Building robust actuarial models in R for the SOA and CAS exams can seem daunting at first, but with the right approach and tools, it becomes a manageable and even enjoyable process. Whether you’re new to R or looking to sharpen your modeling skills, this guide will walk you through the essentials of creating strong actuarial models step-by-step, sharing practical tips and examples along the way.

First, why R? It’s a free, open-source language with a rich ecosystem tailored to statistical analysis and actuarial science. More importantly, it’s widely used in the actuarial profession, making it a valuable skill for your exams and future work. R’s powerful packages can help you implement everything from survival models to generalized linear models (GLMs), which are central to pricing and reserving tasks in actuarial work.

As an actuarial student, you’re likely no stranger to the importance of predicting personal injury claims. Insurance companies rely on accurate predictions to set aside sufficient reserves and manage risk effectively. Traditional methods often involve grouping claims, but machine learning offers a more precise approach by analyzing individual claim behavior. In this tutorial, we’ll explore how to build a machine learning model in R to predict personal injury claims, a skill that’s increasingly valuable in today’s data-driven insurance industry.