Wondering how to bring services to your clients in real time – and on their preferred device? Need to automate your financial supply chain, including risk and compliance functions, and move to a pay for performance model? Learn about use cases from within the big data ecosystem, ranging from AML compliance, trade lifecycle, fraud detection and digital transformation, and introduce their risk data aggregation and compliance initiative. Find out how you can best leverage Open Enterprise Hadoop to achieve these goals.

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Big Data in Financial Services
We Do Hadoop

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Global banking trends
Source: E&Y

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Key focus areas within the financial services industry
Internal domains..
Please note that this is not a comprehensive list of deployed use-cases
across the major domains, just the major areas in which industry shifts are
occurring and where customers are looking to deploy enterprise Big Data in.
External Customer facing domains..
Risk Mgmt
Cyber
Security
Fraud
Detection
Data
Compliance
Digital Banking
360
degree view
Capital Markets
Retail Banking
and Lending
Credit Cards;
Payment Networks
Wealth Mgmt
Corporate Banking
Asset Mgmt
(Brokerage, MF and
Stock exchanges)

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My Experience in Risk & Compliance
Mike DeSanti
November 4th, 2015

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• The move from proprietary to flow-based trading in Capital Markets is affecting
revenue
• Fintech companies are taking over traditional revenue sources in the lending and
payments spaces
• The millenials reliance on mobile technology and instant gratification
• Increased regulatory spending is strangling discretionary spending
• Having to deal with very dated and expensive IT infrastructure
Issues Affecting Most Banks

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• 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 risk systems by risk function
• Credit limit management, traded credit, Basel capital systems, CVA, Market Risk VaR, Stress VaR, Market Risk reporting…
– Multiple compliance systems by business line by compliance initiative
• AML for Retail, AML for Commercial Lending, AML for Capital Markets…
• They are full of proprietary vendor and in-house built solutions that have been
acquired over the years
What I Have Seen at Banks

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Leads to Current State Complexity
• Thousands of point-to point feeds to each
enterprise system from each transaction
system
• Data is independently sourced leading to
timing and data lineage issues
• Close processes are complicated and
error prone
• Reconciliation requires a large effort and
has significant gaps
Book of Record Transaction Systems
Enterprise Risk, Compliance and Finance Systems

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• Centralize business and operation functions
• Incentivize technology to shrink not grow
• Populate a data lake that with a set of canonical feeds from the transaction
systems
• Create a linearly scalable platform to host enterprise applications on top of this
data lake, including hot, warm and cold computing zones
• Develop a partnership with Hortonworks to evolve the platform to meet the
Banking Industry’s needs
How Do We Change?

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• A free open source linearly scalable platform has only become available within
the last few years
• Due to the amount of regulation over the last 15 years all bank enterprise
compliance, risk and finance systems now function essentially the same way
• Banks partnering with an open source partner is very different from partnering
with a vendor who develops proprietary software
• Proprietary software vendors will adopt the new standards since it is in their self
interest to do so
• Regulators can now streamline their regulatory practices by adopting this
platform
Why Will This Work Now?

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Open Platform for Risk & Compliance
David Lattimore-Gay (Eikos Partners)

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What is OPRC ?
• Leveraging Open Source
– Large Development Community
– Innovation
– Reuse not build
• Commodity hardware
– Data Storage
– In Memory Cache
– Computing Power
• Light weight way for ingesting and storing data
– Single Source Data
– Multiple views on the same data with Schema on Read capability
– Built in data lineage
• Unified development environment for analytics
– Partnership between quants and IT developers that will allow IT to package analytics for
deployment rather than recoding them

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Data Lake
OPRC Originating Source System
Source System
Canonica
l
L1
Mapped
to
Schema
Cache
Reference / Transaction Data / Market Data / Static Data / Meta Data
Reference Data
Validation
Normalization
PK/AK
What has
changed ?
Load
L0
Raw Data
Reporting
Derived
Data

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Compute Service
Grid
Web Services
UI Framework
In Memory
API
Data
Fabric
Desktop Mobile
Data Access
API
Task
Calculator Framework
(Job Create/Monitor/Control)
Task
Task
TaskTask Task
Task
Task
Compute Service Strategy Engine
Task Dependency
Scheduler
Task Dependency
Scheduler
Task
Aggregation &
Reporting Engine
Data
Lake
Standardized Book of
Record Transaction
(BORT) & Other
Systems Feeds
ETL
Framework
OPRC High Level Architecture

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Technology Architecture
Nadeem Asghar

