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Insurance Analytics

  • Enseignant(s):   J.Trufin  
  • Titre en français: Analyse de données en assurance
  • Cours donné en: anglais
  • Crédits ECTS: 3 crédits
  • Horaire: Semestre d'automne 2020-2021, 2.0h. de cours (moyenne hebdomadaire)
  •  séances
  • site web du cours site web du cours
  • Formation concernée: Maîtrise universitaire ès Sciences en sciences actuarielles

 

Objectifs

The goal is to provide a solid understanding of some of the most common machine learning methods, and to be able to apply and interpret them on insurance data.

Contenus

  • Model Performance Evaluation (Model selection, Model Asessment)
  • Regression Trees
  • Bagging Trees and Random Forests
  • Boosting Trees and Gradient Boosting Trees
  • Measures for Model Comparison (Measures of Association, Tools to Measure Model Lift)
  • Introduction to Neural Networks

Références

Denuit, M., Hainaut, D., Trufin, J. (2020). Effective Statistical Learning Methods for Actuaries II. Tree-Based Methods. Lecture Notes.

Denuit, M., Hainaut, D., Trufin, J. (2019). Effective Statistical Learning Methods for Actuaries III. Neural Networks and Extensions. Springer Actuarial Lecture Notes.

Hastie, T., Tibshirani, R., Friedman, J. (2009). The Elements of Statistical Learning. Data Mining, Inference, and Prediction. Second Edition. Springer Series in Statistics.

Kuhn, M., Johnson, K. (2013). Applied Predictive Modeling. Springer, New York.

Wüthrich, M. V., Buser, C. (2019). Data analytics for non-life insurance pricing. Lecture notes.

Evaluation

1ère tentative

Examen:
Oral 0h25 minutes
Documentation:
Autorisée
Calculatrice:
Autorisée
Evaluation:

Students will prepare the analysis of a dataset. Examination will be based on a presentation made by the student of its analysis, followed by specific questions on its work but also on the course itself, which will take place on-line.

Rattrapage

Examen:
Oral 0h25 minutes
Documentation:
Autorisée
Calculatrice:
Autorisée
Evaluation:

Students will prepare the analysis of a dataset. Examination will be based on a presentation made by the student of its analysis, followed by specific questions on its work but also on the course itself.



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