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This paper develops a mechanism design framework, grounded in anti-money laundering, to address the information aggregation problem faced by competing firms with fragmented signals about risky customers. It identifies three strategic frictions: compliance moral hazard, adversarial adaptation, and information destruction. The authors propose a temporal value assignment (TVA) mechanism that incentivizes truthful reporting using a strictly proper scoring rule, and demonstrate its superior welfare performance compared to autarky or mandated sharing in a banking competition model.
Mandating information sharing between competing firms can backfire and reduce welfare below no sharing at all, highlighting the critical need for incentive-compatible mechanisms.
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.
