Stop-Loss Normal Approximation Expected Cost Calculator
Estimate the expected aggregate loss above an attachment point under a normal approximation, before expenses or risk loads.
Description
Estimate the expected aggregate loss above an attachment point under a normal approximation, before expenses or risk loads.
Stop-Loss Normal Approximation Expected Cost Calculator provides a focused, reproducible calculation with explicit inputs and a deterministic result. Estimate the expected aggregate loss above an attachment point under a normal approximation, before expenses or risk loads.
Use to approximate expected aggregate loss above an attachment when aggregate loss is modeled as normal.
When to use Stop-Loss Normal Approximation Expected Cost Calculator
Use to approximate expected aggregate loss above an attachment when aggregate loss is modeled as normal.1
How the calculation works
Estimate the expected aggregate loss above an attachment point under a normal approximation, before expenses or risk loads.2
E[(S−d)+] = σφ(z) + (μ−d)[1−Φ(z)] Inputs and interpretation
Keep every input on the same valuation, timing, unit, and assumption basis. Interpret the output at the precision supported by those inputs rather than treating extra decimal places as additional certainty.
Limitations and assumptions
Normal loss models permit negative values and can understate skew or tail risk; the result is expected cost before expenses and risk loads, not a quoted premium.
References
-
The Effect of Trend on Excess of Loss Coverage — Casualty Actuarial Society
-
Actuarial science — Wikipedia contributors
Similar or alternative tools
- Aggregate Excess Loss Layer Calculator
Calculate the aggregate loss falling within a layer above an attachment point, capped by the layer limit.
- Bornhuetter-Ferguson Reserve Calculator
Estimate the IBNR reserve with the Bornhuetter-Ferguson method from earned premium, the expected loss ratio, and the portion of losses not yet reported.
- Contingent Insurance Probability Calculator
Calculate the probability that the first life dies before the second life under constant forces of mortality derived from annual death probabilities.