Anti Money Laundering Typologies

International Tax & Financial ServicesGraduate Programs

19 years online - serving international financial service professionals and government regulators

Prof. William H. Byrnes, IVAssoc. Dean, Graduate & Distance Programs

Law & Economics Workshop

Two Topics

Models

Typologies & Trends

Part I: Survey of Models

Currency Demand Approach Pricing Approach

Multiple Indicators Multiple Solutions (MIMIC)

Measurement of Crimes Model

Newton’s Law of Gravity applied to ML

Research Part II: Predictive Model

Neural Networkings

Concept Mapping

Evolutionary Programming (Generic Algorithms)

Typologies & Trends Part I

Securities

Trade Based

Counterfeit Trade

Sports Clubs (and Equestrian)

Typologies & Trends Part II

Child Pornography & Human Trafficking

Government Contracting / Bribery / PEPs

Internet Based Market Systems – Amazon, E-Bay, Second-Life & World of Warcraft

Crime Specific Estimates

PROCEEDS OF CRIME ×PERCENTAGE LAUNDERED

= MONEY LAUNDERED

NUMBER OF CRIMES ×MONEY LAUNDERED PER RECORDED

CRIME= MONEY LAUNDERED

Measuring a Black Hole

MIMIC Model to Measure ML

y = aML + e (1)

ML= b’x + c(2)Where y = effects and x = causesML= unobservable variable

CAUSES (X)Crime / Bank

Secrecy

Effects (Y)Demand for $ Growth Rates

UnobservableMoney Laundering

(ML)

Newton’s Law of Gravitation

Attractive Force Fij = G* Mi*Mj/(Dij) 2

Fij = the attractive force between objects i and j

Mi = mass of object iMj = Mass of object jDij = Distance between object i and object jG = Gravitational constant

Tinbergen’s Formula

Fij = Rj * Mi*Mj / Dij θ

Rj has replaced the constant G fromNewton’s gravity formula. Rj represents sets of alternatives for importers.M represents economic mass (GDP)

Walker Model: Subjective Calibrations

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

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 SecrecyGA= Govt AttitudeCF = Risk of Conflict CR CorruptionEG – EgmontFD = financial deposits for market size

Modified Walker (Unger)

P = proportion of money from X to Y

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

Topic 1: II – Predictive Modeling

• Neural Networks

• Concept Mapping

• Evolutionary Programming = Genetic Algorithms

http://amlsample.googlepages.com/Securities_and_Trade_ML.html

Topic II: I - Typologies

• Securities

• Trade Based

• Counterfeit Trade

• Sports Clubs (and Equestrian)

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…

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

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)

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

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)