Mechanism Design for Decentralized Risk Detection: Strict Propriety, Network Coalitions, and the Backfiring Mandat

TL;DR

This paper develops a dynamic mechanism design framework for decentralized risk detection among competing firms, addressing strategic frictions and proposing a TVA mechanism that incentivizes truthful reporting, with applications to anti-money laundering and cybersecurity.

Contribution

It introduces a TVA mechanism for truthful reporting in decentralized risk detection, analyzes coalition contributions, and identifies welfare impacts of regulatory regimes including backfiring mandates.

Findings

TVA implements truthful reporting as a Bayes--Nash equilibrium.

Coalition value is proportional to firms' cross-interaction degree.

Information-sharing mandates can reduce welfare below autarky.

Abstract

Competing firms that share a population of risky customers face a decentralized risk detection problem in which each firm holds fragmentary information whose aggregation would generate social value, but private incentives impede truthful sharing. We develop a dynamic mechanism design framework for this setting and identify three strategic frictions that distinguish it from classical mechanism design with decentralized information: compliance moral hazard, adversarial adaptation, and information destruction through intervention. A temporal value assignment (TVA) mechanism credits firms using a strictly proper scoring rule applied to discounted verified outcomes; under stated assumptions, TVA implements truthful posterior reporting as a Bayes--Nash equilibrium (uniquely optimal at each edge in large federations, with $O(1/m)$ shading in finite systems). A network Shapley characterization shows that under edge-additive coalition value, each firm's marginal contribution is proportional to its weighted cross-firm interaction degree, yielding a sharp prescription for coalition design that prioritizes inter-firm volume over firm size. Embedding TVA in a model of competition among firms, we establish a welfare ordering across four regulatory regimes (autarky, voluntary federation, mandated full sharing, TVA) and identify conditions under which information-sharing mandates without compatible incentive design reduce welfare below autarky: a ``backfiring mandate.'' We illustrate the framework on a 1.4M-transaction synthetic anti-money-laundering benchmark; the same machinery extends to platform fraud, cybersecurity threat intelligence, and supply chain risk detection.