Working Paper
Correlated Intelligence: AI Adoption and Systemic Risk
Common AI model adoption can expose banks to unpriced correlated errors, producing too little model diversity and greater systemic risk.
Abstract
Banks delegate decisions to AI models from a common vendor frontier that bundles accuracy with error correlation: the most accurate models share lineage and fail together. The cross-bank cost of that correlation remains unpriced even when lineage is observable, because a credit spread reprices the borrower, not the banks its choice endangers. Model diversity is inefficiently low; under pooled pricing, welfare losses grow quadratically with excess adoption. Frontier improvements can reduce welfare by tipping the market into monoculture. Disclosure makes a correlation surcharge feasible but does not substitute for it.
Type
Working Paper