This open access book discusses the statistical modeling of insurance problems a process which
comprises data collection data analysis and statistical model building to forecast insured
events that may happen in the future. It presents the mathematical foundations behind these
fundamental statistical concepts and how they can be applied in daily actuarial practice.
Statistical modeling has a wide range of applications and depending on the application the
theoretical aspects may be weighted differently: here the main focus is on prediction rather
than explanation. Starting with a presentation of state-of-the-art actuarial models such as
generalized linear models the book then dives into modern machine learning tools such as
neural networks and text recognition to improve predictive modeling with complex features.
Providing practitioners with detailed guidance on how to apply machine learning methods to
real-world data sets and how to interpret the results without losing sight of the mathematical
assumptions on which these methods are based the book can serve as a modern basis for an
actuarial education syllabus.
