If you’re preparing for the SOA Exam C, you’ve probably come across Markov chain models as an essential topic. These models aren’t just theoretical constructs; they’re practical tools that help actuaries analyze systems with multiple states and transitions over time. Implementing Markov chains effectively can be a game-changer for passing the exam and applying those skills in real-world actuarial work. In this guide, I’ll walk you through what Markov chains are, why they matter for the exam, and how to build and implement them using Python—complete with practical tips and examples.

If you’ve ever found yourself tangled in complex actuarial cash flow models, wondering how to make the process more efficient and less error-prone, Python offers a fresh and powerful approach. Actuarial cash flow modeling is essential for predicting future financial outcomes based on policies, assumptions, and behaviors. Traditionally, these models have been developed in spreadsheets or specialized software, which can become cumbersome and hard to maintain as complexity grows. But with Python—particularly using frameworks like cashflower—you can build transparent, flexible, and scalable models that are easier to manage and extend.