Oct 08, 2026 by Offer

WTW Interview Questions 2027: What Is a Markov Chain?

For this Willis Towers Watson interview question, define then apply: a Markov chain is a stochastic process where the next state's probabilities depend only on the current state — the memoryless property. Explain states and the transition matrix, give a one-line example, then the actuarial use: multi-state models (healthy → disabled → dead) for pricing and reserving.

Willis Towers Watson Interview Questions: What the Markov Chain Question Assesses

Technical concept questions like this are commonly reported by candidates in WTW actuarial interviews — they check that your quantitative foundation is real and that you can connect theory to practice. Interviewers are testing two distinct skills: precise technical communication (can you define it without rambling?) and actuarial thinking (do you see where it applies in insurance?). A textbook definition with no application is half an answer; the application is what makes it an actuarial interview answer.

Willis Towers Watson Interview Questions: How to Answer

Build it in three layers — definition, intuition, application:

The definition. "A Markov chain is a sequence of random states where the probability of the next state depends only on the current state — the system has no memory of how it got there. That's the Markov property."
The mechanics. "You describe it with a set of states and transition probabilities — often organized as a transition matrix, where each row gives the probabilities of moving from one state to all possible next states. Multiplying the current state distribution by the matrix steps the process forward."
The intuition. A one-line example: "Weather modelled as sunny/rainy, where tomorrow's probabilities depend only on today — not on last week's weather."
The actuarial application. "In actuarial work, Markov chains underpin multi-state models: a policyholder moves between states like healthy, disabled, and dead, with transition probabilities estimated from data. That structure is used for pricing disability and life products and for setting reserves — because the future liability depends on which state the policyholder is in now, not their full history."

Example line: "So the memoryless property is exactly what makes it tractable for insurance: to value a policy, I only need today's state and the transition matrix — not the policyholder's entire biography."

Common Mistakes

Definition without application. Pure textbook recitation misses the actuarial point of the question.
Overcomplicating. Stationary distributions and absorbing states are great if asked — don't lead with them.
Hand-waving the matrix. Be ready to sketch a tiny 2×2 example if the interviewer probes.

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FAQ

How much depth is expected? Definition, the memoryless property, and one actuarial application — that's the complete answer for most interviews.

What's the difference from a general stochastic process? The memoryless restriction — general processes can depend on full history; Markov chains can't.

Where else do actuaries use them? Credit risk migration matrices and customer lapse modelling follow the same structure.

Should I mention continuous-time chains? Only if the conversation goes there — discrete-time is the right default for this question.

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