When would you use Monte Carlo simulation instead of discrete scenario or sensitivity analysis, and what does it add?
An advanced FP&A question — expect it in final rounds and case-heavy interviews (IB, PE, Big-4 Transaction Services).
THE SHORT ANSWER
Sensitivity analysis flexes one variable at a time; scenario analysis bundles a few coherent cases (base/upside/downside). Monte Carlo goes further: you assign probability distributions to the uncertain inputs (and their correlations), then run thousands of iterations to produce a full distribution of outcomes — not just three points. It adds value when several uncertain drivers interact, when you care about the probability and shape of outcomes (e.g., 'what's the chance we breach the covenant?' or the P10–P90 range of cash), and when correlations between variables matter (revenue and costs moving together). It gives a probabilistic answer — expected value plus the distribution and tail risk — rather than a deterministic point. Caveats: it's only as good as the input distributions and correlation assumptions (garbage in, garbage out), it can give false precision, and it needs clear communication. So use it for genuinely multi-variable, probabilistic questions; stick with scenarios for simpler or board-level storytelling.
WHAT INTERVIEWERS LISTEN FOR
- ✓Monte Carlo: distributions on inputs + correlations, thousands of runs → outcome distribution
- ✓Use when multiple drivers interact and you need probabilities/tail risk
- ✓Captures correlations scenarios miss; gives P10–P90, breach probability
- ✓Caveat: only as good as input distributions; risk of false precision
COMMON MISTAKES
- ✗Using it when a simple scenario suffices
- ✗Ignoring input-distribution/correlation quality
- ✗Presenting false precision to the board
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