Insurance

R in Insurance 2016 Programme

We are delighted to announce that the programme for the 4th R in Insurance conference at Cass Business School in London, 11 July 2016, have been finalised. Register by the end of May to get the early bird booking fee. The organisers gratefully acknowledge the sponsorship of Verisk, Mirai Solutions, Applied AI, Studio, CYBAEA and Oasis, without whom the event wouldn’t be possible. Agenda [09:00 - 10:00] Keynote 1:

Notes from Warsaw R meetup

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. DataI kicked off with some observations of the challenges in insurance pricing. Accidents are thankfully rare events, that’s why we buy insurance.

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.

Loss Developments via Growth Curves and Stan

Last week I posted a biological example of fitting a non-linear growth curve with Stan/RStan. Today, I want to apply a similar approach to insurance data using ideas by David Clark [1] and James Guszcza [2]. Instead of predicting the growth of dugongs (sea cows), I would like to predict the growth of cumulative insurance loss payments over time, originated from different origin years. Loss payments of younger accident years are just like a new generation of dugongs, they will be small in size initially, grow as they get older, until the losses are fully settled.

R in Insurance 2016

Following the successful 3rd R in Insurance conference in Amsterdam this year, we will return to London next year. We will be back at Cass Business School, 11 July 2016. The event will focus again on the use of R in insurance, bringing together experts from industry and academia with a diverse background of disciplines, such as actuarial science, catastrophe modelling, finance, statistics and computer science. We are delighted to announce or keynote speakers already: Dan Murphy and Mario V.

ChainLadder 0.2.2 is out with improved glmReserve function

We released version 0.2.2 of ChainLadder a few weeks ago. This version adds back the functionality to estimate the index parameter for the compound Poisson model in glmReserve using the cplm package by Wayne Zhang. Ok, what does this all mean? I will run through a couple of examples and look behind the scene of glmReserve. However, the clue is in the title, glmReserve is a function that uses a generalised linear model to estimate future claims, assuming claims follow a Tweedie distribution.

Notes from the Kölner R meeting, 18 September 2015

Last Friday the Cologne R user group came together for the 15th time. Since its inception over three years ago the group evolved from a small gathering in a pub into an active data science community, covering wider topics than just R. Still, R is the link and clue between the different interests. Last Friday’s agenda was a good example of this, with three talks touching on workflow management, web development and risk analysis.

Notes from the 3rd R in Insurance Conference

Photo: Arthur CharpentierThe R in Insurance conference in Amsterdam was a sold out success! Congratulations to the organising committee at the University of Amsterdam, and many thanks to our sponsors: Milliman, RStudio, CYBAEA, Deloitte, a.s.r., Triple A Risk Finance, AEGON, Delta Lloyd Amsterdam, QBE Re and APPLIED AI This one-day conference focused once more on applications in insurance and actuarial science that use R. Topics covered included reserving, pricing, loss modelling, the use of R in a production environment and more.

ChainLadder 0.2.1 released

Over the weekend we released version 0.2.1 of the ChainLadder package for claims reserving on CRAN. New FeaturesNew function PaidIncurredChain by Fabio Concina, based on the 2010 Merz & Wüthrich paper Paid-incurred chain claims reserving methodFunctions plot.MackChainLadder and plot.BootChainLadder gained new argument which, allowing users to specify which sub-plot to display. Thanks to Christophe Dutang for this suggestion. Output of plot(MackChainLadder(MW2014, est.sigma=“Mack”), which=3:6)ChangesUpdated NAMESPACE file to comply with new R CMD checks in R-3.

Communicating Risk at the Bay Area R User Group

I will be speaking at the Bay Area User Group meeting tonight about Communicating Risk. Anthony Goldbloom from Kaggle and Karim Chine from ElasticR will be there as well. The meeting will be at Microsoft in Mountain View. Later this week I will give a similar presentation at the R in Finance conference in Chicago. Please get in touch if you are around and would like to share a coffee with me.

Posterior predictive output with Stan

I continue my Stan experiments with another insurance example. Here I am particular interested in the posterior predictive distribution from only three data points. Or, to put it differently I have a customer of three years and I’d like to predict the expected claims cost for the next year to set or adjust the premium. The example is taken from section 16.17 in Loss Models: From Data to Decisions [1]. Some time ago I used the same example to get my head around a Bayesian credibility model.

