Introduction to Machine Learning in Reserving Working Party blog

Introduction to the Foundations Workstream

Learn about getting started in Machine Learning and what the Foundations workstream plans to share.

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last updated: September 2020

General Insurance Machine learning in Reserving working party

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

Recent Posts

Introducing the foundations workstream and articles

Getting started with data science and machine learning (ML) has never been easier or harder. Easier in the sense that there are a wealth of resources available online, but harder in that it can be difficult to know where to start.

Introducing the MLR blog

Introduction It’s probably fair to say that we are living in the era of big data and machine learning. In the actuarial world machine learning (ML) has certainly made inroads into personal lines pricing - tight margins and high competitiveness create a large incentive to extract as much value and insight from data as possible.




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.


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.

Literature review

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


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


The purpose of the Survey workstream is to find out where the market is on progressing the use of machine learning in reserving: what are the barriers? what are the benefits?

Recent & Upcoming Talks

Machine Learning in Reserving

We will share the substantial work of the Institute and Faculty of Actuaries (IFoA) Machine Learning in Reserving Working Party (MLR …

Machine learning in GI reserving

Machine learning is widely used in general insurance pricing, where it has been shown that techniques such as GBMs and neural networks …


Contact the working party via the IFoA Communities Team.