1. Is Online Poker a Valid Platform for Money Laundering in the EU? (Revised version) Conference: International Masters of Gaming Law October 1-4, 2013, Oslo Oslo, October 1-4, 2013© Prof. Dr. Friedrich Schneider Prof. Dr. Friedrich Schneider Department of Economics Johannes Kepler University Linz Altenbergerstraße 69, A-4040 Linz-Auhof, AUSTRIA Phone: 0043-732-2468-8210, Fax:-8209 E-mail: friedrich.schneider@jku.at http://www.econ.jku.at/schneider MoneyLaundering_OnlinePoker_E_2013_Sep.ppt 1 / 13

2. 1. Introduction Proceeds from organized crime are quite large; often billions of US-Dollars are earned. Hence money laundering of the proceeds is essential, if the criminals want to spend this money. Goal of this lecture: (1)Show some facts / figures of worldwide and national criminal proceeds. (2)How relevant is online-gambling / online-poker for money laundering? Oslo, October 1-4, 2013© Prof. Dr. Friedrich Schneider2 / 13

3. Table of Content 1.Introduction 2.Proceeds from TOC (Transnational Organized Crime): Some Facts 3.The German Market for Gambling & Betting 4.Online-Poker used for Money Laundering? 5.Summary & Conclusions Oslo, October 1-4, 2013© Prof. Dr. Friedrich Schneider3 / 13

4. 2. Proceeds from TOC (Transnational Organized Crime): Some Facts – Global / Regional / National Figures (1)The most widely quoted figure for the extent of money laundered has been the IMF consensus range of 2 % to 5 % of global GDP, made public by the IMF in 1998. A more recent analysis of the results from various studies suggests that all criminal proceeds are likely to amount to some 3.6% of global GDP (2.3 % - 5.5 %), equivalent to about USD 2.1 trillion in 2009. (2)Another reliable OECD estimate for the amount available for laundering through the financial system would be equivalent to 2.7 % of global GDP (2.1 % - 4 %) or USD 1.6 trillion in 2009. Oslo, October 1-4, 2013© Prof. Dr. Friedrich Schneider4 / 13

5. 2. Proceeds from TOC (Transnational Organized Crime): Some Facts – Global / Regional / National Figures Oslo, October 1-4, 2013© Prof. Dr. Friedrich Schneider Table 2.1:IMF Estimates of Proceeds from TOC and/or Money Laundered, worldwide, period 1996 to 2009. EstimationMinimumMaximum Mid- point Increase (in %) Average (1996 to 2009) IMF estimates of money laundered (as a percentage of global GDP) 2 %5 %3.5 %--- Estimate for 1996 (in billion USD)6001,5001,050--- Estimate for 2005 (in billion USD)9002,3001,60052 % Estimate for 2009 (in billion USD)1,2002,9002,05028 % Source: OECD Observer, Paris, various years. 5 / 13

6. Oslo, October 1-4, 2013© Prof. Dr. Friedrich Schneider 2. Proceeds from TOC (Transnational Organized Crime): Some Facts – Global / Regional / National Figures: Table 2.2: Proceeds of transnational crime (time range 2003-2009). Source: Global Financial Integrity, Transnational crime in the Developing World, February 2011 and World Bank, Indicators (for current GDP). Kind of Crime (2003-2009) Billion USD In % of total proceeds Sources Drugs 32050 % UNODC, World Drug Report 2005 (data refer to 2003) Counterfeiting 25039 % OECD, Magnitude of Counterfeiting and Piracy of Tangible Products, 2009 Human trafficking 31.65 % P. Belser (ILO), Forced Labor and Human Trafficking: Estimating the Profits, 2005 Oil 10.82 % GFI estimate based on Baker 2005 (quantities) and US Energy Information Administration (prices: 2003- 2010) Art and cultural property 3.4 - 6.30.8 % GFI estimate based on Interpol, International Scientific and Professional Advisory Council of the United Nations Crime Prevention and Criminal Justice Programme In % of global GDP in 2009 1.1 %--- In % of average global GDP, 2000- 2009 1.5 %--- 6 / 13

