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The Effectiveness of Anti-Money Laundering Policy: A Cost-Benefit Perspective

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Abstract

Basically all countries in the world have an anti-money laundering framework in place based on the 40 recommendations of the Financial Action Task Force (FATF), an intergovernmental body established by the G-7 countries in 1989. Now that all these countries are spending tax money to fight money laundering, a natural question to ask is how effective is this policy. Do taxpayers receive value for the money spent? In this chapter we discuss the effectiveness and efficiency of anti-money laundering policies and perform a measurement for countries in the European Union. We use the common policy evaluation tool of a cost-benefit analysis to inform us about the efficiency.

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Notes

  1. 1.

    Joras Ferwerda, ‘The Multidisciplinary Economics of Money Laundering’ (2012) PhD Dissertation Utrecht University. See further Chapter 3 (Bergstrom) in this collection.

  2. 2.

    Personal experience from the EU-financed project ECOLEF in which we travelled to the EU member states to analyse money laundering policies and interview people involved in the fight against money laundering, such as policy makers at the relevant ministry/ministries, public prosecutors, employees of the FIU, compliance officers and relevant law enforcement agencies. For a list of the formal interviews, see Unger and others ‘Report’ (see article note).

  3. 3.

    For an overview, see Chap. 2(2) of Ferwerda (n 1).

  4. 4.

    Martin Gill and Geoff Taylor, Tackling Money Laundering: The Experiences and Perspectives of the UK Financial Sector (2002) Report by the Scarman Centre, University of Leicester, 44. For similar issues concerning counter-terrorist financing, see Chap. 34 (Anand) in this collection.

  5. 5.

    Antony Whitehouse, ‘A Brave New World: The Impact of Domestic and International Regulation on Money Laundering Prevention in the UK’ (2003) 11(2) Journal of Financial Regulations and Compliance 138, 144.

  6. 6.

    Hans Geiger and Oliver Wuensch, ‘The Fight Against Money Laundering: An Economic Analysis of a Cost-Benefit Paradoxon’ (2007) 10(1) Journal of Money Laundering Control 91, 100.

  7. 7.

    These interviews and regional workshops were part of the EU-financed project ECOLEF (n 2).

  8. 8.

    Gill and Taylor (n 4) 44.

  9. 9.

    The data collection presented in this chapter started before Croatia joined the EU. Therefore, only 27 EU Member States are included in the analysis.

  10. 10.

    For such analyses, see Elöd Takáts, ‘A Theory of Crying Wolf: The Economics of Money Laundering Enforcement’ (2007) IMF Working paper 07/81 <www.imf.org/external/pubs/ft/wp/2007/wp0781.pdf> accessed 21 March 2017; Jackie Harvey, ‘Compliance and Reporting Issues Arising for Financial Institutions from Money Laundering Regulations: A Preliminary Cost Benefit Study’ (2004) 7(4) Journal of Money Laundering Control 333.

  11. 11.

    Population statistic from 2010 from Alan Heston, Robert Summers, and Bettina Aten, ‘Penn World Table Version 7.0’ (2011) Center for International Comparisons of Production, Income and Prices at the University of Pennsylvania. The values are also listed in Unger and others ‘Report’ (see article note) Annex 12(1).

  12. 12.

    Price level statistic (p) from 2010 from Heston, Summers, and Aten (n 11). The values are also listed in Unger and others ‘Report’ (see article note) Annex 12(1).

  13. 13.

    See Unger and others ‘Report’ (see article note) Annex 12(1) for these correction factors for each Member State.

  14. 14.

    The results can for instance be biased when certain costs or benefits are not proportional to population (because of fixed costs or economies of scale for example) or when the countries that provided data are not representative for the EU-27.

  15. 15.

    The online surveys and interviews were part of the EU-financed project ECOLEF (n 2).

  16. 16.

    Since the policies against money laundering and terrorist financing have a significant overlap and are often tied together (especially in terms of policy making), the question asked for the overall estimation of both. As a result, the eventual estimations could overestimate the costs of anti-money laundering policy.

  17. 17.

    Throughout this chapter, all values that are not directly derived from statistics but are estimated by the responsible authority are marked with an asterisk (*).

  18. 18.