Predicting events, when they haven't happened yet

Suppose you have to predict the probabilities of events which haven’t happened yet. How do you do this? Here is an example from the 1950s when Longley-Cook, an actuary at an insurance company, was asked to price the risk for a mid-air collision of two planes, an event which as far as he knew hadn’t happened before. The civilian airline industry was still very young, but rapidly growing and all Longely-Cook knew was that there were no collisions in the previous 5 years [1].

ChainLadder 0.2.0 adds Solvency II CDR functions

ChainLadder is an R package that provides statistical methods and models for claims reserving in general insurance. With version 0.2.0 we added new functions to estimate the claims development result (CDR) as required under Solvency II. Special thanks to Alessandro Carrato, Giuseppe Crupi and Mario Wüthrich who have contributed code and documentation. New FeaturesNew generic function CDR to estimate the one year claims development result. S3 methods for the Mack and bootstrap model have been added already:

R in Insurance 2015: Registration Opened

The registration for the third conference on R in Insurance on Monday 29 June 2015 at the University of Amsterdam has opened. This one-day conference will focus again on applications in insurance and actuarial science that use R, the lingua franca for statistical computation. The intended audience of the conference includes both academics and practitioners who are active or interested in the applications of R in insurance. Invited talks will be given by:

First steps with ChainLadder: Import triangle from Excel into R

Taking the first step is often the hardest: getting data from Excel into R. Suppose you would like to use the ChainLadder package to forecast future claims payments for a run-off triangle that you have stored in Excel.

How do you get the triangle into R and execute a reserving function, such as MackChainLadder? Well, there are many ways to do this and the ChainLadder package vignette, as well as the R manual on Data Import/Export has all of the details, but here is a quick and dirty solution using a CSV-file.

Visualising the seasonality of Atlantic windstorms

Last week Arthur Charpentier sketched out a Markov spatial process to generate hurricane trajectories. Here, I would like to take another look at the data Arthur used, but focus on its time component. According to the Insurance Information Institute, a normal season, based on averages from 1980 to 2010, has 12 named storms, six hurricanes and three major hurricanes. The usual peak months of August and September passed without any major catastrophes this year, but the Atlantic hurricane season is not over yet.

ChainLadder 0.1.8 released

Over the weekend we released version 0.1.8 of the ChainLadder package for claims reserving on CRAN. What is claims reserving?The insurance industry, unlike other industries, does not sell products as such but promises. An insurance policy is a promise by the insurer to the policyholder to pay for future claims for an upfront received premium. As a result insurers don’t know the upfront cost for their service, but rely on historical data analysis and judgement to predict a sustainable price for their offering.

Notes from the 2nd R in Insurance Conference

The 2nd R in Insurance conference took place last Monday, 14 July, at Cass Business School London. This one-day conference focused once more on applications in insurance and actuarial science that use R. Topics covered included reserving, pricing, loss modelling, the use of R in a production environment and more. In the first plenary session, Montserrat Guillen (Riskcenter, University of Barcelona) and Leo Guelman (Royal Bank of Canada, RBC Insurance) spoke about the rise of uplift models.

Last chance to register for the R in Insurance conference

The registration for the 2nd R in Insurance conference at Cass Business School London will close this Friday, 4 July. The programme includes talks from international practitioners and leading academics, see below. For more details and registration visit: http://www.rininsurance.com. Still unsure? Review some impressions and presentations from last year’s conference. On behalf of the committee and sponsors, Mango Solutions, Cybaea, RStudio and PwC, we look forward to seeing you in London on 14 July!

Registration for the 2014 'R in Insurance' conference has opened

The registration for the second conference on R in Insurance on Monday 14 July 2014 at Cass Business School in London has opened. This one-day conference will focus again on applications in insurance and actuarial science that use R, the lingua franca for statistical computation. Topics covered may include actuarial statistics, capital modelling, pricing, reserving, reinsurance and extreme events, portfolio allocation, advanced risk tools, high-performance computing, econometrics and more. All topics will be discussed within the context of using R as a primary tool for insurance risk management, analysis and modelling.

R in Insurance Conference, London, 14 July 2014

Following the very positive feedback that Andreas and I have received from delegates of the first R in Insurance conference in July of this year, we are planning to repeat the event next year. We have already reserved a bigger auditorium. The second conference on R in Insurance will be held on Monday 14 July 2014 at Cass Business School in London, UK. This one-day conference will focus again on applications in insurance and actuarial science that use R, the lingua franca for statistical computation.

