1. Knowledge is of the past, wisdom is of the future
Big Data Analytics Using R
AMZ Bank
RITUPARNA SARKAR

2.  Banks look to 'big fast data' to meet regulator demands
 Use cases of Analytics in Banking
◦ Consumer Behavior and Marketing
◦ Risk, Fraud and AML/KYC
◦ Product and PortFollio Optimization
 Examples of Banking Analytics Impact
◦ A 15% increase in assets by designing unique offers for customers
◦ Improve time-to-market by 25%
◦ Cut marketing cost by 20%
 Size and Growth of Analytics in Banking industry

3.  AMZ Bank
◦ Recognized for their high standards and advanced service philosophy
◦ 7th largest lender in terms of assets
◦ Network of over 400 branches in Asia Pacific
◦ Relationships with 1000 banks in 70 countries around the world
 Vision Statement: To become ultra-modern and data driven—an organization enabled to use
any and all of their data to drive business excellence
 Goal and objective:
◦ Wish to reward their active and paying customers and reduce overheads on maintaining not so paying
customers.
◦ Maximize the number of active credit card customers
 better targeting marketing incentives to those most likely to activate and use for their business
transactions
◦ Want to isolate the cards that would likely never be activated to reduce wasted marketing spend
◦ Reduce loss of customers to their competition

4.  How did AMZ get here:
◦ Realized the potential of Big Data that could help them through predictive analytics and guided actions
for their business decisions
◦ Large institution with large data
 ability to scale is mandatory, but they wanted to place themselves at the leading edge
◦ Efficiency was one of their top considerations
 wanted an analytic architecture that was fast, flexible, affordable and simple-to-use, all at the
same time
 Current Situation
◦ Recently set out to create a state-of-the-art analytic environment to support and fuel their fast
growing credit card business
◦ Bank Credit Card Center was already familiar with predictive analytics
◦ Had used conventional products in the past for BI and Reporting for their business outcomes and
decision making.

5. To Visualization & Reporting Layer
Connection to Data-lake
Open-source Message Broker - provides a unified,
high-throughput, low-latency platform for handling real-
time data feeds.
Open-source real-time computation system-
processes unbounded streams of data, doing for realtime
processing what Hadoop did for batch processing.

6. A. Setting up a right Datawarehouse platform: Leveraging from the large data
Many hundreds of systems are distributed throughout the organization; each system is largely
independent; any customer experience data is concentrated within that system
Option A: Traditional Datawarehouse Option B: Big Data (MPP Database) Approach
• extensive data definition work
• extensive transfer of data
• data sources are incomplete, do not use
the same definitions, and not always
available
• Sampling the data would have been very
problematic, as the objective was to
construct a customer view over time from
all the events that took place.
• Timescale to implement considerably high
• Need for elastic scalability, extreme
performance, faster data access,
• Workload Management, Fault-Tolerance,
Advanced Analytics feature support
• Massively Parallel Processing data warehouse
set up
• Can execute complex SQL analytics on very
large data sets at speeds multiple times
faster

7. Connection to Real-time layer
To Visualization & Reporting Layer

8. B. Identifying the predictive analytics software
◦ Need to use the complete data, and not just a sample to get the complete picture towards making the
right decisions
◦ Tool would need to work with massive datasets, support the datawarehouse platform, provide
visualizations/predictive modelling capability
◦ Fully scalable data modeling against all data, improving analytic model accuracy and efficacy.
◦ Faster time from modeling to scoring, delivering rapid results and enabling iteration.
◦ Reduced data movement and latency, thus improving productivity of data analysts and IT staff.
◦ Efficient utilization of data assets and IT resources, reducing costs and increasing ROI ; essential to
factor in the cost of having dedicated data miners versus a tool based approach
◦ Scoring directly within the database, leveraging the database’s common security, auditing and
administration capabilities and reducing data movement and increasing data utilization.

9. Ad-hoc Data Analysis & Reporting
in Excel & R
Automated Reporting & Alerting system
Delivered to all form-factors
Advance analytics capabilities
Data-lake
Visualization & Reporting Platform

10.  With centralization and democratization of data, business user become
more empowered to mitigate risk and propel development.
 Rational and data supported decisions will increase efficiency in
processes
 2-layered data warehouse (MPP & HDFS) bring the best of both worlds.
 Mix of flagship products & open-source gives technical flexibility with
unbounded scope.

11.  Data Model
◦ HDFS
 Unstructured data : JSON formatted Flat File schema.
 Structured data : Flattened Star Schema.
◦ Pivotal Greenplum
 Structured data : Snowflakes schema
◦ Apache Storm
 All data : JSON formatted Flat File schema.

