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.

翻译：服务共同客户群体的竞争企业面临根本性的信息聚合问题：每个企业掌握关于风险客户的碎片化信号，但个体激励阻碍了高效的集体检测。我们以银行网络反洗钱为背景，构建了去中心化风险分析的机制设计框架。三种策略性摩擦凸显了我们的研究场景：合规道德风险、对抗性适应以及干预导致的信息破坏。一种时间价值赋值（TVA）机制——通过基于严格真值评分规则对折现的经核实结果赋予机构信用——能够在大规模联盟中实现真实报告作为贝叶斯-纳什均衡（每个节点唯一最优）。将TVA嵌入银行竞争模型后，我们发现竞争压力放大了合规道德风险，而设计不当的强制要求可能使社会福利降低至低于自给自足水平，这一“适得其反”的结果具有直接政策含义。在基于合成反洗钱基准的模拟中，TVA相比自给自足或无激励设计的强制信息共享实现了显著更高的社会福利。