Delivering Data-Driven Applications at the Speed of Business: Global Banking AML use case. Chief Data Officers in financial services have unique challenges: they need to establish an effective data ecosystem under strict governance and regulatory requirements. They need to build the data-driven applications that enable risk and compliance initiatives to run efficiently. In this webinar, we will discuss the case of a global banking leader and the anti-money laundering solution they built on the data lake. With a single platform to aggregate structured and unstructured information essential to determine and document AML case disposition, they reduced mean time for case resolution by 75%. They have a roadmap for building over 150 data-driven applications on the same search-based data discovery platform so they can mitigate risks and seize opportunities, at the speed of business.

1. © Hortonworks Inc. 2011 – 2016. All Rights Reserved
The Path to a Modern Data
Architecture in Financial Services
Vamsi Chemitiganti
GM for Banking & Financial Services,
Hortonworks
@Vamsitalkstech
© Hortonworks Inc. 2011 – 2016. All Rights Reserved
Lee Phillips
Sr. Director, Product Management,
Attivio

2. © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Speakers
Lee Phillips
Sr. Director, Product Marketing
Attivio
Vamsi Chemitiganti
GM, Financial Services
Hortonworks
Part of the Product Marketing team and responsible for analyst relations at
Attivio, Lee brings over 35 years of experience in product, marketing, and
business development in software and information solutions. His background
includes MSE, management, and senior management positions for market
innovators such as Lotus, Borland, Ziff-Davis, FAST, and NewsEdge.
Vamsi is responsible for driving Hortonwork's technology vision from a client
business standpoint. The clients Vamsi engages with on a daily basis span
marquee financial services names across major banking centers in Wall Street,
Toronto, London & Asia, including businesses in capital markets, core banking,
wealth management and IT operations.

3. Agenda
• Introductions
• Trends in Financial Services Risk & Compliance
• Trends in the AML Space
• Why Open Enterprise Apache Hadoop for Modern Data Architectures
• Architectures & Work Streams
• An AML Case Study
• Q & A

4. Page 4 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Big Data in the Financial Services Industry
Page 4 © Hortonworks Inc. 2011 – 2016. All Rights Reserved

5. Hortonworks Key Focus Areas in Financial Services
Common Focus AreasSegments of Banking
Risk Mgmt
Cyber
Security
Fraud
Detection
Predictive Analytics
Data
AML Compliance
Digital Banking
360 degree
view
Customer Service
Capital Markets
Corporate Banking and
Lending
Credit Cards &
Payment Networks
Retail Banking
Wealth & Asset
Management
Stock Exchanges &
Hedge Funds
+

6. Demand drivers for Big Data in
Retail Banking & Capital markets
Catalyst Definition Example
Larger data sets Larger data sets allow analysts to query and conduct
experiments with fewer iterations
Omnichannel data, Tickers, price, volume and
longer time horizons. Social media/ third party
data
New types of data New data types that need to be synthesized for
traditional relational databases
Business process data, Social Data, Sensor &
device data. OTC contracts and public filings.
Analytics and
visualization
More powerful analytics and visualization tools to
explain and explore patterns – Fraud, Compliance &
Segmentation
Complex Event Processing (CEP), predictive
analytics. Portfolio and risk management
dashboards
Tools and lower-cost
computing
Open source software tools. Lower server and
enterprise storage costs
Hadoop, NoSQL. Commodity hardware. Elastic
compute capacity.

7. Transformation
--- Maturity Stages à
OptimizationExplorationAwareness
---MaturityStagesà
Peer Competitive Scale
Standard among peer
group
Common among peer
group
Strategic among peer
group
New Innovations
No Use Case Name
1 Single View of Ins/tu/on
2 Predict Risk Exposures
3 Predict Counterparty Default
4
Automa/on of Client Due Diligence for
consumer onboarding
5 Enhanced Transac/on Monitoring
6 Enhance SAR Accuracy
7 Credit Risk Calcula/on
8a
Regulatory Risk Calcula/ons – Basel III &
CCAR
8b
Regulatory Risk Calcula/ons – Basel III &
CCAR
9a
Calcula/ng VaR across mul/ple trading
desks
9b
Calcula/ng VaR across mul/ple trading
desks
10
Calculate credit risks across a variety of
loan porRolios
11 Internal Surveillance of Trade Data
12
CAT (Consolidated Audit Trail)/OATS
Repor/ng
13 EDW Offload
Corporate &
IT Functions
Trading Desks
Retail Banking Use Cases are available at different levels of maturity
Surveillance
Security & Risk
2
8a
5
7
1
6
3
4
9a
10
11 12
8b
9b
13

8. ©2015 Attivio, | Proprietary and Confidential
GOVERNANCE, RISK, AND COMPLIANCE TRENDS
REGULATORY PRESSURE,
ENFORCEMENT SCRUTINY
Multiple frameworks increase
the economic cost of monitoring
A CRISIS FOR DATA
MANAGEMENT
Increasing volume, velocity, and
variety of Risk & Compliance data
QUEST FOR EFFICIENCY
& EFFECTIVENESS
Shift from manual to cognitive and
automated processes

