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Insurance Analytics

  • Teacher(s):   P.Hieber  
  • Course given in: English
  • ECTS Credits: 3 credits
  • Schedule: Autumn Semester 2021-2022, 2.0h. course (weekly average)
  •  sessions
  • site web du cours course website
  • Related programme: Master of Science (MSc) in Actuarial Science
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Objectives

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.

Contents

  • 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

References

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

First attempt

Exam:
Written 1h30 hours
Documentation:
Not allowed
Calculator:
Allowed
Evaluation:

Grades are based on a final exam.

No documentation allowed.

Due to health developments linked to COVID-19, the study plans (and evaluation criteria) may experience adaptations during the semester.

Retake

Exam:
Written 1h30 hours
Documentation:
Not allowed
Calculator:
Allowed
Evaluation:

Grades are based on a final exam.

No documentation allowed.

Due to health developments linked to COVID-19, the study plans (and evaluation criteria) may experience adaptations during the semester.



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