anti money laundering optimisation by alexon bell, compliance director

Copyright © 2015, SAS Insti tute Inc. Al l r ights reserved.

ANTI MONEY LAUNDERING

OPTIMISATION

ALEXON BELL, COMPLIANCE DIRECTOR - SAS

Copyright © 2015, SAS Insti tute Inc. Al l r ights reserved. 2

AML

COMPLIANCEAGENDA

• Global Drivers, Challenges and Background

• How Global Institutions are Addressing the New Wave of Regulations

• Using advanced analytics to minimise investigation cycles and save

investigator time;

• Realising benefits through co-existence with existing AML solutions (without

the need to rip and replace);

• How technology can be leveraged to uncover new trends and meet new

regulations;

• Incremental steps towards a more sophisticated holistic monitoring capability.


GLOBAL DRIVERS, CHALLENGES AND

BACKGROUND


GLOBAL DRIVERS &

CHALLENGES

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

THE FOCUS ON COMPLIANCE CONTINUES

40 New FATF Recommendations

American and EU Regulators

Increase Checks

Ultimate Beneficial Ownership

(UBO)

FATCA /OECD Compliance

Financial Crime Intelligence Units

New Focus Areas: Trade-based AML

Correspondent Banking Risk Based Approach

Domestic PEP

AML Optimisation & Governance


AML FINES

CONTINUE


MANAGING MULTIPLE SOLUTIONS

High volumes of alerts and false positives

• Inefficient processes and weak detection rates

• Too much noise – “needle in the haystack”

• Must investigate everything due to regulatory pressure

Existing controls are rigid and black box

• Cannot plug in new data and they run slowly

• What is the risk just below the threshold?

• Money launderers and fraudsters continually adapt

No quick and easy way to explore disparate data sources and discover new MOs

• No comprehensive ah-hoc analysis or discovery of new modus operandi

• No visibility of emerging trends in non alert data

𝑥 + 𝑎 𝑛 =

𝑘=0

𝑛𝑛

𝑘𝑥𝑘𝑎𝑛−𝑘

X

CHALLENGES


HOW GLOBAL BANKS ARE ADDRESSING THE

NEW WAVE OF REGULATIONS


MEETING THE

GLOBAL CHALLENGEKEY PROGRAMME THEMES AND DRIVERS

Holistic approach

Global case management

Data & data sharing cross

siloes & borders

Leverage existing

investments / co-existence

Analytics, optimisation and tuning

FCIU


ENTERPRISE CASE

MANAGEMENT

THREATS, COUNTER MEASURES, STRENGTHS AND

WEAKNESSES

H

Holistic

• Single Financial Crime Repository

• Global Search

• Intelligence

Information Sharing

• Best Practice

• Consistency

• DPA Compliance

Analysis

• Emerging Threats

• Process Weaknesses

• Automation


ITS ALL ABOUT

THE DATAYOUR BEST FRIEND AND WORST ENEMY

Question your data

• Who

• What

• Why

How do you question?

• Analytics

• Visualisation

• Business Knowledge

What will you find?

• Information

• Intelligence

• Insight

What does it tell you?

• Existing Typologies

• Emerging Trends

• Gaps

What will it give you?

• Known Knowns

• Known Unknowns

• Unknown Unknowns

How will you use it?

• Proactive

• Get more data

• Visualisation

• When

• Where

• How?


Ale

rts

LEVERAGE

INVESTMENTSCO-EXISTENCE AND ENHANCEMENT

Entity Consolidation

Calibration

Ultimate

Beneficial

Owner

Optimisation

Payment and

Correspondent

Banking

Automation

Visualisation, Simulation, MI Environment

Social

Network

Analysis

Global

Federated

Search and

Retrieval


AML

OPTIMISATIONSOLUTION OVERVIEW

• Co-existence with existing AML vendors in the current estate

• Capabilities aimed at• Entity Consolidation – “Single View” of Customers, internal and external (correspondent banking and KYCC)

• Calibration – tuning the rules inside your existing Transaction Monitoring System

• Optimisation – 2nd pass, were the existing system runs and “alerts” SAS runs before cases are raised and re-prioritise / hybernate

• Customer Screening

• Payments Screening

• Transaction Monitoring Post Alert – Advanced Triage

• Automation – No ETL process to get additional data needed by analysts to make decisions

• Social Network Analysis – Prioritisation and SAR linkages

• Global Federated Search and Retrieval – Find all your customers and retrieve all relevant data – DPA aware

