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This study examines the relationship between corporate secrecy and financial crime, presenting an analytical framework to strengthen risk assessment efforts. We develop secrecy indicators using corporate ownership data from over 2.6 million firms across eight European countries. These indicators are validated using machine learning models built upon evidence of crime committed by firms and/or their owners. The results demonstrate robust predictive power: firms with complex structures and owners from high-risk jurisdictions show a higher likelihood of engaging in illicit activities. Incorporating macro-level information, such as geographic location and economic sector, enhances the understanding of this phenomenon. These findings advance empirical knowledge about the nexus between secrecy firms and crime, offering anti-money laundering authorities novel machine learning tools for effective risk assessment. [ABSTRACT FROM AUTHOR]

Corporate ownership secrecy has become a central issue in the global debate as the use of legitimate companies in illicit schemes has increased dramatically in recent times. While several measures have been implemented worldwide to increase the transparency of firms and their owners, empirical evidence and knowledge on the subject remain limited to few small-scale case studies. In addition, there is a lack of tools specifically designed for risk assessment and risk monitoring of firms to be used by public authorities. The present paper, based on the results of the EU-funded project DATACROS, addresses these gaps by (i) proposing and validating novel risk indicators of corporate ownership opacity in a large sample of companies, and (ii) implementing them in a user-friendly platform to be used by public institutions, a tool capable of identifying companies at risk of involvement in corruption and money laundering. Machine learning results confirm the relevance of corporate ownership opacity in the facilitation of financial crime. Firms with (i) more complex structures, (ii) links to secrecy jurisdictions, and (iii) links to opaque corporate vehicles, are, in fact, more prone to engage in illicit activities. This urgently calls for the innovation of risk assessment activities based on the intelligent use of corporate ownership information. As such, the present contribution could be used to support LEAs and other authorities in combating financial crime in the sometimes overwhelming and ever-evolving digital age. [ABSTRACT FROM AUTHOR]

Corporate ownership secrecy has become a central issue in the global debate as the use of legitimate companies in illicit schemes has increased dramatically in recent times. While several measures have been implemented worldwide to increase the transparency of firms and their owners, empirical evidence and knowledge on the subject remain limited to few small-scale case studies. In addition, there is a lack of tools specifically designed for risk assessment and risk monitoring of firms to be used by public authorities. The present paper, based on the results of the EU-funded project DATACROS, addresses these gaps by (i) proposing and validating novel risk indicators of corporate ownership opacity in a large sample of companies, and (ii) implementing them in a user-friendly platform to be used by public institutions, a tool capable of identifying companies at risk of involvement in corruption and money laundering. Machine learning results confirm the relevance of corporate ownership opacity in the facilitation of financial crime. Firms with (i) more complex structures, (ii) links to secrecy jurisdictions, and (iii) links to opaque corporate vehicles, are, in fact, more prone to engage in illicit activities. This urgently calls for the innovation of risk assessment activities based on the intelligent use of corporate ownership information. As such, the present contribution could be used to support LEAs and other authorities in combating financial crime in the sometimes overwhelming and ever-evolving digital age. [ABSTRACT FROM AUTHOR]

The present study aims to support existing risk assessment tools by proposing an innovative network-oriented methodology based on ownership information. The approach involves calculating company-level indicators that are then transformed into red flags and used to rate risk. To this end, we collect data on companies active in the division of gambling and betting activities in Malta, and further combine them with information on enforcement actions imposed on Maltese companies, their beneficial owners, intermediate shareholders and subsidiaries. Correlation analysis and statistical testing were performed to assess the individual relevance of company-level indicators, while machine learning methods were employed to validate the usefulness of the indicators when used collectively. We conclude that the intelligent use of ownership information and proper analysis of ownership networks greatly supports the detection of firms involved in financial crime, hence the recommendation to adopt akin approaches to improve risk assessment strategies. [ABSTRACT FROM AUTHOR]