Motor Pricing and Reserving in Python (P3)

Explore utilising advanced data science techniques in the context of pricing and reserving, including traditional actuarial methods, and data cleaning, in this end to end walkthrough of predicting the frequency of claims for non-life insurance contracts to assess risk.

Introduction

If you’re looking to learn how to utilise advanced applications of Python in an insurance context, 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 assistyou on your data science journey.

You can also request to access to a coding project 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

In this case study, we utilise data science techniques to analyse motor claims. 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 non-life insurance company.

By fitting advanced data science models, we aim to predict the frequency of these claims to assess risk in the context of pricing and reserving. These techniques will be applied to a particular dataset 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.

If you're not already familiar with Python, we recommend that you start with our Foundations in Python course.

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Motor Pricing and Reserving in Python

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Course Structure

Chapter 1 is the Introduction & Problem Specification, which discusses the business context and provides further readings and resources.

Chapter 2 addresses Data Collection & Data Management, in which we discuss the libraries we use in the course and provide an overview of the data.

Chapter 3 outlines Model Building, addressing the training and testing sets split, “Dummy” estimators, automating the process, and various models.

Chapter 4, the Conclusion, concludes the course by discussing visualisations and next steps.

Chapter 5 is the Appendix & Further Reading.

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Who's this course suitable for?

  • Individuals with grasp of the fundamentals of Python who are looking to expand their applications into non-life insurance.
  • Individuals who currently use more traditional techniques such as GLMs and wish to understand how more advanced machine learning techniques can be used
  • Individuals wanting to learn how to utilise Python for pricing and reserving.

Why is this topic important?

  • Understanding the risk of future claims is a key part of actuarial work in a non-life insurance company.
  • Insurers would wish to know how to best-estimate the risk premium. Analytic and machine learning techniques shown in this case study can be useful in determining it.
  • A key component of this case study is explainability and interpretability and what factors influence the risk premium and claim frequency, which may be necessary when reporting 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

Motor Pricing and Reserving in Python

Sign up for a free preview of this advanced python case study

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Preview

£150 Once-off (3-month access)

Enroll Today

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