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Anti Money Laundering Typologies
The document discusses various models and typologies related to money laundering, including approaches such as currency demand and pricing strategies, alongside predictive modeling techniques like neural networks. It highlights trends in crime related to securities, trade-based activities, counterfeit trade, and social issues like human trafficking. Additionally, it presents economic impacts in sectors such as equestrian activities and government contracting.
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Anti Money Laundering Typologies
- 1. International Tax &Financial Services Graduate Programs 19 years online - serving international financial service professionals and government regulators Prof. William H. Byrnes, IV Assoc. Dean, Graduate & Distance Programs Law & Economics Workshop
- 2. Two Topics Models Typologies & Trends
- 3. Part I: Surveyof Models Currency Demand Approach Pricing Approach Multiple Indicators Multiple Solutions (MIMIC) Measurement of Crimes Model Newton’s Law of Gravity applied to ML
- 4. Research Part II:Predictive Model Neural Networkings Concept Mapping Evolutionary Programming (Generic Algorithms)
- 5. Typologies & TrendsPart I Securities Trade Based Counterfeit Trade Sports Clubs (and Equestrian)
- 6. Typologies & TrendsPart II Child Pornography & Human Trafficking Government Contracting / Bribery / PEPs Internet Based Market Systems – Amazon, E-Bay, Second-Life & World of Warcraft
- 7. Crime Specific Estimates PROCEEDSOF CRIME × PERCENTAGE LAUNDERED = MONEY LAUNDERED NUMBER OF CRIMES × MONEY LAUNDERED PER RECORDED CRIME = MONEY LAUNDERED
- 8.
- 9. MIMIC Model toMeasure ML y = aML + e (1) ML= b’x + c (2) Where y = effects and x = causes ML= unobservable variable CAUSES (X) Crime / Bank Secrecy Effects (Y) Demand for $ Growth Rates Unobservable Money Laundering (ML)
- 10. Newton’s Law ofGravitation Attractive Force Fij = G* Mi*Mj/(Dij) 2 Fij = the attractive force between objects i and j Mi = mass of object i Mj = Mass of object j Dij = Distance between object i and object j G = Gravitational constant
- 11. Tinbergen’s Formula Fij =Rj * Mi*Mj / Dij θ Rj has replaced the constant G from Newton’s gravity formula. Rj represents sets of alternatives for importers. M represents economic mass (GDP)
- 12. Walker Model: SubjectiveCalibrations per country & bi-lateral Fij/Mi = Attractiveness j /Distance ij2 BS Bank Secrecy GA Govt Attitude CF Conflict CR Corrupt Fij/Mi = (GNP/capita)j * (3BSj+GAj+SWIFTj-3CFj –CRj +15)/ Distance ij2 Rich Developed Countries $ Pakistan (drugs) Netherlands
- 13. Unger’s Calibrating Revisions Attractiveness= (GDP/capita)*(3BS+GA+SWIFT+FD-3CF-CR-EG+10) Distance DIST = language+3*colonial background+3*trade+physical distance BS = Bank Secrecy GA= Govt Attitude CF = Risk of Conflict CR Corruption EG – Egmont FD = financial deposits for market size
- 14. Modified Walker (Unger) P= proportion of money from X to Y
- 15. Walker Model: Results bi-lateral E14.5 NL + E4 E18.5 (5% NL GDP) Antilles, Spain, Turkey, Colombia Netherlands USA, Russia, Italy, Germany, UK, France
- 16. Topic 1: II– Predictive Modeling •Neural Networks •Concept Mapping •Evolutionary Programming = Genetic Algorithms http://amlsample.googlepages.com/Securities_and_Trade_ML.html
- 17. Topic II: I- Typologies •Securities •Trade Based •Counterfeit Trade •Sports Clubs (and Equestrian)
- 18. Topic II: I- Securities If incidence of Opaqueness relative to Transparency; with higher Liquidity is an indicator for Launderer, then Securities •GEM, AIM, B®IC •Hedge Funds •Audit firms, banks, law firms = $100B in tax losses = $T alleged w/o business purpose and w/o risk, IMAGINE what launders can do…
- 19. Topic II: I– Trade Based Transfer Pricing L.A. goods hP a USA Dump (harm market price & higher risk detection) Smurf purchases to avoid CTRs Switzerland USA $31B Russia USA $9B
- 20. Topic II: I– Counterfeit Trade • 2% - 7% of World Trade $200B - $650B • CF currency • CF goods perceived by BRIC as less harmful than drugs This weakness in perception can allow substantial $ flows from other crimes (B movie industry)
- 21. Topic II: I- Equestrian Equestrian > Hollywood Economic Impact = $25B / $100B indirect # of Americans Involved = 7.1 Million # of Full-Time Jobs = 1.4 Million # of Horses = 6.9 Million Total Taxes Paid = $1.9 Billion
- 22. Topic 2: II- Trends • Child Pornography & Human Trafficking • Government Contracting / Bribery / PEPs • Internet Based Market Systems • Amazon & E-Bay • Second-Life (Direct) & World of Warcraft (3rd Party)