1st Edition

Actuarial Loss Models A Concise Introduction

By Guojun Gan Copyright 2025
    240 Pages 21 B/W Illustrations
    by Chapman & Hall

    Actuarial loss models are statistical models used by insurance companies to estimate the frequency and severity of future losses, set premiums, and reserve funds to cover potential claims. Actuarial loss models are a subject in actuarial mathematics that focus on the pricing and reserving for short-term coverages.

    This is a concise textbook written for undergraduate students majoring in actuarial science who wish to learn the basics of actuarial loss models. This book can be used as a textbook for a one-semester course on actuarial loss models. The prerequisite for this book is a first course on calculus. The reader is supposed to be familiar with differentiation and integration.

    This book covers part of the learning outcomes of the Fundamentals of Actuarial Mathematics (FAM) exam and the Advanced Short-Term Actuarial Mathematics (ASTAM) exam administered by the Society of Actuaries. It can be used by actuarial students and practitioners who prepare for the aforementioned actuarial exams.

    Key Features:

    • Review core concepts in probability theory.
    • Cover important topics in actuarial loss models.
    • Include worked examples.
    • Provide both theoretical and numerical exercises.
    • Include solutions of selected exercises.


    1. Probability Theory

    2. Frequency Models

    3. Severity Models

    4. Aggregate Loss Models

    5. Coverage Modifications

    6. Model Estimation

    7. Model Selection

    8. Credibility Models

    9. Risk Measures

    A. Useful Results from Calculus

    B. Special Functions

    C. Normal Distribution Table

    D. R Code

    E. Solutions to Selected Exercises


    List of Symbols



    Guojun Gan is an Associate Professor in the Department of Mathematics at the University of Connecticut, Storrs, Connecticut, USA. He received a BS degree from Jilin University, Changchun, China, in 2001 and MS and PhD degrees from York University, Toronto, Canada, in 2003 and 2007, respectively.  His research interests are in the interdisciplinary areas of actuarial science and data science.