Detecting corruption in single-bidder auctions via positive-unlabelled learning
This addresses corruption detection in public procurement, but appears incremental as it applies an existing method to a specific domain without claiming major breakthroughs.
The paper tackled the problem of distinguishing corrupt from uncompetitive single-bidder auctions in public procurement by applying positive-unlabeled learning to Russian data, but the abstract does not specify concrete results or numbers.
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.