Course Contents

    1. 1. Welcome to Industry Resource Library

    2. 2. Contents and References

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

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

    3. 3. Predictive Analytics with Python: Case Study of the Insurance Industry (Ref 1.3)

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

    5. 5. Thematic Review Report General insurance: involvement of actuaries in pricing for UK home and motor insurance (Ref 1.5)

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

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

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

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

    10. 10. BAJ - What data science means for the future of the actuarial profession: Abstract of the London Discussion (Ref 1.10)

    11. 11. Neural networks for insurance pricing with frequency and severity data: a benchmark study from data preprocessing to technical tariff (Holvoet et al. 2023)

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

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

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

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

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

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

    7. 11. Milliman Data Science Survey Report (Ref 1.11)

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

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

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

    6. 8. Machine Learning Cheat Sheet (Ref 3.8)

    7. 9. Insurance risk pricing with XGBoost (Ref 3.9)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

    24. 28. Statistical Foundations of Actuarial Learning and its Applications

    25. 29. APR Modelling: Inheriting Actuarial Code

    26. 30. The next generation of pricing: AutoML

    27. 31. Data Science & Machine Learning Consulting

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

    29. 33. Bringing actuarial models into the future

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

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

    2. 2. SOA: Actuarial Modeling Systems (Ref 5.2)

    3. 3. APR Modelling: Inheriting Actuarial Code

    4. 4. The next generation of pricing: AutoML

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

About this course

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  • 141 lessons
  • 0 hours of video content