Detecting corruption in single-bidder auctions via positive-unlabelled learning

TL;DR

This paper employs positive-unlabelled learning to differentiate potentially corrupt auctions from fair ones in Russian public procurement, addressing the challenge of identifying corruption amidst structural factors influencing auction competitiveness.

Contribution

It introduces a novel application of positive-unlabelled classification to detect suspicious auctions, accounting for structural factors beyond corruption.

Findings

Effective separation of suspicious and fair auctions

Identification of structural factors influencing auction outcomes

Potential for improved anti-corruption monitoring

Abstract

In research and policy-making guidelines, the single-bidder rate is a commonly used proxy of corruption in public procurement used but ipso facto this is not evidence of a corrupt auction, but an uncompetitive auction. And while an uncompetitive auction could arise due to a corrupt procurer attempting to conceal the transaction, but it could also be a result of geographic isolation, monopolist presence, or other structural factors. In this paper we use positive-unlabelled classification to attempt to separate public procurement auctions in the Russian Federation into auctions that are probably fair, and those that are suspicious.