Actuartech

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)

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

4. Thematic Review Report General insurance: involvement of actuaries in pricing for UK home and motor insurance (Ref 1.4)

5. Practical Application of Machine Learning within Actuarial Work (Ref 1.5)

6. IFoA Data Science in Insurance - Some introductory case studies (Ref. 1.6)

7. ADS Actuarial Data Science An initiative of the Swiss Association of Actuaries (Ref 1.7)

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

2. Data wrangling, exploration, and analysis with R (Ref 2.2)

3. PyMC3 Examples: GLM with Custom Likelihood for Outlier Classification (Ref 2.3)

4.1 Data Gov UK - Road Safety Data, Published by: Department for Transport (Ref 2.4.1)

4.2 Bayesrules: Datasets and Supplemental Functions from Bayes Rules! Book (Ref 2.4.2)

4.3 Insurance Datasets - Cran R Package (Ref 2.4.3)

4.4 Insurance Datasets - CASdatasets Package (Ref 2.4.4)

4.5 Synthetic Dataset Generation of Driver Telematics - University of Connecticut (Ref 2.4.5)

4.6 Driver Telematics Dataset - Yun Solutions (Ref 2.4.6)

4.7 Polish Companies' Bankruptcy Dataset (Ref 2.4.7)

4.8 Taiwanese Bankruptcy Prediction Dataset (Ref 2.4.8)

4.9 Breast Cancer Wisconsin (Diagnostic) Dataset (Ref 2.4.9)

4.10 Early Stage Diabetes Risk Prediction Dataset (Ref 2.4.10)

4.11 Credit Card Transactions Fraud Detection Dataset (Ref 2.4.11)

4.12 Mortality Dataset - Deutsche Aktuarvereinigunge.V. (Ref 2.4.12)

4.13 FitBit Fitness Tracker Dataset (Ref 2.4.13)

4.14 Medical Insurance Cost Dataset (Ref 2.4.14)

4.15 Flood Risk Areas [UK] (Ref 2.4.15)

4.16 COVID-19 by Our World in Data (Ref 2.4.16)

4.17 British Geological Survey [UK] (Ref 2.4.17)

4.18 Temperature [UK and regional series] (Ref 2.4.18)

4.19 Rainfall [UK and regional Series] (Ref 2.4.19)

4.20 Climate Change Data (Global) (Ref 2.4.20)

1. Methodology to train neural networks to perform non-life reserving projections from triangulated loss data (Ref 3.1)

2. The Actuary and IBNR Techniques: A Machine Learning Approach (Ref 3.2)

3. Insurance risk pricing with XGBoost (Ref 3.3)

4. Time Series Forecasting using LSTM Neural Networks in Python (Ref 3.4)

5. Mind the Gap - Safely Incorporating Deep Learning Models into the Actuarial Toolkit by Ronald Richman (Ref 3.5)

6. IFoA Non-life Machine Learning Resources (Ref 3.6)

7. Proxy Modelling using Machine Learning: LSMC case study (Ref 3.7)

8. GAMs, GLMs and GBMs in insurance pricing (Ref 3.8)

9. HDtweedie: The Lasso for the Tweedie's Compound Poisson Model Using an IRLS-BMD Algorithm (Ref 3.9)

10. Local GLMnet: Interpretable Deep Learning (Ref 3.10)

11. Report on Probability and Regression in Python and R (Ref 3.11)

12. Predictive Analytics with Python: Case Study of the Insurance Industry (Ref 3.12)

13. Tasting Python Machine Learning : Insurance Claim Prediction (Ref 3.13)

14. Neural networks for insurance pricing with frequency and severity data (Ref 3.14)

1. Data visualisation as a powerful means of communication (Ref 4.1)

1. SOA: Actuarial Modeling Systems (Ref 5.1)

2. Technical Documentation in Software Development: Types, Best Practices, and Tools (Ref 5.2)

3. Git: Branches, Merges, Remotes and Git Intermediate Techniques (Ref 9.3)

4. Version Control with Git (Ref 5.4)

5. Pro GIT Book (Ref 5.5)

6. APR Modelling: Inheriting Actuarial Code (Ref 5.6)