AML Optimization - SAS · Statistical Approach for segmentation and optimization As part of the preliminary segmentation analysis and validation component, various analytical approaches

Copyr i g ht © 2016, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

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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

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

Copyright © SAS Inst i tute Inc. Al l r ights reserv ed.

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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


Threshold #1 Value


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

Company Conf ident ial – For Internal Use Only


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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

%


All known Shareholders automatically retrieved

All known Officers automatically

retrieved and matched against

Sanctions lists

All known filings


Officers and shareholders are

automatically included in the social

network (sanction list matches in red!)


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

Documents

AML Optimization - SAS · Statistical Approach for segmentation and optimization As part of the preliminary segmentation analysis and validation component, various analytical approaches