401-3936-00L Data Analytics for Non-Life Insurance Pricing
Semester | Spring Semester 2021 |
Lecturers | C. M. Buser, M. V. Wüthrich |
Periodicity | yearly recurring course |
Language of instruction | English |
Courses
Number | Title | Hours | Lecturers | ||||
---|---|---|---|---|---|---|---|
401-3936-00 V | Data Analytics for Non-Life Insurance Pricing | 2 hrs |
| C. M. Buser, M. V. Wüthrich |
Catalogue data
Abstract | We study statistical methods in supervised learning for non-life insurance pricing such as generalized linear models, generalized additive models, Bayesian models, neural networks, classification and regression trees, random forests and gradient boosting machines. |
Objective | The student is familiar with classical actuarial pricing methods as well as with modern machine learning methods for insurance pricing and prediction. |
Content | We present the following chapters: - generalized linear models (GLMs) - generalized additive models (GAMs) - neural networks - credibility theory - classification and regression trees (CARTs) - bagging, random forests and boosting |
Lecture notes | The lecture notes are available from: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2870308 |
Prerequisites / Notice | This course will be held in English and counts towards the diploma of "Aktuar SAV". For the latter, see details under www.actuaries.ch Good knowledge in probability theory, stochastic processes and statistics is assumed. |
Performance assessment
Performance assessment information (valid until the course unit is held again) | |
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ECTS credits | 4 credits |
Examiners | M. V. Wüthrich, C. M. Buser |
Type | session examination |
Language of examination | English |
Repetition | The performance assessment is offered every session. Repetition possible without re-enrolling for the course unit. |
Mode of examination | oral 30 minutes |
Additional information on mode of examination | Language of examination: English or German / Prüfungssprache: Deutsch oder Englisch |
This information can be updated until the beginning of the semester; information on the examination timetable is binding. |
Learning materials
No public learning materials available. | |
Only public learning materials are listed. |
Groups
No information on groups available. |
Restrictions
Priority | Registration for the course unit is only possible for the primary target group |
Primary target group | Data Science MSc (261000)
Mathematics BSc (404000) Quantitative Finance MSc (435000) Mathematics MSc (437000) Applied Mathematics MSc (437100) Doctorate Mathematics (439002) Actuary SAV (448100) |