ReSurv

ReSurv is an R software for predicting IBNR claims. The software includes tools for synthetic data generation, data pre-processing, hyperparameters tuning, model estimation and prediction.

The package is based on the approach illustrated in Hiabu M., Hofman E., and Pittarello G. (2023) and estimates feature dependent development factors using individual reserving data.

Installation

The developers version of the package can be installed from GitHub.

devtools::install_github('https://github.com/edhofman/ReSurv')

We suggest to import the package in R using

library(ReSurv)
reticulate::use_virtualenv('pyresurv')

Available Machine Learning (ML) models

There is a one-to-one relationship between development factors and hazard rates (Hiabu et al. (2023)). The package implements extends the following machine learning algorithms for proportional hazard models:

• Cox model with splines (COX, Gray (1992)).

• Neural Networks (NN, Katzman et al. (2018)).

• eXtreme Gradient Boosting (XGB, Chen et al. (2016)).

ReSurv extends COX, NN, and XGB to account for ties in left-truncated and right-censored observations.

Further reading material

Visit the package vignettes for learning:

• Synthetic data genration.
• Bayesian Hyperparameters tuning, Snoek (2012).
• Hiabu et al. (2023) replication code.

The full replication code the main manuscript can be found at the GitHub folder.

References

• Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794).

• Gray, R. J. (1992). Flexible methods for analyzing survival data using splines, with applications to breast cancer prognosis. Journal of the American Statistical Association, 87(420), 942-951.

• 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.

• Snoek, J., Larochelle, H., & Adams, R. P. (2012). Practical bayesian optimization of machine learning algorithms. Advances in neural information processing systems, 25.

• 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, 1-12.