Insurance Data Science Resources

Archived Resource Library

Our rich library of additional Actuartech and curated resources to supplement your data science learning journey. Free if you subscribe to any of our courses or £5/month.

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Course Contents

1. 1. Welcome to Resource Library!
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2. 2. Contents
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1. 1. Data Science & Actuaries: A Practical Overview Webinar (Ref 1.1)
2. 2. Beyond theoretical data science: application to actuarial work: lapse analysis (Ref 1.2)
3. 3. Automation examples using off the shelf software (Ref 1.3)
4. 4. Insurtech and Actuarial & Finance Functions - what do we need to know? (Ref 1.4)
5. 5. The Actuary in a World of Data and Technology Video(Ref 1.5)
6. 6. Insurtech: application of image, video and audio technology innovations (Ref 1.6)
7. 7. Practical Application of Machine Learning within Actuarial Work (Ref 1.7)
8. 8. ADS Actuarial Data Science An initiative of the Swiss Association of Actuaries (Ref 1.8)
9. 9. Insurance Data Science.Org Conference References (Ref 1.9)
10. 10. Practical Data Science for Actuarial Tasks (Ref 1.10)
11. 11. Milliman Data Science Survey Report (Ref 1.11)
12. 12. An article on The Use of Predictive Analytics in the Canadian Life Insurance Industry (Ref 1.12 )
13. 13. Southampton Data Science Academy (SDSA) and Institute and Faculty of Actuaries (IFoA) Certificate in Data Science Candidate information pack and policies (Ref 1.13)
14. 14. IFoA Data Science in Insurance - Some introductory case studies (Ref. 1.14)
15. 15. A report on Machine learning in UK financial services by Bank of England (Ref 1.15)
16. 16. Cambridge CORE article - What data science means for the future of the actuarial profession: Abstract of the London Discussion (Ref 1.16)
17. 17. Thematic Review Report General insurance: involvement of actuaries in pricing for UK home and motor insurance (Ref 1.17)
18. 18. Beyond Theoretical Data Science: A Benchmarking of Actuarial Departments
19. 19. The Impact of the 4th Industrial Revolution on the Role of the Actuary- Webinar
1. 1. Machine Learning Application for Non Life Pricing and Profitability Analysis (Ref 2.1)
2. 2. Are we in the era of actuarial data science modelling? (Ref 2.2)
3. 3. Titanic : Who do you think survived Analysis (Ref 2.3)
4. 4. Singapore Actuarial Society Data Analytics Committee Information and Resources (Ref 2.4)
5. 5. Predicting the probability of a Severe bodily injury in a Car accident in Tel Aviv (Ref 2.5)
6. 6. Cran R Project - Package ‘insuranceData’ February 20, 2015 (Ref 2.6)
7. 7. Digitalising the actuarial operating model: the future of work is here: Webinar (Ref 2.7)
8. 8. Data wrangling, exploration, and analysis with R (Ref 2.8)
9. 9. A selection of ABI statistical publications (Ref. 2.9)
10. 10. Data Gov UK - Road Safety Data, Published by: Department for Transport (Ref 2.10)
11. 11. Beyond theoretical data science: Application to actuarial work: lapse analysis (Ref 2.11)
12. 12. The Actuary in a World of Data and Technology Video (Ref 2.12)
13. 13. Insurtech: application of image, video and audio technology innovations (Ref 2.13)
14. 14. ADS Actuarial Data Science An initiative of the Swiss Association of Actuaries (Ref 2.14)
15. 15. IFoA Data Science in Insurance - Some Introductory Case Studies (Ref 2.15)
