derive_tariff_segments: Derive insurance tariff segments

In insurancerating: Actuarial Tools for Insurance Pricing Models

Derive insurance tariff segments

Description

Derives data-driven tariff segments for a continuous risk factor from a fitted "riskfactor_gam" object produced by risk_factor_gam(). The segments help translate a smooth GAM response pattern into practical categorical rating factors for a GLM tariff.

Usage

derive_tariff_segments(
  object,
  complexity = 0,
  max_iterations = 10000,
  population_size = 200,
  seed = 1,
  alpha = NULL,
  niterations = NULL,
  ntrees = NULL
)

Arguments

object	An object of class "riskfactor_gam", produced by risk_factor_gam(). Objects with the old "fitgam" class are still supported for backward compatibility.
complexity	Numeric. Controls the complexity penalty used when deriving segments. Higher values generally yield fewer tariff segments. Default = 0.
max_iterations	Integer. Maximum number of search iterations used by the underlying grouping algorithm. Default = 10000.
population_size	Integer. Number of candidate trees used by the underlying grouping algorithm. Default = 200.
seed	Integer, seed for the random number generator (for reproducibility).
alpha	Deprecated. Use complexity instead.
niterations	Deprecated. Use max_iterations instead.
ntrees	Deprecated. Use population_size instead.

Details

Evolutionary trees (via evtree::evtree()) are used as a technique to bin the fitted GAM object into candidate tariff segments. This method is based on the work by Henckaerts et al. (2018). See Grubinger et al. (2014) for details on the parameters controlling the evtree fit.

Value

A list of class "tariff_segments" with components:

gam_prediction

Data frame with the fitted GAM curve.

risk_factor

Name of the continuous risk factor.

model_type

Model type: "frequency", "severity", or "pure_premium".

classification_data

Data frame used to derive the segments.

risk_factor_values

Observed risk factor values in portfolio row order.

segment_boundaries

Numeric vector with segment boundaries.

assigned_segments

Factor with the tariff segment assigned to each observed risk factor value.

For backward compatibility, the old components prediction, x, model, data, x_obs, splits, class_boundaries, assigned_groups, and tariff_classes are also returned.

Author(s)

Martin Haringa

References

Antonio, K. and Valdez, E. A. (2012). Statistical concepts of a priori and a posteriori risk classification in insurance. Advances in Statistical Analysis, 96(2), 187–224. \Sexpr[results=rd]{tools:::Rd_expr_doi("doi:10.1007/s10182-011-0152-7")}

Grubinger, T., Zeileis, A., and Pfeiffer, K.-P. (2014). evtree: Evolutionary learning of globally optimal classification and regression trees in R. Journal of Statistical Software, 61(1), 1–29. \Sexpr[results=rd]{tools:::Rd_expr_doi("doi:10.18637/jss.v061.i01")}

Henckaerts, R., Antonio, K., Clijsters, M., & Verbelen, R. (2018). A data driven binning strategy for the construction of insurance tariff classes. Scandinavian Actuarial Journal, 2018(8), 681–705. \Sexpr[results=rd]{tools:::Rd_expr_doi("doi:10.1080/03461238.2018.1429300")}

Wood, S.N. (2011). Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. JRSS B, 73(1), 3–36. \Sexpr[results=rd]{tools:::Rd_expr_doi("doi:10.1111/j.1467-9868.2010.00749.x")}

Examples

## Not run: 
library(dplyr)

# Recommended new usage (SE)
age_segments <- risk_factor_gam(MTPL,
                                risk_factor = "age_policyholder",
                                claim_count = "nclaims",
                                exposure = "exposure") |>
  derive_tariff_segments()

MTPL |>
  add_tariff_segments(age_segments, name = "age_policyholder_segment")

## End(Not run)

insurancerating documentation built on July 30, 2026, 5:09 p.m.