Structural asymmetry as a fraud signature: detecting collusion with Heron's Information Coefficient

TL;DR

This paper introduces Heron's Information Coefficient, a geometric measure that detects collusive patterns in procurement networks by analyzing structural asymmetries, providing a novel tool for fraud detection in large-scale systems.

Contribution

The paper presents Heron's Information Coefficient as a new geometric metric for identifying collusion in procurement networks, outperforming traditional indicators in revealing structural shifts.

Findings

HIC effectively detects collusive patterns in real-world procurement data.

Synthetic experiments show high detection accuracy across various network conditions.

HIC offers a complementary tool for auditors to monitor procurement integrity.

Abstract

Fraud in public procurement remains a persistent challenge, especially in large, decentralized systems like Brazil's Unified Health System. We introduce Heron's Information Coefficient (HIC), a geometric measure that quantifies how subgraphs deviate from the global structure of a network. Applied to over eight years of Brazilian bidding data for medical supplies, this measure highlights collusive patterns that standard indicators may overlook. Unlike conventional robustness metrics, the Heron coefficient focuses on the interaction between active and inactive subgraphs, revealing structural shifts that may signal coordinated behavior, such as cartel formation. Synthetic experiments support these findings, demonstrating strong detection performance across varying corruption intensities and network sizes. While our results do not replace legal or economic analyses, they offer an effective complementary tool for auditors and policymakers to monitor procurement integrity more effectively. This study demonstrates that simple geometric insight can reveal hidden dynamics in real-world networks better than other Information Theoretic metrics.