16. 16. 'HDTweedie' package - Motor Insurance Dataset (Ref 2.16)
1. 1. Contingencies Article - Model Behavior—Applications of Artificial Intelligence in Actuarial Science (Ref 3.1)
2. 2. AI and Automation Working Party – Short Term Output by AI and Automation Working Party (Ref 3.2)
3. 3. The Evolving Role of the Actuary (a South African Perspective) (Ref 3.3)
4. 4. Abstract on Synthetic Data and Artificial Neural Networks for Insurance Loss Reserving (Ref 3.4)
5. 5. Methodology to train neural networks to perform non-life reserving projections from triangulated loss data (Ref 3.5)
6. 6. The power of computational notebooks: introducing Jupyter Webinar (Ref 3.6)
7. 7. Machine Learning Plus - Logistic Regression – A Complete Tutorial With Examples in R (Ref 3.7)
8. 8. Introduction to Statistical Learning by James, Witten, Hastie and Tibshirani (Ref 3.8)
9. 9. The Actuary and IBNR Techniques: A Machine Learning Approach (Ref 3.9)
10. 10. Machine Learning Cheat Sheet (Ref 3.10)
11. 11. Insurance risk pricing with XGBoost (Ref 3.11)
12. 12. Time Series Forecasting using LSTM Neural Networks in Python (Ref 3.12)
13. 13. Mind the Gap - Safely Incorporating Deep Learning Models into the Actuarial Toolkit by Ronald Richman (Ref 3.13)
14. 14. IFoA Non-life Machine Learning Resources (Ref 3.14)
15. 15. Kaggle Competition – Modelling of claims costs (Ref 3.15)
16. 16. Proxy Modelling using Machine Learning: LSMC case study (Ref 3.16)
17. 17. Are we in the era of actuarial data science modelling? (Ref 3.17)
18. 18. Machine Learning Application for Non Life Pricing and Profitability Analysis (Ref 3.18)
19. 19. Beyond theoretical data science: application to actuarial work: lapse analysis (Ref 3.19)
20. 20. Machine learning in Reserving Working Party - UK survey findings (Ref 3.20)
21. 21. ADS Actuarial Data Science An initiative of the Swiss Association of Actuaries (Ref 3.21)
22. 22. Predicting the probability of a Severe bodily injury in a Car accident in Tel Aviv (Ref 3.22)
23. 23. IFoA Data Science in Insurance - Some introductory case studies (Ref 3.23)
24. 24. GAMs, GLMs and GBMs in insurance pricing (Ref 3.24)
25. 25. IFOA: General Insurance Machine Learning in Reserving working party (Ref 3.25)
26. 26. HDtweedie: The Lasso for the Tweedie's Compound Poisson Model Using an IRLS-BMD Algorithm (Ref 3.26)
27. 27. Local GLMnet: Interpretable Deep Learning (Ref 3.27)
28. 28. Road testing: Machine learning and the efficiency of fraud detection
1. 1. Blog Post - Data Visualisation (OK, boomer!) (Ref 4.1)
2. 2. Data visualisation as a powerful means of communication (Ref 4.2)
3. 3. Mages’s Blog - Visualising theoretical distributions of GLMs (Ref 4.3)
4. 4. Predicting the probability of a Severe bodily injury in a Car accident in Tel Aviv (Ref 4.4)
1. 1. Automation examples using off the shelf software (Ref 5.1)
2. 2. AI and Automation Working Party – Short Term Output by AI and Automation Working Party (Ref 5.2)
3. 3. SOA: Actuarial Modeling Systems
1. 1. Report on Probability and Regression in Python and R (Ref 6.1)
2. 2. Why to Buy Insurance? An Explainable Artificial Intelligence Approach (Ref 6.2)
3. 3. SoA & Deloitte Research on Emerging Technologies and their Impact on Actuarial Science (Ref 6.3)
4. 4. SOA Research on Interpretable Machine Learning for Insurance (Ref 6.4)
5. 5. Most popular programming languages (2004 to 2021) (Ref 6.5)
6. 6. Explainable Machine Learning Webinar (Ref 6.6)
7. 7. Interpretable Machine Learning Webinar (Ref 6.7)
