SAS was analytics sponsor at AML Summit by Fintelekt in Bangladesh and Mr. Rohan Langley, SAS Fraud & Security Expert AP presented on topic Effective AML Compliance. SAS Anti-Money Laundering protects your assets by using advanced analytics to uncover illicit activity and comply with AML and CTF regulations.

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Effective AML Compliance
AML Compliance and Technology
Rohan Langley CAMS, Regional Solution Specialist

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SAS AML Solution Agenda
• Background: AML Regulatory Changes
• Best Practices: Expectations from AML Technology
• Focus: Machine Learning & Artificial intelligence
• Key Considerations in Evaluating an AML Platform

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Background
AML Regulatory Changes

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2008 2011 2013 2015 2016 2017 2018?
AML Regulatory Changes
Banks and Regulators are under pressure
Rules
Based
Client
Rating &
Model
Validation
Analytics
Based
CDD and
KYC
Ad-hoc
Discovery
Western Europe Asia Pacific
•TBML
•UBO
U.S.
Regulators
AI, RPA,
Crypto
Ongoing
Review &
Tuning
Other
Regulators

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AML Regulatory Changes
Rapid changes, new concerns
OCC2011-12 Model
Risk Management
Risk Based Approach, UBO
Tax Compliance NY DFS 504
Crypto Currencies
TBML

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Compliance
Trends
40 FATF
Recommendations
(2012 Revised)
EU 4th AML Directive:
PEP Enhancements,
UBO, CDD/EDD
Regulator Trends:
Increased Scrutiny, Data
Governance, White Box,
Scenario Tuning, STR
Analysis, Model Risk
Management
UK Bribery Act
and FCPA
FATCA /CRS
Compliance
AML/Screening
Optimization
Financial Crime
Intelligence Units
AML Regulatory Changes
Increased focus on compliance, globalisation

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Global Best Practices
Expectations from AML Technology

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Compliance Framework
Global Best Practices
Top 10
Adopt a Risk
Based
Approach
Implement a
Culture of
Compliance
Document
and Verify
Choose
Appropriate
Technology
Know Your
Customer
External
Validation
Compliance
and Business
Regulatory
Reporting
Review and
Refine, MRM
Flexibility in
the Program

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“Tick in the Box”
Global Best Practices
Key takeaways compliance
Start at the Top
Model Risk Management
Risk Based Approach
Systems Transparent and Flexible
Know Your Data

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Increase in false positive rates of
traditional AML systems are
becoming inefficient and expensive
Expectations that firms have the
holistic picture of the activity at time
of decision
Need to address monitoring of “faster
payment” transactions through real-
time analytics and decision models
Pressure to use Machine learning
techniques to identify abnormal
behavior
What is going on in the AML market?

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Heavier attention on sanctions and
high risk customer screening
mechanisms and testing
Analysts/investigators use majority of
time collecting data and information
for review vs. investigating
Emphasis on know your customer, and
understanding the nature and purpose
of their accounts
Requirements to collect and leverage
beneficial ownership of legal entity
customers to better understand your risk
What is going on in the AML market?

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Machine Learning & Artificial Intelligence
SAS’ Approach…

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“increasingly, regulators recognise that rules
alone are not an effective manner of
detection and are pressuring banks to
include more sophisticated analytics.”
Aite Group.

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Machine Learning
Our Approach…
Artificial Intelligence
Automation of information extraction
(patterns both known / unknown) from
data, expressing it in a comprehensible
form so that it can be applied to new and
unseen data within a system and learn
from the outcomes of the system.
The science of training computers to
perform tasks that typically require human
intervention to complete. Computer learns
how to do those processes more efficiently.

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Key Considerations in Evaluating an AML Platform

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Compliance
The AML Puzzle
KYC / CDD /
EDD
AML
(Transaction
Monitoring &
Customer Screening)
Sanctions
screening
Correspondent
Banking
FATCA / CRS AML Analytics
Trade based
AML UBO

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Spotlight on AML, CDD and Sanctions
Challenge
• New Regulations – 4th EU directive, OCC
2011/12
• Tactical solutions may seem like a quick fix
but are non upgradable
• TCO – cost reductions
Your AML and CDD systems need
to be able to quickly comply with
rapidly changing regulations
What you need
• Complete end to end solution
• White box solution
• Data Lineage and Flexibility
• Out of the box library of scenarios and
risk factors
• Statistic Scoring of Alerts
• Integration of AML and CDD
Analytics & Alerting
• Expose the data so that it can be
analysed and explored visually
• Make sure it is all “white box”
meaning both the data model as
well as the way scenarios work.
Regulatory Reporting & MI/BI
• Framework to support regulatory
reporting.
• AML out of the box reports.
• Flexibility to create new reports.
Look For
Case Management & Workflow
• Be sure you have a fully audit-
trailed Case Management
environment
• Ask for Workflow and
configurable user interface
• Ask for a role-based approach to
alert triage and case
investigation.

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Spotlight on AML Analytics
Challenge
• Overwhelming volume of alerts with
significant staffing costs
• High false positive rate
• View of zero value from incumbent
solutions
Ability to optimise existing AML
alerting systems to target a
reduction in workload and increased
efficiency.
What you need
• Co-existence with existing solutions
• White box solution
• Reduced False Positives – Higher quality
& relevancy of alerts generated / worked
• More efficient team which can focus on
the high risk genuine alerts
Assess Data
• Data exploration and discovery
of emerging risks
• Data quality & usage
• Data transformation
Optimise
• Develop and author scenarios,
segmentation, and/or post alert
scoring models
• Add new data sources to
existing models
Look for
Test
• Hypothesis testing and
simulation
• Champion challenger
• Above/below the line testing
Monitor
• Operationalize scenarios
• Ongoing monitoring of
effectiveness
• Identify ineffective scenarios
& prioritize

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Q&A

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