Notes from Warsaw R meetup

December 1, 2015

(This article was first published on mages' blog, and kindly contributed to R-bloggers)

I had the great pleasure time to attend the Warsaw R meetup last Thursday. The organisers Olga Mierzwa and Przemyslaw Biecek had put together an event with a focus on R in Insurance (btw, there is a conference with the same name), discussing examples of pricing and reserving in general and life insurance.

Experience vs. Data

I kicked off with some observations of the challenges in insurance pricing. Accidents are thankfully rare events, that’s why we buy insurance. Hence, there is often not a lot of claims data available for pricing. Combining the information from historical data and experts with domain knowledge can provide a rich basis for the assessment of risk. I presented some examples using Bayesian analysis to understand the probability of an event occurring. Regular readers of my blog will recognise the examples from earlier posts. You find my slides on GitHub.

Download slides

Non-life insurance in R

Emilia Kalarus from Triple A shared some of her experience of using R in non-life insurance companies. She focused on the challenges in working across teams, with different systems, data sets and mentalities.

As an example Emilia talked about the claims reserving process, which in her view should be embedded in the full life cycle of insurance, namely product development, claims, risk and performance management. Following this thought she presented an idea for claims reserving that models the live of a claim from not incurred and not reported (NINR), to incurred but not reported (IBNR), reported but not settled (RBNS) and finally paid.

Stochastic mortality modelling

The final talk was given by Adam Wróbel from the life insurer Nationale Nederlanden, discussing stochastic mortality modelling. Adam’s talk on analysing mortality made me realise that life and non-life insurance companies may be much closer to each other than I thought.

Although life and non-life companies are usually separated for regulatory reasons, they both share the fundamental challenge of predicting future cash-flows. An example where the two industries meet is product liability.

Over the last century technology has changed our environment fundamentally, more so then ever before. Yet, we still don’t know which long term impact some of the new technologies and products will have on our life expectancy. Some will prolong our lives, others may make us ill.

A classic example is asbestos, initial regraded as a miracle mineral, as it was impossible to set on fire, abundant, cheap to mine, and easy to manufacture. Not surprisingly it was widely used until it was linked to cause cancer. Over the last 35 years the non-life insurance industry has paid well in excess of hundred billion dollars in compensations.

To leave a comment for the author, please follow the link and comment on their blog: mages' blog. offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...

If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.

Search R-bloggers


Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)