Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 1 CON2161 Big Data in Financial Services: Technologies, Use Cases and Implications.

Copyright © 2013, Oracle and/or its affiliates. All rights reserved.1

CON2161Big Data in Financial Services: Technologies, Use Cases and ImplicationsJim AckerGlobal Solution Manager for Big DataIndustry Business Unit, Financial Services


Understanding the DriversExecutives frustrated with their data gathering and distribution systems

Executives’ Biggest Data Management Gripes:*

#1

#2

#3

#4

#5

Don’t have the right systems in place to gather the

information we need (38%)

Can’t give our business managers access to the

information they need; need to rely on IT (36%)

Systems are not designed to meet the specific needs

of our industry (29%)

Can’t make sense of the information we have and

translate it into actionable insight (25%)

Information is no longer timely by the time it makes it

to our business managers (24%)

* Source: Oracle Overload to Impact Study 2012


695,000 Status updates

510,040 Comments 2,000,000

Search Queries

204,166,667 Emails

571NewWebsites

The data problem just got a lot biggerLeveraging untapped data for commercial gain


The Big Data Opportunity

Big Data: Techniques and Technologies that Enable Enterprises to Effectively and Economically Analyze All of their Data


Big Data is ALL DataUnstructured, Semi-Structure and Structured

There is always structure. But its not formally definedor anticipated.Social Media, RSS feeds, Videos, DOCs, PDFs, Graphics

Semi-Structured. Does not conform to DB tables, butstill contains tags or semantic elements.Emails, log files, machine generated content

What is the main difference in this data?

Volume, Velocity, Variety, Value

These Characteristics Challenge your Existing Architecture

and your Thought Processes


Contrast in Big Data ModelsDemands a new holistic look into data architecture

SQL RDBMSSchema on

Write

Relational DB

HDFSSchema on

ReadMap-Reduce

Distributed File System

No / Minimal Data Model ExplicitExtreme Scale Scale Large Scale

Batch / slow – getting faster Processing Real time and batchMinimal Security Robust

Flexibility and time to value Advantages Optimized and familiar


Pulling it ALL Together for Business Value

Create value from the full range of data sources– Its about using ALL your data

– No more sampling

Value First– Let the data drive the questions, or …

– Test a hypothesis against all your data

Still Need Information Management– Once you find value, incorporate IM

– Big Data is NOT a Silo


A Word of CautionGartner Hype Cycle for Big Data

You are Here


Big Data in Financial Services


ALLDATA

DiscoverAnalyze

PlanPredict

BETTERDECISIONS FASTER ACTION

Big Data is About Analytics

ACQUIRE

ORGANIZE

10Copyright © 2012, Oracle and/or its affiliates. All rights reserved.


Big Data Use Cases Today

Finding and Monetizing Unknown Relationships

Correlating Diverse Data Sets

Drive OpportunityReduce Cost


Big Data Solutions for Financial ServicesTwo main patterns for how customers are using Big Data

IT Optimization

Big Data Analytics

• ETL and batch processing • Extended Data Warehouse• Mainframe offloading • Archiving

• Customer 360 • Omni-channel CX• Cross-selling / Geo-fencing • Payment Analytics• AML / Anti-Fraud • Risk Management• Pricing Management • Compute Offload (VAR)


IT Optimization


Big Data Usage PatternETL and Batch Processing Workloads on Hadoop

Integrate

SQL

SQL

NoSQL

• Scalable• Flexible• Cost

Effective

DW & BI

Analytics

Web

Mainframe


Regions Bank

Objectives

Meet ever evolving regulatory requirements Consolidate existing deposit, loan and

customer databases

Solution

Big Data Appliance and Exadata ODS for single, reliable, cleansed data source

ODS is single landing zone and archival repository for internal, external, structured, semi-structured, and unstructured data

Results & Benefits• Faster access to all their data• Reduced IT costs by eliminating duplicate

data stores


Thomson Reuters

Objectives

Maximize cross-sell opportunities Lower cost and complexity

Solution

Economically capture all customer activity Testing 50M events/sec ingest rates into

the Oracle Big Data Appliance Feeds Exadata EDW for customer

profitability & segmentation analysis

Rick KingChief Operating Officer for TechnologyThomson Reuters

“Oracle's engineered systems… are geared toward high performance big data delivery - and that is exactly the type of work we do”

BDA Exadata Exalytics

EDWSandbox & DR

Event Capture & Store

Interactive Analytics

Research Applications

Upsell/Cross Sell


Big Data Usage PatternExpand Data Warehouse with Granular Data Store

MartsData Warehouse

Σ Σ

BusinessIntelligence

Archiving

• Online• Scalable• Flexible• Cost

Effective

Data Factory


End-to-end business information environment that provides accurate, transparent and timely information to shareholders, regulators and management

Objectives

Tier 1 Global BankNew Information Management Architecture

Results & BenefitsReduce complexity and risk of changesReduce cost of operation Increased stability & performance

Results & BenefitsReduce complexity and risk of changesReduce cost of operation Increased stability & performance

Solution

7 Exadata Racks 16 Node Hadoop Cluster – 33TB Oracle Loader for Hadoop (pending)


Big Data Analytics


Ad-hoc

Big Data Usage PatternScale-out Information Discovery


Effective

Data FactoryContinuous On-Demand


Enable customers to learn about stocks and increase buying confidence

Cultivate the advisor-client relationship online and acquire smaller clients

Objectives

Credit SuisseIncreased sales through instant access to information

Results & Benefits Incremental sales for Bank based on this

application for 5 years. Improved customer relationships

Results & Benefits Incremental sales for Bank based on this

application for 5 years. Improved customer relationships

Solution Information Discovery on pooled research

data sets in multiple unstructured formats Oracle powers their internal application that

advisors utilize to quickly find information on financial metrics


Big Data Usage PatternInstant Responses based on Historical Analysis

BusinessIntelligence


Effective

Integrate

Event Decisions


NoSQL for Fraud ScoringFinancial Services coordinated theft prevention

Objectives

Solution

Combine data sources for complex scoring Detect, alert analyst with low latency Handle burst seasonal transaction volumes

