Machine Learning for Actuaries Valerie du Preez Steven

Working Party Overview Research New Approaches New Areas Professional Implications The views expressed in

Actuaries & Data Science 1 2 3 4 What aspects of data science do

Data Science Process & the Actuarial Control Cycle Model Validation Model Refinement Model Aging

Data Science Benefits to Actuaries Improved Data Quality • Machine learning is a key

Machine Learning Risks Relating to Existing Actuarial Tasks Building models which are poorly understood

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Machine Learning for Actuaries Valerie du Preez Steven Perkins 16 July 2018

Working Party Overview Research New Approaches New Areas Professional Implications The views expressed in this presentation are those of the presenters 2

Actuaries & Data Science 1 2 3 4 What aspects of data science do all actuaries need to know about? What is data science bringing to actuaries? What are actuaries bringing to data science? What should the IFo. A do to support learning in this area? 3

Data Science Process & the Actuarial Control Cycle Model Validation Model Refinement Model Aging Interaction with Environment 3. Monitor Outcome 1. Define Problem 2. Design Solution Aims Supervised/Unsupervised Classification/Regression Obtain/Prepare Data Select Inputs Variables Select Accuracy Metric Select Model 4

Data Science Benefits to Actuaries Improved Data Quality • Machine learning is a key driver for companies to improve data capture and storage New Data Sources • Machine learning potentially opens up opportunities for actuaries to explore alternative data sources Speed of Analysis • Machine learning models can generally be fitted and validated in a short space of time New Modelling Techniques • Utilising alternative modelling approaches allows different perspectives to be gained on data New Approaches to Problems • Produce a wider variety of models quickly - better ability to select the appropriate modelling approach for a given problem Improved Data Visualisations • Increasing power to produce stunning visualisations of data which can itself provide new perspectives on a task 5

Machine Learning Risks Relating to Existing Actuarial Tasks Building models which are poorly understood Actuarial models built by individuals with little / no actuarial knowledge Challenges around reviewing coded models vs spreadsheet Gaining stakeholder buy in Regulatory compliance Wider Risks • Automation leading to fewer junior staff being trained • Job losses and widening inequality as a result of automation 6

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