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    Statistical Foundations of Actuarial Learning and its Applications

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    Author(s)
    Wüthrich, Mario V.
    Merz, Michael
    Language
    English
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    Abstract
    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.
    URI
    https://library.oapen.org/handle/20.500.12657/60157
    Keywords
    Deep Learning; Actuarial Modeling; Pricing and Claims Reserving; Artificial Neural Networks; Regression Modeling
    DOI
    10.1007/978-3-031-12409-9
    ISBN
    9783031124099, 9783031124099
    Publisher
    Springer Nature
    Publisher website
    https://www.springernature.com/gp/products/books
    Publication date and place
    Cham, 2023
    Grantor
    • Swiss Re - [...]
    Imprint
    Springer
    Series
    Springer Actuarial,
    Classification
    Applied mathematics
    Probability and statistics
    Machine learning
    Algorithms and data structures
    Artificial intelligence
    Pages
    605
    Rights
    http://creativecommons.org/licenses/by/4.0/
    • Imported or submitted locally

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    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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