What is the role of Digital
Transformation in AML/CFT?

25th September 2019
Yoshiyuki Minegishi , NTT DATA Getronics Corporation, General Sales Manager
Catarina Soares, NTT DATA UK, Data & Analytics everis Banking UK© 2019 NTT DATA Corporation

Brief Introduction to NTT DATA

© 2019 NTT DATA Corporation

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Let’s start by introducing ourselves…

Yoshiyuki Minegishi
General Manager of Financial
Solution Sales in NTT DATA
Getronics Corporation
Yoshiyuki has been working in thePayment and Compliance area for
more than 15 years.

© 2019 NTT DATA Corporation

Catarina Soares
Senior Manager and Head Data &Analytics Banking in everis UK,NTT Data company
Catarina has been working in theBanking Industry for the last 12years in large transformation
programs around Data & Analytics.

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NTT Data Group at a glance
NTT DATA (as part of NTT group) is a global consulting firm with a worldwide reach.
We support you with your digital transformation and operations of integrated IT platforms. With a newperspective on banking’s digital future, NTT DATA helps create digital value to your business and a smootherbanking experience for your customers.

Operations inMore than88

242,000,Countries$105BnIn revenues
We work with80 of theTOP100

companies listed inFortune Global 100© 2019 NTT DATA Corporation

NTT DATA
supports
nearly twothirds of theFinancial
Servicescompanies onthe GlobalFortune 500.

25,000 banking+$2Bn

Professionals

invested annually in R&D

Ranked#6
In the IT Services field4Banking Capabilities Worldwide
NTT Data is composed by professionals with different backgrounds and experiences that can cover the fullrange of services/technologies:

Big Data &Analytics

DigitalExperience

MachineLearning

Biometrics

Hybrid Cloud &DevOpsInfrastructure& Application
Transformation

Artificial
Intelligence

TestingServices© 2019 NTT DATA Corporation

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Robotic Process
AutomationBlockchain

AML
NTT DATA Getronics Corporation© 2019 NTT DATA Corporation

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AML functional landscape
NTT DATA Getronics Corporation has worked on the below components from systems perspective
Customer
Information
CaptureDocument
Management

OCR

Identification &Verification

Ultimate
Beneficiary

Screening

Hits
Investigation

PEP Check

Negative MediaCheck

Credit Check

Transaction
Monitoring

SAR

Workflow

Case
Management

TBML

CIF Integration

Interface withcompanies’
databases

Risk
Categorization

ReportingCustomer Risk
Scoring

No experience

Has experience© 2019 NTT DATA Corporation

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Fircosoft Overview
Strategic partner with Fircosoft since 2003.
Extensive experience in implementationof Firco Filtering Suite#12

Only Japanese vendor with a partnershipwith Fircosoft

Consulting services provided by ACAMS
certified experts

SolutionOverview

Firco Continuity

Firco Continuity is a real time watchlist filtering solution designed to ensure compliance and detect & blocktransactions including listed parties. The solution is multi-alphabet ready and can be interfaced to any banking
application, operates on all major platforms and filters any message type including SWIFT, FEDWIRE andSEPA. The solution can filter against any sanctions list (such as EU OFAC, HMT and UN), third party lists orinternal Watchlists.

Firco Trust

Firco Trust is a customer and entity screening solution that combines filtering intelligence with highperformance processing capabilities. With Firco Trust, users can implement KYC programs by filtering existingand new customers, counterparties or beneficiaries against any sanctions, PEPs or Negative newslists. Firco
Trust enables a risk based approach to scan customer databases, and offers a web based user interface tostreamline and expedite decision making.

© 2019 NTT DATA Corporation8Oculus®: in-house AML package solutions
Match clients data against watch-list data
CIF filtering（Oculus®-filter）

Hit resultsCore bankingsystems
Transaction monitoring（Oculus®-monitor）

STR related infro.
Past transaction data

Monitoring suspicious activities

CDD(Oculus®-CDD)
Customer risk scoring* “Oculus” are trademarks of NTT DATA Getronics Corporation registered in Japan.
* Trademarks of the other products/corporations/organizations are omitted on this paper.
© 2019 NTT DATA Corporation

Hit results data9Challenges in current AML/CFT field
1.

More sophisticated money-laundering methods are getting being used than ever•2.

BIG DATA is getting more and more available as means of AML than ever•3.

Techniques are getting more sophisticated & complicatedExpected to be applied to reduction of False Positives & findings of True Positives

Much stricter sanctions & regulations are getting imposed to Financial Institutions•

5th Anti-Money Laundering Directive (AMLD), FinCEN CDD final rule, U.K. Senior
Managers Certification Regime (SMCR), etc.

© 2019 NTT DATA Corporation

10Know Your Data (KYD): a substantial means of KYC
1.

Define your “Customers” in consistent rules- Definition of Customers is a first step in KYC/CDD.
- If the definition of Customers is inconsistent over jurisdictions/regions, the data to beaggregated as “Customers” would be inconsistent.

2.

Aggregate & collate “Data” of “Customers” in consistent rules- Under consistent definition and rules, the data of Customers could be aggregated andcollated consistently.

