Conspiracy of Corporate Networks in Corruption Scandals

Abstract

Corruption in public procurement transforms state institutions into private entities where public resources get diverted for the benefit of a few. On this matter, much of the discussion centers on the legal fulfillment of the procurement process, while there are fewer formal analyses related to the corporate features which are most likely to signal organized crime and corruption. The lack of systematic evidence on this subject has the potential to bias our understanding of corruption, making it overly focused on the public sector. Nevertheless, corruption scandals worldwide tell of the importance of taking a better look at the misuse and abuse of corporations for corrupt purposes. In this context, the research presented here seeks to contribute to the understanding of the criminal conspiracy of companies involved in public procurement corruption scandals under a network and complexity science perspective. To that end, we make use of a unique dataset of the corporate ownership and management information of four important and recently documented cases of corruption in Mexico, where hundreds of companies were used to embezzle billions of dollars. Under a bipartite network approach, we explore the relations between companies and their personnel (shareholders, legal representatives, administrators, and commissioners) in order to characterize their static and dynamic networked structure. In terms of organized crime and using different network properties, we describe how these companies connect with each other due to the existence of shared personnel with role multiplicity, leading to very different conspiracy networks. To best quantify this behavior, we introduce a heuristic network-based conspiracy indicator that together with other network metrics describes the differences and similarities among the networks associated with each corruption case. Finally, we discuss some public policy elements that might be needed to be considered in anti-corruption efforts related to corporate organized crime.

Network analysis in peace and state building: revealing power elites

“…Metrics examining the structure and dynamics of corruption and criminal networks have provided insights into various forms, including hierarchical or familial networks, organized crime, diverse illicit markets (Morselli et al, 2007; Framis, 2014; DellaPosta, 2017), and even conspiracies and favoritism (Nicolás-Carlok and Luna-Pla, 2021; Falcón-Cortés et al, 2022). The structure and dynamics of these networks evolve in response to changes and regulations within their environment, shaped by a multitude of factors and cohesive actors (Barabási, 2016; Carrington, 2016; Campedelli et al, 2019; Luna-Pla and Nicolás-Carlock, 2020).…”

A complexity science approach to law and governance

“…Since the pioneering works on legal complexity, the vibrant community of legal scholars and practitioners, complexity scientists and artificial intelligence (AI) experts has steadily grown over the years, leading to a much wider range of topics being investigated with a variety of new tools. To name just a few: the study of legal citation networks [ 26 , 27 ], machine-learning and network analysis of statutes, treaties and court litigation [ 28 , 29 ], stat-mech models of judicial decisions [ 30 , 31 ] and of structural complexity of legal texts [ 32 , 33 ], corruption scandals [ 34 ], as well as the study of legal language and semantics using quantitative models [ 35 ].…”

Red flag algorithms for Brazilian electronic invoices: outlier detection and price risk classification

“…However, network analysis and natural language processing allow more assertive results. Nicolás‐Carlock and Luna‐Pla (2021) used a network approach to explore the relations between companies involved in documented corruption cases in Mexico. They used data about companies' shareholders, representatives, administrators, and commissioners to describe how these companies connect due to shared personnel with multiple roles.…”