Courses

An Introduction to AI Risk Management

Learn how to manage risks associated with artificial intelligence (AI) in an actuarial context, with a focus on machine learning.

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Risk Management

AI

General

Fundamentals of Machine Learning

In this course, we will explore the fundamentals of machine learning in the context of actuarial work, including exploring considerations when applying ML, actuarial use cases of various software packages, and actuarial case studies utilising ML.

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Application

AI

IFRS 17

A Practical Guide to KPIs under IFRS 17

Explore how Key Performance Indicators (KPIs) are impacted by IFRS 17, how to transition to new KPIs, and how to calculate KPIs under IFRS 17.

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IFRS 17

DevOps

Fundamentals of DevOps for Actuarial Data Science

Learn the fundamentals of DevOps to develop system-based solutions and understand the current techniques in designing, developing, testing, iterating, deploying, and maintaining system-based solutions.

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Solutionising

AI

SQL

Foundations in SQL

Learn the fundamentals of SQL through interactive Jupyter Notebooks, discover basic and complex SQL functionalities, data manipulation, and overall data management tools & techniques.

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Getting Started

AI

Other Practical Applications of Machine Learning in Non-Life Insurance

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

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Application

AI

Advanced Methods of Non-Life Pricing in R

Discover how to use advanced techniques for non-life pricing such as regression models and calibrating machine learning models in R through Notebooks.

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Application

AI

Visualisations Conceptual Introduction

We introduce the benefits of data visualisation and types of visualisations and discuss the considerations and challenges linked to visualisations, before taking a brief look at some tools, platforms, and real-life examples.

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Introduction to Data Science in Insurance

In this introduction of data science in insurance, we discuss data analysis and data sources and provide an overview of some data science techniques, before touching on the ethical considerations in data science and giving examples of the application of data science in insurance.

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Lapse Experience Analysis Python

Discover how to use advanced techniques in Python, including fitting regression and classification machine learning models, data cleaning, feature engineering, preliminary visualisations, and reporting to investigate lapse rates in this end to end walkthrough.

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IFRS 17

Introduction to IFRS 17: Hands On Training

Discover how to interpret the principal requirements of IFRS 17 through learning the basics of IFRS 17, including measurement models, terminology, and hands-on calculations.

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IFRS 17

Motor Pricing and Reserving in Python

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.

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Application

AI

Mortality Forecasting in Python

Discover how to use advanced techniques in Python, including time series analysis and the Lee-Carter model, data cleaning, and visualisations to forecast mortality rates in this end to end walkthrough of mortality modelling.

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Application

AI

Foundations in Python

Learn the fundamentals of Python through interactive Jupyter Notebooks, discover data management tools & techniques, statistical packages, and explore regression analysis, building your first model, validation and visualisation.

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Getting Started

AI

Basics of Non-life pricing in R

Explore building a technical tariff through GLMs in R to learn the basics of non-life pricing using data science techniques through Notebooks.

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Application

AI

Visualisations Deep Dive in R

Discover how to use visualisation techniques in R to present key messages from complex data with hands-on examples, including building a dashboard to aid in identifying the impact, patterns, and trends present in a complex dataset.

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Application

AI

Mortality Experience Analysis in R

Discover how to use advanced techniques in R, including fitting various machine learning models, data cleaning, and visualisations to understand the drivers of mortality in this end to end walkthrough of mortality modelling.

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Application

AI

Lapse Experience Analysis in R

Discover how to use advanced techniques in R, including fitting regression and classification machine learning models, data cleaning, feature engineering, preliminary visualisations, and reporting to investigate lapse rates in this end to end walkthrough.

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Application

AI