We propose an original application of screening methods using machine learning to detect collusive groups of firms in procurement auctions. As a methodical innovation, we calculate coalition-based screens by forming coalitions of bidders in tenders to flag bid-rigging cartels. Using Swiss, Japanese and Italian procurement data, we investigate the effectiveness of our method in different countries and auction settings, in our cases first-price sealed-bid and mean-price sealed-bid auctions. We correctly classify 90% of the collusive and competitive coalitions when applying four machine learning algorithms: lasso, support vector machine, random forest, and super learner ensemble method. Finally, we find that coalition-based screens for the variance and the uniformity of bids are in all the cases the most important predictors according to the random forest.

我们提出了一种使用机器学习的筛选方法的原始应用，以检测采购拍卖中的串通公司集团。作为一项有条不紊的创新，我们通过在投标中形成投标人联盟来标记操纵投标卡特尔来计算基于联盟的筛选。使用瑞士、日本和意大利的采购数据，我们调查了我们的方法在不同国家和拍卖环境中的有效性，在我们的案例中是第一价格密封投标和平均价格密封投标拍卖。在应用四种机器学习算法时，我们正确分类了 90% 的共谋和竞争联盟：套索、支持向量机、随机森林和超级学习器集成方法。最后，