9. ©2015 Attivio, | Proprietary and Confidential
THE MOST COMMONLY CITED CHALLENGES
Global Inconsistency
Absence of uniformity
across jurisdictions raises
regulatory scrutiny
Lack of Cognitive
Understanding
Must make sense of an
explosion in unstructured
information
Information
Fragmentation
Multiple silos, solutions,
and sources create
expensive friction

10. ©2015 Attivio, | Proprietary and Confidential
Achieve Certain,
Global Impact
A single-view of the
transaction or entity,
across jurisdictions
Correlate Information
for Understanding
Discovery of the structure
inherent in unstructured
information
Unify Information
Virtual integration across
multiple silos, solutions,
and sources
REQUIRED: A HOLISTIC, COGNITIVE SOLUTION

11. Page 11 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Illustrative Use Case – Anti Money Laundering
Page 11 © Hortonworks Inc. 2011 – 2016. All Rights Reserved

12. General Trends in AML
Trends
• Increasing levels of criminal sophistication
• Illicit activities span geographies, products and accounts
• Expert systems and rules-engine approaches are becoming less effective
• Inefficient investigation tools and processes aren’t keeping up
Impacts for AML
• Programs must evaluate multiple, varied data sources
• Require a 360-degree view across much larger data sets
• Automated, predictive approaches must replace manual, reactive programs

13. The Current State of AML Data Analysis
• Investigators demand interactive, visually appealing user interfaces
• Data discovery and predictive analytics can show deeper customer trends
• Aging technologies and their supporting approaches should be retired
• Companies are adopting advanced risk classification approaches
• New technologies help reduce the number of “false positives”

14. ©2015 Attivio, | Proprietary and Confidential
ANALYTICS DRIVE COGNITIVE SEARCH
BEHAVIORAL
ANALYTICS
Surprise Factor
Improbability Scores
Outlier Detection
IMPROVED RISK
SCORING
Rule Management
Alert Logic
Layered Scoring
STATISTICAL
EXTRACTION
Stock Tickers
Credit Card Numbers
CUSIPS
RUNTIME
ENHANCEMENTS
Extreme Scale
Rapid Document Processing
Immediate Rule Applications

15. Page 15 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
How Current AML Solutions Fall Short
Page 15 © Hortonworks Inc. 2011 – 2016. All Rights Reserved

16. What We Have Seen at Banks
Fragmented Book of Record Transaction systems
• Lending systems along geographic and business lines
• Trading systems along desk and geographic lines
Fragmented enterprise systems
• Multiple general ledgers
• Multiple Enterprise Risk Systems
• Multiple compliance systems by business line
• AML for Retail, AML for Commercial Lending, AML for Capital Markets…
• Lack of real time data processing, transaction monitoring and historical analytics
Proprietary vendor and in-house built solutions
• Acquisitions over the years have built up a significant technological debt
• Unable to keep pace with the progress of technology
• Move to combine Fraud (AML, Credit Card Fraud & InfoSec) into one platform
• Issues with flexibility, cost and scalability

17. ©2015 Attivio, | Proprietary and Confidential
AML: STRENUOUS CHALLENGES
Speed, transparency, and
auditability for each new
framework
Increased Expectations
of Regulators
Complexity Integrating
Application & Data Silos
Manual Process Wastes
Millions in OpEx
“Overtime reviewers made
more than our Execs…”
Chief Data Officer
Typical case reviews
involve over 125 facts
from 20 sources

18. …And the Data Complexity Continues to Grow
• Tens of point-to point feeds to
each enterprise system from each
transaction system
• Data is independently sourced,
leading to timing and data lineage
issues
• Business processes are
complicated and error-prone
• Reconciliation requires a large
effort and has significant gaps
Book of Record Transac/on Systems
Enterprise Risk, Compliance and Finance Systems

19. ©2015 Attivio, | Proprietary and Confidential
CLASSIC CONFIGURATION OF DATA SOURCES

20. ©2015 Attivio, | Proprietary and Confidential
HOLISTIC COMBINATION OF DATA SOURCES
GRC DATA UNIFICATION PLATFORM

21. Page 21 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Illustrative AML Use Cases and Work Streams
Page 21 © Hortonworks Inc. 2011 – 2016. All Rights Reserved

22. Leading AML Use Cases
• Large transfers across geographies
• Single view of a customer with multiple accounts
• Linked entity analysis
• Watch-list monitoring and data mining
• Credit card fraud detection

23. Major areas of activity around AML..
• Automating Due Diligence around KYC data
– Simple information collected during customer onboarding
– More complex information for certain entities
– Applying sophisticated analysis to such entities
– Automating Research across news feeds (LexisNexis, DB, TR, DJ,
Google etc)
• Efficient Case Management
• Applying Advanced Analytics (two sub Use Cases)
– Exploratory Data Science
– Advanced Transaction Intelligence

