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@DeutscheAktuarvereinigung

DeutscheAktuarvereinigung

README.md

Deutsche Aktuarvereinigung

The German Association of Actuaries (Deutsche Aktuarvereinigung.V., DAV) is the professional representation of all actuaries in Germany. It was founded in 1993 and has more than 5,400 members today. More than 700 members are involved in thirteen committees and in over 60 working groups as a voluntary commitment.

The given repositories have been created by committees and working groups and serve as an aid for our members and interested persons to support them in their work with machine learning methods and data science issues in an actuarial context.

Working group results

Use Cases

See here for more detailed descriptions.

  1. Claim frequency modeling
  2. Insurance SCR data
  3. Mortality modeling
  4. Forecasting rare events on credit scoring

Data Science Challenges

Training

Best Notebook Awards

Disclaimer

Please note that the repositories provided on GitHub are published by the DAV. The content of linked websites is the sole responsibility of their operators. The DAV is not responsible for the code and data linked to external sources.

Popular repositories Loading

  1. claim_frequency claim_frequency Public

    GLM, Neural Network and Gradient Boosting for Insurance #, Part 1: Claim Frequency

    Jupyter Notebook 9 5

  2. WorkingGroup_eXplainableAI_Notebooks WorkingGroup_eXplainableAI_Notebooks Public

    Notebooks of the eXplainableAI working group of the German actuarial association

    HTML 9 1

  3. Mortality_Modeling Mortality_Modeling Public

    Multi-Population Mortality Modeling With Neural Networks

    Jupyter Notebook 7 3

  4. Deriving-NHANES-data-set-CDC-for-mortality-analysis Deriving-NHANES-data-set-CDC-for-mortality-analysis Public

    Deriving of a NHANES-data set (CDC) for a mortality analysis

    Jupyter Notebook 7 1

  5. insurance_scr_data insurance_scr_data Public

    How to Work With Comprehensive Internal Model Data for Three Portfolios

    Jupyter Notebook 6 2

  6. Data_Science_Challenge_2020_Betrugserkennung Data_Science_Challenge_2020_Betrugserkennung Public

    In this notebook we take a look at a relevant project that is frequently encountered by insurers: Fraud Detection. For this purpose we use a car data set from a public source and will show the nece…

    Jupyter Notebook 2

Repositories

Showing 10 of 15 repositories
  • WorkingGroup_eXplainableAI_Notebooks Public

    Notebooks of the eXplainableAI working group of the German actuarial association

    DeutscheAktuarvereinigung/WorkingGroup_eXplainableAI_Notebooks’s past year of commit activity
    HTML 9 GPL-3.0 1 0 0 Updated Dec 13, 2024
  • Python_fuer_Aktuare Public

    Notebooks der DAA Fortbildung "Python für Aktuare"

    DeutscheAktuarvereinigung/Python_fuer_Aktuare’s past year of commit activity
    Jupyter Notebook 0 GPL-3.0 0 0 0 Updated Dec 4, 2024
  • .github Public

    Spezielles repository für das README mit der Repository Übersicht

    DeutscheAktuarvereinigung/.github’s past year of commit activity
    0 0 0 0 Updated Nov 18, 2024
  • 2024_CADS_Immersion_Best_Notebooks Public

    Winner notebooks of the 2024 CADS Immersion exams

    DeutscheAktuarvereinigung/2024_CADS_Immersion_Best_Notebooks’s past year of commit activity
    HTML 0 0 0 0 Updated Nov 5, 2024
  • ADS_Use_Cases Public

    Notebooks etc. for Actuarial Data Science use cases

    DeutscheAktuarvereinigung/ADS_Use_Cases’s past year of commit activity
    Jupyter Notebook 0 0 0 0 Updated Apr 23, 2024
  • Deriving-NHANES-data-set-CDC-for-mortality-analysis Public

    Deriving of a NHANES-data set (CDC) for a mortality analysis

    DeutscheAktuarvereinigung/Deriving-NHANES-data-set-CDC-for-mortality-analysis’s past year of commit activity
    Jupyter Notebook 7 GPL-3.0 1 0 1 Updated May 8, 2023
  • Data_Science_Challenge_2022_Python-Notebook_zur_Erstellung_von_Schadenhaeufigkeitsmodellen Public

    In this Python notebook, based on a large French. The results are compared and the interpretability of the models is analyzed and evaluated with SHAP and PDP plots. In addition, the four tools TPOT, Auto-Sklearn, H2O and FLAML are tested or used.

    DeutscheAktuarvereinigung/Data_Science_Challenge_2022_Python-Notebook_zur_Erstellung_von_Schadenhaeufigkeitsmodellen’s past year of commit activity
    0 GPL-3.0 0 0 0 Updated Dec 9, 2022
  • Use-Case-zur-Modellierung-von-Cyberrisiken Public
    DeutscheAktuarvereinigung/Use-Case-zur-Modellierung-von-Cyberrisiken’s past year of commit activity
    HTML 1 GPL-3.0 0 0 0 Updated Nov 8, 2022
  • Impact_of_the_COVID-19_Pandemic Public

    Modeling and Forecasting using Affectedness Variables

    DeutscheAktuarvereinigung/Impact_of_the_COVID-19_Pandemic’s past year of commit activity
    Jupyter Notebook 0 0 0 0 Updated Feb 22, 2022
  • Data-Science-Challenge2021_Explainable-Machine-Learning Public

    The notebook on the main topic of interpretable machine learning is a descriptive and instructive analysis of a car data set from a public source.

    DeutscheAktuarvereinigung/Data-Science-Challenge2021_Explainable-Machine-Learning’s past year of commit activity
    Jupyter Notebook 2 GPL-3.0 1 0 0 Updated Jan 10, 2022
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