Reserving using machine learning - an advanced example in R from a recent paper (Part 2)

last updated: 23 November 2020

General Insurance Machine learning in Reserving working party

Institute and Faculty of Actuaries

General Insurance Machine Learning in Reserving working party

The General Insurance Machine Learning in Reserving working party is an international group of over 40 actuaries, bringing together experts in this field from around the globe.

Our starting premise is that whilst machine learning techniques are widespread in pricing, they are not being adopted ‘on the ground’ in reserving (certainly in the UK). The idea of the working party is to help move this forward, by identifying what the barriers are, communicating any benefits, and helping develop the research techniques in pragmatic ways. At the same time we understand the resource and time pressures that reserving actuaries are under and the aim is not to replace existing reserving methods per se, but to start the journey to understanding if and how machine learning may help us in our day to day work.

Our intention is to develop and undertake our own research. To this end, we have a number of workstreams addressing different issues. Currently these are:

Foundations
Literature Review
Survey
Data
Research

We anticipate adding additional workstreams covering issues such as pragmatic considerations, and trust and ethics, as our research develops.

Chair: Sarah MacDonnell

Membership: 48

Established: 2019

DISCLAIMER

The views expressed on this site are those of invited contributors and not necessarily those of the Institute and Faculty of Actuaries. The Institute and Faculty of Actuaries do not endorse any of the views stated, nor any claims or representations made in this publication and accept no responsibility or liability to any person for loss or damage suffered as a consequence of their placing reliance upon any view, claim or representation made in this publication. The information and expressions of opinion contained in this publication are not intended to be a comprehensive study, nor to provide actuarial advice or advice of any nature and should not be treated as a substitute for specific advice concerning individual situations. On no account may any part of this publication be reproduced without the written permission of the Institute and Faculty of Actuaries.

Recent Posts

Reserving using machine learning - an advanced example in R (Part 2)

This article introduces the second of a series of three R notebooks. The notebooks provide R code to replicate the central scenario in the paper of Maximilien Baudry “NON-PARAMETRIC INDIVIDUAL CLAIM RESERVING IN INSURANCE”.

Nigel Carpenter

Nov 23, 2020 2 min read

Reserving using machine learning - an advanced example in R (Part 1)

This article introduces the first of a series of three R notebooks. The notebooks provide R code to replicate the central scenario in the paper of Maximilien Baudry “NON-PARAMETRIC INDIVIDUAL CLAIM RESERVING IN INSURANCE”.

Nigel Carpenter

Nov 13, 2020 2 min read

Gap opening up in motor insurance market; why are some companies investing in machine learning for reserving and others not?

Enthusiasm from reserving actuaries but stakeholder engagement low This is the first in a series of two posts detailing the results of a survey into machine learning use in reserving in the UK and Canada.

Sarah MacDonnell

Nov 10, 2020 2 min read

Wrap-up from 2020 Autumn conference talks

Between September and November 2020, a number of working party members presented at three conferences: CAS CLRS 2020 CIA Appointed Actuary Virtual Seminar GIRO 2020 Our talks provided an update on the activities of the working party from some of our workstreams.

Nigel Carpenter

Nov 2, 2020 6 min read

R's data.table - a useful package for actuaries

This is the second of two articles that discusses packages in R that are useful for data manipulation. The first article covers the tidyverse ecosystem of packages. What is data.

Grainne McGuire

Oct 19, 2020 5 min read

See all posts

Workstreams

Literature review

Review related papers or work and highlight those of particular use in reserving.

Survey

The purpose of the Survey workstream is to find out where the market is on progressing the use of machine learning in reserving. We have spoken to companies in the UK and Canada to find out whether our presumption was correct that machine learning is not being used ‘on the ground’ in reserving.

Foundations

The Foundations workstream aims to provide a path to gaining competency in common statistical and machine learning techniques by: creating a roadmap of methods to learn gathering together relevant learning materials and, developing notebooks in R and Python with example code, where the methods are applied to reserving data sets.

Data

Identify publicly available datasets that can be used to illustrate reserving techniques. Provide a summary of the features available in each dataset. Provide notebook examples of how to generate simulated datasets.

Research

This workstream focuses on research carried out by the working party. Further details will be added in due course.

Recent & Upcoming Talks

Machine Learning in Reserving - IFoA Working Group

The aim of the General Insurance Machine Learning in Reserving Working Party (MLR WP) is to help develop the use of machine learning (ML) techniques in reserving. The session will include:

Nov 2, 2020 Virtual conference

Sarah MacDonnell, Jacqueline Friedland, Grainne McGuire, Nigel Carpenter, Kevin Kuo

Project Project Project