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Summarise an excess-loss allocation

Source: R/excess_loss.R
summary.excess_allocation.Rd

Return the allocation audit table from an object produced by allocate_excess_loss().

Usage

# S3 method for class 'excess_allocation'
summary(object, compare_to_empirical = FALSE, ...)

Arguments

object

An object returned by allocate_excess_loss().

compare_to_empirical

Logical. If TRUE, append allocation_difference and allocation_difference_ratio to compare the credibility-weighted allocation with historically observed excess loss.

...

Unused.

Value

A data.frame with aggregated allocation statistics. The returned table has one row per risk-factor level. The original risk-factor and allocation-weight names are preserved. "Observed" refers to historical excess loss in the input data; "allocated" refers to the credibility-weighted allocation. Loadings are amounts per unit of allocation weight and losses are total amounts for the level. The returned columns are:

<risk_factor>

Risk-factor level. For example, risk_factor = "sector" returns a sector column. When no risk factor is supplied, this column is named risk_factor and contains "portfolio".

<allocation_weight>

Total allocation weight for the level. For example, allocation_weight = "earned_exposure" returns an earned_exposure column.

<claim_count>

Number of claims in the level. When claim_count is supplied, its original column name is preserved. When claim_count = NULL, inferred counts are returned as claim_count.

excess_claim_count

Number of claim records with excess loss.

observed_excess_loss

Historically observed excess loss.

observed_excess_loading

Observed excess loss per unit of allocation weight.

<risk_factor>_excess_loading

Risk-factor-specific excess loading before pooling. When no risk factor is supplied, this column is named risk_factor_excess_loading.

<risk_factor>_credibility

Credibility weight assigned to the risk-factor-level estimate.

portfolio_excess_loading

Portfolio-level excess loading per unit of allocation weight.

blended_excess_loading

Credibility-weighted blend of the risk-factor and portfolio estimates. For risk_factor = "sector", this is calculated as sector_excess_loading * sector_credibility + portfolio_excess_loading * (1 - sector_credibility).

expected_excess_loss

Total expected excess loss allocated to the level, calculated as blended_excess_loading * <allocation_weight>.

allocation_difference

Expected minus observed excess loss, included when compare_to_empirical = TRUE. A positive value means the level receives more allocated excess loss than it generated historically.

allocation_difference_ratio

Allocation difference relative to observed excess loss, included when compare_to_empirical = TRUE. When observed excess loss is zero this value is NA_real_, because a relative comparison with zero observed excess loss is undefined.

See also

allocate_excess_loss()

Author

Martin Haringa