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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ARCHIHANDI 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.

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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: Archihandi@gmail.com

Training

ARCHIHANDI DATA SCIENCE TRAINING: GET STARTED

Structure

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