Deeptriangle Save
DeepTriangle: A Deep Learning Approach to Loss Reserving
DeepTriangle
This is the companion repository to the paper DeepTriangle: A Deep Learning Approach to Loss Reserving.
Experiments
To get started, either clone the repo and build the R package, or install with
devtools::install_github("kasaai/deeptriangle")
You will also need the insurance package, which can be installed with
devtools::install_github("kasaai/insurance")
The experiments can be found in analysis/main.R. It is recommended
that you use a GPU since many instances of the models are fit.
For convenience, we provide a predictions.feather file in the release.
predictions <- feather::read_feather("datasets/predictions.feather")
model_results <- dt_compute_metrics(predictions) %>%
bind_rows(stochastic_model_results) %>%
bind_rows(read_csv("datasets/automl_results.csv")) %>%
gather(metric, value, mape, rmspe)
dt_tabulate_metrics(model_results, metric = "mape") %>%
knitr::kable(booktabs = "T", digits = 3)
| lob | Mack | ODP | CIT | LIT | AutoML | DeepTriangle |
|---|---|---|---|---|---|---|
| commercial_auto | 0.060 | 0.217 | 0.052 | 0.052 | 0.068 | 0.043 |
| other_liability | 0.134 | 0.223 | 0.165 | 0.152 | 0.142 | 0.109 |
| private_passenger_auto | 0.038 | 0.039 | 0.038 | 0.040 | 0.036 | 0.025 |
| workers_compensation | 0.053 | 0.105 | 0.054 | 0.054 | 0.067 | 0.046 |
To create actual vs. predicted plots, use the dt_plot_predictions()
function. Here are successful and unsuccessful examples of the model’s
forecasting attempts.
Company 1767 commercial auto.
Company 337 workers’ compensation.
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 keras model. See
the source code of dt_model() for a template.
For more details on the keras R package, visit https://keras.rstudio.com/.
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