Oracle Coherence cluster for real time transaction object management

Oracle NoSQL Database for fraud model and customer profile management

Oracle Database for statistics and fraud modeling-related data

Application Data Ingestion

Tra

nsa

ctio

n A

uth

oriz

atio

nP

roce

sso

r

NoSQL DB Driver

Results & Benefits Simple data model, flexible transactions Scalable, Low Latency data management Easy configuration and administration Enterprise Support


Real-time Location-Based OffersTier 1 Global Bank

Objectives

Customer profile enrichment with Big Data Capture credit card POS and merchant data with

event processor Determine geo location of POS and nearby bank

wholesale customers Leverage real-time decision engine to generate

offer to mobile device

Solution

Increase revenue through real-time, location based offers


Tier 1 Global BankOffer Workflow

Capture credit card

transactions & identify customer location

Derive next best offer

using customer

information and

propensity

Evaluate offers

based on customer location

Make offer through mobile text message

Locate and identify customer Select next best offer

Identify next best offers Make offer

Analyze customer acceptance/rejection

Enrich propensity based on acceptance/rejection


ATM MACHINE

POS DEVICE

SMART PHONE APP

Event Capture and Co-relation

Temporal cache based customer identification

REAL TIME EVENT CAPTURE

TCPIPIF

XX

ML

Routing

Integration adapters

Rules

Mapping

Real-time/Near Time, Batch

DATA TRANSPORT LAYER

XML

IFXT

CP

IP

Real time decision

Real time intervention – click to chat, click to call

Adaptive self- learning

Near real-time analysis and dashboarding

INTELLIGENT INTERVENTION PLATFORM

WEBSERVICES MQ

NE

XT

B

ES

T

AC

TIO

N

EXECUTION

Near time/Batch for acceptance/rejection data

Near time/Batch to performmodel update

FACTORY

BANK REPOSITORIES

Client profile, historical transactions, Good life data, segment info, profit info, risk info, Opt-in

information etc.

KEY VALUE PAIRS

Map information, social networks, device logs, smart app interfaces etc.

STAGING

Structured, Non-structured, Semi-structured

MapReduce + NLP Derived outputs- intent, segment, enhanced customer masteringETL/Real-Time

Statistical modeling – Propensity, segments etc.

Natural language processing

Intent and semantic inference

Advanced model free visualization

DATA VISUALIZATION LAYER

DATA PROCESSING LAYER

DATA STORAGE LAYER

System ArchitectureOracle Big Data at Work

LEGEND


Product Roadmap


Engineering the Oracle Big Data Solution

Stream Acquire – Organize – Analyze

In-D

ata

ba

se

A

na

lyti

cs

DataWarehouse

Oracle Advanced Analytics

OracleDatabase

Oracle BI Enterprise Edition

Oracle Real-TimeDecisions

Endeca Information Discovery

Decide

Oracle Event Processing

Apache Flume A

pp

lic

ati

on

s

Oracle NoSQL

Database

Cloudera Hadoop

Oracle R Distribution

Oracle Big Data Connectors

Oracle DataIntegrator

Unified Analytics APIs


Why Make Big Data a Divided World?

VS


Goal: Unified Data Analytics Environment

VS

•Real-Time Analytics

•Thousands of Users

•Secure and Available

•All Data On-line and Ready to Use

•Large Scale Systems

•Cost Effective


Unified Data Analytics EnvironmentUnified Analytics API

SQL R MR

Unified Analytics Processing Platform

Hadoop RDBMS

IB

Management Framework and Tools


Analyze Data across your Oracle Systems

SQL Analytics on ALL data Expand the data pool for

analytics leveraging Hadoop

Stream Hadoop resident data

through Big Data Connectors

for SQL processing

Use the full power of Oracle

SQL on all data

Or use Oracle Loader for

Hadoop to integrate data in

Oracle Database

SQL

Hadoop Oracle Database

IB


Analyze Data across your Oracle SystemsR Analytics on ALL data

Expand the data pool for

analytics leveraging Hadoop

Improve scalability and

performance for R without

changes to your programs

Dynamically leverage Hadoop

through Big Data Connectors

to execute R analytics

R

Hadoop Oracle Database

IB


Unified Data Analytics Environment

Real-Time Analytics

Thousands of Users

Secure and Available

All Data On-line and Ready to Use

Large Scale Systems

Cost Effective


Unified Big Data Environment

VS&


Oracle Big Data Solution

Stream Acquire – Organize – Analyze

In-D

ata

ba

se

A

na

lyti

cs

DataWarehouse

Oracle Advanced Analytics

OracleDatabase

Oracle BI Enterprise Edition

Oracle Real-TimeDecisions

Endeca Information Discovery

Decide

Oracle Event Processing

Apache Flume A

pp

lic

ati

on

s

Oracle NoSQL

Database

Cloudera Hadoop

Oracle R Distribution

Oracle Big Data Connectors

Oracle DataIntegrator

• Complete

• Integrated

• Scalable



Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 1 CON2161 Big Data in Financial Services: Technologies, Use Cases and Implications.

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