  Adding to the literature on the data-driven detection of bid-rigging cartels,
we propose a novel approach based on deep learning (a subfield of artificial
intelligence) that flags cartel participants based on their pairwise bidding
interactions with other firms. More concisely, we combine a so-called
convolutional neural network for image recognition with graphs that in a
pairwise manner plot the normalized bid values of some reference firm against
the normalized bids of any other firms participating in the same tenders as the
reference firm. Based on Japanese and Swiss procurement data, we construct such
graphs for both collusive and competitive episodes (i.e when a bid-rigging
cartel is or is not active) and use a subset of graphs to train the neural
network such that it learns distinguishing collusive from competitive bidding
patterns. We use the remaining graphs to test the neural network's
out-of-sample performance in correctly classifying collusive and competitive
bidding interactions. We obtain a very decent average accuracy of around 90% or
slightly higher when either applying the method within Japanese, Swiss, or
mixed data (in which Swiss and Japanese graphs are pooled). When using data
from one country for training to test the trained model's performance in the
other country (i.e. transnationally), predictive performance decreases (likely
due to institutional differences in procurement procedures across countries),
but often remains satisfactorily high. All in all, the generally quite high
accuracy of the convolutional neural network despite being trained in a rather
small sample of a few 100 graphs points to a large potential of deep learning
approaches for flagging and fighting bid-rigging cartels.