Not only verbs but also believes can be conjugated

Following on from last week, where I presented a simple example of a Bayesian network with discrete probabilities to predict the number of claims for a motor insurance customer, I will look at continuous probability distributions today. Here I follow example 16.17 in Loss Models: From Data to Decisions [1]. Suppose there is a class of risks that incurs random losses following an exponential distribution (density $f(x) = \Theta {e}^{- \Theta x}$) with mean $1/\Theta$.

Predicting claims with a Bayesian network

Here is a little Bayesian Network to predict the claims for two different types of drivers over the next year, see also example 16.15 in [1]. Let’s assume there are good and bad drivers. The probabilities that a good driver will have 0, 1 or 2 claims in any given year are set to 70%, 20% and 10%, while for bad drivers the probabilities are 50%, 30% and 20% respectively. Further I assume that 75% of all drivers are good drivers and only 25% would be classified as bad drivers.

Why models need a certain culture to flourish

About half a year ago Ian Branagan, Chief Risk Officer of Renaissance Re - a Bermudian reinsurance company with a focus on property catastrophe insurance, gave a talk about the usage of models in risk management and how they evolved over the last twenty years. Ian’s presentation, titled with the famous quote of George E.P. Box: “All models are wrong, but some are useful”, was part of the lunch time lecture series of talks at Lloyd’s, organised by the Insurance Institute of London.

ChainLadder 0.1.6 released with chain-ladder factor models

Version 0.1.6 of the ChainLadder package has been released and is already available from CRAN. The new version adds the function CLFMdelta. CLFMdelta finds consistent weighting parameters delta for a vector of selected age-to-age chain-ladder factors for a given run-off triangle. The added functionality was implemented by Dan Murphy, who is the co-author of the paper A Family of Chain-Ladder Factor Models for Selected Link Ratios by Bardis, Majidi, Murphy. You find a more detailed explanation with R code examples on Dan’s blog and see also his slides from the CAS spring meeting.

R in Insurance: Presentations are online

The programme and the presentation files of the first R in Insurance conference have been published on GitHub. Front slides of the conference presentations Additionally to the slides many presenters have made their R code available as well: Alexander McNeil shared the examples of the CreditRisk+ model he presented. Lola Miranda made a Windows version of the double chain-ladder package DCL available via the Cass knowledge web site.Alessandro Carrato’s 1-year re-reserving code is hosted on the ChainLadder project web site.

Quick review: R in Insurance Conference

Yesterday the first R in Insurance conference took place at Cass Business School in London. I think the event went really well, but as a member of the organising committee my view is probably skewed. Still, we had a variety of talks, a full house, a great conference dinner and to top it all, the Tower Bridge opened while we had our drinks at the end of the evening. I will post a more complete review in the future with links to the files of the presentations and R code, once we had a chance to collate all the information.

Claims Inflation - a known unknown

Over the last year I worked with two colleagues of mine on the subject of inflation and claims inflation in particular. I didn’t expect it to be such a challenging topic, but we ended up with more questions than answers. The key question and biggest challenge is to define what inflation, or indeed claims inflation actually is and how to measure it. We published a summary of our thoughts and findings in this month’s issue of The Actuary.

R in Insurance: Programme and Abstracts published

I am delighted to announce that the programme and abstracts for the first R in Insurance conference at Cass Business School in London, 15 July 2013, have been published. The conference committee received strong abstracts from academia and the industry, covering: PricingReservingData miningCapital modellingAutomate reportingCatastrophe modellingHigh-performance computingSoftware development managementRegister by the end of May to get the early bird booking fee. We gratefully acknowledge the sponsorship of Mango Solutions and CYBAEA, without whom the event wouldn’t be possible.

ChainLadder 0.1.5-6 released on CRAN

Last week we released version 0.1.5-6 of the ChainLadder package on CRAN. The ChainLadder package provides statistical models, which are typically used for the estimation of outstanding claims reserves in general insurance. The package vignette gives an overview of the package functionality. Output of plot(MackChainLadder(GenIns)) Since the last CRAN release Dan Murphy added new features to the MackChainLadder function and we fixed a bug in BootChainLadder. Here are he details:

Submit a talk for the first R in Insurance conference

The registration for the first R in Insurance is open and there is still time to submit a talk / lightning talk. The conference will take place at Cass Business School in London on Monday, 15 July 2013. This is the Monday following the useR! 2013 conference in Spain. Thus, if you come from overseas to Spain, why not stop in London on your way back? All further information and registration details are available on the Cass Business School conference site.

