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AML OptimizationChristopher Ghenne
SAS – Fraud & Security Intelligence EMEA
SAS® FORUM
PORTUGAL 2017
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QUIZ
How much US$ are laundered in the world ? ?Every minute in the world how many US dollars are laundered in the world ?
• 500 000 US$
• 750 000 US$
• 1 000 000 US$
The correct answer is: 3 800 000 US$
https://www.unodc.org/unodc/en/money-
laundering/globalization.html
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PUT INTO CONTEXT
• 3 805 166 US$ per minute
• 228 310 000 US$ per hour
• 5 479 000 000 US$ per day
• 2 000 000 000 000 US$ per year
• 2000 Billion US$ (or 2 Trillions)=
• GDP India 2250 Billion USD
• 10% of the US GDP in 2016 or
• More or less 10 times the GDP of Portugal
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COMPLIANCE LANDSCAPE
Compliance Trends
40 New FATF Recommendations
New Focus Areas: Trade-based AML
Correspondent BankingRisk Based Approach
Domestic PEP
American and EU Regulators Increase
Checks
Ultimate Beneficial
Ownership (UBO)
FATCA /OECD Compliance
AML Optimisation& Governance
Financial Crime Intelligence Units
4th and 5th EU Directives
Demonstrate DiligenceTransparency
American and EU Regulators
Increase Checks
Ultimate Beneficial Ownership
(UBO)
FATCA /CRS Compliance
Financial Crime Intelligence Units
Terrorist Cell IndicatorsHuman Trafficking
New Focus Areas: Trade-based AML
Correspondent Banking Risk Based Approach
Domestic PEPTax Offences
AML Optimisation &
Governance
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OPTIMIZATION
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DIFFERENT TYPES OF OPTIMISATIONS
Upstream Vs Downstream
Drowning under alerts flowing from everywhere
Reduce the flow upstream Reduce the flow downstream
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PredictiveDiagnosticDescriptive
TM OPTIMISATION
Ongoing AML Program Improvement
Reports / Dashboards
Data Exploration
Predictive Analytics
Data ScientistData Stewards / CDOsBusiness Analysts
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DECISION TREES DECISION TREES
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OUR METHODOLOGY
Statistical Approach for segmentation and optimization
As part of the preliminary segmentation
analysis and validation component, various
analytical approaches are utilized to explore
the data to determine the relationships
between the variables and to identify key
attributes that can be used to segment similar
customers and accounts together.
Graphical approaches used include items
such as scatter plots, frequency histograms,
box plots, stacked bar charts, pie charts, etc.
Statistical tests on the segments’ means and
variances are performed to verify that they
are in fact from different populations. Tests to
identify outliers are performed as well as
tests for normality of the population.
Distributional metrics including skewness,
kurtosis, coefficient of variation, mean, and
median are all produced to assist in
determining the similarity of the segments.
Various statistical clustering procedures are
performed to assist in identifying groups of
entities with similar characteristics.
ILLUSTRATIVE ANALYSIS
Businesses Corporations Government MSBs
Total Monthly Transaction Amounts
Total Amount Vs. Total Volume
Distributional Metrics
The distributional analysis shown
here is only an example of the
types of analysis performed. The
analysis performed as part of this
component of the segmentation is
significantly more extensive.
LOB Variable Min 25th Pct Median Mean 75th Pct Max STD CoV Skewness Kurtosis
Business Amount 100 120 144 173 207 249 55.8 0.3 23.3 30.3
Volume 10 70 121 151 182 1,000 369.7 2.4 21.9 28.5
Corporations Amount 125 150 180 216 259 311 69.8 0.3 20.6 26.8
Volume 25 175 203 254 305 1,500 542.2 2.1 19.4 25.2
Government Amount 500 650 845 1,099 1,428 1,856 509.7 0.5 18.2 23.6
Volume 65 455 502 628 753 3,200 1,134.3 1.8 17.1 22.2
MSB Amount 6500 7,800 9,360 11,232 13,478 16,174 3,627.5 0.3 16.1 20.9
Volume 21 147 180 225 270 5,523 2,187.6 9.7 15.1 19.6
Num
ber of E
ntities
Num
ber of E
ntities
Num
ber of E
ntities
Num
ber of E
ntities
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SINGLE VARIANT ANALYSIS EXAMPLE
MULTIPLE VARIANT ANALYSIS EXAMPLE
Threshold Value
Current
Production
Threshold
Value
Recommended
Threshold
Value
Productive AlertNon-productive Alert
SINGLE VARIANT ANALYSIS EXAMPLE
MULTIPLE VARIANT ANALYSIS EXAMPLE
Threshold Value
Current
Production
Threshold
Value
Recommended
Threshold
Value
Productive AlertNon-productive Alert
Threshold #1 Value
Productive AlertNon-productive Alert
Thre
shold
#2 V
alu
e
Current
Production
Threshold Values
Recommended
Threshold
Values
This illustration depicts how the current threshold value
and the testing region are determined within the alert
population.
