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P076. Missing Data: Patterns Left Visible – Uncovering Financial Crimes Through Beneficial Ownership Opacity and Non-Compliance

Thu, September 4, 6:45 to 8:00pm, Other Venues, Poster Venue

Abstract

Beneficial ownership (BO) registers have been created in several countries to combat financial crime, but there is a great heterogeneity in data quality, regulatory frameworks, and enforcement across jurisdictions. This paper examines how missing, incomplete, and unavailable BO data can reveal patterns of non-compliance and financial secrecy, which may facilitate money laundering and corruption. This paper seeks to assess the completeness, reliability, and accessibility of BO registers in six countries: Denmark, Estonia, Latvia, Slovakia, Ukraine, and the UK.
The analysis integrates three core components: (1) a comparative legal analysis of BO regulations and enforcement mechanisms, (2) an empirical assessment of missing data patterns in BO registers, and (3) interviews with key stakeholders—including regulators, financial intelligence units, and private sector actors—to understand institutional challenges in data collection and verification. The study explores the relationship between BO data quality and regulatory effectiveness, testing hypotheses on whether stronger legal frameworks, verification mechanisms, and institutional capacity correlate with improved BO transparency. Additionally, it considers how financial secrecy jurisdictions exploit data gaps to obscure ownership structures.


Findings highlight structural weaknesses in BO regimes, the role of external pressures (such as EU directives) in shaping transparency, and the ways in which poor data quality can be leveraged for illicit financial flows. This research contributes to the broader discourse on corporate accountability, offering policy recommendations to enhance BO data integrity and enforcement. By reframing missing BO data as an analytical lens rather than a limitation, this study demonstrates how data opacity itself can be a signal of risk in financial crime detection.

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