# Hierarchical Loss Reserving with Stan

I continue with the growth curve model for loss reserving from last week’s post. Today, following the ideas of James Guszcza [2] I will add an hierarchical component to the model, by treating the ultimate loss cost of an accident year as a random effect. Initially, I will use the `nlme`

R package, just as James did in his paper, and then move on to Stan/RStan [6], which will allow me to estimate the full distribution of future claims payments.

The growth curve describes the proportion of claims paid up to a given development period compared to the ultimate claims cost at the end of time, hence often called development pattern. Cumulative distribution functions are often considered, as they increase monotonously from 0 to 100%. Multiplying the development pattern with the expected ultimate loss cost gives me then the expected cumulative paid to date value.

However, what I’d like to do is the opposite, I know the cumulative claims position to date and wish to estimate the ultimate claims cost instead. If the claims process is fairly stable over the years and say, once a claim has been notified the payment process is quite similar from year to year and claim to claim, then a growth curve model is not unreasonable. Yet, the number and the size of the yearly claims will be random, e.g. if a windstorm, fire, etc occurs or not. Hence, a random effect for the ultimate loss cost across accident years sounds very convincing to me.

Here is James’ model as described in [2]:

\[ \begin{aligned} CL_{AY, dev} & \sim \mathcal{N}(\mu_{AY, dev}, \sigma^2_{dev}) \\ \mu_{AY,dev} & = Ult_{AY} \cdot G(dev|\omega, \theta)\\ \sigma_{dev} & = \sigma \sqrt{\mu_{dev}}\\ Ult_{AY} & \sim \mathcal{N}(\mu_{ult}, \sigma^2_{ult})\\ G(dev|\omega, \theta) & = 1 - \exp\left(-\left(\frac{dev}{\theta}\right)^\omega\right) \end{aligned} \]

The cumulative losses \(CL_{AY, dev}\) for a given accident year \(AY\) and development period \(dev\) follow a Normal distribution with parameters \(\mu_{AY, dev}\) and \(\sigma_{dev}\).

The mean itself is modelled as the product of an accident year specific ultimate loss cost \(Ult_{AY}\) and a development period specific parametric growth curve \(G(dev | \omega, \theta)\). The variance is believed to increase in proportion with the mean. Finally, the ultimate loss cost is modelled with a Normal distribution as well.

Assuming a Gaussian distribution of losses doesn’t sound quite intuitive to me, as loss are often skewed to the right, but I shall continue with this assumption here to make a comparison with [2] possible.

Using the example data set given in the paper I can reproduce the result in R with`nlme`

:
The fit looks pretty good, with only 5 parameters. See James’ paper for a more detailed discussion.

Let’s move this model into Stan. Here is my attempt, which builds on last week’s pooled model. With the generated quantities code block I go beyond the scope of the original paper, as I try to estimate the full posterior predictive distribution as well.The ‘trick’ is the line `mu[i] <- ult[origin[i]] * weibull_cdf(dev[i], omega, theta);`

where I have an accident year (here labelled origin) specific ultimate loss.

The notation `ult[origin[i]]`

illustrates the hierarchical nature in Stan’s language nicely.

The estimated parameters look very similar to the `nlme`

output above.

This looks all not too bad. The trace plots don’t show any particular patterns, apart from \(\sigma_{ult}\), which shows a little skewness.

The`generated quantities`

code block in Stan allows me to get also the predictive distribution beyond the current data range. Here I forecast claims up to development year 12 and plot the predictions, including the 95% credibility interval of the posterior predictive distribution with the observations.
The model seems to work rather well, even with the Gaussian distribution assumptions. Yet, it has still only 5 parameters. Note, this model doesn’t need an additional artificial tail factor either.### Conclusions

The Bayesian approach sounds to me a lot more natural than many classical techniques around the chain-ladder methods. Thanks to Stan, I can get the full posterior distributions on both, the parameters and predictive distribution. I find communicating credibility intervals much easier than talking about the parameter, process and mean squared error.

James Guszcza contributed to a follow-up paper with Y. Zhank and V. Dukic [3] that extends the model described in [2]. It deals with skewness in loss data sets and the autoregressive nature of the errors in a cumulative time series.

