Uncovering the Hidden Financial Crime œ the SAS Anti ...

Copyright © 2003, SAS Institute Inc. All rights reserved.

Uncovering the Hidden Financial Crime �the SAS Anti-Money Laundering Solution

Sascha Schubert

SAS EMEA

Copyright © 2003, SAS Institute Inc. All rights reserved. 2

What is Money Laundering?

! The process by which large amounts of illegally obtained money is given the appearance of having originated from a legitimate source (United Nations)

! Estimations� 3-5% of global gross product - $500 Billion to $1.2

Trillion annually (IMF)� $590 billion and $1.5 trillion (FATF)� $300 Billion is laundered in the United States annually

(US Administration)


! Money laundering is the criminal practice of filtering illegally obtained money (or lawfully derived money intended to be used for illicit purposes) through a series of transactions to break the investigative trail

! Money laundering is a diverse and often complex processand plays a fundamental role in facilitating the ambitions of drug traffickers, terrorists, organized criminals, insider traders, and tax evaders as well as many others who need to avoid the kind of attention that "sudden" wealth brings

! A professional money launderer is limited only the breadth of his/her imagination. No business, whether it takes cash or operates under a strict, no cash policy, is immune from becoming the unwitting accomplice of the money launderer

Source: Deloitte & Touche

What is Money Laundering?


Business Pains for Financial Institutions

! Legal and regulatory issues.

! Negative publicity.

! Damage to corporate reputation and goodwill.

! Civil or criminal prosecution.

! An adverse effect on the corporate bottom line.


Four Key Goals of an AML Solution1. Facilitate compliance with the EU Directive, and other

Regulatory requirements.! KYC! Report suspicious activity

2. Minimize the number of false positives � Reviewing money laundering alerts is a time-consuming manual effort. The goal of such a solution must be to reduce the number of people required to review alerts by learning from past experiences.

3. Provide collaboration and flexibility � The solution needs to be adaptable to changes in regulations and criminal behavior. Italso needs to facilitate collaboration amongst the organization and user community to exchange best practices.

4. Allow scalability � The solution needs to be able to process large amounts of data in a nightly batch process looking for suspicious activity. Data volumes can exceed many millions of transactions a night.


SAS AML Solution Overview Aberdeen Group

Business Rules

Configure Sensitivity

Configure Workflow

Historical Analysis

TransactionCustomer/Product

Rules/ScenarioEngine

Statistical Analysis

Suspicious Activity

Detection

EscalationsLogging

Reporting Legal Inquiry

Legal Notification

Internal Experience

NewTrends

Government Changes

Refinement

ExceptionsNormal workflow

No Yes

Administration

Data Management

Alert Engine

Surveillance System

Regulatory

Discovery


Components of the SAS AML Solution

Discovery


Process of the SAS AML Solution

AlertSystem

SurveillanceSystem

DataSources

Reports

SARsFinCEN

ElectronicTransfer

Knowledge Center

DiscoveryAdministration

Data Management

Regulatory

Profiles

Data Store

Watch Lists


Component ArchitectureData

ManagementAlert Engine Investigation

Administration

Watch Lists

Profiles

Data Store

0102030405060708090

1st Qtr 2nd Qtr 3rd Qtr 4th Qtr

SARsFinCEN

Feedback

DiscoveryNew

scenarios


Data Management

! Open Metadata-Driven Architecture

! Robust Data Access Engines

! Comprehensive Transformation Suite, Data Quality

! Framework for executing production rules and creating alarms

! Framework for rules management

! Logical & Physical Data Models

Customer &Account

Transactions

Product


Data Management - ETL Process


Alert Engine

Differentiating Between Monitoring Approaches

Issues� Detect known and

unknown patterns of money laundering

� Keep track with new patterns of money laundering

� Not exactly knowing what to look for

ProfilingBuild statistical

Profiles ofAccounts andtransactions

Examples:Mean, standard

deviation, quartiles,distributions

AdvancedAnalytics

Knowledge discoveryIn databasesand machine

learning

Examples:Neural networks

Fuzzy logicGenetic algorithms

HybridCombination of

all existingapproaches

Examples:Genetic algorithm and

Statistics plusNeural network

RulesSet up rules

to filter suspicioustransactions

Examples:AMOUNT > 20�000

ANDCOUNTRY IN HOTLIST

BestPractice

Suitable for known

patterns

Suitable for unknownpatterns

Suitable for complexpatterns


SAS AML Alert Engine

Large Scale

Report accounts which have (number) of transactions above the (transaction limit)�

Multi Trans

Where deposit is followed by withdrawal within (percentage) of deposit with (number of hours) of Deposit�

