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 when contracts can change residual risk. Under complete contracting, prevention is supplied by its least-cost source. Prediction and prevention can lower claims but the best prevention package may attract the people most likely to need it. Competition then creates a risk-design trilemma: plans may have to weaken prevention, abandon separation, or subsidize high-risk enrollment. Adverse selection penalizes prevention only when high-risk consumers value it more, not when it lowers insurers’ claims. Because broader coverage lets insurers capture more of those savings, it can increase certified prevention. If patients or firms cannot replace missing services privately, screening changes more than enrollment and premiums: it leaves society with more avoidable losses overall.

JEL-codes: D4 D47 D81 D82 D86 G22 G52 I13 O33 (search for similar items in EconPapers)
Date: 2026-07
New Economics Papers: this item is included in nep-des
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