7. Oslo, October 1-4, 2013© Prof. Dr. Friedrich Schneider SegmentGGR 2009 (in million EUR)Total share 2009 (in %) Slot machines3.34032.3% Lottery 6 aus 492.25021.7% Other lottery products1.08010.4% Super 6, Spiel 778107.8% Casinos7707.4% Stationary betting (offices)4804.6% Online-poker3403.3% Online-betting3002.9% Black market betting2302.2% Online-casinos2102.0% PS-Sparen, Gewinnsparen2102.0% Online-lottery1401.4% Oddset / football pools1001.0% Betting on horses600.6% Online-games300.3% Total10.3100.0 % 1) Gross gaming revenues (GGR) = game stakes less the winnings paid out. Source: Goldmedia (May 2010); Own calculations. 3. The German Market for Gambling & Betting. Table 3.1: Breakdown Total Annual Gross Gaming Revenues (GGR) 1), in Mio. (2009). 7 / 13

8. Oslo, October 1-4, 2013© Prof. Dr. Friedrich Schneider Table 4.1: Gambling & Betting in Germany (2009): Total Annual Gross Gaming Revenues (GGR). Gambling & BettingGGR 2009 (in billion EUR)Total share 2009 (in %) Regulated8.5583 % Unregulated *1.7517 % Sum10.3100 % Online-segment GGR 2009 (billion EUR) Share Online Share unregulated [100% = 1.75 bn EUR] Total share 2009 [100% = 10.3 bn EUR] Online-poker0.3438.8 %19.4 %3.3 % Online-betting0.3033.7 %16. 9 %2.9 % Online-casinos0.2124.2 %12.1 %2.1 % Online-games0.033.3 %1.6 %0.9 % Sum Online0.88100 %50.0 %8.5 % Source both tables: Goldmedia (May 2010); Own calculations. Table 4.2:Online-Gambling & Betting in Germany (2009): Relation Annual Gross Gaming Revenues (GGR) to online- & unregulated & total market, in billion EUR & per cent. 4. Online-Poker and Money-Laundering? *Unregulated market subsumes all those in Germany privately offered gambling & betting products, which are either illicit by German regulation or have an ambiguous legal status (e.g. legal license in another EU-country). 8 / 13

9. Oslo, October 1-4, 2013© Prof. Dr. Friedrich Schneider Online-segment GGR 2009 (in billion EUR) Total share 2009 [100% = 9.9 bn EUR] 1) Online-poker0.343.4 % Online-betting0.303.0 % Online-casinos0.212.1 % Online-games0.030.3 % Sum Online0.888.8 % 1) Figure of 9.9 bn of national German criminal flows are from Schneider (2013). Source: Own calculations. 4. Online-Poker and Money-Laundering? Table 4.3: Online-Gambling & Betting in Germany (2009): hypothetical simulation annual online gross gaming revenues (GGR) in per cent of national criminal money flows (assumption: 100% of the revenues are laundered money). 9 / 13

10. Oslo, October 1-4, 2013© Prof. Dr. Friedrich Schneider Year National criminal money flows (in million EUR) GGR online-poker (in million EUR) Total share (in %) 20057,239.0103.31.4% 20067,903.0201.62.6% 20078,645.0263.43.0% 20089,243.0322.63.5% 20099,897.0339.43.4% 201010,450.0361.0 (p) 3.5% 201111,432.0364.0 (p) 3.2% 4. Online-Poker and Money-Laundering? Table 4.4: Online-Poker in Germany: hypothetical simulation online-poker annual gross gaming revenues (GGR) in per cent of annual national criminal money flows, period 2005 to 2011 (assumption: 100% of the revenues are laundered money). (p) = Prognosis Goldmedia. Source: Goldmedia (May 2010); Own calculations. 10 / 13

11. 5. Summary & Conclusions (1)The necessity of money laundering is obvious as a great number of illegal (criminal) transactions are done by cash. This amount of cash from criminal activities must be white washed in order to have a legal profit and to be able to invest or consume these profits. (2)Tax fraud and/or illegal cross-border capital flows are by far the biggest/highest share of all illegal transactions (quite often 66% of all illegal capital flows/proceeds!). Oslo, October 1-4, 2013© Prof. Dr. Friedrich Schneider11 / 13

12. 5. Summary & Conclusions (3)In 2009 online-poker, -games, -betting and -casinos did not play a substantial role in money laundering in Germany, as only 8.8 % (or 880 million EUR) of the total share of criminal proceeds of 9.9 billion EUR could hypothetically be laundered. Extreme assumption: all revenues (100%) from online- poker, -betting and -casinos come from criminal proceeds. Realistic assumption: 8-12%. For other countries and for the whole EU, the figures will be similar! (4)Laundering via online-gambling and -betting induces high transaction costs (up to 30 % of the amount) and high risks of detection; hence other methods will be chosen. Oslo, October 1-4, 2013© Prof. Dr. Friedrich Schneider12 / 13

