mages / deeptriangle

DeepTriangle: A Deep Learning Approach to Loss Reserving

DeepTriangle: A Deep Learning Approach to Loss Reserving

This is the companion repository to the DeepTriangle paper. A preprint can be found at https://arxiv.org/abs/1804.09253.

Reproducing experiments

To get started, either clone the repo and build the R package, or install with

devtools::install_github("kevinykuo/deeptriangle)

You will also need the insurance package, which can be installed with

devtools::install_github("kevinykuo/insurance")

The following lines of code will instantiate and fit the model for each line of business then combine the forecasts in a data frame:

library(deeptriangle)
library(tidyverse)
library(keras)

# set seed for reproducibility
use_session_with_seed(2018)

data <- dt_data_prep(insurance::schedule_p, dt_group_codes)

lobs <- c("workers_compensation", "commercial_auto",
          "private_passenger_auto", "other_liability")

predictions <- lobs %>%
  map(
    function(x) {
      # clear session and instantiate model
      k_clear_session()
      model <- dt_model()

      c(training_data, validation_data, full_training_data) %<-%
        dt_train_validation_split(data[[x]])

      message("Training - ", x)

      # determine number of epochs
      epochs_to_train <- dt_optimize_epochs(
        model, training_data, validation_data
      )

      # clear session and reinstantiate model
      k_clear_session()
      model <- dt_model()

      # fit model to all training data
      history <- model %>%
        fit(x = full_training_data$x,
            y = full_training_data$y,
            batch_size = 128,
            epochs = epochs_to_train,
            verbose = 0)
      dt_compute_predictions(model, data[[x]])
    }) %>%
  bind_rows()

We can then compute performance metrics…

model_results <- dt_compute_metrics(predictions) %>%
  bind_rows(stochastic_model_results) %>%
  gather(metric, value, mape, rmspe)

and tabulate the results:

dt_tabulate_metrics(model_results, metric = "mape") %>%
  knitr::kable(booktabs = "T", digits = 3)

lob	Mack	ODP	CIT	LIT	CSR	DeepTriangle
commercial_auto	0.060	0.217	0.052	0.052	0.074	0.060
other_liability	0.134	0.223	0.165	0.152	0.292	0.121
private_passenger_auto	0.038	0.039	0.038	0.040	0.037	0.026
workers_compensation	0.053	0.105	0.054	0.054	0.075	0.039

dt_tabulate_metrics(model_results, metric = "rmspe") %>%
  knitr::kable(booktabs = "T", digits = 3)

lob	Mack	ODP	CIT	LIT	CSR	DeepTriangle
commercial_auto	0.080	0.822	0.076	0.074	0.126	0.085
other_liability	0.202	0.477	0.220	0.209	0.843	0.171
private_passenger_auto	0.061	0.063	0.057	0.060	0.055	0.036
workers_compensation	0.079	0.368	0.080	0.080	0.159	0.064

To create actual vs. predicted plots, use the dt_plot_predictions() function.

# devtools::install_github("thomasp85/patchwork")
library(patchwork)
paid_plot <- dt_plot_predictions(predictions, "337", "workers_compensation",
                    "paid_loss")
case_plot <- dt_plot_predictions(predictions, "337", "workers_compensation",
                    "claims_outstanding")
paid_plot + case_plot + plot_layout(ncol = 1)

Testing different architectures

If you would like to try out different architectures or hyperparameters, you can do so by providing a function that returns a compiled keras model. See the source code of dt_model() for a template. In order to utilize other functions in this package, the inputs and outputs of your custom function have to match those of dt_model(). You can also implement different early stopping criteria by providing a function similar to dt_optimize_epochs().

For more details on the keras R package, visit https://keras.rstudio.com/.