Course Contents

  • 1

    1. Problem Specification

    • 1. Practical Application of Machine Learning within Actuarial Work (Ref 1.1)

    • 2. Practical Data Science for Actuarial Tasks (Ref 1.2)

    • 3. Data Science & Actuaries: A Practical Overview Webinar

    • 4. Machine Learning Application for Non Life Pricing and Profitability Analysis

    • 5. Are we in the era of actuarial data science modelling?

    • 6. Beyond theoretical data science: application to actuarial work: lapse analysis

    • 7. Automation examples using off the shelf software

    • 8. Interpretable Machine Learning: A Guide for Making Black Box Models Explainable - Christoph Molnar, 2020-10-11 (Ref 1.3)

    • 9. Digitalising the actuarial operating model: the future of work is here: Webinar

    • 10. Insurtech and Actuarial & Finance Functions - what do we need to know?

    • 11. The Actuary in a World of Data and Technology Video

    • 12. Panel discussion: The evolving role of the actuary

    • 13. The Evolving Role of the Actuary (a South African Perspective)

    • 14. Insurtech: application of image, video and audio technology innovations

    • 15. Methodology to train neural networks to perform non-life reserving projections from triangulated loss data (Ref 1.4)

    • 16. Abstract on Synthetic Data and Artificial Neural Networks for Insurance Loss Reserving (Ref 1.5)

    • 17. Insurance Data Science.Org Conference References (Ref 1.6)

    • 18. An article on The Use of Predictive Analytics in the Canadian Life Insurance Industry (Ref 1.7 )

    • 19. Milliman Data Science Survey Report (Ref 1.9)

    • 20. ADS Actuarial Data Science An initiative of the Swiss Association of Actuaries (Ref 1.10)

    • 21. CAS working paper (Ref 1.11)

    • 22. Cran R Project - Package ‘insuranceData’ February 20, 2015 (Ref 1.12)

    • 24. Predictive Analytics Pan African Data Science Courses Website and Information (Ref 1.14)

    • 25. Contingencies Article - Model Behavior—Applications of Artificial Intelligence in Actuarial Science (Ref 1.15)

    • 26. Proxy Modelling using Machine Learning: LSMC case study (Ref 1.16)

    • 27. A report on Machine learning in UK financial services by Bank of England (Ref 1.17)

    • 29. Predicting the probability of a Severe bodily injury in a Car accident in Tel Aviv (Ref. 1.19)

    • 30. Accident Severity Prediction Calculator (Ref. 1.20)

    • 31. IFoA Data Science in Insurance - Some introductory case studies (Ref. 1.21)

    • 32. Singapore Actuarial Society Data Analytics Committee Information and Resources (Ref 1.22)

    • 33. Excerpt from Cambridge University Predictive Modelling Capabilities in Data Science Volume II Cast Studies in Insurance (Ref 1.23)

    • 34. Cambridge CORE article - What data science means for the future of the actuarial profession: Abstract of the London Discussion (Ref 1.24)

    • 35. Lynda - The Fundamentals of Data Science, a basic introduction to the careers, tools and techniques of modern data science (Ref 1.24)

    • 36. Proxy Modelling using Machine Learning: LSMC case study (Ref 1.25)

    • 37. AI and Automation Working Party – Short Term Output by AI and Automation Working Party (Ref 1.26)

    • 38. Titanic : Who do you think survived Analysis

  • 2

    2. Data Collection

    • 1. Data wrangling, exploration, and analysis with R (Ref 2.1)

    • 2. A selection of ABI statistical publications (Ref. 2.2)

    • 3. Data Gov UK - Road Safety Data, Published by: Department for Transport (Ref 2.3)

  • 3

    3. Model Building & Model Validation

    • 1. The power of computational notebooks: introducing Jupyter Webinar

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    • 2. Explainable Machine Learning Webinar

    • 3. Interpretable Machine Learning Webinar

    • 4. Interpretable Machine Learning: A Guide for Making Black Box Models Explainable - Christoph Molnar, 2020-10-11 (Ref 1.3)

    • 5. Machine Learning Plus - Logistic Regression – A Complete Tutorial With Examples in R (Ref 3.1)

    • 6. Introduction to Statistical Learning by James, Witten, Hastie and Tibshirani (Ref 3.3)

    • 7. The Actuary and IBNR Techniques: A Machine Learning Approach (Ref 3.10)

    • 8. Machine Learning Cheat Sheet Ref 3.11

    • 9. Insurance risk pricing with XGBoost (Ref 1.18)

    • 10. Time Series Forecasting using LSTM Neural Networks in Python (Ref 3.12)

  • 4

    4. Visualisation

    • 1. Blog Post - Data Visualisation (OK, boomer!) (Ref 3.5)

    • 2. Data visualisation as a powerful means of communication (Ref 3.4)

    • 3. Mages’s Blog - Visualising theoretical distributions of GLMs (Ref 3.2)

  • 5

    5. Professionalism, Ethics and Risk Management in Data Science

    • 1. A Guide for Ethical Data Science (Ref 5.1)

    • 2. Exploring Ethics & AI within Financial Services Webinar

    • 3. AI Governance & Risk Management Framework in Insurance (Virtual Roundtable)

    • 4. Open Risk Management, Open Source - The Tools of the Trade (Ref 5.2)

    • 5. Royal Statistical Society Paper - Additional information and resources: ethical data science (Ref. 5.3)

    • 6. IFoA Ethical and professional guidance on Data Science: A Guide for Members By the Regulation Board (Ref 5.4)

  • 6

    6. Practical Tips in R

    • 1. An Introduction to R: Examples for Actuaries by Nigel De Silva 28 Jan 2006 (Ref 6.1)

    • 2. The caret Package - Max Kuhn, 2019-03-27 (Ref 6.2)

    • 3. Try R (Ref 6.3)

    • 4. R-bloggers (Ref 6.4)

    • 5. R documentation (Ref 6.5)

    • 6. METACRAN (Ref 6.6)

    • 7. Cran Task View (Ref 6.7)

    • 8. r graph (Ref 6.8)

    • 9. R and RStudio Guides (Ref 6.9)

    • 10. New Sections for R-studio and Jupyter Notebooks

    • 11. R Datasets

    • 12. Text Mining with R (Ref 6.10)

    • 13. TensorFlow and Keras in R (Ref 6.11)

    • 14. IFoA Guide - Get up and running with R (Ref 6.13)

    • 15. RStudio Cheatsheets (Ref 6.14)

    • 16. StackOverflow (Ref 6.15)

    • 17. GitHub (Ref 6.16)

    • 18. R for Data Science Website (Ref 6.17)

  • 7

    7. Practical Tips in Python

    • 1. Actuarial models in Python (Ref 7.1)

    • 2. Open Actuarial group for the promotion of open approaches to actuarial problems (Ref 6.12)

    • 3. Python: Examples of AutoML Libraries

    • 4. Predictive Analytics with Python: Case Study of the Insurance Industry

    • 5. Tasting Python Machine Learning : Insurance Claim Prediction

    • 6. Pyliferisk Examples:

    • 7. Allstate Claims Severity - How severe is an insurance claim? Example codes:

  • 8

    8. Practical Coding Tips

    • The tidyverse style guide

  • 9

    9. References

    • Reference Library