On suspicious tracks: Machine-learning based approaches to detect cartels in railway-infrastructure procurement

Abstract

In railway infrastructure, construction and maintenance is typically procured using competitive procedures such as auctions. However, these procedures only fulfill their purpose – using (taxpayers’) money efficiently – if bidders do not collude. Employing a unique dataset of the Swiss Federal Railways, we present two methods in order to detect potential collusion: First, we apply machine learning to screen tender databases for suspicious patterns. Second, we establish a novel category-managers’ tool, which allows for sequential and decentralized screening. To the best of our knowledge, our study represents the first attempt to adapt and implement machine-learning-based price screens within the context of a railway-infrastructure market.