06-09, 14:00–14:20 (Europe/Vilnius), Panel Room I - I-414
The use by criminals of complex cross-border corporate structures represents one of the greatest challenges for financial crime investigators. This study has developed a predictive model which improves the early detection of high-risk firms and owners through the identification of red-flags and anomalies in firms’ ownership structures. To this end, we collect and analyse business ownership data on a sample of more than 3 million firms registered in nine European countries. To validate the risk indicators, we implement several machine learning models that are trained and tested against evidence of sanctions and enforcement. The proposed risk models have a strong predictive power. Firms with (i) more complex structures, (ii) owners from high-risk jurisdictions and (iii) links to opaque corporate vehicles are more likely to engage in illicit activities. The inclusion of macrolevel information, such as geographic location and business sector, significantly improves the understanding of the phenomenon. The risk assessment model is then incorporated into a novel prototype tool which would allow supervisory and investigative authorities to real-time screen and investigate large samples of firms, allowing them to prioritise and speed-up investigations. This paper is partially based on the results of EU co-funded project DATACROS.
Paper co-authored with Maria Jofre and Michele Riccardi.
Maria Jofre is a postdoctoral research fellow at Transcrime. She holds a PhD in Business Analytics (The University of Sydney), as well as a Master in Operations Management and a degree in Industrial Engineering (University of Chile).
Maria is an expert in machine learning, corporate data analytics (ownership and financial), risk assessment models and open-source media analysis. Her main expertise and interests lie in the development of analytical solutions aimed at improving the assessment of transnational crimes and related risks.
Antonio Bosisio is Senior Researcher at Transcrime and Data Analytics Manager at Crime&tech. His research focuses on organised crime, corruption, money laundering and financial crime. In this domain, he has partcicipated to numerous research projects at national and international level, and he is currently coordinating the EU-funded project DATACROS II. He holds a Master’s degree in Economics and Social Sciences at Università Commerciale Luigi Bocconi in 2016. In his previous experiences, he worked at the European Commission (DG COMP), and at the Competition and Markets Authority in the UK.