Download This PaperInverse selection
20 Pages Posted: 21 May 2020 Last revised: 7 Jul 2025
Date Written: June 27, 2025
Abstract
AI, big data, and machine learning inverts adverse selection problems; it allows insurers to infer statistical information, thus reversing the information advantage from insuree to insurer. However, standard insurance models assume private information only on the insuree’s side. In this paper, the underlying risk is two-dimensional; one of which is exclusively known to the insuree and the insurer knows the correlation between the two. The insurer faces a new obfuscation-vs-discrimination trade-off, and by controlling the statistical information about the correlation, she can significantly increase her profits, especially if the insurer cannot do the appropriate Bayesian inference.
Keywords: Insurance, Big Data, Informed Principal, Obfuscation, Price Discrimination
JEL Classification: G22, D82, D86, C55
Suggested Citation: Suggested Citation
Markus Konrad Brunnermeier
Princeton University - Department of Economics ( email )
Bendheim Center for Finance
Princeton, NJ
United States
609-258-4050 (Phone)
609-258-0771 (Fax)
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