Compliance Moral Hazard and the Backfiring Mandate
For regulators and firms in banking networks, the paper shows that poorly designed information-sharing mandates can backfire, reducing welfare below autarky, and provides a mechanism to avoid this.
The paper develops a mechanism design framework for decentralized risk analytics in settings like anti-money laundering, introducing a temporal value assignment (TVA) mechanism that incentivizes truthful reporting. In simulations, TVA achieves substantially higher welfare than autarky or mandated sharing without incentive design.
Competing firms that serve shared customer populations face a fundamental information aggregation problem: each firm holds fragmented signals about risky customers, but individual incentives impede efficient collective detection. We develop a mechanism design framework for decentralized risk analytics, grounded in anti-money laundering in banking networks. Three strategic frictions distinguish our setting: compliance moral hazard, adversarial adaptation, and information destruction through intervention. A temporal value assignment (TVA) mechanism, which credits institutions using a strictly proper scoring rule on discounted verified outcomes, implements truthful reporting as a Bayes--Nash equilibrium (uniquely optimal at each edge) in large federations. Embedding TVA in a banking competition model, we show competitive pressure amplifies compliance moral hazard and poorly designed mandates can reduce welfare below autarky, a ``backfiring'' result with direct policy implications. In simulation using a synthetic AML benchmark, TVA achieves substantially higher welfare than autarky or mandated sharing without incentive design.