Simulating Actuarial Claims Data with R¶
Simulation Script (Transactional)¶
- Source: https://gist.github.com/jimbrig/30262ae72e1ba20d0235fdedcc435150
- Carbon: https://carbon.now.sh/MDUZMTZI1ERkJTzBamSt
- Imagur: https://imgur.com/k4Y16Zp
Gist¶
jimbrig/30262ae72e1ba20d0235fdedcc435150
Code¶
# source: https://gist.github.com/jimbrig/30262ae72e1ba20d0235fdedcc435150
# simulate transactions on insurance claims
# transactions will include claim closures, payments and changes in case reserves
# this is all completely made up and does not accurately resemble actual claims
library(lubridate)
library(dplyr)
library(randomNames)
# number of claims
n_claims <- 1000
beg_date <- as.Date("2012-01-01")
end_date <- Sys.Date()#as.Date("2019-02-17")
accident_range <- as.numeric(end_date - beg_date)
set.seed(1234)
accident_date <- sample(0:accident_range, size = n_claims, replace = TRUE)
payment_fun <- function(n) rlnorm(n, 7.5, 1.5)
claims <- tibble::tibble(
claim_num = paste0("claim-", 1:n_claims),
accident_date = beg_date + lubridate::days(accident_date),
state = sample(c("TX", "CA", "GA", "FL"), size = n_claims, replace = TRUE),
claimant = randomNames::randomNames(n_claims),
report_date = rnbinom(n_claims, 5, .25),
# 0 if claim closed when reported
status = rbinom(n_claims, 1, 0.96),
# initial payment amount
payment = payment_fun(n_claims)) %>%
dplyr::mutate(report_date = accident_date + report_date,
# set payment to zero if closed when reported
payment = ifelse(status == 0, 0, payment),
case = payment * runif(n_claims, 0.25, 8.0),
transaction_date = report_date) %>%
dplyr::arrange(accident_date)
## simulate transaction dates
# simulate number of transactions for each claim
n_trans <- rnbinom(n_claims, 3, 0.25)
# simulate lag to each transaction
trans_lag <- lapply(n_trans, function(x) rnbinom(x, 7, 0.1))
trans_lag <- lapply(trans_lag, function(x) {
if(length(x) == 0) 0 else x
})
for (i in seq_len(n_claims)) {
trans_lag[[i]] <- data_frame(
"trans_lag" = trans_lag[[i]],
"claim_num" = paste0("claim-", i)
)
}
trans_tbl <- bind_rows(trans_lag)
trans_tbl <- trans_tbl %>%
group_by(claim_num) %>%
# switch from incremental to cumulative lag
mutate(trans_lag = cumsum(trans_lag)) %>%
ungroup()
# separate all zero claims from the claims that have payments
zero_claims <- dplyr::filter(claims, status == 0)
first_trans <- dplyr::filter(claims, status == 1)
subsequent_trans <- left_join(trans_tbl, first_trans, by = "claim_num") %>%
filter(!is.na(accident_date))
n_trans <- nrow(subsequent_trans)
# simulate subsequent transaction payments
subsequent_trans <- subsequent_trans %>%
mutate(payment = payment_fun(n_trans),
case = pmax(case * rnorm(n_trans, 1.5, 0.1) - payment, 500),
transaction_date = report_date + trans_lag) %>%
select(-trans_lag)
trans <- bind_rows(zero_claims, first_trans, subsequent_trans) %>%
arrange(transaction_date)
# add in a transaction number
trans$trans_num <- 1:nrow(trans)
# set final trans status to closed and case to 0
trans <- trans %>%
arrange(trans_num) %>%
group_by(claim_num) %>%
mutate(final_trans = ifelse(trans_num == max(trans_num), TRUE, FALSE),
status = ifelse(final_trans, 0, 1),
case = ifelse(final_trans, 0, case),
status = ifelse(status == 0, "Closed", "Open"),
paid = round(cumsum(payment), 0),
case = round(case, 0),
payment = round(payment, 0)) %>%
select(-final_trans) %>%
arrange(accident_date) %>%
ungroup()
saveRDS(trans, file = "trans.RDS")
Related Work¶
- Simulation Machine by KasaAI
- rsvr package by KasaAI and it corresponding Google Design Doc
Related¶
Backlinks:
list from [[Simulating Actuarial Claims Data with R]] AND -"Changelog"