13. (5)Final conclusion and answering the question in the headline: online-poker is by no means relevant for money laundering in the EU! (6)Moreover, there do not exist serious (and published) scientific studies which would demonstrate that online-poker is extensively used for money laundering. THANK YOU FOR YOUR ATTENTION! Oslo, October 1-4, 2013© Prof. Dr. Friedrich Schneider 5. Summary & Conclusions 13 / 13

14. 6. APPENDIX Appendix Part A1: Methods & Stages of Money Laundering Appendix Part A2: Further Facts & Figures: Global / Regional / National Appendix Part B: References Oslo, October 1-4, 2013© Prof. Dr. Friedrich Schneider

15. Oslo, October 1-4, 2013© Prof. Dr. Friedrich Schneider 6. Appendix Part A1: Methods & Stages of Money Laundering Figure A.1: MIMIC estimation of the turnover of transnational crime for 20 highly developed OECD countries over the periods 1994/95, 1997/98, 2000/01, 2002/03, 2003/04, 2004/05 & 2006/07. Test-Statistics: RMSEA a) = 0.008 (p-value 0.910) Chi-squared b) = 24.93 (p-value 0.930) TMCV c) = 0.041 AGFI d) = 0.752 D.F. e) = 62 a) Steigers Root Mean Square Error of Approximation (RMSEA) for the test of a close fit; RMSEA < 0.05; the RMSEA-value varies between 0.0 and 1.0. b) If the structural equation model is asymptotically correct, then the matrix S (sample covariance matrix) will be equal to Σ (θ) (model implied covariance matrix). This test has a statistical validity with a large sample (N 100) and multinomial distributions; both is given for this equation using a test of multi normal distributions. c) Test of Multivariate Normality for Continuous Variables (TMNCV); p-values of skewness and kurtosis. d) Test of Adjusted Goodness of Fit Index (AGFI), varying between 0 and 1; 1 = perfect fit. e) The degrees of freedom are determined by 0.5 (p + q) (p + q + 1) – t; with p = number of indicators; q = number of causes; t = the number for free parameters. Functioning of the legal System Index: 1=worst, and 9=best Amount of criminal activities of illegal weapon selling Amount of criminal activities of illegal drug selling Amount of criminal activities of illegal trade with human beings Amount of criminal activities of faked products Amount of criminal activities of fraud, computer crime, etc. Amount of domestic crime activities Real policy expenditures per capita per country Per capita income in USD Confiscated money Cash per capita Prosecuted persons (number of persons per 100.000 inhabitants) Turnover of Transnational criminal activities +0.402** (2.85) +1.00 (Residuum) -0.154 (*) (-1.49) -0.038* (2.09) +0.214** (3.02) +0.361** (4.11) +0.245* (2.59) +0.142* (2.59) +0.084 (1.41) +0.104 (1.59) -0.245* (-2.51) +0.193* (1.74) Source: Own calculations.

16. Oslo, October 1-4, 2013© Prof. Dr. Friedrich Schneider 6. Appendix Part A1: Methods & Stages of Money Laundering Table A.1: Calculations of the Turnover of Transnational Crime of 20 OECD countries using the MIMIC estimations (1995-2006). Source: Own calculations, calibrated figures from the MIMIC estimations. Year Volume of money laundering (billion USD, 20 OECD countries) Volume of money laundering in % of GDP 20 OECD countries 19952731.33 % Australia, Austria, Belgium, Canada, Denmark, Germany, Finland, France, Greece, Great Britain, Ireland, Italy, Japan, Netherlands, New Zealand, Norway, Portugal, Switzerland, Spain and USA. 19962941.37 % 19973151.40 % 19983321.42 % 19993591.46 % 20003841.47 % 20014121.52 % 20024361.56 % 20034751.63 % 20045121.66 % 20055611.72 % 20066031.74 %

17. Oslo, October 1-4, 2013© Prof. Dr. Friedrich Schneider 6. Appendix Part A1: Methods & Stages of Money Laundering Figure A.2: Framework for analysing the costs of cybercrime. Source: Anderson, et al. (2012, p. 5).