Registration for 'R in Insurance' conference has opened

The registration for the first conference on R in Insurance on Monday 15 July 2013 at Cass Business School in London has opened. The intended audience of the conference includes both academics and practitioners who are active or interested in the applications of R in insurance. The 2013 R in Insurance conference builds upon the success of the R in Finance and R/Rmetrics events. We expect invited keynote lectures by:

New Data Scientist role at Lloyd's

Reserving based on log-incremental payments in R, part III

This is the third post about Christofides’ paper on Regression models based on log-incremental payments [1]. The first post covered the fundamentals of Christofides’ reserving model in sections A - F, the second focused on a more realistic example and model reduction of sections G - K. Today’s post will wrap up the paper with sections L - M and discuss data normalisation and claims inflation. I will use the same triangle of incremental claims data as introduced in my previous post.

Reserving based on log-incremental payments in R, part II

Following on from last week’s post I will continue to go through the paper Regression models based on log-incremental payments by Stavros Christofides [1]. In the previous post I introduced the model from the first 15 pages up to section F. Today I will progress with sections G to K which illustrate the model with a more realistic incremental claims payments triangle from a UK Motor Non-Comprehensive account:# Page D5.17

Reserving based on log-incremental payments in R, part I

A recent post on the PirateGrunt blog on claims reserving inspired me to look into the paper Regression models based on log-incremental payments by Stavros Christofides [1], published as part of the Claims Reserving Manual (Version 2) of the Institute of Actuaries. The paper is available together with a spread sheet model, illustrating the calculations. It is very much based on ideas by Barnett and Zehnwirth, see [2] for a reference.

R in Insurance Conference, London, 15 July 2013

The first conference on R in Insurance will be held on Monday 15 July 2013 at Cass Business School in London, UK. The intended audience of the conference includes both academics and practitioners who are active or interested in the applications of R in insurance. This one-day conference will focus on applications in insurance and actuarial science that use R, the lingua franca for statistical computation. Topics covered may include actuarial statistics, capital modelling, pricing, reserving, reinsurance and extreme events, portfolio allocation, advanced risk tools, high-performance computing, econometrics and more.

Data.table rocks! Data manipulation the fast way in R

I really should make it a habit of using data.table. The speed and simplicity of this R package are astonishing. Here is a simple example: I have a data frame showing incremental claims development by line of business and origin year. Now I would like add a column with the cumulative claims position for each line of business and each origin year along the development years. It’s one line with data.

Claims reserving in R: ChainLadder 0.1.5-4 released

Last week we released version 0.1.5-4 of the ChainLadder package on CRAN. The R package provides methods which are typically used in insurance claims reserving. If you are new to R or insurance check out my recent talk on Using R in Insurance. The chain-ladder method which is a popular method in the insurance industry to forecast future claims payments gave the package its name. However, the ChainLadder package has many other reserving methods and models implemented as well, such as the bootstrap model demonstrated below.

From guts to data driven decision making

Source: Wikipedia, License: CC0 There is a wonderful cartoon by Loriot, a German humorist (1923 - 2011), about a couple sitting at a breakfast table, arguing about how to boil a four-and-a-half minute egg. The answer appears simple, but husband and wife argue about how to measure the time using experience, feelings and expert judgment (wife) or a clock (husband). The whole sketch is hilarious and is often regarded as a fine observation of miss-communication.

Using R in Insurance, Presentation at GIRO 2012

Every year the UK’s general insurance actuarial community organises a big conference, which they call GIRO, short for General Insurance Research Organising committee. This year’s conference is in Brussels from 18 - 21 September 2012. Despite the fact that Brussels is actually in Belgium the UK actuaries will travel all the way to enjoy good beer and great talks. On Wednesday morning I will run a session on Using R in insurance.

Stochastic reserving with R: ChainLadder 0.1.5-1 released

Today we published version 0.1.5-1 of the ChainLadder package for R. It provides methods which are typically used in insurance claims reserving to forecast future claims payments. Claims development and chain-ladder forecast of the RAA data set using the Mack methodThe package started out of presentations given at the Stochastic Reserving Seminar at the Institute of Actuaries in 2007, 2008 and 2010, followed by talks at CAS meetings in 2008 and 2010.