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How to Dodge Drowning in Data? Rule- and Risk-Based Anti Money Laundering Policies Compared

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  • Unger Brigitte

    (Utrecht University School of Economics)

  • van Waarden Frans

    (University College, Utrecht University, The Netherlands)

Abstract

In the past decade, anti-money laundering policy has switched both in the US and in Europe from a rule- to a risk-based reporting system in order to avoid over-reporting by the private sector. However, reporting instead increased in most countries, while the quality of information decreased. Governments were drowned in data because private agents feared sanctions for not reporting. However, unlike in other countries, this "crying wolf problem" (Takats, 2007) did not happen in the Netherlands, where the number of reports diminished, but information quality improved. The reasons for this can be found in differences in legal institutions and legal culture, notably the contrast between US adversarial legalism and Dutch cooperative informalism. The established legal systems also provide for resistance to change. Thus, lowering sanctions in order to reduce over-reporting may not be a realistic option in a legal system which traditionally uses deterrence by fierce criminal and private legal sanctions. Furthermore, a risk-based approach may not be sustainable in the long run, as litigation may eventually replace a risk-based approach again by a rule-based one, now with precise rules set by the courts.

Suggested Citation

  • Unger Brigitte & van Waarden Frans, 2009. "How to Dodge Drowning in Data? Rule- and Risk-Based Anti Money Laundering Policies Compared," Review of Law & Economics, De Gruyter, vol. 5(2), pages 953-985, December.
  • Handle: RePEc:bpj:rlecon:v:5:y:2009:i:2:n:7
    DOI: 10.2202/1555-5879.1423
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    2. Mario Gara & Claudio Pauselli, 2020. "Looking at ‘Crying Wolf’ from a Different Perspective: An Attempt at Detecting Banks Under- and Over-Reporting of Suspicious Transactions," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 6(2), pages 299-324, July.
    3. Raffaella Barone & Donato Masciandaro, 2011. "Organized crime, money laundering and legal economy: theory and simulations," European Journal of Law and Economics, Springer, vol. 32(1), pages 115-142, August.
    4. Lucia Pellegrina & Giorgio Maio & Donato Masciandaro & Margherita Saraceno, 2023. "Are Bankers “Crying Wolf”? Type I, Type II Errors and Deterrence in Anti-Money Laundering: The Italian Case," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 9(2), pages 587-615, July.
    5. Joras Ferwerda & Ioana Sorina Deleanu & Brigitte Unger, 2019. "Strategies to avoid blacklisting: The case of statistics on money laundering," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-13, June.
    6. Rasmus Ingemann Tuffveson Jensen & Joras Ferwerda & Christian Remi Wewer, 2023. "Searching for Smurfs: Testing if Money Launderers Know Alert Thresholds," Papers 2309.12704, arXiv.org.

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