Deep learning for detecting bid rigging: Flagging cartel participants based on convolutional neural networks

TL;DR

This paper introduces a deep learning approach using convolutional neural networks and graph representations of bidding data to effectively detect bid-rigging cartels across different countries, achieving around 90% accuracy.

Contribution

It presents a novel method combining CNNs with graph-based bid data to identify collusive bidding patterns, demonstrating high accuracy and cross-country applicability.

Findings

Achieved around 90% classification accuracy in detecting bid-rigging.

Method performs well across Japanese, Swiss, and mixed datasets.

Predictive performance remains satisfactory even in cross-country tests.

Abstract

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.