insurancerating provides actuarial tools and building blocks for analysing, modelling, refining, and validating insurance rating models in R.

The package is designed around common GLM-based pricing tasks and focuses on the translation of statistical model output into interpretable and controllable tariff structures.

The package supports common tasks that often occur in actuarial pricing work:

• exploratory analysis of risk factors
• estimation of GLM-based pricing models
• controlled refinement of model coefficients
• construction and interpretation of tariff structures
• evaluation of model performance and stability

The focus is on reproducibility, interpretability, and consistency across models.

Install the CRAN version:

install.packages("insurancerating")

Or development version:

# install.packages("remotes")
remotes::install_github("MHaringa/insurancerating")

library(insurancerating)

# Factor analysis
fa <- factor_analysis(
  MTPL,
  risk_factors = "zip",
  claim_count = "nclaims",
  exposure = "exposure",
  claim_amount = "amount"
)

autoplot(
  fa,
  metrics = c("frequency", "average_severity", "risk_premium")
)

# Fit model
mod <- glm(
  nclaims ~ zip,
  offset = log(exposure),
  family = poisson(),
  data = MTPL
)

rating_table(mod)

##   risk_factor       level   est_mod
## 1 (Intercept) (Intercept) 0.1402024
## 2         zip           0 1.0000000
## 3         zip           1 1.0254064
## 4         zip           2 0.9238016
## 5         zip           3 0.9757522

# Refine coefficients
zip_df <- data.frame(
  zip = c("0", "1", "2", "3"),
  zip_adj = c(0.90, 0.95, 1.00, 1.10)
)

mod_refined <- prepare_refinement(mod) |>
  add_restriction(zip_df) |>
  refit()

rating_table(mod_refined)

##   risk_factor       level est_mod_refined exposure
## 1 (Intercept) (Intercept)        0.136653       NA
## 2     zip_adj           0        0.900000      207
## 3     zip_adj           1        0.950000    11081
## 4     zip_adj           2        1.000000     7783
## 5     zip_adj           3        1.100000     7588

A possible sequence of steps is:

factor_analysis()      # analyse portfolio
glm()                  # estimate model
prepare_refinement()   # apply adjustments
rating_table()         # interpret coefficients

factor_analysis() provides aggregated portfolio metrics such as:

• frequency
• average severity
• risk premium
• loss ratio

These are used to assess the behaviour and credibility of risk factors.

Models are estimated using widely used GLM specifications:

• Poisson for frequency
• Gamma for severity
• Gamma (log-link) for premium

rating_table() expresses model output in terms of original factor levels.

Model output can be adjusted using:

prepare_refinement(model) |>
  add_smoothing(...) |>
  add_restriction(...) |>
  add_relativities(...) |>
  refit()

This step is used to impose structure or incorporate expert judgement.

extract_model_data(model)
rating_grid(model)

These functions expose the underlying model structure and allow aggregation at model-point level.

model_performance(model)
bootstrap_performance(model, data)

Used to assess predictive accuracy and stability.

This package is intended for general actuarial pricing work. It does not contain proprietary models, data, or business logic.

Full documentation and examples are available in the articles:

• Getting started
• Refinement building blocks
• Model validation
• Pricing workflow building blocks