8. 8. Interpretable Machine Learning: A Guide for Making Black Box Models Explainable - Christoph Molnar, 2020-10-11 (Ref 6.8)
9. 9. Beyond theoretical data science: application to actuarial work: lapse analysis (Ref 6.9)
10. 10. ADS Actuarial Data Science An initiative of the Swiss Association of Actuaries (Ref 6.10)
11. 11. Titanic : Who do you think survived Analysis (Ref 6.11)
1. 1. An Introduction to R: Examples for Actuaries by Nigel De Silva 28 Jan 2006 (Ref 7.1)
2. 2. The caret Package - Max Kuhn, 2019-03-27 (Ref 7.2)
3. 3. Try R (Ref 7.3)
4. 4. R-bloggers (Ref 7.4)
5. 5. R documentation (Ref 7.5)
6. 6. METACRAN (Ref 7.6)
7. 7. Cran Task View (Ref 7.7)
8. 8. r graph (Ref 7.8)
9. 9. R and RStudio Guides (Ref 7.9)
10. 10. New Sections for R-studio and Jupyter Notebooks (Ref 7.10)
11. 11. R Datasets (Ref 7.11)
12. 12. Text Mining with R (Ref 7.12)
13. 13. TensorFlow and Keras in R (Ref 7.13)
14. 14. IFoA Guide - Get up and running with R (Ref 7.14)
15. 15. RStudio Cheatsheets (Ref 7.15)
16. 16. StackOverflow (Ref 7.16)
17. 17. Understanding Model Parameters From A Previously Fitted Model in R (Ref 7.17)
18. 18. R for Data Science Website (Ref 7.18)
19. 19. Integrating R and SQL Queries (Ref 7.19)
20. 20. The tidyverse style guide (Ref 7.20)
21. 21. Cran R Project - Package ‘insuranceData’ February 20, 2015 (Ref 7.21)
22. 22. Data wrangling, exploration, and analysis with R (Ref 7.22)
23. 23. 'HDTweedie' package - Motor Insurance Dataset (Ref 7.23)
1. 1. PyMC3 Examples: GLM with Custom Likelihood for Outlier Classification (Ref 8.1)
2. 2. Open Actuarial group for the promotion of open approaches to actuarial problems (Ref 8.2)
3. 3. Python: Examples of AutoML Libraries (Ref 8.3)
4. 4. Predictive Analytics with Python: Case Study of the Insurance Industry (Ref 8.4)
5. 5. Tasting Python Machine Learning : Insurance Claim Prediction (Ref 8.5)
6. 6. Pyliferisk Examples: (Ref 8.6)
7. 7. Allstate Claims Severity - How severe is an insurance claim? Example codes (Ref 8.7)
8. 8. Actuarial models in Python (Ref 8.8)
9. 9. ML modelling on triangles - a Python example (Ref 8.9)
10. 10. Python and Finance – Power Up Your Spreadsheets (Ref 8.10)
11. 11. Tools for Working with Excel and Python
1. 1. Git: Branches, Merges, Remotes and Git Intermediate Techniques (Ref 9.1)
2. 2. Version Control with Git (Ref 9.2)
3. 3. Pro GIT Book (Ref 9.3)
4. 4. GitHub (Ref 9.4)
1. 1. Integrating R and SQL Queries (Ref 10.1)
1. 1. Tesla AI Day (Ref 11.1)
2. 2. A Decentralised Future for Financial Technologies (Ref 11.2)
3. 3. Longevitas Survival Modelling (Ref 11.3)
4. 4. Panel discussion: The evolving role of the actuary (Ref 11.4)
5. 5. RPD Toolkit: Climate Change and Sustainability (Ref 11.5)
6. 6. The Evolving Role of the Actuary (a South African Perspective) (Ref 11.6)
7. 7. Insurtech: application of image, video and audio technology innovations (Ref 11.7)
8. 8. Insurtech and Actuarial & Finance Functions - what do we need to know? (Ref 11.8)
9. 9. ADS Actuarial Data Science An initiative of the Swiss Association of Actuaries (Ref 11.9)
10. 10. Leading Experts Reflect on the use of Data during the Pandemic (Ref 11.10)
11. 11. Python and Finance – Power Up Your Spreadsheets (Ref 11.11)
12. 12. Road testing: Machine learning and the efficiency of fraud detection
13. 13. The Impact of the 4th Industrial Revolution on the Role of the Actuary- Webinar
14. 14. The Evolving Role of Actuaries - Montoux Study Report