24. Stream Processing
Storm/Spark ML
Reference Architecture for Fraud/AML/Compliance
Stream
Flume
Sink to
HDFS
Transform
Dashboard
UI Framework
ELT
Hive
Storage
HDFS/Spark ML
Stream
Kafka
Stream to Kafka
Stream to
Flume
Forward to
Storm
Monitoring / KPI
NoSQL
HBase
Real-Time Index
Search
Solr
ELT
Pig
Batch Index
Alerts
Bolt to
HDFS
Dashboard
Silk
JMS
Alerts
Interactive
HiveServer
Visualization
Tableau/SAS/ETC
Reporting
BI ToolsBatch Load
High Speed Real Time
and Batch Ingest
Real-Time
Batch Interactive
Machine Learning
Improved Models
Load to
Hdfs
SOURCE DATA
Customer
Account Data/
CRM/MDM
Transaction
Data
Order
Management
Data
Click Stream
Log//Social Data
Documents
EDW
File
REST
HTTP
Streaming
RDBMS
Sqoop
JMS

25. ©2015 Attivio, | Proprietary and Confidential
AN EFFICIENT, SCALABLE, AND ANALYTIC ANSWER

26. ©2015 Attivio, | Proprietary and Confidential
ANTI-MONEY LAUNDERING
Case Study
26

27. ©2015 Attivio, | Proprietary and Confidential
SOLUTION REQUIREMENTS
Generates automatic case summaries and narratives from all
relevant R&C systems, providing a consistent, holistic view of
suspect transactions:
• Gathers relevant facts from every R&C solution or data source
• Provides multi-lingual text analytics that support key phrase detection,
entity extraction, and synonym expansion in unstructured content
sources
• Initiates alerts and triggers when specific words, phrases, or
content are detected during processing
• Provides –best-in-class search capabilities that power forensic
investigation
Provides proactive monitoring and compliance across the
entire organization

28. ©2015 Attivio, | Proprietary and Confidential
“SINGLE-PANE” SOLUTIONS
Assignment
Investigation
Narrative

29. ©2015 Attivio, | Proprietary and Confidential
INTEGRATE & OPTIMIZE : RESOLVE CASES FASTER
Challenge – Achieve a
productivity breakthrough to
reduce compliance cost
Attivio Solution – Deliver
all evidence to a single screen
for review and reporting
Outcome – 75% reduction in
MTTR for case investigations

30. ©2015 Attivio, | Proprietary and Confidential
INTEGRATE & CORRELATE : REDUCE “False Positives”
Challenge – Reduce ‘false
positive’ costs without missing
true positives
Attivio Solution – Deeper
analytics adds risk scoring to
violation screening
Outcome – Reduced ‘rules’
footprint and over 85% decrease
in ‘false positives’

31. ©2015 Attivio, | Proprietary and Confidential
Achieve Global Impact Act With Certainty
Crush Your DeadlineTransform Productivity
$27M
to
$54M
Instantiate consistency and improve accuracy Confidently seize opportunities and mitigate risks by
considering the right information in context
Unify and enrich all evidence silos to save time Immediately discover and provision new evidence, when
needed, for timely insight
$2M
to
$3M
$29M
to
$34M
$8M
to
$9M
THE VALUE : $66mm - $100mm ANNUALLY
• Discover, profile and correlate all internal and
external data for agile insight
§ Reduce time for Investigators to review,
research and gather to close cases more
quickly
• Reduce reliance on IT to provision data
• Connect or modify evidence sources as
regulatory frameworks evolve
• Use outcomes analysis to increasing alerting
accuracy- reduce ‘false positives’
§ Protect the brand and reduce risk resulting
due to inaccurate or delayed reporting of
suspicious activity
§ Scale AML solution globally
§ Expedite access to case information to
efficiently assign, research and close cases
§ Uniform risk-scoring
§ Close all cases; eliminate sampling and
backlogs

32. ©2015 Attivio, | Proprietary and Confidential
PRINCIPAL BENEFITS
Increases investigation
throughput by up to 300%
Transforms Investigator
Productivity
Reduces Complexity by
Integrating All Sources
Reduces Risk to Brand
Value
Close 100% of cases, even
the most complex
Provide all evidence on a
‘single-screen’

33. The Advantages of Big Data AML Solutions
• Hortonworks Data Platform (HDP) is a linearly scalable platform already in
use at many of the world’s largest financial services companies
• Hortonworks takes a 100% open-source approach to Connected Data
Platforms that manage data-in-motion and data-at-rest
• Partnering with an open source vendor gives banks more options than
choosing a proprietary software platform
• Regulators are streamlining their regulatory practices by adopting a Big Data
approach
Contact Hortonworks to discuss your journey to
actionable intelligence for AML

34. Questions?
Page 34 © Hortonworks Inc. 2011 – 2016. All Rights Reserved

35. Thank You
Vamsi Chemitiganti
GM, Banking & Financial Services, @Vamsitalkstech
Hortonworks
hortonworks.com
Lee Phillips
Sr. Director, Product Marketing
Attivio
attivio.com
Page 35 © Hortonworks Inc. 2011 – 2016. All Rights Reserved