    Calculation example of how these numbers are calculated: first the three relevant budgets are multiplied by the overall correction factors mentioned in Unger and others ‘Report’ (see article note) Annex 12(1). This means we have 3 estimates of this budget: 760,500; 1,813,000 and 116,762. The average of these three numbers is 896,754, which is our best estimate. The lowest (116,762) and highest (1,813,000) estimates indicate the bandwidth.

  19. 19.

    The number of supervisors is based on the specifications in the relevant law, inaccuracies can arise because of unspecified, regional and unclear grouped supervisors.

  20. 20.

    Financial Action Task Force, Third Mutual Evaluation Report on France (2011) 420 (footnote) <www.fatf-gafi.org/media/fatf/documents/reports/mer/MER%20France%20ful.pdf> accessed 21 March 2017.

  21. 21.

    Irish Prison Service, ‘Annual Report’ (2010) 4 <www.irishprisons.ie/images/pdf/annualrepo rt2010.pdf> accessed 21 March 2017.

  22. 22.

    Kern Alexander, ‘The International Anti-Money Laundering Regime: The Role of the Financial Action Task Force’ (2000) 1 Financial Crime Review 9, 11.

  23. 23.

    Harvey (n 10) 341.

  24. 24.

    Price Waterhouse Coopers LLP, ‘Anti-Money Laundering Current Customer Review Cost Benefit Analysis’ (2003) Report prepared for the FSA, 19 <www.fsa.gov.uk/pubs/other/ml_cost-benefit.pdf> accessed 21 March 2017.

  25. 25.

    To make a similar type of estimate for a cost-benefit analysis as the Annex of UK’s Money Laundering Regulations 1993, the HM Treasury sent out 1000 requests, of which only 60 responded and of which only 1 respondent attempted to quantify these costs.

  26. 26.

    Brief van de Algemene Rekenkamer, Bestrijden Witwassen en Terrorismefinanciering, Tweede Kamer der Staten-Generaal, vergaderjaar 2007–2008, 31 477 no 1. This letter reports the estimate and cites another source, namely, Financiën (2007) Vaststelling van de begrotingsstaten van het Ministerie van Financiën (IXB) voor het jaar 2008. Tweede Kamer, vergaderjaar 2007–2008, 31 200 IXB, no 2. Den Haag: Sdu in which we were unable to find the cited estimate.

  27. 27.

    This estimate is probably an underestimation, since Institut der deutschen Wirtschaft Köln, Consult GmbH (2006) Bürokratiekosten in der Kreditwirtschaft, 9 estimates the costs for AML for the financial sector in Germany at €775 million (if we were to use that figure, the estimate for our hypothetical country would be €93 million). Unfortunately, this report focuses on the financial sector only, and since there is no estimate for the other reporting institutions in Germany, we could not use this report directly for an estimation on our component ‘duties of the private sector’.

  28. 28.

    Geiger and Wuensch (n 6) 98.

  29. 29.

    See for example Donato Masciandaro, ‘Crime, Money Laundering and Regulation: The Microeconomics’ (1998) 8(2) Journal of Financial Crime 103; Geiger and Wuensch (n 6).

  30. 30.

    Ernesto U Savona, Mario A Maggioni, and Barbara Vettori (eds), ‘Cost Benefit Analysis of Transparency Requirements in the Company/Corporate Field and Banking Sector Relevant for the Fight Against Money Laundering and Other Financial Crime’ (2007) Study financed by the European Commission—DG JLS <www.transcrime.it/wp-content/uploads/2013/11/CBA-Study_Final_Report_revised_version.pdf> accessed 21 March 2017.

  31. 31.

    Harvey (n 10) 338.

  32. 32.

    The average is over the period 2005–2010 for the years for which statistics are available. The statistics for Hungary are the answers from our online survey, the other statistics come from Cynthia Tavares, Geoffrey Thomas and Mickaël Roudaut, Money Laundering in Europe, Report of Work Carried Out by Eurostat and DG Home Affairs (2010).

  33. 33.

    The daily rate differs from defendant to defendant and is for natural persons 360th of the yearly proceeds, reduced or augmented up to 30% taking into consideration its overall economic situation. See IMF, ‘Detailed Assessment Report on Anti-Money Laundering and Combating the Financing of Terrorism’ (2009) Report 9/298 <www.imf.org/external/pubs/ft/scr/2009/cr09298.pdf> accessed 21 March 2017.

