Probabilistic Forecasting with Neural Networks Applied to Loss Reserving

last updated: 30 September 2021

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

Probabilistic Forecasting with Neural Networks Applied to Loss Reserving

Here we highlight the 2020 thesis by Muhammed Taher Al-Mudafer, which applies an innovative approach to reserving using probabilistic forecasting and neural networks.

Sarah MacDonnell

Sep 30, 2021 2 min read

A brief history of papers looking at using neural networks in reserving

This content was first presented by Kevin Kuo at the CAS Casualty Loss Reserving Seminar, the CIA Appointed Actuary Virtual Seminar and at GIRO in the Autumn of 2020.

Kevin Kuo

Jul 26, 2021 7 min read

Getting to grips with GLM, GAM and XGBoost

Introduction Congratulations! You’ve just completed your introductory course in data science and analytics in R, and you’re ready to start enriching your day-to-day actuarial work with new and exciting models.

Thomas Saliba

Jul 5, 2021 3 min read

ML modelling on triangles - a worked example

Introduction In this article we look at applying different machine learning (ML) models to a data set. Our goal is to illustrate a work flow for: setting up a data set for ML applying different ML models to this data tuning the ML hyper-parameters comparing and contrasting performance for the past fitted values and the future predictions.

Grainne McGuire, Jacky Poon

May 30, 2021 60 min read

Self-assembling claim reserving models using the LASSO

In this post we look at how regularised regression techniques such as the LASSO can be used to set reserves for triangles. The Foundation Workstream’s first post set out a suggested path to getting to grips with machine learning.

Grainne McGuire, Greg Taylor

May 17, 2021 31 min read

See all posts

Workstreams

Literature Review

The literature review workstream collects published research on Machine Learning (ML) in reserving techniques. The literature collected so far discuss the following methods: Neural Networks (NN), Gradient Boosting Machines (GBM) and random forests (RF).

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.

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.

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 modelling on triangles - a worked example

Grainne McGuire and Jacky Poon from the Machine Learning in Reserving working party (MLR WP) will take you through their practical example of applying different machine learning (“ML”) models to a reserving data set using R.

May 21, 2021 09:30 — 10:30 Virtual conference

Grainne McGuire, Jacky Poon

Code Project Slides

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