Package {ReSurv}

Type:	Package
Depends:	R (≥ 4.1.0)
Title:	Machine Learning Models for Predicting Claim Counts
Version:	1.1.0
Description:	Prediction of claim counts using the feature based development factors introduced in the manuscript Hiabu M., Hofman E. and Pittarello G. (2023) <doi:10.48550/arXiv.2312.14549>. Implementation of Neural Networks, Extreme Gradient Boosting, and Cox model with splines to optimise the partial log-likelihood of proportional hazard models.
URL:	https://github.com/edhofman/ReSurv, https://edhofman.github.io/ReSurv/
BugReports:	https://github.com/edhofman/ReSurv/issues
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Imports:	stats, dplyr (≥ 1.1.0), actuar, fastDummies, data.table, purrr, tidyr, ggplot2, lubridate, survival, SynthETIC, xgboost
Encoding:	UTF-8
Suggests:	bshazard, clmplus, knitr, torch, rmarkdown, rpart, testthat (≥ 3.0.0)
VignetteBuilder:	knitr, rmarkdown
RoxygenNote:	7.3.3
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2026-09-15 08:29:57 UTC; pwt887
Author:	Emil Hofman [aut, cre, cph], Gabriele Pittarello [aut, cph], Munir Hiabu [aut, cph]
Maintainer:	Emil Hofman <emil_hofman@hotmail.dk>
Repository:	CRAN
Date/Publication:	2026-09-15 08:50:02 UTC

ReSurv: Individual claim-count reserving

Description

Fit reverse-time hazard models with ReSurv, following preprocessing with IndividualDataPP. Use ReSurvCV for hyperparameter selection, predictReserve for count forecasts, and Score_Reserving to evaluate realized claims.

Author(s)

Maintainer: Emil Hofman emil_hofman@hotmail.dk [copyright holder]

Authors:

• Gabriele Pittarello gabriele.pittarello@uniroma1.it (ORCID) [copyright holder]

• Munir Hiabu mh@math.ku.dk (ORCID) [copyright holder]

See Also

Useful links:

• https://github.com/edhofman/ReSurv

• https://edhofman.github.io/ReSurv/

• Report bugs at https://github.com/edhofman/ReSurv/issues

Individual Data Pre-Processing

Description

This function pre-processes the data for the application of a ReSurv model.

Usage

IndividualDataPP(
  data,
  id = NULL,
  continuous_features = NULL,
  categorical_features = NULL,
  accident_period,
  calendar_period,
  input_time_granularity = "months",
  output_time_granularity = "quarters",
  years = NULL,
  calendar_period_extrapolation = FALSE,
  continuous_features_spline = NULL,
  degrees_cf = 3,
  degrees_of_freedom_cf = 4,
  degrees_cp = 3,
  degrees_of_freedom_cp = 4
)

Arguments

Details

Accident and reporting periods are indexed from one. Development time is DP_i = RP_i - AP_i + 1; reverse development time is DP_rev_i = DP_max - DP_i + 1, and truncation time is TR_i = AP_i - 1. Training retains observed rows with DP_rev_i > TR_i.

The conversion factor is the input time unit divided by the output time unit (for example, 1/3 for months to quarters). Accident and calendar periods are grouped using ceiling; development periods also account for the position of the accident period within each output period. Days use a 360-day year for the development horizon.

Value

An IndividualDataPP list containing training.data (observed rows), full.data (all encoded rows), and data_information (conversion factor, input/output formulas, feature names, time units, horizon, and original column names).

After pre-processing, we provide a standard encoding for the time components. This regards the output in training.data. In the ReSurv notation:

• AP_i: Input granularity accident period.

• AP_o: Output granularity accident period.

• DP_i: Input granularity development period in forward time.

• DP_rev_i: Input granularity development period in reverse time.

• DP_rev_o: Output granularity development period in reverse time.

• TR_i: Input granularity truncation time.

• TR_o: Output granularity truncation time.

• I: event indicator, under this framework is equal to one for each entry.

References

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)

individual_data <- IndividualDataPP(data = input_data_0,
categorical_features = "claim_type",
continuous_features = "AP",
accident_period = "AP",
calendar_period = "RP",
input_time_granularity = "years",
output_time_granularity = "years",
years = 2)

Fit ReSurv models on the individual data.

Description

This function fits and computes the reserves for the ReSurv models

Usage

ReSurv(
  IndividualDataPP,
  hazard_model = "COX",
  tie = "efron",
  baseline = "spline",
  continuous_features_scaling_method = "minmax",
  random_seed = 1,
  hparameters = list(),
  percentage_data_training = 0.8,
  grouping_method = "exposure",
  check_value = 1.85,
  eta = 0.5,
  simplifier = TRUE
)

## Default S3 method:
ReSurv(
  IndividualDataPP,
  hazard_model = "COX",
  tie = "efron",
  baseline = "spline",
  continuous_features_scaling_method = "minmax",
  random_seed = 1,
  hparameters = list(),
  percentage_data_training = 0.8,
  grouping_method = "exposure",
  check_value = 1.85,
  eta = 0.5,
  simplifier = TRUE
)

## S3 method for class 'IndividualDataPP'
ReSurv(
  IndividualDataPP,
  hazard_model = "COX",
  tie = "efron",
  baseline = "spline",
  continuous_features_scaling_method = "minmax",
  random_seed = 1,
  hparameters = list(),
  percentage_data_training = 0.8,
  grouping_method = "exposure",
  check_value = 1.85,
  eta = 0.5,
  simplifier = TRUE
)

Arguments

Details

The model fit uses the theoretical framework of Hiabu et al. (2023), that relies on the correspondence between hazard models and development factors:

Neural networks use the native R torch backend. Install its runtime with torch::install_torch() before fitting a neural network.