  34. 34.

    Initially the idea was to use this statistic in combination with the number of conviction to make a reasonable estimate for the total amount confiscated per year. However, this question was only answered by the countries that had exact and publicly available statistics on confiscation. Since there is no need to make an estimate when exact statistics are available, their answers for this question were not used in our research.

  35. 35.

    The amount changes considerably per year: 350,000 in 2006, 415,000 in 2007, 286,000 in 2008, 5,700,000 in 2009 and 7,600,000 in 2010, retrieved from Moneyval, ‘Mutual Evaluation Report Bulgaria’ (2011) 77–79 <www.coe.int/t/dghl/monitoring/moneyval/Evaluations/Progress%20reports%202y/MONE YVAL(2011)5_ProgRep2_BLG.pdf> accessed 21 March 2017.

  36. 36.

    The amount changes considerably per year: 5605 in 2005, 2,645,039 in 2006, 7,388,602 in 2007, 34,853 in 2008, 5,457,236 in 2009, the data comes from our online survey.

  37. 37.

    The amount changes considerably per year: 174,000 in 2005, 17,676 in 2006, 3,130,383 in 2007 and 8,074,795 in 2008, retrieved from Moneyval, ‘Second Progress Report Latvia’ (2009) 67–68 <www.coe.int/t/dghl/monitoring/moneyval/Evaluations/Progress%20reports%202y/MONEYVAL(2009)39-ProgRep2LAT_en.pdf> accessed 21 March 2017.

  38. 38.

    The amount confiscated then becomes for Bulgaria 175,000 in 2006, 207,500 in 2007, 11,400 in 2008, for Cyprus 0 in 2005, 0 in 2006, 0 in 2007, 1584 in 2008 and for Latvia 58,000 in 2005, 4419 in 2006, 0 in 2007 and 0 in 2008. Shares of convictions for third-party money laundering are calculated from Tavares, Thomas and Roudaut (n 32); when it is not possible to distinguish the conviction statistics between self-laundering and third-party laundering, we assume a 50–50 division between self-laundering and third-party laundering.

  39. 39.

    Harvey (n 10) 343.

  40. 40.

    Geiger and Wuensch (n 6) 92.

  41. 41.

    This overview is an updated version of the literature overview that has been published in Brigitte Unger, The Scale and Impacts of Money Laundering (Edward Elgar Publishing 2007) 110–113 and Ferwerda (n 1). Not in all sources it is clear whether the effects of money laundering are described, or also (or only) the effect of anti-money laundering policy.

  42. 42.

    Brent L Bartlett, ‘The Negative Effects of Money Laundering on Economic Development’ (2002) 77 Platypus Magazine 18.

  43. 43.

    Michael Levi and Peter Reuter, ‘Money Laundering’ (2006) 34 Crime and Justice 289, 294.

  44. 44.

    Brigitte Unger and others, ‘The Amounts and Effects of Money Laundering’ (2006) Dutch Ministry of Finance Report <https://pdfs.semanticscholar.org/06d7/b2a51b10c96018fd92fa5eec19f389304f52.pdf> accessed 21 March 2017.

  45. 45.

    Geiger and Wuensch (n 6) 94.

  46. 46.

    Raymond W Baker, Capitalism’s Achilles Heel, Dirty Money and How to Renew the Free-Market System (John Wiley, 2005) 173–74.

  47. 47.

    Mariano-Florentino Cuellar, ‘The Tenuous Relationship Between the Fight against Money Laundering and the Disruption of Criminal Finance’ (2003) 93(2/3) The Journal of Criminal Law and Criminology 311.

  48. 48.

    Alain Bolle, ‘Le Blanchiment des Capitaux de la Criminalite Organisee’ in Ludovic Francois, Pascal Chaigneau, and Marc Chesney (eds), Blanchiment et Financement du Terrorisme (Sentinel 2004).

  49. 49.

    Whitehouse (n 5) 144.

  50. 50.

    See Unger and others ‘Report’ (see article note) Annex 12(1) for these correction factors for each Member State.

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Ferwerda, J. (2018). The Effectiveness of Anti-Money Laundering Policy: A Cost-Benefit Perspective. In: King, C., Walker, C., Gurulé, J. (eds) The Palgrave Handbook of Criminal and Terrorism Financing Law. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-64498-1_14

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