Big Data Analytics in light of Financial Industry

Big Data & Analytics
Niklas Karlsson
niklas.karlsson@capgemini.com
BIM lead Sweden

Big Data – What is all the fuss about?
http://youtu.be/LrNlZ7-SMPk

Business Information Management
Big Data & Analytics | October 2013
Copyright © 2013 Capgemini. All rights reserved.

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Big Data – What is all the fuss about?
“The effective use of Big Data has the
potential to transform economies,
delivering a new wave of productivity
growth…Using Big Data will become a
key basis for competition…”

“We estimate that a retailer embracing Big Data
has the potential to increase operating margin by
more than 60%”
“$300bn – the potential saving in US healthcare”

“$250bn – the potential saving in European Public Sector”
McKinsey Institute – Big Data: The next frontier for innovation, competition and productivity – May 2011

“Data-Driven Decision-making can explain a 5-6% increase in output and productivity, beyond what
can be explained by traditional inputs and IT usage.”
MIT – Strength in Numbers – April 2011

“Survey participants estimate that, for processes where Big Data analytics has been applied, on
average, they have seen a 26% improvement in performance over the past three years, and they
expect it will improve by 41% over the next three.”

&

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BIG DATA IN ACTION
In September 2012, California passed a law
allowing self-driving cars to be tested on its
roads.
In 2040, it is anticipated people will not need to
get driver’s licenses. Cars will be able to drop
someone off and then go find a parking space.

Take a ride in a self-driving car.
http://youtu.be/cdgQpa1pUUE


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Use Cases
Understanding the customer
Through social media, how they navigate on web pages,
telecoms usage… gives a step change in understanding
and tailoring offers for / retention of the customer

Internet of things
Equipment everywhere is getting real-time remote
monitoring. (>4bn connected IPs). Analyzing this data give
opportunities for preventative maintenance and proactive
system response
Business Performance
Understanding market perception of your company and
products from call center voice and social media sources,
detailed analysis of operations from machine sensor data
and competitor analysis from market data

Smart Meters and Grid
Vast volumes of data will be generated. Getting insights
to optimize the grid, provide customer energy advice and
offers will need Big Data processing
Planes, boats and trains
Now provide continuous telemetry data – allows
performance to be optimized, risks are identified early and
support is more effective
Extended Supply Chain
RFID allows a whole new level of supply chain monitoring
and optimization

Risk Mitigation
Understanding systems and processes better and
customer sentiment early can radically reduce risk

A company whose offers are 10% more effective, which is able to provide the right service at the right time
10% better and its supply network 10% cheaper, is the company that will be around tomorrow.

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What if…
You could detect a neonatal
infections sooner?

Solution
120 children monitored :120K message
per sec, billion messages per day

24 hour
earlier detection of infections

Big Data enabled doctors from University of Ontario to apply neonatal infant
monitoring to predict infection in ICU 24 hours in advance

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WHAT IS BUSINESS ANALYTICS?
Analytics has been defined as “the extensive use of
data, statistical and quantitative analysis,
explanatory and predictive models, and fact-based
management to drive decisions and actions”
 “There is considerable evidence that decisions based on analytics
are more likely to be correct than those based on intuition.”
 “Decision making and the techniques and technologies to support
and automate it will be the next competitive battleground for
organizations. Those who are using business rules, data mining,
analytics and optimization today are the shock troops of this next
wave of business innovation.”
Thomas Davenport, author of Competing on Analytics

Analytics in Action
http://youtu.be/yGf6LNWY9AI

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Source: Davenport, T. H., & Patil, D. J. (2012). Data Scientist. Harvard business review


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We have a Big Data Methodology
We have developed a Big Data strategy, methodology and delivery
capability to help clients take advantage of Big Data:
 Big Data Process Model
New Business Model or Business Process Improvement

Acquisition
Collection of data

Marshalling
Organization and
storing of data

Analysis

Action

Finding insights
Predictive modelling

Changing business
outcomes

Data Governance

Big Data PoV

 Development and Implementation Considerations
Managing
Data
Integration integration of

Data
Integrity

Master data,
governance &
data quality

Business,
Architecture Functional
and
Technical

Data
Storing

Structured, non
structured
modelling...

data sources

Privacy &
Security

Dealing with
new customer
data sources

Action

M2M, ERP
injection, dialog
with suppliers...

Analytics
Value

Models that
deliver
business value

First use

Be sure the
first project
step will be a
success !

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Our structured, but flexible, approach to developing Big Data
Strategies
1. Stakeholder meetings

2. Analysis & Design

3. Big Data Strategy

Policies &
Standards
Systems
Integration
Compliance

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4
3
2

Document
Management
Information
Quality

1

Governance

Knowledge
Management

0

Performance
Management

Lifecycle
Management

Business
Intelligence

Security
Culture

Desired Position
As Is Position

 A kick-off to convey importance &
challenges associated with Big Data
 A rapid assessment using Focused
Interviews with the key stakeholders
from business and IT
 We use our enhanced information
diagnostic to support the capture of
feedback
 This identifies “burning platforms” and
assessment against best practice
 Establishes business justification for
change with key stakeholders

 A detailed assessment using output
from the stakeholder interviews
 Additional information gathering
interviews with client and Capgemini
Subject Matter Experts
 Analyze available unstructured & semistructured data sources to build Big
Data analytics
 This identifies opportunities with
supporting evidence
 Where possible, it also provides
benchmarking against other
organizations

 An information vision agreed by
stakeholders from business and IT with
respect to Big Data assessment
framework developed by Capgemini
 A transformation roadmap, agreed by
stakeholders from business and IT,
required to achieve the vision
 Business case(s) to support the
roadmap (or key steps within it)
 The initial steps on the roadmap need to
be pragmatic and prioritised to deliver
benefits quickly


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Big Data players


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If we only knew?