The ReSurv package assumes proportional hazard models. Given an i.i.d. sample \left\{y_i,x_i\right\}_{i=1, \ldots, n} the individual hazard at time t is:

\lambda_i(t)=\lambda_0(t)e^{y_i(x_i)}

Composed of a baseline \lambda_0(t) and a proportional effect e^{y_i(x_i)}.

Currently, the implementation allows to optimize the partial likelihood (concerning the proportional effects) using one of the following statistical learning approaches:

• COX

• Neural Networks

• eXtreme Gradient Boosting

Value

A ReSurvFit list containing model.out (design matrix and fitted backend model), hazard_frame (risk scores, baseline, hazards, development factors and survival probabilities), data_information (preprocessing and reserving metadata), and fit_information (model name, training loss is_lkh, validation loss os_lkh, and eta). Use predict() or predictReserve() to obtain claim-count predictions.

References

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.

Therneau, T. M., & Lumley, T. (2015). Package ‘survival’. R Top Doc, 128(10), 28-33.

Katzman, J. L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., & Kluger, Y. (2018). DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC medical research methodology, 18(1), 1-12.

Chen, T., He, T., Benesty, M., & Khotilovich, V. (2019). Package ‘xgboost’. R version, 90, 1-66.

Examples

input_data_0 <- data_generator(
random_seed = 1964,
scenario = "alpha",
time_unit = 1,
years = 4,
period_exposure = 100)

individual_data <- IndividualDataPP(data = input_data_0,
categorical_features = "claim_type",
continuous_features = "AP",
accident_period = "AP",
calendar_period = "RP",
input_time_granularity = "years",
output_time_granularity = "years",
years=4)


resurv_fit_cox <- ReSurv(individual_data,
hazard_model = "COX",
eta = 0)

K-fold cross-validation of a ReSurv model

Description

K-fold cross-validation of a ReSurv model

Usage

ReSurvCV(
  IndividualDataPP,
  model,
  hparameters_grid,
  folds,
  random_seed,
  continuous_features_scaling_method = "minmax",
  print_every_n = 1L,
  nrounds = NULL,
  early_stopping_rounds = NULL,
  epochs = 1,
  parallel = FALSE,
  ncores = 1,
  num_workers = 0,
  verbose = FALSE,
  verbose.cv = FALSE,
  cv_data_subsample = 1
)

Arguments

Value

An object of class ReSurvCV containing out.cv (all combinations and mean training and validation losses), out.cv.best.oos (the row with smallest validation loss), and hparameters.best, suitable for the hparameters argument of ReSurv(). Cross-validation does not refit the final model.

Score reserving predictions

Description

Compare predictions with realized claim counts in the lower triangle.

Usage

Score_Reserving(
  models,
  newdata,
  scoring_metrics = c("EI", "R-tot", "R-cell-wise", "R-cal-wise"),
  granularity = c("output", "input"),
  chain_ladder = TRUE,
  clmplus_benchmark = NULL,
  ...
)

Arguments

Value

A list of score tables, one per requested metric, with class Score_Reserving and a granularity attribute.

Individual data generator

Description

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

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)

Plot of the development factors

Description

Plots the development factors by group code.

Usage

## S3 method for class 'ReSurvPredict'
plot(
  x,
  granularity = "input",
  group_code = 1,
  color_par = "royalblue",
  linewidth_par = 2.5,
  ylim_par = NULL,
  ticks_by_par = NULL,
  base_size_par = NULL,
  title_par = NULL,
  x_text_par = NULL,
  plot.title.size_par = NULL,
  ...
)

Arguments

Value

ggplot2 of the development factors

Predict IBNR frequency

Description

This function predicts the results from the ReSurv fits.

Usage

## S3 method for class 'ReSurvFit'
predict(
  object,
  newdata = NULL,
  lower_triangular_output = FALSE,
  minimal_output = FALSE,
  check_value = 1.85,
  ...
)

Arguments

Value

A ReSurvPredict object with fitted predictions, long triangle outputs, predicted counts, and optional lower-triangle outputs.

Predict deterministic reserve table

Description

Predict deterministic reserve table

Predict deterministic reserve table from a ReSurvFit object

Usage

predictReserve(object, ...)

## S3 method for class 'ReSurvFit'
predictReserve(object, granularity = c("output", "input"), ...)

Arguments

Value

A data.table with accident period AP, development period DP, calendar period CP, and predicted claim count IBNR. These are claim counts, not monetary reserves.

A data.table with columns AP, DP, CP, IBNR.

See Also

predict.ReSurvFit

Print method for ReSurvCV objects

Description

Print method for ReSurvCV objects

Usage

## S3 method for class 'ReSurvCV'
print(x, ...)

Arguments

Print summary of IBNR predictions

Description

Gives overview of IBNr predictions

Usage

## S3 method for class 'summaryReSurvPredict'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

Arguments

Value

print of summary of predictions

Summary of IBNR predictions

Description

Gives overview of IBNR predictions

Usage

## S3 method for class 'ReSurvPredict'
summary(object, granularity = "input", ...)

Arguments

Value

Summary of predictions