Non-Life Course in R
Announcement from Actuartech organiser:
Basics in non-life pricing: Building a technical tariff with GLM in R
This course will allow you to feel confident to start using R in the workplace and to explore utilising it within the context of building a technical tariff with generalised linear models (GLMs). GLMs are a common techniques for non-life insurers and this course offers an open-source alternative for building GLMs. This allows greater flexibility in model building. Furthermore, this course considers risk classification, Poisson regression, and some severity models.
We will be presenting a live lesson to accompany this course on 23 November at 13:00-14:30 GMT.
FEE: £300 per student for 3 months' access to all course work.
Advance methods of non-life pricing with R
After working through this course, you should feel confident in using R to explore advanced methods for non-life pricing. We discuss regression models and we deal with calibrating machine learning models, after discussing the general methodology of machine learning and machine learning in insurance. The advanced models discussed in this course can offer improved accuracy over GLMs, and assist in identifying the importance and interaction of rating factors, assisting with risk management and monitoring claim drivers.
We will be presenting a live lesson to accompany this course on 30 November at 13:00-14:30 GMT.
FEE: £300 per student for 3 months' access to all course work.
Other practical applications of machine learning in non-life insurance
This course offers a hands-on approach which will allow students to use R to explore practical applications of machine learning (ML) in non-life pricing. We discuss unsupervised machine learning algorithms, as well as profitability and competition analysis. We place emphasis on feature engineering and model interpretability, which can assist with more competitive pricing strategies and allow for effective communication to stakeholders.
We will be presenting a live lesson to accompany this course on 7 December at 13:00-15:00 GMT.
FEE: £300 per student for 3 months' access to all course work.