18. Oslo, October 1-4, 2013© Prof. Dr. Friedrich Schneider 6. Appendix Part A1: Methods & Stages of Money Laundering Table A.2: An estimation of the cost components (partly proceeds) of cyber crime. Type of cybercrimeUK estimatesGlobal estimates 1. Cost of genuine cybercrime Online banking fraud -phishing$16 m$320 m -malware (consumer)$ 4 m$70 m -malware (businesses)$ 6 m$ 200 m -bank tech. countermeasures$ 50 m $ 1.000 m Fake antivirus$ 5 m$ 97 m Copyright-infringing software$ 1 m$ 22 m Copyright-infringing music etc.$ 7 m$ 150 m Patent-infringing pharma$ 14 m$ 288 m Stranded traveler scam$ 1 m$ 10 m Fake escrow scam$ 10 m$ 200 m Advance-fee fraud$50 m$ 1.000 m SUM of 1.$ 164 m (0.9%)$ 3.457 m (1.6%) 2. Cost of transitional cybercrime Online payment card fraud$ 210 m$ 4.200 m Offline payment card fraud -domestic$ 106 m$ 2.100 m -international$ 147 m$ 2.940 m -bank / merchant defence costs$ 120 m$ 2.400 m Indirect costs of payment fraud -loss of confidence (consumers)$ 700 m$ 10.000 m -loss of confidence (merchants)$ 1.600 m$20.000 m PABX fraud$ 185 m$ 4.960 m SUM of 2.$ 3.068 m (6.7 %)$ 44.200 m (19.8 %)

19. Oslo, October 1-4, 2013© Prof. Dr. Friedrich Schneider Type of cybercrimeUK estimatesGlobal estimates 3. Cost of cybercriminal infrastructure Expenditure on antivirus$ 170 m$ 3.400 m Cost to industry of patching$ 50 m$ 1.000 m ISP clean-up expenditures$ 2 m$ 40 m Cost to users of clean-up$ 500 m$ 10.000 m Defense costs of firms generally$ 500 m$ 10.000 m Expenditure on law enforcement$ 15 m$ 400 m SUM of 3.$ 1.237 m (16.7%)$ 24.840 m (11.9%) 4. Fraud against public institutions Welfare fraud$ 1.900 m$ 20.000m Tax fraud$ 12.000 m$125.000m Tax filing fraud--$ 5.200 m SUM of 4.$ 13.900 m (75.7%)$ 150.200m (67.5%) SUM of 1. – 4.$ 18.369 m (100%)$ 222.697m (100%) Source: Anderson, et al. (2012, p. 24). 6. Appendix Part A1: Methods & Stages of Money Laundering Table A.2: An estimation of the cost components (partly proceeds) of cyber crime (cont.).

20. Oslo, October 1-4, 2013© Prof. Dr. Friedrich Schneider Goals of money laundering Integration 6. Appendix Part A1: Methods & Stages of Money Laundering Figure A.3: Goal-model. Source: Ackermann (1992, p. 11) and Schneider, Dreer, Riegler (2006, p. 39).. Investment Tax: circumvention, evasion, fraud Financing of crime Options to act Support factors Home country Foreign country Offshore Front companies Major companies Securities Savings accounts Tangible assets Not submitting Counterfeiting Financing more criminal acts Corruption Banking secrecy, International factor, Factor of the inadequate financial market supervision and of the lacking coordination in fighting domestic money laundering, Protection factor of secrets, Offshore-factor, Factor of the envelope function of legal persons, Layering-factor, mixing-factor, counterfeiting-factor, Factor of cashless payment transactions.

21. Oslo, October 1-4, 2013© Prof. Dr. Friedrich Schneider Study [St.]MethodResultEvaluation DIRECTESTIMATION METHODS [A]----- Discrepancy analysis of internat. balance of payments & world bal. of current account World balance of current account deficit of around 100 billion USD (due to non- registered interest income) Basically interesting approach. BUT: too unreliable data for offshore banks; lack of differentiation between legal & illicit source. [B] St. for the Netherlands (van Duyne, 1994) Money circulation method Return of Dutch guilder in the amount of 3.7 billion HFL (according to estimates by van Duyne 1 billion of that with illegal origin) Method can be used as an indication for existence of money laundering & for plausibility check. BUT: Assumption of cash dependency, other reasons for transfer payment very obvious; dependency on method; very little relevance of currency in neighboring countries (abroad) [C] Case from the USA after change in fight against drugs Change in cash holdings of national banks Transfer of drug money to the U.S. in the billions Good approach for detection of money laundering centers. BUT: no reliable statements to volume (distinction legal & illicit funds; significant change in anti-money laundering measures required) [D] St. to measure annually exported amount of money from the USA to offshore centres (Blum, 1981) Estimates based on the inflows into offshore financial centers 100 billion USD funds from illegal sources; 20-25 billion USD (according to Gutmann's study) annually leaving USA in direction offshore centers Highlights importance of offshore centers for money laundering. BUT: lack of distinction between legal & illicit funds; in calculations only limited comprehendible approach from Blum [E]----- Calculation based on confiscated assets or individual money laundering cases No data on total amount of actually confiscated assets; money laundering in the millions Too vague, since it can be assumed that the confiscated assets represent only a fraction of true extent 6. Appendix Part A1: Methods & Stages of Money Laundering Table A.3: Summary evaluation of estimation methods and their studies.

