Getting started with actxps"

In actxps: Create Actuarial Experience Studies: Prepare Data, Summarize Results, and Create Reports

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

actxps:::set_actxps_plot_theme()

This article is based on creating a termination study using sample data that comes with the actxps package. For information on transaction studies, see vignette("transactions").

Simulated data set

The actxps package includes a data frame containing simulated census data for a theoretical deferred annuity product with an optional guaranteed income rider. The grain of this data is one row per policy.

library(actxps)
library(dplyr)

census_dat

The data includes 3 policy statuses: Active, Death, and Surrender.

(status_counts <- table(census_dat$status))

Let's assume we're interested in calculating the probability of surrender over one policy year. We cannot simply calculate the proportion of policies in a surrendered status as this does not represent an annualized surrender rate.

# incorrect
prop.table(status_counts)

Creating exposed data

In order to calculate annual surrender rates, we need to break each policy into multiple records. There should be one row per policy per year.

The expose() family of functions is used to perform this transformation.

exposed_data <- expose(census_dat, end_date = "2019-12-31",
                       target_status = "Surrender")

exposed_data

These functions create exposed_df objects, which are a type of data frame with some additional attributes related to the experience study.

Now that the data has been "exposed" by policy year, the observed annual surrender probability can be calculated as:

sum(exposed_data$status == "Surrender") / sum(exposed_data$exposure)

As a default, the expose() function calculates exposures by policy year. This can also be accomplished with the function expose_py(). Other implementations of expose() include:

• expose_cy = exposures by calendar year
• expose_cq = exposures by calendar quarter
• expose_cm = exposures by calendar month
• expose_cw = exposures by calendar week
• expose_pq = exposures by policy quarter
• expose_pm = exposures by policy month
• expose_pw = exposures by policy week

See vignette("exposures") for further details on exposure calculations.

Experience study summary function

The exp_stats() function creates a summary of observed experience data. The output of this function is an exp_df object.

exp_stats(exposed_data)

See vignette("exp_summary") for further details on exposure calculations.

Grouped experience data

If the data frame passed into exp_stats() is grouped using dplyr::group_by(), the resulting output will contain one record for each unique group.

exp_res <- exposed_data |>
  group_by(pol_yr, inc_guar) |>
  exp_stats()

exp_res

Actual-to-expected rates

To derive actual-to-expected rates, first attach one or more columns of expected termination rates to the exposure data. Then, pass these column names to the expected argument of exp_stats().

expected_table <- c(seq(0.005, 0.03, length.out = 10), 0.2, 0.15, rep(0.05, 3))

# using 2 different expected termination rates
exposed_data <- exposed_data |>
  mutate(expected_1 = expected_table[pol_yr],
         expected_2 = ifelse(exposed_data$inc_guar, 0.015, 0.03))

exp_res <- exposed_data |>
  group_by(pol_yr, inc_guar) |>
  exp_stats(expected = c("expected_1", "expected_2"))

exp_res

autoplot() and autotable()

The autoplot() and autotable() functions create visualizations and summary tables. See vignette("visualizations") for full details on these functions.

autoplot(exp_res)

# first 10 rows showed for brevity
exp_res |> head(10) |> autotable()

summary()

Calling the summary() function on an exp_df object re-summarizes experience results. This also produces an exp_df object.

summary(exp_res)

If additional variables are passed to ..., these variables become groups in the re-summarized exp_df object.

summary(exp_res, inc_guar)

Shiny App

Passing an exposed_df object to the exp_shiny() function launches a Shiny app that enables interactive exploration of experience data.

exp_shiny(exposed_data)

actxps documentation built on June 26, 2024, 9:07 a.m.