In light of the current situation regarding COVID-19 and government regulations preventing indoor gatherings, we have made the decision to turn our upcoming conferences into VIRTUAL EVENTS.

The conferences will have to be held from your notebook/desktop.  This will mean more people can join the event, since there’s no travel involved or venue capacity restrictions in place. Whilst there will not be face-to-face audience contact, we will aim to make the virtual event as interactive as possible.  

In this new, digitally-driven environment, we hope you will be able to join us virtually and help maintain a sense of community in these challenging and uncertain times, wherever you are in the world.



A huge increase in data generation, data capture and data storage combined with significantly increased computing power is providing insurers with a unique opportunity to re-evaluate the value that their data can provide; and the technologies available to do that.

Enabling actuaries to embrace modern day data science tools and to work closely with data scientists is an important link that could give strategic advantages to insurers in the further development of actuarial modelling software.

Looking forward, the actuary will continue to evaluate key sources of data and need to find ways to incorporate data science that uses state of the art machine- learning and data technologies together with the actuary’s business insights. We need to refresh our methods and make use of emerging technological advances.

Some are turning to programming languages like Julia, Python and R; among other. With the rise of open-source execution environments computational notebooks, programming is becoming more accessible and easy to use.

This provides an interesting alternative for actuaries to execute large amounts of statistical calculations and see the results with the latest data visualisation techniques.

We have included a summary of our Introduction to Data Science in Insurance course.

Learning Outcomes

Big Data

  • Introduction to Big Data
  • What is Data Science
  • Data sources
  • Key Ethical issues

Data Management

  • Data preparation
  • Data quality and Data validation
  • Data management tools and storage
  • Data governance
  • Importance of data engineering

Data Visualisation

  • Benefits of visual representation of dataV
  • Visualisation tools
  • Types of visualisations and when to use them
  • Tailoring visuals for a specific audience
  • Communication results using visuals

Predictive Modelling

  • Introduction to Predictive Modelling
  • Specifying objectives of predictive modelling
  • Type of predictive models

Machine Learning I

  • Terminology and why machine learning is used
  • Supervised vs unsupervised learning
  • Unsupervised learning example – Clustering customers into groups
  • Supervised learning and when is it used – Regression and classification

Machine Learning II

  • Supervised learning example – customer retention classification
  • Splitting data into training and test sets
  • Training, model evaluation and prediction
  • Communication of results

Case Studies: Data Science in Insurance

  • Investigating drivers of lapse using advanced descriptive and predictive analytics
  • Claim Fraud Detection
  • Experience Analysis Using Data Visualisation and Dashboarding
  • Non Life Insurance Pricing
  • Data quality Analysis and Anomaly Detection

Wrap up

  • Ethical issues and risks
  • Regulations
  • Interpretability of models
  • Best practice and Good Governance within Data Science
  • Data Strategy


We structure this as 8x1 hour lessons and, depending on the audience could either be structured as a ‘Foundations’ course focussed on the principles of data science or a longer ‘Foundations plus Practice’ course that includes practical training examples.

Modules will be adapted to ensure relevant material and use cases for specific clients are covered. We can tailor this to suit your needs – contact us today:


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