Assess possible excess-loss thresholds — assess_excess

Compare candidate thresholds for capped severity and large-loss pricing work.

assess_excess_threshold() is a diagnostic helper. It does not choose a threshold automatically. It shows how many claims and how much historical claim cost sit above candidate thresholds, and how much pure premium would remain after capping claims at each threshold.

Use this before calculate_excess_loss() to understand the effect of the threshold on the portfolio. The output is useful for tariff notes, pricing reviews and governance discussions around capped severity models.

Usage

assess_excess_threshold(
  data,
  claim_amount,
  thresholds,
  exposure = NULL,
  group = NULL
)

Arguments

data: A data.frame with claim-level observations.
claim_amount: Character string. Claim amount column.
thresholds: Numeric vector of candidate thresholds.
exposure: Optional character string. Exposure column. If supplied, pure premium before and after capping is calculated.
group: Optional character string. Grouping column used to assess thresholds by segment.

Value

A data.frame with class "excess_threshold_assessment".

Author

Martin Haringa

Examples

claims <- data.frame(
  sector = rep(c("Industry", "Retail"), each = 5),
  claim_amount = c(1000, 25000, 120000, 50000, 175000,
                   2000, 40000, 90000, 150000, 300000),
  earned_exposure = rep(1, 10)
)

thresholds <- assess_excess_threshold(
  data = claims,
  claim_amount = "claim_amount",
  thresholds = c(25000, 50000, 100000, 150000),
  exposure = "earned_exposure",
  group = "sector"
)

autoplot(thresholds, y = "premium_impact")