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Individual data generator

Source: R/data_generator.R
data_generator.Rd

This function generates individual claims data on the requested time scale using the SynthETIC package. This simple function allows to simulate from a sand-box to test out the ReSurv approach. Some parameters of the simulation can be changed.

Usage

data_generator(
  ref_claim = 2e+05,
  time_unit = 1/360,
  years = 4,
  random_seed = 1964,
  period_exposure = 200,
  period_frequency = 0.2,
  scenario = 1
)

Arguments

ref_claim

integer, reference claim size.

time_unit

Numeric, fraction of a year per output period, for example 1/360 (the default) for days, 1/12 for months, or 1 for years.

years

integer, number of years to be simulated.

random_seed

integer, random seed for replicable code.

period_exposure

integer, volume (number of policies) underwritten each period.

period_frequency

numeric, expected frequency in each period.

scenario

character or numeric, one of the scenarios described in the accompanying manuscript. Possible choices are 'alpha' (0), 'beta' (1), 'gamma'(2), 'delta'(3),'epsilon'(4). Our simulated data are constituted of a mix of short tail claims (claim_type 0) and claims with longer resolution (claim_type 1). We chose the parameter of the simulator to resemble a mix of property damage (claim_type 0) and bodily injuries (claim_type 1). each scenario has distinctive characteristics. Scenario Alpha is a mix of claim_type 0 and claim_type 1 with same number of claims volume at each accident period. Differently from scenario Alpha, in scenario Beta the volumes of claim_type 1 are decreasing in the most recent accident periods. In scenario Gamma we add an interaction between claim_type 1 and accident period: in a real world setting this can be motivated by a change in consumer behavior or company policies resulted in different reporting patterns over time. In scenario Delta, we introduce a seasonality effect dependent on the accident period for claim_type 0 and claim_type 1. In the real word, scenario Delta resembles seasonal changes in the workforce composition. Scenario Epsilon does not satisfy the proportionality assumption. Additional scenarios are "zeta" (5), with age, property value and business-use covariates, and "eta" (6), with business-use and accident-period effects.

Value

Individual claims data. It contains the following columns:

  • claim_number: Policy ID.

  • claim_type: Type of claim (0 or 1) in scenarios alpha through epsilon. Zeta instead includes age, property_value, and business_use; eta includes business_use.

  • AP: Accident period

  • RP: Reporting period.

References

Avanzi, B., Taylor, G., Wang, M., & Wong, B. (2021). SynthETIC: an individual insurance claim simulator with feature control. Insurance: Mathematics and Economics, 100, 296-308.

Hiabu, M., Hofman, E., & Pittarello, G. (2023). A machine learning approach based on survival analysis for IBNR frequencies in non-life reserving. arXiv preprint arXiv:2312.14549.

Examples

input_data_0 <- data_generator(
random_seed = 1964,
scenario = "alpha",
time_unit = 1,
years = 2,
period_exposure = 100)