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Actuarial Applications of Predictive Analytics: From GLMs to Machine Learning and AI
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0.68 kg
0.68 kg
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Amazon
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USA
Sobre este producto
- The actuarial profession has always been rooted in data, mathematics, and judgment. From the earliest mortality tables to today’s sophisticated stochastic simulations, actuaries have relied on their ability to analyze patterns, quantify uncertainty, and guide decisions under risk. Yet, in the last two decades, the scope and scale of available data have expanded beyond what traditional actuarial techniques were designed to handle. Enter predictive analytics, machine learning, and artificial intelligence—fields once considered the domain of computer science and data science, now firmly intertwined with actuarial practice. This ninth volume of the Actuary Mastery Series II – The Advanced Collection focuses on bridging the classical with the modern. We begin with generalized linear models (GLMs)—a long-standing statistical tool familiar to actuaries—before extending into more powerful approaches such as random forests, gradient boosting machines, and neural networks. These methods are not presented as replacements for actuarial techniques, but rather as complementary tools that can enrich the actuary’s toolkit, particularly when analyzing claims, policyholder behavior, or large unstructured datasets. Equally important, this book emphasizes ethical AI in actuarial practice. Predictive models, while powerful, are not without risk: biases in training data, opaque decision-making (“black boxes”), and questions of fairness and accountability are all challenges actuaries must navigate. By engaging with these issues, actuaries can ensure that their work remains not only technically sound but also aligned with professional standards and societal trust. This book follows naturally from Book 8: Advanced Reserving and Capital Models, where we explored simulation-based methods for capital and reserves. Predictive analytics adds another dimension to actuarial decision-making, one that draws on both structured statistical reasoning and cutting-edge computational approaches. Looking ahead, Book 10: Professionalism and Ethics in Complex Risk Environments will complete this series by addressing the professional obligations, global standards, and ethical considerations that underlie all actuarial practice—especially when applying advanced tools such as AI. As you read this volume, I encourage you to view predictive analytics not as a replacement for actuarial science but as its evolution—an opportunity to integrate traditional rigor with modern innovation. The future of actuarial work will demand this balance, and it is my hope that this book will help you embrace it. Oluchi Ike
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