1. Welcome to Industry Resource Library

2. Contents and References

1. Insurance Data Science.Org Conference References (Ref 1.1)

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

4. An article on The Use of Predictive Analytics in the Canadian Life Insurance Industry (Ref 1.4)

6. Practical Application of Machine Learning within Actuarial Work (Ref 1.6)

7. IFoA Data Science in Insurance - Some introductory case studies (Ref. 1.7)

8. ADS Actuarial Data Science An initiative of the Swiss Association of Actuaries (Ref 1.8)

9. A report on Machine learning in UK financial services by Bank of England (Ref 1.9)

1. Titanic : Who do you think survived Analysis (Ref 2.1)

2. Singapore Actuarial Society Data Analytics Committee Information and Resources (Ref 2.2)

3. Predicting the probability of a Severe bodily injury in a Car accident in Tel Aviv (Ref 2.3)

8. ADS Actuarial Data Science An initiative of the Swiss Association of Actuaries (Ref 2.8)

9. IFoA Data Science in Insurance - Some Introductory Case Studies (Ref 2.9)

10. PyMC3 Examples: GLM with Custom Likelihood for Outlier Classification (Ref 2.10)

11. Milliman Data Science Survey Report (Ref 1.11)

1. Contingencies Article - Model Behavior—Applications of Artificial Intelligence in Actuarial Science (Ref 3.1)

2. AI and Automation Working Party – Short Term Output by AI and Automation Working Party (Ref 3.2)

3. Abstract on Synthetic Data and Artificial Neural Networks for Insurance Loss Reserving (Ref 3.3)

4. Methodology to train neural networks to perform non-life reserving projections from triangulated loss data (Ref 3.4)

7. The Actuary and IBNR Techniques: A Machine Learning Approach (Ref 3.7)

8. Machine Learning Cheat Sheet (Ref 3.8)

9. Insurance risk pricing with XGBoost (Ref 3.9)

11. Mind the Gap - Safely Incorporating Deep Learning Models into the Actuarial Toolkit by Ronald Richman (Ref 3.11)

12. IFoA Non-life Machine Learning Resources (Ref 3.12)

13. Kaggle Competition – Modelling of claims costs (Ref 3.13)

14. Proxy Modelling using Machine Learning: LSMC case study (Ref 3.14)

15. Machine learning in Reserving Working Party - UK survey findings (Ref 3.15)

16. ADS Actuarial Data Science An initiative of the Swiss Association of Actuaries (Ref 3.16)

17. Predicting the probability of a Severe bodily injury in a Car accident in Tel Aviv (Ref 3.17)

18. IFoA Data Science in Insurance - Some introductory case studies (Ref 3.18)

19. GAMs, GLMs and GBMs in insurance pricing (Ref 3.19)

20. IFOA: General Insurance Machine Learning in Reserving working party (Ref 3.20)

21. HDtweedie: The Lasso for the Tweedie's Compound Poisson Model Using an IRLS-BMD Algorithm (Ref 3.21)

22. Local GLMnet: Interpretable Deep Learning (Ref 3.22)

23. Road testing: Machine learning and the efficiency of fraud detection (Ref 3.23)

24. Machine learning modelling on triangles - A worked example (Ref 3.24)

25. 'HDTweedie' package - Motor Insurance Dataset (Ref 3.25)

27. Machine learning in UK financial services -Bank of England (Ref 3.27)

28. Statistical Foundations of Actuarial Learning and its Applications

29. APR Modelling: Inheriting Actuarial Code

30. The next generation of pricing: AutoML

31. Data Science & Machine Learning Consulting

32. Data Science and Machine learning: Unlocking new frontier in claims

33. Bringing actuarial models into the future

1. Blog Post - Data Visualisation (OK, boomer!) (Ref 4.1)

2. Data visualisation as a powerful means of communication (Ref 4.2)

4. Predicting the probability of a Severe bodily injury in a Car accident in Tel Aviv (Ref 4.4)

1. AI and Automation Working Party – Short Term Output by AI and Automation Working Party (Ref 5.1)

2. SOA: Actuarial Modeling Systems (Ref 5.2)

3. APR Modelling: Inheriting Actuarial Code

4. The next generation of pricing: AutoML

5. Technical Documentation in Software Development: Types, Best Practices, and Tools

1. Report on Probability and Regression in Python and R (Ref 6.1)

2. Why to Buy Insurance? An Explainable Artificial Intelligence Approach (Ref 6.2)

3. SoA & Deloitte Research on Emerging Technologies and their Impact on Actuarial Science (Ref 6.3)

4. SOA Research on Interpretable Machine Learning for Insurance (Ref 6.4)

5. Most popular programming languages (2004 to 2021) (Ref 6.5)

6. Interpretable Machine Learning: A Guide for Making Black Box Models Explainable - Christoph Molnar, 2020-10-11 (Ref 6.6)

