Financial-risk analysis is dominated by tools that price the past distribution accurately — chain-ladder triangulations, credit scoring, retention modeling, pricing engines, M&A diligence frameworks. They are valuable for the question they answer. The question they answer is not always the question the decision actually requires.
Each case below opens with what the standard analysis was saying, names the decision that was waiting on it, and walks through what the causal model showed instead. Model files ship with every case.
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▸Property Insurance — The intervention that makes things worse.
A board considering a 15% rate increase on a $412M coastal portfolio. The causal model showed the increase would accelerate adverse selection — and identified the cheaper fix the register couldn’t see.
▸Insurance Reserving — Chain-ladder tells you the pattern. It cannot tell you why the pattern is changing.
Social inflation, attorney involvement, severity creep, or a mix — each carries different reserve implications. Regulators are increasingly asking for the why; the standard tooling cannot supply it.
▸Insurance Attribution — The expert who was right for the wrong reason.
The senior adjuster apportions every claim correctly — drawing on jurisdiction, injury pattern, and two decades of intuition. When she retires, the reasoning goes with her. The correlate may not hold in the next case.
▸Self-Insurance Decision — Most self-insurance analysis treats future losses as a fixed distribution.
The act of self-insuring changes the distribution — through claims behavior, loss-prevention investment, and risk culture. A causal model represents the change; an actuarial mean does not.
▸Bank Churn — The program that looked like it worked.
£2.4M spent on a retention campaign. The data showed it working. The causal model asked the right question: would those customers have stayed regardless?
▸Credit Risk — We measured program effectiveness by looking at enrolled borrowers.
Borrowers selected into the program based on intent to stay. The observed effect is mostly the selection, not the program. The standard analysis confounded the two; the causal model separates them.
▸M&A Due Diligence — The team that resisted the retention clause is the most capable team.
The negotiating signal is informative about exactly the variable the acquirer wants to retain. A causal model represents how the diligence process itself selects on the outcome it is trying to measure.
▸Rent vs Buy — A rate rise makes buying more expensive. It also cools prices.
The two effects move in opposite directions for the buyer. A spreadsheet that treats rates and prices as independent inputs answers a different question than the one the decision requires.
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For the methods behind these cases, see Confounding, Querying (interrogating a built model for decisions), and Bayesian Risk Decisions. For the wider portfolio across all five risk types, see About Risk.