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.