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Actuarial Claims Data - Simulation R Script

Simulation Script (Transactional)

  • Source: https://gist.github.com/jimbrig/30262ae72e1ba20d0235fdedcc435150
  • Carbon: https://carbon.now.sh/MDUZMTZI1ERkJTzBamSt
  • Imagur: https://imgur.com/k4Y16Zp

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")

jimbrig/30262ae72e1ba20d0235fdedcc435150