• Correspondent Banking – Profiling and visualisation of data

• Visualisation and Reporting – Transaction flows, Interactive drill-down


ENTITY

CONSOLIDATIONENTITY CONSOLIDATION PROCESS – EXAMPLE RESULT

TRANSACTION RECORD

BENEFICIARY ADDRESS 1 BENEFICIARY ADDRESS 2 BENEFICIARY ADDRESS 3 BENEFICIARY ADDRESS 4

UNITED ADVANTADGE SIGNS INC 208 TOWER DRIVE OLDSMAR FLORIDA 34677 U S A

REFERENCE TABLE

REFERENCE BENEFICIARY NAME REFERENCE BENEFICIARY ADDRESS 2 REFERENCE BENEFICIARY ADDRESS 3

REFERENCE BENEFICIARY ADDRESS 4

UNITED ADVANTADGESIGNS FLAT RM S 10 F TACK BLDG 48 GILMA ST CENTRAL HONGKONG

INC UNITED ADVANTADGE SIGNS 208 TOWER DRIVE OLDSMAR Washington 37365

Unitetadvantadgesigns 22 Otakau Rd Milford 0620 New Zealand

UNITED ADVANTADGE SIGNS INC TOWER DR 208 OLDSMAR FLORIDA 34677 U S A

UNITETADVANTADGESIGNS GHANA PARA BRAZI

UNITED of ADVANTADGE SIGNS 103 RITZ RD ADENTA ACCRA 00233 GHANA

UNITED ADVANTADGE SIGNS INC FLAT RM S 10 F TACK BLDG 48 GILMA ST CENTRAL HONGKONGM

atc

hes


CORRESPONDENT

BANKINGKEY COMPONENTS – REPORTS AND INSIGHT


UBOs

UBO ULTIMATE BENEFICIAL OWNERSHIP

Bank A

Alpha Exports Ltd

Bank B

Alpha Regional Ltd

Alpha Parent Ltd

Director

Director

Arms Manufacturer

Bank B

http://smartweb.latinamericanspeakers.net/Capacitacion Y Entrenamiento/Temarios/index.html





SEARCH GLOBAL FEDERATED SEARCH AND RETRIEVAL

Mexico UK MalaysiaGermanyOthers

Security and Permission Models

Connected

Parties


CALIBRATION TRANSACTION MONITORING - TUNING

• Tuning existing rules, profiles and thresholds in the incumbent system

• OCC 2011-12 “Aligned” Methodology

• 7 Work packages covering

• Segmentation Model (Peer Groups)

• Scenario Effectiveness (BLU)

• Scenario Threshold Distribution

• Below the Line Testing

• Above the Line Productive Alert Analysis

• Model Validation and Testing

• Look Back

• 8 to 16 week engagement depending on how many work packages selected

Payments

ScreeningCustomer

Systems

Txn

Monitroing

Alert

Investigation

Near Real Time


OPTIMISATION TRANSACTION MONITORING – ADVANCED TRIAGE

• Second Screening approach

• SAS Advanced Analytics and Decisioning Rules run

after overnight batch

• Disclosure prediction model

• False positive prediction model

• Alert re-prioritisation model

• Multi-peer group comparisons

• “Auto Close” alerts based on auditable business

rules and logic

Payments

ScreeningCustomer

Systems

Txn

Monitoring

Alert

Investigation

Near Real Time

Mange different strategies for “Re-Prioritisation”

• Specific Line of Business Strategies

• RBS Retail, Coutts, NatWest

• Peer Group Strategies

• High Account Type or Low Account Type or both

• Corporates, Government

• Mass Market Retail Current Account, SME Current

Account

• Specific Customer Type Strategies

• Non Resident Alien

• Solicitors


OPTIMISATION CUSTOMER SCREENING


• SAS Advanced Matching and Decisioning Rules run

after overnight batch

• 240+ Screening Rules for Individuals

• 40+ Screening Rules for Organisations

• Elimination Rules

• Conflict Rules

• 40 + Prioritisation/Elimination/Automation Rules

• Transliteration and Native Script Matching

Customer

ScreeningScreening

Engine

Customer

Systems

Alert

Investigation

Over Night Batch

• Mange different strategies for matching and “Re-

Prioritisation”

• PEP

• SIP

• Sanctions

• Arabic/ Hispanic/Russian/ Slavic, etc. Strategies


OPTIMISATION PAYMENT SCREENING


• SAS Advanced Matching and Decisioning Rules run after the payment is blocked

• Contextual Matching – field parsing and categorisation

• Matching Rules for Individuals, Organisations, Vessels, Sanctions, Town, Cities and Ports

• Imbedding Human Decision Process into matching logic and prioritisation rules

• Identifying data in fields that can release the payment

• Mange different strategies for matching and “Re-Prioritisation”