22. Oslo, October 1-4, 2013© Prof. Dr. Friedrich Schneider Study [St.]MethodResultEvaluation INDIRECTESTIMATION METHODS [F] St. for Vienna (Siska, 1999); St. for Western Europe (BND, 1993), St. for the USA (ONDCP, 2000) Quantification based on the estimated drug use Around 700 million EUR sales revenues from hashish & heroin trade in Vienna; around 40 billion EUR sales revenues from hashish & heroin trade in Western Europe; around 12 billion USD sales revenues from heroin trade in the USA Regional application of this method meaningful. BUT: prices diverge nationally / internationally very heavily; consumption individually different [G]----- Quantification based on the estimated drug production ----- Heavy price differences; estimations for production volume very different [H] St. for the USA (Preston, 1989) Quantification based on confiscated illegal drugs Amount of laundered money from drug trafficking for the U.S. 50 - 65 billion USD Heavy differences in success rates of prosecution authorities; very uncertain extrapolation from confiscated quantity to actual quantity Source: Own depiction. 6. Appendix Part A1: Methods & Stages of Money Laundering Table A.3: Summary evaluation of estimation methods and their studies (cont.).

23. Oslo, October 1-4, 2013© Prof. Dr. Friedrich Schneider 6. Appendix Part A1: Methods & Stages of Money Laundering Figure A.4: Infiltration of the Legal Economy by Transnational Organized Crime (TOC). Source: Yepes (2008); Own remarks. Payments to firms sympathizing with terrorism Donations via informal bank circuits Commercial criminal activity: Stone and metals, oil Classical criminal activities (drug-, arms-, human trafficking Infiltration in the internat. financial markets through corporate vehicles International control and purchase of companies Means / Instruments of Infiltration of the TOC in the Official Economy With threat of violence Use of financial resources Bribery of employees or functionaries: corruption

24. (1)If only flows related to drug trafficking of transnational organized crime activities were considered, the proceeds would be on average USD 650 billion per year over 2001 to 2010, and for 2009 equivalent to 1.5 % of global GDP or USD 870 billion. Oslo, October 1-4, 2013© Prof. Dr. Friedrich Schneider Appendix A2: Further Facts & Figures: Global / Regional / National

25. Oslo, October 1-4, 2013© Prof. Dr. Friedrich Schneider Appendix A2: Further Facts & Figures: Global / Regional / National Table A.4: Cross-border flows of global dirty money (including financial and tax fraud), in trillion USD. Source: R. W. Baker, Capitalisms Achilles Heel: Dirty Money and How to Renew the Free-Market System, New Jersey, 2005, p. 172 and World Bank, Indicators (for GDP). Variable 2000-2005extrapolated to 2009 lowhigh in % of GDP 2000-2005 lowhigh mid- point Overall amounts laundered 1.11.62.9 - 4.3 %1.72.52.1 Of which criminal component in % of overall 0.3 (27%) 0.5 (31%) 0.9 - 1.5 % 0.5 (29%) 0.9 (36%) 0.7 (33%)

26. Oslo, October 1-4, 2013© Prof. Dr. Friedrich Schneider Appendix A2: Further Facts & Figures: Global / Regional / National Table A.5: Amount & Top 20 Destinations of Laundered Money (2005). Source: Unger (2007, p. 80). RankDestination % of worldwide money laundering Walker estimate 2.85 trillion USD Amount in billion USD IMF estimate of 1.5 trillion worldwide Amount in billion USD 1United States18.9 %538.145283.50 2Cayman Islands4.9 %138.32973.50 3Russia4.2 %120.49363.00 4Italy3.7 %105.68855.50 5China3.3 %94.72649.50 6Romania3.1 %89.59546.50 7Canada3.0 %85.44445.00 8Vatican City2.8 %80.59642.00 9Luxembourg2.8 %78.46842.00 10France2.4 %68.47136.00 11Bahamas2.3 %66.39834.50 12Germany2.2 %61.31533.00 13Switzerland2.1 %58.99331.50 14Bermuda1.9 %52.88728.50 15Netherlands1.7 %49.59125.50 16Liechtenstein1.7 %48.94925.50 17Austria1.7 %48.37625.50 18Hong Kong1.6 %44.51924.00 19United Kingdom1.6 %44.47824.00 20Spain1.2 %35.46118.00 SUM of 20 countries67.1 %1,910.9221,006.50