7. ADS Actuarial Data Science An initiative of the Swiss Association of Actuaries (Ref 6.7)

8. Titanic : Who do you think survived Analysis (Ref 6.8)

9. Understanding Model Parameters From A Previously Fitted Model in R (Ref 9.6)

10. Understanding AI - Explainable AI (XAI) techniques in practice

11. InterpretML - Open source python package for interpretability techniques

1. An Introduction to R: Examples for Actuaries by Nigel De Silva 28 Jan 2006 (Ref 7.1)

2. The caret Package - Max Kuhn, 2019-03-27 (Ref 7.2)

3. R-Bloggers (Ref 7.3)

4. R documentation (Ref 7.4)

5. METACRAN (Ref 7.5)

6. Machine learning modelling on triangles - A worked example (Ref 7.6)

7. Cran Task View (Ref 7.7)

8. R Graph Gallery (Ref 7.8)

9. R and RStudio Guides (Ref 7.9)

10. New Sections for R-studio and Jupyter Notebooks (Ref 7.10)

11. R Datasets(Ref 7.11)

12. Text Mining with R (Ref 7.12)

13. TensorFlow and Keras in R (Ref 7.13)

14. IFoA Guide - Get up and running with R (Ref 7.14)

15. RStudio Cheatsheets (Ref 7.15)

16. StackOverflow (Ref 7.16)

17. Understanding Model Parameters From A Previously Fitted Model in R (Ref 7.17)

18. R for Data Science Website (Ref 7.18)

19. Integrating R and SQL Queries (Ref 7.19)

20. The tidyverse style guide(Ref 7.20)

21. Cran R Project - Package ‘insuranceData’ February 20, 2015 (Ref 7.21)

22. Data wrangling, exploration, and analysis with R (Ref 7.22)

23. 'HDTweedie' package - Motor Insurance Dataset (Ref 7.23)

1. PyMC3 Examples: GLM with Custom Likelihood for Outlier Classification (Ref 8.1)

2. Open Actuarial group providing guidance to open approaches to actuarial problems (Ref 8.2)

3. Python: Examples of AutoML Libraries (Ref 8.3)

4. Predictive Analytics with Python: Case Study of the Insurance Industry (Ref 8.4)

5. Tasting Python Machine Learning : Insurance Claim Prediction (Ref 8.5)

6. Pyliferisk Examples (Ref 8.6)

8. lifelib: Actuarial models in Python (Ref 8.8)

9. ML modelling on triangles - a Python example (Ref 8.9)

10. Python and Finance – Power Up Your Spreadsheets (Ref 8.10)

11. Tools for Working with Excel and Python (Ref 8.11)

1. Git: Branches, Merges, Remotes and Git Intermediate Techniques (Ref 9.1)

2. Version Control with Git (Ref 9.2)

3. Pro GIT Book (Ref 9.3)

4. GitHub (Ref 9.4)

1. Integrating R and SQL Queries (Ref 10.1)

1. TuringGLM: Bayesian Generalized Linear models

2. Introduction to Turing: Bayesian Estimation of Differential Equations

1. Tesla AI Day (Ref 11.1)

2. A Decentralised Future for Financial Technologies (Ref 11.2)

3. Longevitas Survival Modelling (Ref 11.3)

4. RPD Toolkit: Climate Change and Sustainability (Ref 11.4)

5. Road testing: Machine learning and the efficiency of fraud detection (Ref 11.5)

6. Leading Experts Reflect on the use of Data during the Pandemic (Ref 11.6)

7. Python and Finance – Power Up Your Spreadsheets (Ref 11.7)

8. New Risk On The Block - Climate Risk & Sustainability: A Crash Course For Actuaries

9. Actuaries Climate Index

10. Multi-Impact Growth Fund Annual Report

11. Impact Report

1. Machine learning in UK financial services -Bank of England (Ref 12.1)

2. IFOA's: The Actuaries' Code (Ref 12.2)

3. IFoA APS X1: Applying Standards to Actuarial Work (Ref 12.3)

4. IFoA APS X2: Review of Actuarial Work (Ref 12.4)

5. IFoA: Actuarial Software and Calculations - Professional Responsibilities (Ref 12.5)

6. IFoA Risk Management in a Digital World working party's paper on blockchain (Ref 12.6)

7. IFoA: A Guide for Ethical Data Science (Ref 12.7)

8. IFoA: Ethical and professional guidance on Data Science (Ref 12.8)

9. IFoA: Guidance on the application of Technical Actuarial Standard 100 (Ref 12.9)

10. FRC: Technical Actuarial Standard 100: Principles for Technical Actuarial Work (Ref 12.10)

11. FRC: Technical Actuarial Standard 200: Insurance (Ref 12.11)

12. AAE: European Standards of Actuarial Practice 1 (Ref 12.12)

13. AAE: European Standards of Actuarial Practice 2 (Ref 12.13)

14. AAE: European Actuarial Note 1 on ESAP 3 and ORSA (Ref 12.14)

15. AAE: European Actuarial Note 2 on Actuarial Function under IORP II (Ref 12.15)

16 . ASSA: APN 901: General Actuarial Practice (Ref 12.16)

17. ASSA: COVID-19 Considerations for Life Assurance Actuaries (Ref 12.17)

18. ASSA: APN 401- Establishing Technical Provisions for Short-Term Insurers (Ref 12.18)

19. EU Artificial Intelligence Act: The European Approach to AI (Ref 12.19)

20. Data Privacy- POPIA and the insurance industry (RSA) (Ref 12.20)

21. Data Privacy- General Data Protection Regulation (GDPR) (Ref 12.21)

22. SOA regulation: Actuarial Data Science Portal (Ref 12.22)