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：服务于共同客户群体的竞争性企业面临一个基本的信息聚合问题：各企业持有关于风险客户的碎片化信号，但个体激励阻碍了有效的集体检测。我们构建了一个去中心化风险分析的机制设计框架，该框架以银行网络中的反洗钱活动为现实基础。三个战略摩擦使我们的场景区别于其他情形：合规道德风险、对抗性适应以及干预导致的信息破坏。一种时间价值分配（TVA）机制，该机制通过使用基于折现已验证结果的严格适当评分规则对机构进行信用评级，在大型联邦中实现了如实报告作为贝叶斯-纳什均衡（在每条边上唯一最优）。将TVA嵌入银行竞争模型后，我们证明了竞争压力会放大合规道德风险，且设计不当的强制性政策可能使福利低于自给自足水平——这一“适得其反”的结论具有直接的政策含义。在基于合成反洗钱基准的模拟中，TVA实现的福利显著高于自给自足或缺乏激励设计的强制性共享方案。