27. Oslo, October 1-4, 2013© Prof. Dr. Friedrich Schneider Appendix A2: Further Facts & Figures: Global / Regional / National Table A.6: Annual money-laundering by region, period 2000 to 2005*, in billion USD. * projection Source: Celent, Anti-Money Laundering: A Brave New World for Financial Institutions, September 2002. Region / Year200020022005* America31337.8%32838.3%35037.7% Asia-Pacific24629.7%25429.7%29231.5% Europe23027.8%23427.3%24126.0% Middle East / Africa384.6%404.7%444.7% Total827100%856100%927100% In % of GDP2.7 %2.6 %2.0 %

28. Oslo, October 1-4, 2013© Prof. Dr. Friedrich Schneider Appendix A2: Further Facts & Figures: Global / Regional / National Table A.7: Estimates of worldwide turnover of organized crime. Origin / studyYear Volume (worldwide) in trillion USD As a percentage of global GDP M. Schuster19940.5-0.8 trillion0.9 - 3.0 % International Monetary Fund & Interpol19960.5 trillion1.6 % UN estimates1994/980.7-1 trillion2.4 - 3.4 % S. Kerry19970.42-1 trillion1.4 - 3.3 % J. Walker19982.85 trillion9.5 % National Criminal Intelligence Service 19981.3 trillion4.3 % 20011.9 trillion5.9 % 20032.1 trillion5.6 % I. Takats (2007)20050.6-1.5 trillion1.3 - 3.3 % J.D. Agarwal and A. Agarwal (2006)20052.0-2.5 trillion4.4 -5.5 % Global Financial Integrity (2011) (estimate for transnational crime) 2000-20090.65 trillion1.5 % J. Walker (based on J. Walker & B. Unger) (2009) 20011 trillion3.4 %

29. Oslo, October 1-4, 2013© Prof. Dr. Friedrich Schneider Origin / studyYear Volume (worldwide) in trillion USD As a percentage of global GDP F. Schneider (University of Linz) 20010.8 trillion 2.5 % 20020.96 trillion 2.9 % 20031.2 trillion 3.2 % 20041.4 trillion 3.3 % 20051.5 trillion 3.3 % 20061.7 trillion 3.4 % Tentative estimate*2009*2.0 trillion 3.4 % Median of all estimates2009**1.9 trillion 3.3 % Inter-quartile range of all estimates2009**1.5-2.4 trillion 2.6 - 4.1 % Average of all estimates2009**2.1 trillion 3.6 % Confidence interval of mean (95%)2009**1.6-2.6 trillion 2.7 - 4.4 % * Tentative estimate, assuming that Schneiders proportion of turnover of organized crime expressed as a percentage of GDP remained unchanged over 2006-2009 period. ** Extrapolated to global GDP in 2009. Source: See appendix. Appendix A2: Further Facts & Figures: Global / Regional / National Table A.7: Estimates of worldwide turnover of organized crime (cont.).

30. Oslo, October 1-4, 2013© Prof. Dr. Friedrich Schneider Appendix A2: Further Facts & Figures: Global / Regional / National Table A.8: FATF estimates of global amounts of laundered money from 1988 to 2009. Source: Organization for Economic Co-operation and Development, Financial Action Task Force on Money Laundering, Paris, 1990, p. 6. quoted in UNDCP, Economic and Social Consequences of Drug Abuse and Illicit Trafficking, UNDCP Technical Series No. 6, Vienna 1998, p, 26; International Monetary Fund, Financial System Abuse, Financial Crime and Money Laundering- Background Paper, February 2010. Estimate of drug sales in key markets (1988)USD 124 billion As a percentage of global GDP (1988)0.8 % Assumed proportion that is laundered (1988)66 – 70 % Estimate of amounts laundered related to drugsUSD 85 billion Proportion in % of global GDP (1988)0.5 % of GDP Estimated proportion of drugs in total amounts laundered25 % Estimated total amounts (all crimes) laundered in 1988USD 340 billion As a percentage of global GDP2.0 % of GDP Extrapolated to global GDP in 2000USD 0.6 trillion Extrapolated to global GDP in 2009USD 1.2 trillion