3. Then, analyze “Customers data” which are aggregated & collated correctly- By looking into the data which accurately represents the “Customers” of the organization,

the organization can assess the risk of “Customers” correctly.

© 2019 NTT DATA Corporation

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New concepts of AML as data analytics
1.

“Actors based” monitoring•

Usually Monitoring rules and models are set based on transactions. But currently
there are new ways available to set them by criminal groups and networks.

2.

Behavior-based detection•

Segment customers by behaviors•

Analyze which segments were detected for escalation, which activities seem to be
involved in money laundering, which activities are close to false positives•

Connecting them together will enable more effective detection

3. Link Analysis•Detecting suspicious or hidden relationships© 2019 NTT DATA Corporation

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New concepts of AML as data analytics
1.

Monitor in “Actors” base, not only in “Transactions” base.
Actor A

Actor B

Transactions

Actor C

Transactions

Actor X
Transactions

Transactions

…..

2.

Behavior-based detection

3.

Link Analysis…..

Counterparty

Counterparty

Buyer© 2019 NTT DATA Corporation

13Seller…..

Technology as a lever fortransformation© 2019 NTT DATA Corporation

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Big Data & Analytics

Artificial Intelligence
Howto leverage
emerging
technologies totransform banks?

© 2019 NTT DATA Corporation

Blockchain

Robotics &Automation
Machine Learning

15Biometrics

Data X
2 every
2 years

How can Big
Data & Analytics
improve AML/KYC processes ?

© 2019 NTT DATA Corporation

33
trillion
GB 2018

175
trillion
GB 2025

Data consistency=Wider investigations
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Case Study UK Bank – Starting point
Complex ecosystem of systems
End user computing sources/tools
Data Governance structure but no processes defined
Lack of internal skills/knowledge
Resistance to change© 2019 NTT DATA Corporation

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Case Study UK Bank – 3 step approach
APIsAnalytics
ReportingInstalling the RoofDevelopment
Frameworks

FramingArchitecture &Methodology
Target Operating Model
Data Strategy, DataManagement, Data
Governance© 2019 NTT DATA Corporation

18Foundation

From static rules to a model based on selflearning that identifies patterns
Lower false positives
Increase speed of analysis
How can Machine
Learning & AI
improve AML/KYC processes ?

© 2019 NTT DATA Corporation

Automate the analysis of low risk cases
Focus on higher risk cases

19Adopted by 15% of Business,but 30% plans to use it
Robotics market is expected togrow by 175% over the next decade.
How can Robotics& Automation
improve AML/KYC processes ?

© 2019 NTT DATA Corporation

In Europe between 1999 and 2010,
the number of employees grew intandem with automation
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Case Study EMEA Banking Retail Group
Starting point:

Long processing time
Time consuming activities
Human errors
Re-work© 2019 NTT DATA Corporation

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Case Study EMEA Banking Retail Group
Initial Assessment+ Business Case

Validado presencialmente com Paula Borges no dia 20/02.

© 2019 NTT DATA Corporation

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Case Study EMEA Banking Retail Group
Robots activities:•Filing of documentation.

•Request information / documentation.

•Information Upload on Systems andPortal.

•

Search and compile information foranalysis.

•

Validation of information /documentation.
© 2019 NTT DATA Corporation

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Savings€1.5
millionCase Study EMEA Banking Retail Group
Implementation of Robots:• Filing of documentation.
•Request information / documentation.

•

Information Upload on Systems andPortal.

•

Search and compile information foranalysis.

•Validation of information /documentation.
© 2019 NTT DATA Corporation

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Savings€1.5
millionBlockchain can provide
an unique client registry
Reduce Fraud
Reduce KYC Costs
How canBlockchain
improve AML/KYC processes ?

© 2019 NTT DATA Corporation+Interoperability
Improve Customer experience25

Voice-based biometrics fortelephony contact centres

How canBiometrics
improve AML/KYC processes ?

© 2019 NTT DATA Corporation

Device-based biometrics
for digital interactions
Facial Recognition26

Before reaching out to newtechnology for transformation© 2019 NTT DATA Corporation

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First, analyse data from multiple aspect to capture the core issue
It is not as simple as putting the new technology (such as AI) into the operations that will solve the problem.
Evaluation on Solutions
Solutions

Outputs

Continuous Overall Analysis on AML Operations

OKApproachN/A

QuantitativeCore

Trend Extraction

Issue

PartialN/A

Qualitative

OK
PartialData driven Purpose© 2019 NTT DATA Corporation

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N/A・・・

Purpose driven Solution・・・&Second, clarify how to perceive the new technologies
There are two main aspects that need to be considered and aligned…

Position

Explanation

Question…

Question…

How to utilize the new technology in production?

Does the logic behind the results derived by new technology
is needed?

Further Question…

Further Question…

Is it something that supports human operation?
Is it something that replaces the human operation?
Does the both? But at what level?
Or something else?

To whom this needs to be disclosed? And why?
What is the definition of the clarity?
At what level of the explanation is it needed?
How much information is needed?

Value Alignment© 2019 NTT DATA Corporation29

© 2019 NTT DATA Corporation