Big data analytics in payments

Big Data Ecosystem
Big Data Analytics in Payments
Big Data for Acquiring Segment
Big Data Analytics aiding in Revenue Advantage
Knowing the Pulse of the customer
Operational Efficiency and competitive advantage
Reference Architecture
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Big Data Ecosystem
Variety Volume
Value Velocity
Big Data
Ecosystem
Location
Data
Mobile Fraud Data
RFID
ERP
Sensor
Data Social Networking
Data
ClickStream
Data
Point of
sale
Transaction
Data
Big (Bad)
Data

Big Data: Key FeaturesVolume
Unstructured information
Structured information
Velocity
Variety
Value
Visualization
How to leverage huge volumes of unstructured data to take a calculated business decision
Value
More robust fraud and risk systems
Running Targeted Campaigns for the
customers
Helpful in designing performance based fee
model for merchants on basis of no of
customers etc.
Visualization
Conversion of unstructured data into
relational data
Natural Language processing
Semantics Analysis of consumer data
 Identify trends in fraud, risk, consumer spend
etc.

Action Time
Value vs. Data Latency
Business Idea/Event
Data Ready for Analysis
Information Delivered
Valuelost
Data Latency
Analysis
Latency
Analysis
Latency
Most of the value of
data is lost from the
data recording to an
analysis phase which
encompasses
conversion of
unstructured info. to
structured data
seamlessly for analysis
In some of the cases
a score is provided
depending upon the
latency of data in
order to make it
usable but the
usability of such data
in payments still
remains a question
mark except in macro
level predictive
analysis

As per Forrester Research Most banks indicated that the main hindrance to their big
data adoption plan was cost. Integration costs are not insignificant; for starters, one
needs to integrate their analytics platforms to more data sources and invest in more
hardware to increase compute power and storage capacity. As per the report some of
the One of the leading issuing banks and ISO’s in US said that the price tag of big data
would not provide an ROI from incremental savings for at least another two years.

Big Data Ergonomics
0.8 1.9
7.9
35
0
5
10
15
20
25
30
35
40
2009 2011 2015 2020
Growth of Global Data
Zetta Bytes
•The proliferation of the internet and the mobile era has
increased the rate at which data is created and stored;
hence, there is a need for tools and techniques to
analyze data at an equal speed
•80% of the data available today is unstructured and
includes raw text, audio/video files, click-stream
data, blogs, social media, location coordinates, purchase
patterns etc.
•Banks and ISO’s are increasingly realizing that
unstructured data, if analyzed, can provide a
competitive edge to them and the merchants
Implications for Payment Companies
•Need for large storage capacity
•Need for quick retrieval of data
•Enable informed decision making by effectively leveraging
large datasets
•Example:-
•Turn 12TB of tweets created each day into improved
product sentiment analysis
•Convert 350 billion annual purchases at a certain
merchant store to predict demand forecast and consumer
buying behavior
Storage Costs
*Source: Nasscom
18.9
1.6 0.7
0
5
10
15
20
2005 2011 2015
USD/Gigabyte
*Source: Nasscom

Big Data AnalyticsBig Data Production Big Data Management Big Data Consumption
Transaction History
Transaction Geo
Location
Merchant Category
Code
Website Click stream
Data
Textual Data
ATM Specific Info.
Purchase Channel
Social Media
Crowd sourcing
Customer Database
Customer Complaints
Big Data Quality
Large Scale Gathering of Raw Data
Storage
Security
Analytics
Databases
Improve Big Data Quality
Data Mining
Targeted Marketing
Loyalty
Digital Marketing
Product Innovation
Fraud & Risk
Security
Commercialization

Big Data in payments value chain
Merchants
Issuers
AcquirersISO’s
Card
Networks
New product Ideation and enhancements
 Customer Spending pattern; geography
wise, location wise, time zone wise,
targeted offers
 loyalty
New product launches
 Product enhancements
Customer Spend Analytics
 Chargeback analytics
Customer feedback on
existing products
 Risk and Fraud Analytics
Dismissing false proofs
resulting in improvement of
quality of transactions
Customer Spend
Analytics
Risk and Fraud
Analytics
New product
Ideation
Customer
feedback
Crowd Sourcing
Chargeback Analytics
Merchant fraud analytics
 Aiding analytics for
merchants at all levels
Chargeback Analytics
Merchant fraud analytics
 Aiding analytics for
merchants at all levels

Big Data for Acquiring Segment
The Big Data ecosystem for the Acquirers revolves around the following key
parameters
Revenue
Advantage
Operation
Efficiency
Knowing pulse
of customer
Competitiveness

