Statistical Foundations of Actuarial Learning and its Applications

This open-access book discusses the statistical modeling of insurance problems, presenting the mathematical foundations and applications in daily actuarial practice. It covers state-of-the-art models and modern machine learning tools to improve predictive modeling with complex features, serving as a modern basis for an actuarial education syllabus.

Format: Hardback
Length: 605 pages
Publication date: 23 November 2022
Publisher: Springer International Publishing AG

This comprehensive open-access book delves into the intricate realm of statistical modeling for insurance problems, encompassing a multi-step process that involves data collection, analysis, and the construction of statistical models to predict future insured events. It offers a profound exploration of the mathematical underpinnings of these fundamental statistical concepts, elucidating their practical applications in the daily practices of actuaries.

Statistical modeling finds widespread applications across diverse fields, and the weight given to theoretical aspects can vary depending on the specific application. In this context, the primary emphasis lies in predictive modeling rather than explanatory purposes. The book begins by presenting cutting-edge actuarial models, including generalized linear models, as a foundation for further discussion. It then delves into modern machine learning tools, such as neural networks and text recognition, to enhance predictive modeling capabilities with complex features.

By offering practitioners comprehensive guidance on applying machine learning methods to real-world data sets and interpreting the results while maintaining a firm grasp of the mathematical assumptions underlying these methods, the book serves as a contemporary and valuable resource for actuarial education. It equips readers with the skills and knowledge necessary to navigate the evolving landscape of statistical modeling in insurance and make informed decisions based on rigorous data analysis.

Weight: 1087g
Dimension: 235 x 155 (mm)
ISBN-13: 9783031124082
Edition number: 1st ed. 2023