Construct insurance tariff classes — construct_tariff

Constructs insurance tariff classes for objects of class "fitgam" produced by riskfactor_gam() (formerly fit_gam()). The goal is to bin continuous risk factors into categorical tariff classes that capture the effect of the covariate on the response in an accurate way, while remaining easy to use in a generalized linear model (GLM).

Usage

construct_tariff_classes(
  object,
  alpha = 0,
  niterations = 10000,
  ntrees = 200,
  seed = 1
)

Arguments

object: An object of class "fitgam", produced by riskfactor_gam().
alpha: Complexity parameter passed to evtree::evtree.control(). Higher values yield fewer tariff classes. Default = 0.
niterations: Maximum number of iterations before termination. Passed to evtree::evtree.control(). Default = 10000.
ntrees: Number of trees in the population. Passed to evtree::evtree.control(). Default = 200.
seed: Integer, seed for the random number generator (for reproducibility).

Value

A list of class "constructtariffclasses" with components:

prediction: Data frame with predicted values.
x: Name of the continuous risk factor for which tariff classes are constructed.
model: Model type: "frequency", "severity", or "burning".
data: Data frame with predicted and observed values.
x_obs: Observed values of the continuous risk factor.
splits: Numeric vector with boundaries of the constructed tariff classes.
tariff_classes: Factor with the tariff class each observation falls into.

Details

Evolutionary trees (via evtree::evtree()) are used as a technique to bin the fitted GAM object into risk-homogeneous categories. 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.

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. 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. 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. 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. doi:10.1111/j.1467-9868.2010.00749.x

Author

Martin Haringa

Examples

if (FALSE) { # \dontrun{
library(dplyr)

# Recommended new usage (SE)
riskfactor_gam(MTPL,
               nclaims = "nclaims",
               x = "age_policyholder",
               exposure = "exposure") |>
  construct_tariff_classes()

# Deprecated usage (NSE, still works with warning)
fit_gam(MTPL, nclaims = nclaims, x = age_policyholder, exposure = exposure) |>
  construct_tariff_classes()
} # }