This illustration shows how the current threshold value and the
testing region are determined within the alert distribution
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AML HOT TOPICS IN 2017Related to Optimization
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Watch List Filtering
PaymentStripping
Ultimate Beneficial Owners
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Watch List Filtering
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COUNTRIES ON THE SPOTLIGHT
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WATCH LIST METHODOLOGY OVER TIME
Exact MatchInsufficient
No regulatory guidance
SynonymMethodsVariations requires domain knowledge
Impossible to include every version
Phonetic MethodsSoundex
Produces a code, usually first letter + 3 digits
Different names with the same code Jones/James =J520
Same Names different codes Gaddafi = G310 Qaddafi = Q310
SAS Dynamic Programming
Break the given problem into afew sub-problems and combinethe optimal solution of thesmaller sub-problems to getoptimal solutions to larger ones.
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ANALYTICS FOR MATCHING
Match Codes Creation
Name Match Code (95% Sensitivity) Match Code (90% Sensitivity) Match Code (85% Sensitivity)
John Q Smith 4B7~2$$$$$$$$$$C@B$$$$$$$$Q 4B7~2$$$$$$$$$$C@P$$$$$$$$Q 4B&~2$$$$$$$$$$C@P$$$$$$$$$
Johnny Smith 4B7~2$$$$$$$$$$C@B7$$$$$$$$ 4B7~2$$$$$$$$$$C@P$$$$$$$$$ 4B&~2$$$$$$$$$$C@P$$$$$$$$$
Jonathan Smythe 4BR~2$$$$$$$$$$C@B&~2&B$$$$ 4BR~2$$$$$$$$$$C@P$$$$$$$$$ 4B&~2$$$$$$$$$$C@P$$$$$$$$$
Match code generation process:
• Data is parsed into its components (Given Name, Family Name, …)
• Ambiguities and noise words are removed (e.g. 'the')
• Transformations are made (e.g. 'Jonathon' 'John')
• Phonetics are applied (e.g. 'PH' 'F')• Based on the sensitivity selection, the following occurs
• Relevant components are determined• Only a certain number of characters of the
transformed relevant components are used
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Payment Stripping
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SOLUTION OVERVIEW
Parser
Event Stream Processing (ESP) engine
Match Code Generation
Match CodeComparison
RulesRisk
Assessment Model
Outcomes(match results)
Blocked transactions
Payment Stripping in real time
1
2 4 5
3
6 7
8
Source data (SWIFT messages)
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MATCH CODES IN REAL TIME
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RULES USING THE MATCH CODES
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UBO
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CLIENT ON-BOARDING
Without Analytics
- Search external database
- Identify all possible matches and select the right ones
- Retrieve all UBO’s and Officers
- Investigate them
- 25% ? 10% ? Less ?
- How many levels down ?
- How much time do you have ?
- What level of certainty ?
- When will you be exhausted searching entities where you find nothing…
- Calculate the CDD risk score
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CLIENT ON-BOARDING
- Search external database
- Identify all possible matches and select the right one
- Retrieve all UBO’s and Officers
- Use analytics to know where to search- Fuzzy match all UBOs and officers with sanctions lists
- Fuzzy match them with the database of existing customers
- Check whether alerts and/or cases do exist for any of them
- Check whether any of the linked entities are compromised with Panama Papers and the likes
- Calculate the CDD risk score
With Analytics
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Select the one that most likely matches
Review the Company Details
Select your search options
Select the min. shareholding
%
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All known Shareholders automatically retrieved
All known Officers automatically
retrieved and matched against
Sanctions lists
All known filings
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Officers and shareholders are
automatically included in the social
network (sanction list matches in red!)
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Inactive officers marked
'grey'
Associated
household 'BOB J
TAYLOR'
Customer 'BOB J
TAYLOR' has a SAR filed
against him
Officer 'Robert Michael TYLER'
matches customer 'BOB J
TAYLOR'
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OBRIGADO