Frank Schmid offers a more complex Bayesian analysis of claims reserving in [4], while Jake Morris highlights the similarities between a compartmental model used in drug research and loss reserving [5].

Finally, Glenn Meyers published a monograph on *Stochastic Loss Reserving Using Bayesian MCMC Models* earlier this year [7] that is worth taking a look at.

## Update

Jake Morris and I published a new research paper on Hierarchical Compartmental Reserving Models:

Gesmann, M., and Morris, J. “Hierarchical Compartmental Reserving Models.” Casualty Actuarial Society, CAS Research Papers, 19 Aug. 2020, https://www.casact.org/sites/default/files/2021-02/compartmental-reserving-models-gesmannmorris0820.pdf

### References

[1] David R. Clark. *LDF Curve-Fitting and Stochastic Reserving: A Maximum Likelihood Approach.* Casualty Actuarial Society, 2003. CAS Fall Forum.

[2] James Guszcza. *Hierarchical Growth Curve Models for Loss Reserving*, 2008, CAS Fall Forum, pp. 146–173.

[3] Y. Zhang, V. Dukic, and James Guszcza. *A Bayesian non-linear model for forecasting insurance loss payments.* 2012. Journal of the Royal Statistical Society: Series A (Statistics in Society), 175: 637–656. doi: 10.1111/j.1467-985X.2011.01002.x

[4] Frank A. Schmid. *Robust Loss Development Using MCMC*. Available at SSRN. See also http://lossdev.r-forge.r-project.org/

[5] Jake Morris. *Compartmental reserving in R*. 2015. R in Insurance Conference.

[6] Stan Development Team. *Stan: A C++ Library for Probability and Sampling, Version 2.8.0*. 2015. http://mc-stan.org/.

*Stochastic Loss Reserving Using Bayesian MCMC Models.*Issue 1 of CAS Monograph Series. 2015.

### Session Info

```
R version 3.2.2 (2015-08-14)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: OS X 10.11.1 (El Capitan)
locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ChainLadder_0.2.3 rstan_2.8.0 ggplot2_1.0.1 Rcpp_0.12.1
[5] lattice_0.20-33
loaded via a namespace (and not attached):
[1] nloptr_1.0.4 plyr_1.8.3 tools_3.2.2
[4] digest_0.6.8 lme4_1.1-10 statmod_1.4.21
[7] gtable_0.1.2 nlme_3.1-122 mgcv_1.8-8
[10] Matrix_1.2-2 parallel_3.2.2 biglm_0.9-1
[13] SparseM_1.7 proto_0.3-10 coda_0.18-1
[16] gridExtra_2.0.0 stringr_1.0.0 MatrixModels_0.4-1
[19] lmtest_0.9-34 stats4_3.2.2 grid_3.2.2
[22] nnet_7.3-11 tweedie_2.2.1 inline_0.3.14
[25] cplm_0.7-4 minqa_1.2.4 actuar_1.1-10
[28] reshape2_1.4.1 car_2.1-0 magrittr_1.5
[31] scales_0.3.0 codetools_0.2-14 MASS_7.3-44
[34] splines_3.2.2 rsconnect_0.3.79 systemfit_1.1-18
[37] pbkrtest_0.4-2 colorspace_1.2-6 quantreg_5.19
[40] labeling_0.3 sandwich_2.3-4 stringi_1.0-1
[43] munsell_0.4.2 zoo_1.7-12
```

### Citation

For attribution, please cite this work as:Markus Gesmann (Nov 10, 2015) Hierarchical Loss Reserving with Stan. Retrieved from https://magesblog.com/post/2015-11-10-hierarchical-loss-reserving-with-stan/

@misc{ 2015-hierarchical-loss-reserving-with-stan,

author = { Markus Gesmann },

title = { Hierarchical Loss Reserving with Stan },

url = { https://magesblog.com/post/2015-11-10-hierarchical-loss-reserving-with-stan/ },

year = { 2015 }

updated = { Nov 10, 2015 }

}