31. Oslo, October 1-4, 2013© Prof. Dr. Friedrich Schneider Year Amounts estimated to have been laundered (in billion USD) As a percentage of global GDP Increase (in %) 1988340.02.0 %--- 19961,100.03.5 %223.5 % 20052,300.03.0 %109.1 % Table A.9:FATF Estimate of World-Wide Money Laundering, period 1988 to 2005. Source: International Monetary Fund, Financial System Abuse, Financial Crime and Money Laundering-Background Paper, February 12, 2001, and FATF, 2007. Appendix A2: Further Facts & Figures: Global / Regional / National

32. Oslo, October 1-4, 2013© Prof. Dr. Friedrich Schneider Table A.10: Updated FATF model of global amounts laundered. Source: International Monetary Fund, Financial System Abuse, Financial Crime and Money Laundering- Background Paper, Feb. 2010; UNODC, 2005 World Drug Report, Volume 1, Analysis, Vienna, p. 127. Estimate of drug sales in key markets (UNODC estimate for 2003)USD 322 bn As a percentage of World GDP0.9 % of GDP Assumed proportion that is laundered (initial FATF estimate)66 - 70 % Estimate of amounts laundered related to drugsUSD 220 bn Proportion in % of global GDP (2003)0.6 % of GDP Estimated proportion of drugs in total amounts laundered (initial FATF estimate) 25 % Estimated total amounts (of all crimes) laundered in 2003USD 880 bn As a percentage of GDP in 20032.4 % of GDP Extrapolated to global GDP in 2009USD 1.4 trillion Appendix A2: Further Facts & Figures: Global / Regional / National

33. Oslo, October 1-4, 2013© Prof. Dr. Friedrich Schneider Appendix A2: Further Facts & Figures: Global / Regional / National Figure A.5: Sum of national criminal money flows in Austria, in million EUR (1994-2011). Source: Own calculations. Million EUR

34. Oslo, October 1-4, 2013© Prof. Dr. Friedrich Schneider Year Online-GGR Scenario I (million EUR) Online-GGR Scenario II (million EUR) Online-GGR Scenario III (million EUR) Delta Scenario III-I (million EUR) 2009 875.2 0.0 2010 973.8977.2978.7+4.9 2011 1,020.21,030.61,031.5+11.3 2012 1,058.21,094.31,169.2+111.0 2013 1,097.81,154.31,303.6+205.8 2014 1,132.01,203.91,419.3+287.3 2015 1,164.51,251.31,507.4+342.9 Delta 09/15 (Mio. EUR) +289.3+376.1+632.2+342.9 Delta 09/15 (%) +33.1%+43.0%+72.2%-- Source: Goldmedia (May 2010); Own calculations. Table A.11: Comparison three Scenarios (I, II & III) of Development Gross Gaming Revenues (GGR) Online-Market in Germany (four online-segments: casino, gambling, poker, sports betting; Scenario I = monopoly, Scenario II = partial (stationary) liberalization, Sc. III = full liberalization), in million (2009-2015). Appendix A2: Further Facts & Figures: Global / Regional / National

35. Oslo, October 1-4, 2013© Prof. Dr. Friedrich Schneider Year GGR (in million EUR) change versus previous year (in million EUR) change versus previous year (in %) 2005315--- 2006534+219+69,5% 2007692+158+29,6% 2008825+133+19,2% 2009875+50+6,1% Delta 05/09 (Mio. EUR) +560--- Delta 05/09 (%) +177.8 %--- Source: Goldmedia (May 2010); Own calculations. Appendix A2: Further Facts & Figures: Global / Regional / National Table A.12: Development of Annual Gross Gaming Revenues (GGR) Online-Market in Germany (four online-segments: casino, gambling, poker, sports betting), in million EUR (2005-2009).

36. Oslo, October 1-4, 2013© Prof. Dr. Friedrich Schneider Year GGR (in million EUR) change versus previous year (in million EUR) change versus previous year (in %) 2005103.3--- 2006201.6+98.3+95.2% 2007263.4+61.8+30.7% 2008322.6+59.2+22.5% 2009339.4+16.8+5.2% Delta 05/09 (Mio. EUR) +236.1--- Delta 05/09 (%) +228.6%--- Source: Goldmedia (May 2010); Own calculations. Appendix A2: Further Facts & Figures: Global / Regional / National Table A.13: Development of Annual Gross Gaming Revenues (GGR) Online-Poker in Germany, in million EUR (2005-2009).

