Artificial Intelligence (AI) and machine learning (ML) are no longer futuristic concepts for actuaries—they’re actively reshaping the profession as we move through 2025. If you’re an actuary or aspiring to become one, understanding how to leverage these technologies is no longer optional; it’s essential. The integration of AI and ML into actuarial roles is opening new doors for innovation, enhancing traditional risk modeling, and automating routine tasks, but it also demands new skills and a fresh mindset.

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.