What are the questions that need to be asked?
What are the answers that help us move from data to decisions?
Can we shift insight into action?
How do we tie information to business process?
Who needs what information at what right time?
How often should this information be updated, delivered, and shared?


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Extra slides


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Analytical Sandbox
Analytics Sandbox

Data Visualization

Prebuilt Connectors and Standard Analytical Algorithms

Power User

Machine Data

Weblo
gs
Web Logs

Social
Media
Social Media Data

Unstructured Data

 Readymade environment for customers to start building PoCs

 Ready analytical plug-ins to expedite analytical development (Fraud detection, sentiment analysis etc.)

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Capgemini BIM + Big Data CUBE Lab
Our BIM CUBE hosts the Big Data lab
We are able to show and to build PoCs on these technologies:

What is the BIM CUBE:

Customers can:







Located at Capgemini Mumbai and occupying a space of over 400
sq feet, the CUBE features an interactive kiosk that outlines our BIM
Service Model
Customers can navigate themselves, or have a guided tour, to help
them gain greater insight into the broad spectrum of BIM Solutions





Experience innovative Business Information Management
solutions
Interact with BIM Subject Matter Experts
Witness the solutions created for similar customers
Review proof of concepts and technology innovations, as well as
productivity tools

We are at the forefront of the technology disruptions fuelling information led transformation

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Use Cases - Financial Services
Customer Risk Analysis

Surveillance and Fraud Detection

Build comprehensive data picture of customer side
risk
• Publish a consolidated set of attributes for
analysis
• Map ratings across products

Trade surveillance records activity in a central repository
• Centralized logging across all execution platforms
• Structured and raw log data from multiple applications

Parse and aggregate data from difference sources
• Credit and debit cards, product payments,
deposits and savings
• Banking activity, browsing behaviour, call logs,
e-mails and chats

Pattern recognition detect anomalies/harmful behaviour
• Feature set and timeline vector are very dynamic
• Schema on read provides flexibility for analysis

Merge data into a single view
• A “fuzzy join” among data sources
• Structure and normalize attributes
• Sentiment analysis, pattern recognition


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Central Data Repository

Personalization and Asset Management

Financial Data messy due to many interacting systems
• Personal data is obfuscated for security and records
get out of sync
• Trades need to be “sessionized” into accounts and
products
• Discrepancies are difficult to reconcile, need to track
corrections

Institutional and personal investing services
• Arms investor with sophisticated models for their
positions
• Success measured by upsell and conversion (as
well as profit)

Big Data as a centralized platform for data collection
• Single source for data, processing happens on the
platform
• Metadata used to track information lifecycle
Data served via APIs or in Batch
• Single version of the truth, data processed and
cleansed centrally
• Clear audit trail of data dependencies and usage

Data analysis across distinct data sources
• Market data and individual assets by investor
• Investor strategy, goals and interactive behaviour
Data sources combined in HDFS
• Models written in Pig with UDFs and generated
regularly
• Reports for sales and fed into online
recommendation system


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Market Risk Modeling

Trade Performance Analysis

Evaluating asset risk is very data intensive
• Trade volumes have increased dramatically
• Classic indicators at the daily level don’t provide a
clear picture

Increased Demands on Trade Analytics
• Regulatory requirements for best price trading
across exchanges
• Increased competition and scrutiny adds a focus on
optimization

Trends across complex instruments can be hard to spot
• Models require massive brute force calculation
• Multiple models built in batch and in parallel
Data is primarily structured and sourced from RDBMS
• Transactional data sqooped to combine with market
feeds
• Resulting predictions sqooped and served via
RDBMS

Trade Analytics becomes a Clickstream problem
• Trade execution systems include order trails and
execution logs
• Sessionized across order systems and combined
with system logs
Processing, Analysis and Audit Trail all in Hadoop
• KPIs summarized as regular reports written in Hive
• Data available for historical analysis and discovery


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Big Data Deployments In Financial Services
Global Bank
 Business Challenge:
• Global bank establishing “Analytics” as a core
competency. Bank focusing on Information and Data
as strategic asset.
• Bank is focused on Big Data as key analytics tool and
establishing a Big Data COE to be leveraged into
multiple lines of business of the bank – retail, cards,
commercial

 Solution:
• Capgemini selected by Bank to be its strategic partner
for Big Data. (selected versus Accenture, TCS, Cognizant)
• Big Data established as a “shared service” across
multiple LOBs.
• Capgemini involved in the “ideation” phase with
business and IT sponsors to define business cases.
• Business Cases: Next Best Action, Sentiment Analysis,
Cross-Sell/Upsell, Fraud Analytics, Mortgage
Dispositions


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About Capgemini
With more than 125,000 people in 44 countries, Capgemini is one
of the world's foremost providers of consulting, technology and
outsourcing services. The Group reported 2012 global revenues
of EUR 10.3 billion.
Together with its clients, Capgemini creates and delivers
business and technology solutions that fit their needs and drive
the results they want. A deeply multicultural organization,
Capgemini has developed its own way of working, the
Collaborative Business ExperienceTM, and draws on Rightshore®,
its worldwide delivery model

www.capgemini.com
The information contained in this presentation is proprietary.
© 2013 Capgemini. All rights reserved.
Rightshore® is a trademark belonging to Capgemini.

Big Data Analytics in light of Financial Industry

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