Introduction to Machine Learning to understand Motor Insurance Claims

On Friday, 25 June 2021 at 08:30 BST, we were pleased to present a webinar on the applications of machine learning to motor insurance claims.

This webinar shows you how to utilise data science techniques to analyse motor insurance claims.

By fitting advanced data science models we aim to predict the frequency of these claims to assess risk.

This case study is designed to give a broad overview of basic machine learning techniques that can be used to model data in the context of a motor insurance company.

These techniques will be applied to a particular dataset (see below) in conjunction with more traditional actuarial techniques, including a comparison of performance in both instances with the results produced explained. In addition, we will touch on how certain models could potentially be fine-tuned to yield better and more explainable models, in the context of this dataset.

We will be using a dataset of motor contracts from a French insurer.

The file is named ‘freMTPL2freq’ and can be found on

During this introductory webinar we will show you:

- Overview of how we utilise data science techniques to analyse motor claims using the dataset above.
- How to fit a machine learning model that aim to predict the frequency of these claims to assess risk.
- We demonstrate how these techniques and other data science techniques are being applied to aim to improve reserving and pricing processes within motor insurance.

This webinar will be presented by:
- Valerie du Preez, Founder and Managing Director at Dupro Advisory
- Patrick Moehrke, Junior Actuarial Consultant at Dupro Advisory
- Julien Crespy, Actuary & Data Scientist
- Xavier Marechal, CEO Reacfin

Access the Webinar

Interested in Actuartech Training?

Lorem ipsum dolor sit amet, consectetur adipiscing elit.

Contact Us

You may also like these...


Keep up to date

Sign up to our newsletter for the latest in actuarial technology and data science

Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.