1. Big Data in Finance
Vandana Saini
vandana.saini@td.com

2. Agenda
 What is Big Data
• Big Data Overview
• Big Data over Traditional Platforms
• Competitive Advantage
 Why Big data in Finance?
• Data Drivers for FIs
• Big Data journey in FI
• Applications
 TD: Revolutionizing IT and Banking
 Associated Risks with Big Data
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3. Big Data Overview
Big Data isn't just a technology - it's a business strategy for capitalizing on
information resources.
Linking high velocity of complex present data, with high volume of past
digital footprint, generated at high inconsistent speeds, stored in a variety
of formats to predict the future course of action.
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4. Extended Definition: 4 V's of Big Data
• Data inconsistency &
incompleteness,
ambiguities, latency,
model approximations
• Data in many forms-
structured,
unstructured, text,
multimedia
• Streaming data,
milliseconds to seconds
to respond
• 2.5 Quintillion Bytes
per day
• Terabytes to Exabytes of
existing data to
process
VOLUME
Data at Rest
VELOCITY
Data in
Motion
VERACITY
Data in Doubt
VARIETY
Data in Many
Forms
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5. Big Data over Traditional Database
Technologies
5
https://www.google.ca/search?q=big+data+future&biw=1600&bih=731&espv=2&source=lnms&tbm=isch&sa=X&ved=0ahUKEwj8rfDS_ZvNAhVETlIKH
fhnAHkQ_AUIBygC&safe=active&ssui=on#imgrc=r7q_Vw6SKSo2VM%3A
http://www.itnewsafrica.com/2014/12/is-big-data-making-its-way-to-the-banking-sector/
www.itnewsafrica.com/2014/12/is-big-data-making-its-way-to-the-banking-sector/
Structured & Repetitive Iterative & Explorative
–

6. Competitive Advantage
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According to a research by Gartner, the use of big data has improved the
performance of businesses by an average of 26%
http://cib.db.com/docs_new/GTB_Big_Data_Whitepaper_(DB0324)_v2.pdf
• Enhances risk assessment
process
• Effectively analyze non-
structured data formats
alongside structured data
formats
• Improved fraud risk
decision making
• Planning audits
• High Volume of data,
shorter time period
• Cost effective

7. Why Big data in Financial
Institutions?
7
Market Data
Digital Footprint
• Combining Data Silos
• Most financial firm do
not combine
unstructured with
structured data.
• Major banks often
spend $5 billion a
quarter on technology,
but can’t personalize
offerings for 10 to 30
million people
10% structured-90% unstructured data
-Financial Institutes hold vast arrays of unstructured data
-This data is largely under-analyzed and rarely adds business value
Trading Research
Ideas Reports
News Emails
Twitter Internal Reports
Unstructured
Digital Footprint
ACQUIRE Customers DEVELOP Customers RETAIN Customers
http://www.forbes.com/sites/tomgroenfeldt/2015/07/01/banks-have-a-long-big-data-journey-to-
catch-up-with-google-and-facebook/2/#8c3feef16c6e
Structured Data
Unstructured Data

8. Big Data Drivers for Financial
Institutions
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High cost of storing and analyzing large data sets
Banks biggest impediments to
actionable data insights
Too many silos - data is not pooled for the benefit of the entire organization
Time taken to analyze large data sets
Shortage of skilled people for data analytics
Unstructured content in big data is too difficult to interpret
Big Data Sets are too complex to collect and store

9. Big Data Journey in Financial
Institutions
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Understanding the product lifecycle to
retain their customers
Sophisticated predictive models to
analyze historical transactions and
forecast customer churn.
.
Customer lifecycle events to boost
credit card activations..

10. Big Data Analytics : Making Data work
Quantitative & Algo Trading
• Quick access to historical data
series
• Create circuit breakers on bad
news
• Quotes on market moving
events
Business Oriented
Applications
• Research and forecast
• Filter out noise in
unstructured data
• Derive simple indicators
Market Surveillance
• Enhance surveillance tools
• Optimize ongoing investigations
• Reduce false positives
Risk Management
• Manage event risks
• Forecast volatility and liquidity
• Improve "Risk-on, Risk-off"
strategies
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http://cib.db.com/docs_new/GTB_Big_Data_Whitepaper_(DB0324)_v2.pdf

11. Banks harnessing the Big Data Potential
• Discriminatory power of the models to mitigate risks
• Addressing Security Challenges
• Predictive indicator for the credit behavior with the bank
• Marketing Predictor-models
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Security is being addressed by big
data because the data that has a security
context is huge. (65 billion security events
per month)
By segmenting Bank of America is able
to remove its assumptions about its
customers.
http://www.barclays.co.uk/PersonalBanking/P1242557947640
55%
customers
will demand
24/7 access
Technology Expectations by
2020
53%
customers
will demand
faster access

12. Making Big Data Analytics work:
Look to the Future
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Adapt to fast
changing
environment
Fill talent
gaps
Break
down your
talent
needs

13. Effective Big Data Team Set-up
Technology
IT Operations
Management
Infrastructure & Support
Development & Control
Analytics
Data Scientists
Data Analysts
Data Engineers
Business &
Marketing
Product Owner
SME's
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Big Data-Effective
Collaboration

14. Big Data Analytics Workflow
Overview
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Data Exploration/
Transformation
Feature
Selection
Build
Model
Evaluate
Model
Best
Model
Predictions
Modern innovations in big data technology are ushering in a wave of
new advanced analytics workflow.
Developing a Customer-centric strategy

15. Build a comprehensive customer profile in 30 minutes for every customer
in the TD customer base
60months of transactions data 8Terabytes 20B transactions 11M customers
Identify customer affinity to
600 interest categories
Did you know?
Grocery shopping and gas
dispensers were the interest areas
where competitor products were
used most
Identify customers using
competitor's products
TD : Revolutionizing IT and Banking

16. Making Big Data Analytics Dream a
Reality
11M Customers
THIS TAKES HOURS, IF NOT DAYS, IN TRADITIONAL ENVIRONMENTS
Segment customer base in
10 seconds
Build predictive models in
2minutes
Predict customer behavior in
2 seconds

17. Performance: Recommendation
Model
600Minstances

18. Risks associated with Big Data
Technologies
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New technology for most organizations introduce new vulnerabilities
Open source code implementations potential for unrecognized back doors
A Access to data from multiple sources may not be sufficiently controlled
Regulatory challenges access to logs and audits
Significant opportunity malicious data input & inadequate validation

19. Big Data Analytics Market by 2018
Big Data
$ 114 Billion
CAGR 30%
Financial
Analytics
$ 12 Billion
CAGR 23%
Cloud
Computing
$ 129 Billion
CAGR 22%
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Banks $1.6 B
Insurance Companies
$325 M
Data Vendors $40MM
Big Data Analytics in
Financial Services
Market Size: $3.1 Billion
https://www.youtube.com/watch?v=hUZBfro20H8

20. In Conclusion - Getting Started is
Crucial ..
20
Execute and
Deliver Value!
Pick Your
Spot!
Think Big!

21. 21vandana.saini@td.com
Thank You