Risk Design: AI and Prediction Beyond Screening in Insurance Markets

Alex Chan

No 35444, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: I study insurance markets in which scalable prediction, like AI, designs residual risk rather than merely classifies fixed risk. A complete-contracting benchmark shows that if prevention is observable, contractible, competitively supplied, and fully priced, it does not matter whether consumers, insurers, or vendors supply it. Adverse selection breaks such irrelevance. When high-risk consumers are more "AI-treatable," efficient prevention makes low-risk contracts attractive to them. A contract intended for low-risk consumers faces a risk-design trilemma: separate, prevent efficiently, or avoid cross-subsidy, but not all three. The result extends Rothschild-Stiglitz from distorted coverage to distorted risk-control technology and offers market design insights of AI in insurance markets.

JEL-codes: D4 D47 D81 D82 D86 G22 G52 I13 O33 (search for similar items in EconPapers)
Date: 2026-07
Note: AG EH IO
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