37. Oslo, October 1-4, 2013© Prof. Dr. Friedrich Schneider Year Online-GGR Scenario I (million EUR) Online-GGR Scenario II (million EUR) Online-GGR Scenario III (million EUR) Delta Scenario III-I (million EUR) 2009 339.4 0.0 2010 361.0 360.3-0.7 2011 364.0 364.2+0.2 2012 362.5371.2412.9+50.4 2013 366.9377.3456.1+89.2 2014 371.6381.5491.1+119.5 2015 376.6386.5510.0+133.4 Delta 09/15 (Mio. EUR) +37.2+47.1+170.6+133.4 Delta 09/15 (%) +11.0%+13.9%+50.3%-- Source: Goldmedia (May 2010); Own calculations. Appendix A2: Further Facts & Figures: Global / Regional / National Table A.14: Comparison of three Scenarios (I, II & III) of Development Gross Gaming Revenues (GGR) Online-Poker in Germany (Scenario I = monopoly, Scenario II = partial (stationary) liberalization, Scenario III = full liberalization), in million EUR (2009-2015).

38. 6. APPENDIX – Part B: References Anderson, Ross, et al., Measuring the Cost of Cybercrime. Working paper, 2012. http://weis2012.econinfosec.org/papers/Anderson_WEIS2012.pdf Schmid, Michael, and Solveig Börnsen. Glücksspielmarkt Deutschland 2015: Situation und Prognose des Glücksspielmarktes in Deutschland. Publisher Dr. Klaus Goldhammer. Berlin: Goldmedia GmbH Media Consulting & Research, May 2010. [Goldmedia (May 2010)] Schneider, Friedrich, Dreer, Elisabeth and Wolfgang Riegler, GELDWÄSCHE – Formen, Akteure, Größenordnung – und warum die Politik machtlos ist. Wiesbaden : Gabler, August 2006. Schneider, Friedrich, and Martin Maurhart. Volkswirtschaftliche Analyse des legalen/illegalen Marktes für Glücksspiel in Deutschland. Linz, December 2009. Schneider, Friedrich. The Financial Flows of Transnational Crime and Tax Fraud in OECD Countries: What Do We (Not) Know?. Linz, October 2012. Unger, Brigitte, The Scale and Impacts of Money Laundering. UK: Edward Elgar, March 2007. Oslo, October 1-4, 2013© Prof. Dr. Friedrich Schneider

39. 6. APPENDIX – Part B: References Sources for figures & tables: See text box below respective figure / table; exceptions: tables 2.6, 2.7 & A.7. Sources for Table 2.6 : Peter Reuter, Chasing Dirty Money – the Fight against Money Laundering, Washington 2004; based on Office of National Drug Policy (2000 and 2001); Simon and Witte (1982); GAO (1980); Federal Bureau of Investigations annual Uniform Crime Reports; Internal Revenue Service; International Organization on Migration; Abt. Smith, and Christiansen (1985); Kaplan and Matteis (1967), Carlson et al. (1984) and Key (1979). Sources for Table 2.7 : Brigitte Unger, The Scale and Impacts of Money Laundering, Cheltenham (UK), Edward Elgar Publishing Company, 2007, p. 66, based on studies by Smekens and Verbruggen (2004), Business criminality: Criminaliteit en rechtshandhaving (2001), WODC (2003, p. 60) and NIPO (2002). Sources for Table A.7 : UNODC calculations, based on F. Schneider, Turnover of Organized Crime and Money Laundering: Some Preliminary Findings, in Public Choice, Vol. 144, 2010, pp. 473-486; J. Walker, How Big is Global Money Laundering? Journal of Money Laundering Control, 1999, Vol. 3, No. 1; I. Takats, A theory of crying wolf: the economics of money laundering enforcement. Paper presented at the conference Tackling Money Laundering, University of Utrecht, Utrecht, The Netherlands, November 2–3, 2007; J.D. Agarwal and A. Agarwal, Globalization and international capital flows, Finance India, 19, 2004, pp. 65–99; J.D. Agarwal and A. Agarwal, Money laundering: new forms of crime, and victimization, paper presented at the National Workshop on New Forms of Crime, and Victimization, with reference to Money Laundering. University of Madras, Indian Society of Victimology, Department of Criminology, 2006; Global Financial Integrity, Transnational Crime in the Developing World, February 2011; J. Walker and B. Unger, Measuring Global Money Laundering: The Walker Gravity Model, Review of Law & Economics, vol. 5, issue 2, the Berkeley Electronic Press; F. Schneider, Money Laundering: some preliminary empirical findings, Linz, Nov. 2007, Paper presented at the Conference Tackling Money Laundering, University of Utrecht, the Netherlands, November 2–3, 2007 and World Bank, Indicators (current GDP). Oslo, October 1-4, 2013© Prof. Dr. Friedrich Schneider