Other Practical Applications of Machine Learning in Non-Life Insurance (R7)

Explore applications of advanced methods in non-life pricing in R, such as unsupervised machine learning algorithms and profitability and competition analysis through Jupyter Notebooks.

Introduction

If you’re looking to learn how to utilise R in non-life insurance, this course is ideal for you. An individual subscription gives you 3 months’ online access to:

  • Course materials
  • A personal coding environment through Jupyter Notebooks
  • Discussion forums to engage and collaborate with like-minded individuals
  • Instructional videos
  • Option to ask tutors questions through forums and Q&A sessions
  • Hands-on practical examples linked to actuarial work
  • On demand access

As Well As

Our Data Science Resource Library which features Actuartech and Industry specific curated additional content to assist you on your data science journey.

You can also request to access to coding projects to practice the skills you learn in this course.

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Pick from any of our introductory or advanced courses with bespoke insurance and actuarial specific case studies.

Our platform is easy to use and embeds the coding environment and learning material in one place to enable you to apply data science hands-on.

We provide case studies and projects relevant to actuarial work, and based on relevant datasets provided. You have the option to interact and network with your peers.

Overview

The course starts with data & feature selection and feature engineering in R. Thereafter, we take a deep dive into the interpretability of machine learning (ML) models. After each video there is a short quiz for the student to gauge their understanding of the section before continuing.

The next section is the e-learning section which introduces unsupervised machine learning algorithms, including partitioning and hierarchical, density-based clustering. The interactive e-learning session ends with a short quiz for the student to gauge their understanding of the session before continuing.  

The course ends with the live lesson which introduces profitability and competition analysis. There is reference to four practical experiences, two examples and two hands-on case studies. The first example showcases the binning of continuous variables and data filtering, whereas the second is an example of vehicle categorisation. The case studies are hands-on experiences of interpreting results of ML algorithms and the detection of interaction between variables. The Notebooks, along with their respective memos, are available for completion on the platform.

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Other Practical Applications of Machine Learning in Non-Life Insurance

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Course Structure

Chapter 1 introduces data selection, pre-analysis, and feature selection, including data quality, pre-treatment, missing values, etc. The chapter also discusses interpretability of machine learning.

Chapter 2 offers an introduction to unsupervised ML algorithms.

Chapter 3 contains an example of binning continuous variables and data filtering as well as hands-on case studies on interpreting results of ML algorithms and detection of interactions between variables.

Chapter 4 discusses profitability and competition analysis, including an example of reverse engineering market prices.

The Appendix contains further resources to assist the student in their data science journey.

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Who's this course suitable for?

  • Individuals with some experience in and basic knowledge of R.
  • Individuals interested in learning how ML can assist with pricing and how it can offer advanced insights into data.
  • Individuals wanting to learn how to interpret ML models and detect interactions.
  • Individuals who need to communicate results.
  • Individuals working in pricing and reserving.

Why is this topic important?

  • Data processing is necessary to get data into the correct format for the model, reducing the risk of model error.
  • Feature engineering techniques can create more accurate GLMs and assist with more competitive pricing strategies.
  • Model interpretability is necessary for effective communication of results to stakeholders.

Short note to say really enjoyed today’s webinar. It had a very clear message. […] fully in agreement with the comments that it is imperative we maintain our professional and ethical stance at all times if we want to continue to be trusted and relied on.

Webinars

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Short note to say really enjoyed today’s webinar. It had a very clear message. […] fully in agreement with the comments that it is imperative we maintain our professional and ethical stance at all times if we want to continue to be trusted and relied on.

Webinars

I just wanted to say what an interesting presentation that was. Thank you so much for taking the time to put this on for us, it is very much appreciated by all – especially the flexibility around hosting as a webinar instead of the original [in-person] format. It worked very well indeed!

Webinars

I think I’m [one of the first actuaries in my area] who are pointing towards Data Science, creating the new [role] of Actuarial data Scientist. For this reason i [sic] decided to follow a post graduate master in Business Intelligence and Big data analytics. I'm actively following your company and i [sic] think it is one of the best Actuarial consulting company [sic] who [sic] is pointing towards data Science!

Webinars

I love your videos - being free and accessible really helped me. The Q&A session was fantastic! It always comes down to execution and I feel this should always accompany your presentations - answering the question of how will your participants use what you give them. Keep up the great work!

Round Table

Thank you for having me along. I really found it the most motivating conversation I’ve had in a while, and made me think about what I’m trying to achieve within this area. We all need evenings like that to get some perspective on what we *think* is going on and what actually is. It was a very good evening.

Round Table

It was a really good introduction to Data Science and afterwards I felt that I now have a platform that I could use to further my understanding in this area.

Webinars

I am really happy to have been part of the talk. It was very insightful and please keep doing more of this. I am a data science student currently but I have an actuarial background. I worked in life insurance for about 5 months before resigning to do my masters in Data science so that I blend the actuarial world and Data science together. The talk gave me perspective. Even suggested some potential topics for my Thesis.

Webinars
Get started

Other Practical Applications of Machine Learning in Non-Life Insurance

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£300 once-off (3-month access)

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