big data analytics in light of financial industry

Big Data & Analytics

Niklas Karlsson

[email protected]

BIM lead Sweden

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Business Information Management

Copyright © 2013 Capgemini. All rights reserved.

Big Data & Analytics | October 2013

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

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Big Data – What is all the fuss about?

“We estimate that a retailer embracing Big Data

has the potential to increase operating margin by

more than 60%”

“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…”

McKinsey Institute – Big Data: The next frontier for innovation, competition and productivity – May 2011

“$300bn – the potential saving in US healthcare”

“$250bn – the potential saving in European Public Sector”

“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.”

“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.”

MIT – Strength in Numbers – April 2011

&

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BIG DATA IN ACTION

Take a ride in a self-driving car.

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.

http://youtu.be/cdgQpa1pUUE

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Use Cases

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

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

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

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

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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24 hour earlier detection of infections

You could detect a neonatal

infections sooner?

What if…

Big Data enabled doctors from University of Ontario to apply neonatal infant

monitoring to predict infection in ICU 24 hours in advance

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

Solution

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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

Development and Implementation Considerations

Acquisition Marshalling Analysis Action

New Business Model or Business Process Improvement

Collection of data Organization and

storing of data

Finding insights

Predictive modelling

Changing business

outcomes

Data Governance

Big Data PoV

Managing

integration of

data sources

Data

Integration

Master data,

governance &

data quality

Data

Integrity

Dealing with

new customer

data sources

Privacy &

Security

Models that

deliver

business value

Analytics

Value

Business,

Functional

and

Technical

Architecture M2M, ERP

injection, dialog

with suppliers...

Action

Be sure the

first project

step will be a

success !

First use Structured, non

structured

modelling...

Data

Storing

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1. Stakeholder meetings

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 & semi-

structured data sources to build Big

Data analytics

This identifies opportunities with

supporting evidence

Where possible, it also provides

benchmarking against other

organizations

Our structured, but flexible, approach to developing Big Data Strategies

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

0

1

2

3

4

5

Policies &

Standards

Document

Management

Information

Quality

Knowledge

Management

Lifecycle

Management

Security

Culture

Business

Intelligence

Performance

Management

Governance

Compliance

Systems

Integration

Desired Position

As Is Position

2. Analysis & Design 3. Big Data Strategy

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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

Readymade environment for customers to start building PoCs

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

Machine Data

Unstructured Data

Weblo

gs

Web Logs

Social

Media

Social Media Data

Prebuilt Connectors and Standard Analytical Algorithms

Analytics Sandbox

Power User

Data Visualization

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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:

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

Customers can:

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

http://www.oracle.com/index.html

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Use Cases - Financial Services

Customer Risk Analysis

Build comprehensive data picture of customer side

risk

• Publish a consolidated set of attributes for

analysis

• Map ratings across products

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

Merge data into a single view

• A “fuzzy join” among data sources

• Structure and normalize attributes

• Sentiment analysis, pattern recognition

Surveillance and Fraud Detection

Trade surveillance records activity in a central repository

• Centralized logging across all execution platforms

• Structured and raw log data from multiple applications

Pattern recognition detect anomalies/harmful behaviour

• Feature set and timeline vector are very dynamic

• Schema on read provides flexibility for analysis

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Central Data Repository

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

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

Personalization and Asset Management

Institutional and personal investing services

• Arms investor with sophisticated models for their

positions

• Success measured by upsell and conversion (as

well as profit)

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

Evaluating asset risk is very data intensive

• Trade volumes have increased dramatically

• Classic indicators at the daily level don’t provide a

clear picture

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 Performance Analysis

Increased Demands on Trade Analytics

• Regulatory requirements for best price trading

across exchanges

• Increased competition and scrutiny adds a focus on

optimization

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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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

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

Big Data Deployments In Financial Services

Global Bank

The information contained in this presentation is proprietary.

© 2013 Capgemini. All rights reserved.

Rightshore® is a trademark belonging to Capgemini.

www.capgemini.com

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

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big data analytics in light of financial industry

Business

effective use of big

big data processing

machine sensor data

continuous telemetry

datadriven decisionmaking

grid vast volumes of

potential saving

supply network