio Expected loss
Auto-calculated on submit
Auto-calculated on submit

Claims Detail
Primary and excess portions are split using the split point.
Claim ID Incurred amount Primary Excess
Split on submit Split on submit
Split on submit Split on submit
Split on submit Split on submit
Example Data
Use this sample to understand the expected-loss and claim split structure.
Type Item Value Notes
Class 8810 payroll 500,000 Payroll used to build expected losses
Class 8810 ELR 0.250 Expected loss per $100 payroll
Class 8810 D-ratio 0.400 Expected primary share of expected losses
Claim CLM-1044 incurred 52,000 Primary is capped by split point
Parameter Split point 18,500 Primary = min(incurred, split point)
Formula Used
This calculator models a transparent experience modifier using primary/excess splits and stabilizing terms.
Expected losses
ExpectedLoss = Σ (Payroll ÷ 100 × ELR)
ExpectedPrimary = Σ (ExpectedLoss × D-ratio)
ExpectedExcess = ExpectedLoss − ExpectedPrimary
Actual losses
PrimaryPerClaim = min(Incurred, SplitPoint)
ExcessPerClaim = max(Incurred − SplitPoint, 0)
ActualPrimary = Σ PrimaryPerClaim
ActualExcess = Σ ExcessPerClaim
Experience mod
EMR = (ActualPrimary + W×ActualExcess + B) ÷ (ExpectedPrimary + W×ExpectedExcess + B)
In “Estimate parameters” mode, the calculator uses: W = ExpectedLosses ÷ (ExpectedLosses + K) and B = K × (ExpectedPrimary ÷ ExpectedLosses).
How to Use This Calculator
  1. Enter the split point and your manual premium (optional).
  2. Add one or more class rows with payroll, ELR, and D-ratio.
  3. Enter claims with incurred amounts for the experience period.
  4. Select “Estimate parameters” for a quick model, or enter W and B manually.
  5. Click Calculate to see EMR and premium impact above the form.
  6. Use Download CSV or Download PDF for sharing and documentation.

Why experience rating matters

An experience modifier often translates prior losses into a pricing factor. A mod of 0.85 reduces a 120,000 manual premium to 102,000, while 1.20 lifts it to 144,000. Many mid-size accounts see movements of 0.05 to 0.15 year over year, so small changes can swing cash flow materially.

Interpreting payroll and ELR inputs

Expected Losses are built from payroll and the expected loss rate (ELR) per 100 of payroll. For example, 500,000 payroll with an ELR of 0.25 produces 1,250 expected losses. Adding a second class with 300,000 payroll and 0.40 ELR adds 1,200 more. Higher ELRs typically reflect higher hazard classes, making accurate classification critical.

Primary and excess loss behavior

Claims are split at the selected split point. With an 18,500 split point, a 12,000 claim is fully primary, while a 52,000 claim is 18,500 primary and 33,500 excess. Primary losses drive the modifier most strongly, while excess losses are partially weighted, preventing one shock loss from dominating the result.

Parameter sensitivity and stability

The weighting value W governs how much excess losses count. In estimate mode, W increases as expected losses grow, reflecting greater credibility for larger employers. The ballast B adds stability to both numerator and denominator. A higher K generally lowers volatility, but can also slow improvement when loss performance gets better.

Using results for pricing decisions

Use the calculated EMR to preview renewal impact and to test “what-if” scenarios. Reducing one 30,000 claim by 10,000 at an 18,500 split point cuts excess first, improving the mod less than reducing primary. Track expected primary versus actual primary and focus on frequency controls, return-to-work, and claim closure timing. Underwriters treat 1.00 as average. Each 0.10 change equals 10% change on mod lines. Use exports to document assumptions and share with stakeholders during budgeting, quoting, and reviews.

FAQs

1) What does an EMR of 1.10 mean?

It means the modifier increases your mod-applied premium by about 10%. If your manual premium is 120,000, an EMR of 1.10 estimates an adjusted premium near 132,000.

2) Which split point should I enter?

Use the split point applicable to your rating plan or bureau circular for the experience period. Changing the split point shifts losses between primary and excess, which can materially change the modifier.

3) Why do I need ELR and D-ratio?

ELR converts payroll into expected losses, and D-ratio allocates expected losses into expected primary and expected excess. Both inputs define the denominator that your actual losses are compared against.

4) What is the weighting value (W)?

W controls how much excess losses influence the mod. Higher W gives excess losses more credibility, usually for larger accounts. In estimate mode, W is derived from expected losses and the stabilizing constant.

5) Should claims be paid, incurred, or reserved?

Use incurred amounts consistent with your loss runs: paid plus case reserves, and any reportable medical and indemnity components. Consistency across the experience period is more important than the specific accounting view.

6) Why does reducing a claim sometimes help less than expected?

Because reductions may apply to excess first. If the claim already exceeds the split point, lowering the excess portion can have a smaller effect than lowering primary. Frequency reduction typically improves modifiers faster than severity changes.

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Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.