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

39. Thematic Review Report General insurance: involvement of actuaries in pricing for UK home and motor insurance

40. A Decentralised Future for Financial Technologies

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)

4. Tesla AI Day

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)

11. Mind the Gap - Safely Incorporating Deep Learning Models into the Actuarial Toolkit by Ronald Richman (Ref 3.13)

12. IFoA Non-life Machine Learning Resources (Ref 3.14)

13. Modelling of claims costs and a deep dive into the winning solution (Ref 3.15)

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)

IFoA Risk Management in a Digital World working party's paper on blockchain

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)

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)

19. Integrating R and SQL Queries (Ref 6.18)

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:

The tidyverse style guide

Most popular programming languages (2004 to 2021)

PyMC3 Examples: GLM with Custom Likelihood for Outlier Classification

1. Leading Experts Reflect on the use of Data during the Pandemic

1. Defining the Discount Rate

2. International Actuarial Note 100: Application of IFRS 17 Insurance Contracts

RPD Toolkit: Climate Change and Sustainability

Reference Library