Acct Verify

Where opening more than (account limit) of accounts with last (number of days)�


Administration

! Secure Infrastructure

! Fine-tune subsystems by adapting rule parameters

! System efficiency


Rule Administration


Investigation Component

! Web Based Reporting

! Security� User and Group Level

! Reports based on defined rules

! Manage flow of Information

! Capture Pertinent Information


Workflow Engine � Process Definition


Regulatory � Government Interaction! Electronic Filing of Suspicious Activity Reports (SAR)

! Document Process and Events to support Audits


Discovery

Facilitate Basic and in-depth analysis

! Ad-hoc query �I just read about a Money Laundering Scheme in WSJ and I want to see if any of our accounts is doing similar activities.�

! Reduce False Positives �History has shown us that Accounts with these characteristics are clean, therefore, I don�t want to see them on an alert.�

! Data Visualization


Discovery � Ad-hoc Query


Discovery - Visualization

Analysis

Data Repository

Data


Discovery - Visualization

Customer 151

Customer 41

Customer 156

Branch 010

Customer 40

Branch 205

Branch 004

Analysis

Copyright © 2003, SAS Institute Inc. All rights reserved.

Discovery � In-depth Analysis

! Detect data-driven rules of ML � Anomalies in behavior −Related to peer-groups−Related to historical behavior

� Profiling� Fine-tuning of scorecards� Build predictive models for

known ML cases� Textual analysis


Auditing

! All changes to rules are tracked and documented for auditing purposes

! All changes to the system and all actions taken are documented and accessible for auditing purposes

! All actions are documented in the Knowledge Centre to create data for analytical intelligence


SAS Consortium

! SAS encourages AML customers to make rules available to consortium

! SAS will provide updates of rules library in regular cycles


SAS AML Logical Architecture

Knowledge Center

Core

Data Management

& Alert Server(UNIX, MVS, WinNT)

ScenarioAdministration

Client(Windows)

LOCAL

RDBMS Middleware

Application Server

Web Server,J2EE App Server,

LDAP Server(UNIX, WinNT)

InvestigationOperational

Data

Web Clients

Ad hoc client administration

(WinNT)

Discovery/Ad hoc Clients(Windows)


Key Features ! End-to-end solution to cover entire Intelligence process

(IVC)! Capabilities to build comprehensive AML Warehouse

� More than just transaction monitoring �need to link the characteristics of the single transaction with other data (customer information, history, external information)

! Advanced analytics� Uncover hidden relationships between subsequent

transactions, different accounts, different account holders� Monitor the performance of the system in terms of compliance

and accuracy

! Information Dissemination� Use web services to make information available where and

when needed

! Integration into larger Banking Intelligence Solution


Integrated Data Model

Corporate Performance Management

Market RiskCredit Risk

Operational Risk�Intrusion Detection

�Risk Mgt for Banking� (Basel II)

Anti-Money Laundering

Fraud � Kiting�Credit Card�Loans

Channel Mgt

ATM Forecasting/ Optimization

Branch/Web/ Call Center Mgt

Offer Optimization

Web Analysis

IT Mgt

Product/Channel analysis

Customer ProfitabilityLife time value

Credit Scoring Wealth Mgt

Target marketingSegmentation &

Profiling

Human Resource Mgt

Broker Surveillance

Compliance Reporting Portal

Cross-sell/up-sell

Campaign Mgt

Customer AcquisitionCustomer Retention

Asset/Liability Mgt.

OperationalCustomerRisk

�Credit Scoring

SAS Financial Service Intelligence

Financial Mgt


Customers of the SAS AML Solution! 3 of the top 10 Financial Institutions in US

� Morgan Stanley� Bank of America

! Unity Trust Bank, UK (http://www.sas.com/success/unitytrust.html)

! "We can focus on suspicious behavior rather than suspicious transactions. This in turn lets us concentrate our resources on investigation and action, rather than detection." Neil Hartley, head of internal audit and compliance, Unity Trust Bank

http://www.sas.com/success/unitytrust.html


Analysts on the SAS AML Solution

! "SAS should enjoy a significant advantage over its competitors. It takes a tremendous amount of time and money to detect money laundering. [Money laundering] techniques change every week. You want a firm that's going to be there if problems develop."

» Richard Delotto, Gartner


Analysts on the SAS AML Solution

�I've been very impressed with what SAS has to offer. It's not just a point solution for anti-money laundering. The technical capabilities of SAS in terms of the warehousing of the information and the analysis tools make it a strategic solution.

Alan YongResearch Director, Aberdeen Group


Summary

! Comprehensive & stable suite of technology

! Long-standing relationship with Financial Service Organizations

! Rules sharing � Financial Consortium for AML Rules

! Beyond simple �if then� rules�Predictive analysis

! SAS is Financially Strong Organization


Questions ??

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