Economic crimes including corruption, fraud, collusion, and tax evasion impose significant costs to societies all around the world. Beyond their direct economic costs, these behaviors reduce mutual trust and cohesion in society. The erosion of these fundamental elements of a healthy society is hypothesized to contribute to growing inequality and the strengthening of political populism. Altogether there are significant incentives to study economic crimes. However, until recently, its investigation remained largely the preserve of law enforcement, which has resources to investigate only a tiny minority of cases.
Researchers now have more data than ever to investigate these phenomena, but face several unique challenges. The lack of unbiased ground-truth data hinders the straightforward application of machine learning. Publicly available data often contains only suggestive traces of illegal activity. Though economic crimes are increasingly international, data availability and quality varies highly across borders. Despite these difficulties, recent years have witnessed a remarkable increase in scientific activity in this area. Studying economic crime from a data science perspective offers unique insights and can inform the design of novel solutions. The results of such research are of eminent interest to governments, law enforcement, organizations, companies and civil society watchdogs. In light of this recent activity, there is a need to survey the field, to reflect on progress, shortcomings, and open problems, and to highlight promising new methods.
In this special issue, we gather research that highlights novel applications of data science to the problems and challenges of economic crime. We also welcome data-critical studies and mixed-methods papers, recognizing that data-driven methods complement rather than substitute for other approaches.
Topics of interest include, but are not limited to:
- Estimating levels and trends of economic crimes using open source data
- Corruption in public procurement
- Collusion and cartels
- Network science perspectives on economic crime
- Agent-based models of economic crime
- Detecting fraud in transaction data
- Critical perspectives on the application of data science to economic crime (i.e. pitfalls and biases of predictive policing and profiling)
- Data-driven analyses of organized crime and mafia-type groups
- Tax evasion and money laundering
- Terrorist financing
- The political organization of economic crimes
- Lobbying networks and political favoritism
- Social and communication networks of criminal conspiracies
- Transactions on the darknet and the role of crypto-currency in economic crime
- Data-driven journalism and OSINT perspectives on economic crime
- Novel datasets for measuring and tracking economic crime
- Mixed-methods approaches to studying economic crime
Lead Guest Editor
Johannes Wachs
Vienna University of Economics and Business, & Complexity Science Hub Vienna, Austria
Guest Editors
Janos Kertesz
Department of Network and Data Science, Central European University, Vienna, Austria
Mihaly Fazekas
School of Public Policy, Central European University, Vienna, Austria
Elizabeth David-Barrett
Department of Politics/Centre for the Study of Corruption, University of Sussex, UK