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OPRC Functional Requirements
LANDING DATA
ZONE
L0
STANDARDIZED
DATA ZONE
L1
CANONICAL
DATA ZONE
L2 Regulatory
Reports
Internal
Reports
External
Reports
Search
REPORTING/
ANALYTICS ZONE
L3
Golden Source &
Feeds
Master Data
Contrats
Balances
Transaction
Positions
Factors/
Scenarios
Market Data
Unstructured
Data
Original
Data
RAW Data
Standardized
Data
Materialized
View
Extract & Load
Extract & Load
Connectors/
Adaptors
Standardized
Data
DQ & Validation
Normalization
Mapping &
Schemas
Data Cleansing
Standardized
Data
Data Join/merging
Data
Transformation
Business Rules
Model Rep &
Validation
Canonical
Position
Data
Canonical
Transaction
Data
Data Aggregation
Calculators
Scenarios Run
Revision Hisotry
Scenarios
Results
Data
Aggregations
Analytics/
Reports
Revision
History
Common Repositories/Meta Data Management
Security
Connectors Schema Mapping Catalogging End to End Lineage Tracing
Authenication RBAC Authorization Encryption Audit

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Proposed Solution based on Hortonworks Stack
Hortonworks Data Platform
LANDING DATA
ZONE
L0
STANDARDIZED
DATA ZONE
L1
CANONICAL
DATA ZONE
L2 Regulatory
Reports
Internal
Reports
External
Reports
Search
REPORTING/
ANALYTICS ZONE
L3
Golden Source &
Feeds
Master Data
Contrats
Balances
Transaction
Positions
Factors/
Scenarios
Market Data
Unstructured
Data
(hdfs)
Original
Data
(hdfs)
RAW Data
hdfs)
Standardized
Data
(Hive/orc)
Materialized
View
(Hive/orc)
sqoop/hadoop fs/
nfs
Kafka/Storm
Java/Scala
Standardized
Data
(Hive/Orc)
Hive/Spark/Scala
Hive/Spark/Scala
MHive/Spark/Scal
Hive/Spark/Scala
Standardized
Data
(Hive/Orc)
Hive/Spark/Scala
Hive/Spark/Scala
Hive/Spark/Scala
TBD??
Canonical
Position
Data
(hive/orc)
Canonical
Transaction
Data
(Hive/orc)
Hive/Spark
Scala/Python/R
etc
Scala/Java
Hive/Spark
Scenarios
Results
(Hive/orc)
Data
Aggregations
(Hive/orc/
Hbase)
Analytics/
Reports
(Hive/orc/
HBase)
Revision
History
(Hive/orc)
Common Repositories/Meta Data Management
Security
Apache Atlas/Falcon/ Custom Solution
Apache Ranger/ Atlas and Custom/Partner Solution

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OPRC Game Plan:
Solution Based on IP(Above the line)/ Open Source(Below the Line)

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Wrap up..

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Hadoop is a Platform Decision
Adoption follows a consistent journey
Data architecture efficiencies, new analytic apps, and
ultimately to a “data lake”.
HDP: A centralized architecture built on YARN
Any application, any data, anywhere.
HDP: A completely open data platform
Platforms are ultimately defined by open communities.
HDP subscription supports entire lifecycle
World class experience to ensure success from architecture to
production to expansion.

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Cautionary Statement Regarding Forward-Looking Statements
This presentation contains forward-looking statements involving risks and uncertainties. Such
forward-looking statements in this presentation generally relate to future events, our ability to
increase the number of support subscription customers, the growth in usage of the Hadoop
framework, our ability to innovate and develop the various open source projects that will enhance
the capabilities of the Hortonworks Data Platform, anticipated customer benefits and general
business outlook. In some cases, you can identify forward-looking statements because they
contain words such as “may,” “will,” “should,” “expects,” “plans,” “anticipates,” “could,” “intends,”
“target,” “projects,” “contemplates,” “believes,” “estimates,” “predicts,” “potential” or “continue” or
similar terms or expressions that concern our expectations, strategy, plans or intentions. You
should not rely upon forward-looking statements as predictions of future events. We have based
the forward-looking statements contained in this presentation primarily on our current expectations
and projections about future events and trends that we believe may affect our business, financial
condition and prospects. We cannot assure you that the results, events and circumstances
reflected in the forward-looking statements will be achieved or occur, and actual results, events, or
circumstances could differ materially from those described in the forward-looking statements.
The forward-looking statements made in this prospectus relate only to events as of the date on
which the statements are made and we undertake no obligation to update any of the information in
this presentation.
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