• Arabic/ Hispanic/Russian/ Slavic, etc. Strategies

• Strategies for Sanctioned Locations – Korea, Middle East, Cuba

• Vessel Strategies – Laura 1, Patricia

• High False Positive Strategies – New York, Santa Clara, Trinidad

Payments

ScreeningPayment

Systems

Screening

Engine

Alert

Investigation

Near Real Time


OPTIMISATION FOR PAYMENT SCREENING

Broker

Screening

SWIFTGateway

Payment

System 1

Payment

System 2

Payment

System n

Case

Management CM Database Second

Screening

MQ

MQ

Real Time

Off Line

• Integration with the Case Management

Database

• SAS Polls Database for new

alerts/records

• Pulls details to screen

• Writes outcome to the database

• Changes Workflow State

• Use the existing tool’s capabilities to

release the payment

• Possibly new workflow for SAS released

payments.


INSIGHT INTO

INVESTIGATIONSCURRENT VIEW OF ALERT GENERATION

Source

Data

Stage

Data

Transaction

Monitoring

Alert

Review

(L1)

Case

Review

(L2)

SAR

Filing

(L3)

L1 Decision>100 data points

Prioritise Alert50-60 data points

Alert Creation<30 data points


ALERT AND

INVESTIGATION

ENHANCEMENTS

NEW VIEW OF ALERT GENERATION

Source

Data

Stage

Data

Transaction

Monitoring

Alert Creation

Event

Enrichment

Event

Analytics

Event Creation• <30 data points

• TM alerts “downgraded”

Get Additional

Data Points• 100 + data points

• Using no ETL

Technology

• Able to use data to

predict outcome

• Continuous

Improvement

Enhanced Investigation• Investigation time reduced from 40 mins to <10

• Investigator throughput up from 1.5 alerts per

hour to 5 to 12 alerts per hour

• Investigators presented with additional facts

• More robust, consistent, thorough Investigation

• Human eyes on data and still make the

decisions

Prioritise• 50% reduction

in Work Items –

100% SAR

Capture Rate

• 75% reduction

in Work Items –

97% SAR

Capture Rate

Alert

Review

(L1)

Case

Review

(L2)

SAR

Filing

(L3)

Event Creation


SUCCESSES


CASE STUDY 1:

MAJOR UK BANK

Screening for Individuals

• 17,000 matches from Original System

• 4,000 matches after SAS processing

• All true positives correctly identified 0

5000

10000

15000

20000

Incumbent SAS

Individuals

76%

Reduction

99%

Reduction

Screening for Organisations

• 4,000 matches from Original System

• 40 matches after SAS processing

• All true positives correctly identified 0

2000

4000

6000

Incumbent SAS

Organisations


BUSINESS ISSUE

• Improve the quality of SARs

• Improve the relevancy of work items to analysts

• Reduce false positives and reduce staffing costs

SAS APPROACH

• SAS’ hybrid approach to analyze past investigations during the prior 6 months

• Business rules applied to normalise the data between relevant & irrelevant work items

• A number of models were deployed with varying business objectives ranging from

workload reduction to SAR retention

RESULTS

• Estimated savings of $1 million in first year based upon 25 investigators

• Adopted a methodology that will scale as the institution acquires other banks

• Reduced work items by 46% with 100% SAR capture

• Reduced work items by 75% with 97% SAR capture

• Ability to auto-triage work items in objective and repeatable manner

CASE STUDY 2:

TOP 20 US BANK


Bank 3 – International Dutch Retail Bank

• Incumbent AML Solution producing 16,000 alerts per month

• “Auto Close” 13,000 per month deemed very risky

• Alerts after SAS Optimisation – 2,500

• 84% reduction in false positives with 100% SAR capture

• Greatly reduced risk profile

Bank 4 – International Spanish Bank

• Incumbent AML Solution producing 13,000

alerts for 1 scenario over 12 months

• After SAS Optimisation 30% FP Reduction

with100% SAR capture

• Potential Savings $3m per year

• For 1 Scenario

CASE STUDY 3 & 4:

TOP 20 EUROPEAN

BANKS


MEETING THE

GLOBAL CHALLENGEKEY PROGRAMME THEMES AND DRIVERS

Holistic approach

Global case management

Data & data sharing cross

siloes & borders

Leverage existing

investments / co-existence

Analytics, optimisation and tuning

FCIU


ANTI MONEY LAUNDERING

OPTIMISATION

ALEXON BELL, COMPLIANCE DIRECTOR - SAS

anti money laundering optimisation by alexon bell, compliance director

Technology

sas institute

new data

new trends

compliance director

new mos

discovery of new

new wave of regulations

global institutions