Big Data Levers for Merchants
Functions Big Data Levers
Marketing
Operations
Merchandise
Loyalty
New Business Models
•Cross Selling of similar products
•Customer Micro-segmentation
•Sentiment Analysis for merchants
•Geo-Location & Clock based Marketing
•Price Comparison Services
•Web Based Markets/New Store Placements
•Dispute management services
•Clear reconciliation of gross reportable sales from
store level to TIN level data
•Tracking of the transaction data of the merchants
to initiate the loyalty loop for merchants
•Mobile coupons in case of usage of mobile wallets
•The merchandise to be sold online/offline
•Product innovation/ Existing product augmentation

Macro Level Data Analytics
Economy Level Data Analytics
• The payment processors/networks can provide a holistic overview of economic outlook based
on spending data
• This can aid merchants in taking investment decisions for new geographies
Brand Perception (Comparative Analysis)
• A comparative intelligence about the brand when compared to its competition
• Aids merchants in benchmarking, and understanding consumer preferences/attitudes
Diversification of Payment Channels
• This diversification can be judged by analysis of transactions data via understanding channel
preferences on the basis of customer spend analytics
Finding Best Partners on Basis of Transaction Data Analytics
• The analytics data helps in improving the value proposition of co-branded cards
• Finding out the most profitable merchant partner
Chargeback and Retrieval Analytics
• This feature would help in avoiding chargeback due to non retrieval/failure to provide copy of
transaction receipts and in turn would improve in making the quality of transactions better

Big Data in Loyalty
Method Dimensionality Predictive
Power
Scalability Real Time
Potential
Application Areas
Next best offer
analytics
    Customer retention and
loyalty, personalization
Social network and
influencer analytics
  Customer retention and
loyalty, new product
development
Geospatial/location
analytics
   Targeting, personalization,
site selection
Customer journey/
path analysis
   Marketing attribution,
customer life-cycle
planning, offer
management
Social & voice-of
customer
text
Analysis
  Message and content
development, customer
service

Big Data: How it augers for Merchants
Consumer Profiling
None of the single player in the payments value chain can provide for consumer profiling on the basis
of big data this calls for consolidation of big data from different sources to make it more meaningful .
But in addition to that regulatory constrains have to be kept in mind for usage of consumer data by
different sectors
Consumer
Non Financial Product or
service consumption
Account
Transaction
Details
Identity Behavioral
Traits
Preferences
•Card spend
data from
Networks,
Issuers
•Bank Accounts
Activity from
Issuers
•Payment
Channels used
from Card
Networks
•Federal Authorities
•Credit rating
agencies as FICO
•Credit reports from
Equifax etc.
•Merchant level purchase Data via tracking
merchant loyalty cards/subscriptions
•Location
•Cultural aspects
•Reaction to
different forms of
Media
•Online
purchase
•Product search
behavior
•Crowd sourcing
New product
Innovation
Targeted Marketing
Better Consumer
Experience
High RoI from
Marketing
Micro level
segmentation
Very high
operational efficiencies
Better time to
market
Reach inside the
consumers mind
Results

Fraud Management
Fraud Losses
•Credit
•Debit
•Prepaid
USD11.27
Billion in
2012 up by
14.6% over
‘11
Most prominent
merchant categories
Ecommerce
Travel
Healthcare
Insurance
Customer
Experience
Operational
Efficiency
Fraud
Management
The Big Data Triad In Fraud Management
Better Understanding of Patterns of Fraud
and criminal Activity
Ability to analyze transaction data from peer
organizations to give early warning systems
Grater accuracy and lowering false positive
rates
Adding to user experience via GPS
Geolocation data & Behavioral profile Data for
Authentication
Converting data to BI aids in revenue growth
Implications of Big Data Triad
Current Fraud and Risk
Engines
Rule Engines
Statistical Data
Models
Risk Scoring

Big Data in Fraud Management
Application Log Files
Network and Click
Stream Data
Data from Social
Networks
Link and Entity Data
Unstructured Textual
Data
Traditional Data
Sources (Payments,
Transactions etc.
Data
Integration
Self
Learning
and
Streaming
Models
More Fraud
Caught
Less Money Spent
on Fraud
Management
More Satisfied
Customers
Better Business
Decisions
Improved Cyber
security
Big Data Analytics would mean a
reduction in Rule based risk
scoring systems which don’t
provide sufficient throughput and
performance, in addition rule
testing and maintenance is
cumbersome beyond 150 to 200
rules. Usage of analytics and
behavioral modeling will help in
meeting tactical and newly
emerging fraud detection
requirements

The Reference Architecture
Senior Management
Dashboard
Customer Analytics
Behavioral offer
targeting
Statistical and Algorithmic Models
Operational
Data Store/
Organizational
Data
Warehouse
MapReduce(Re
duced Data
sets)
Structured Data Unstructured Data
ETL’s

Big data analytics in payments

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