Training

We provide face to face or online training. We offer a combination of structured, pre-defined courses or could tailor these to meet the needs of you or your organisation.

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ACTUARIAL DATA SCIENCE TRAINING: GET STARTED

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.


Lesson
Learning Outcomes
1

Big Data

  • Introduction to Big Data
  • What is Data Science
  • Data sources
  • Key Ethical issues
2

Data Management

  • Data preparation
  • Data quality and Data validation
  • Data management tools and storage
  • Data governance
  • Importance of data engineering
3

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
4

Predictive Modelling

  • Introduction to Predictive Modelling
  • Specifying objectives of predictive modelling
  • Type of predictive models
5

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
6

Machine Learning II

  • Supervised learning example – customer retention classification
  • Splitting data into training and test sets
  • Training, model evaluation and prediction
  • Communication of results
7

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
8

Wrap up

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

Structure

We would 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 via the use of Jupyter Notebooks.

Clients would generally require access to a set number of training hours. 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: info@actuartech.com

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