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15. 15. SOA - Actuarial Technology, A Roundtable Discussion on Current Issues
1. 1. Royal Statistical Society Paper - Additional information and resources: ethical data science (Ref 12.1)
2. 2. Interpretable Machine Learning: A Guide for Making Black Box Models Explainable - Christoph Molnar, 2020-10-11 (Ref 12.2)
3. 3. Exploring Ethics & AI within Financial Services Webinar (Ref 12.3)
4. 4. AI Governance & Risk Management Framework in Insurance (Virtual Roundtable) (Ref 12.4)
5. 5. Open Risk Management, Open Source - The Tools of the Trade (Ref 12.5)
6. 6. IFoA Risk Management in a Digital World working party's paper on blockchain (Ref 12.6)
7. 7. IFoA Ethical and professional guidance on Data Science: A Guide for Members By the Regulation Board (Ref 12.7)
8. 8. A Guide for Ethical Data Science (Ref 12.8)
9. 9. Digitalising the actuarial operating model: the future of work is here: Webinar (Ref 12.9)
10. 10. ADS Actuarial Data Science An initiative of the Swiss Association of Actuaries (Ref 12.10)
11. 11. Ethical Use of Artificial Intelligence for Actuaries - SOA
12. 12. Big Data And Algorithms In Actuarial Modelling And Consumer Impacts
1. 1. Machine learning in UK financial services -Bank of England (Ref 13.1)
2. 2. IFOA's: The Actuaries' Code (Ref 13.2)
3. 3. IFoA APS X1: Applying Standards to Actuarial Work (Ref 13.3)
4. 4. IFoA APS X2: Review of Actuarial Work (Ref 13.4)
5. 5. IFoA: Actuarial Software and Calculations - Professional Responsibilities (Ref 13.5)
6. 6. IFoA: Actuarial Software and Calculations - Professional Responsibilities Questions and Answers (Ref 13.6)
7. 7. IFoA: A Guide for Ethical Data Science (Ref 13.7)
8. 8. IFoA: Ethical and professional guidance on Data Science (Ref 13.8)
9. 9. IFoA: Guidance on the application of Technical Actuarial Standard 100 (Ref 13.9)
10. 10. FRC: Technical Actuarial Standard 100: Principles for Technical Actuarial Work (Ref 13.10)
11. 11. FRC: Technical Actuarial Standard 200: Insurance (Ref 13.11)
12. 12. AAE: European Standards of Actuarial Practice 1 (Ref 13.12)
13. 13. AAE: European Standards of Actuarial Practice 2 (Ref 13.13)
14. 14. AAE: European Actuarial Note 1 on ESAP 3 and ORSA (Ref 13.14)
15. 15. AAE: European Actuarial Note 2 on Actuarial Function under IORP II (Ref 13.15)
16. 16 . ASSA: APN 901: General Actuarial Practice (Ref 13.16)
17. 17. ASSA: COVID-19 Considerations for Life Assurance Actuaries (Ref 13.17)
18. 18. ASSA: APN 401- Establishing Technical Provisions for Short-Term Insurers (Ref 13.18)
19. 19. EU Artificial Intelligence Act: The European Approach to AI (Ref 13.19)
20. 20. Data Privacy- POPIA and the insurance industry (RSA) (Ref 13.20)
21. 21. Data Privacy- General Data Protection Regulation (GDPR) (Ref 13.21)
22. 22. SOA regulation: Actuarial Data Science Portal (Ref 13.22)
23. 23. Ethical Use of Artificial Intelligence for Actuaries - SOA

About this course

£5.00 / month
172 lessons
29.5 hours of video content

Archived Resource Library

Course Contents

0. Introduction

1. Problem Specification

2. Data Collection And Management

3. Model Building & Model Validation

4. Visualisation

5. Deployment

6. Reporting/Explainability

7. Practical Tips in R

8. Practical Tips in Python

9. Practical Tips in Git

10. Practical Tips in SQL

11. Wider Themes

12. Professionalism, Ethics & Risk Management

13. Regulation & Governance

About this course