12.  Analyzing the issue 1: Reduce the number of inactive credit cards /
Maximize the number of active credit card customers
 Definition, Benefits and Cause
◦ In-active credit cards - No transaction over an year
◦ Benefits of reducing In-active Credit Cards
 Restricted lines of credit can be re-distributed among active users
and hence opening new opportunities of earning.
 Suggested Solution
◦ Design dynamic products that grows and shrinks with customers
behavior.
◦ Restrict pushing low fees cards.
◦ Define in-activity period limits, apply re-activation strategy and
churn customers on expiry.
◦ Increase presence of ATMs, POS Card Swipe Machines, online
merchant partnership to tap more information.
◦ Design system to push customized 1-to-1 based on location and
situation.
Possible
Causes
Low
Quality
Customer
Low
Quality
Products
& Eco-
system
Customer
Product
Mismatch

13.  Analyzing the issue 2: Reduce customer churn
 Definition, Benefits and Cause
◦ Loss to competition - Customers shifting to competitor’s product
◦ Benefits – Steady revenue, focused incentives, increased loyalty
 Suggested Solution
◦ Push products with high exit barrier to customer with multiple short term credit
lines.
◦ Improve brand value and trust by showcasing the strength.
◦ Empower customer support with updated-till-last-minute details of customer and
prescribed recommendation to serve better.
◦ Revamp the customer acquisition process with best mix of online and offline
processes.
◦ Achieve all regulatory and compliance certification
◦ Design dynamic products that grows and shrinks with customers behavior.
◦ Restrict pushing low fees cards.
◦ Define in-activity period limits, apply re-activation strategy and churn customers
on expiry.
◦ Increase presence of ATMs, POS Card Swipe Machines, online merchant
partnership to tap more information.
◦ Design system to push customized 1-to-1 based on location and situation.
Possible
Causes
Weaker
brand
value
Higher
Cost of
ownershi
p
Low
quality
customer
service
Innovativ
e
products

14.  Data Sources
Internal Data External Data
• Customer acquisition history – Sales Team : How, what, when,
why and who
• Customer lifetime history – Customer Relations Team
• Current & Historical satisfaction status
• Movement between products
• Movement in customer’s life an its effect on behavior
• Marketing & Products data – Marketing Teams
• Products
• Marketing campaigns
• Customer identification across borders
• Across border relationship
• Credit scores and credit reports
• Analyze open lines of credit
• Analyze metrics like debt ratio, credit utilization etc.
• Domestic and International Fraud and Crime Data
• Prevent fraud and money laundering
• Partner Banks, Merchants and Institutions
•Professional Data from LinkedIn
•Network and personal life from Facebook & Instagram
•Social behavior from Twitter
Social
media
•Sensors, Mobile devices and applications
•PoS, ATM
•Web logs and Online shopping
Others

15.  Devise 3600 view of customer
◦ Develop customers spend signature to prevent fraud using past transaction data, social media behavior, sensor data from mobile etc.
◦ Analyze all interactions like emails, call-center calls etc. with customer to devise the current satisfaction status
◦ Develop a view of customers personal life and social connections like “Last 5 significant events”.
◦ Link and analyze customer social media activities
◦ Develop recommendations for customer which can include offers, merchant offers, upgrade offer etc.
 Revamp Marketing and Sales
◦ Build Social Media listening and monitoring center to capture, mediate and intervene in Social Media conversations.
◦ Analyze live data sources along with customer spend signature to push location-based, situation-based 1-to-1 targeted offers and notification.
◦ Devise aggregated regional potential, market share, brand sentiment to formulate resource allocation, sales targets etc.
◦ Empower ground staff with next-gen marketing & sale tools to help them maximize their personal ROI.
◦ Design and deploy market watch system to monitor and generate alerts on competitor activity like new product launch, change in interest rates
etc.
 Empowering Operations
◦ Revamped dashboards with details like customer satisfaction status, customer value, last 5 call highlights etc to be delivered with least time.
◦ Design special call routing to build executive – customer relationship.
◦ Develop 1-click recommendation and live offer system which will use data from live current conversation to facilitate best cross selling.
◦ Deploy Robotic Process Automation tools to increase efficiency.

17. Setting up a MPP Data warehouse
 EMC Greenplum Enterprise Data Cloud (EDC) as a powerful MPP data warehouse platform
◦ Greenplum Database® is an advanced, fully featured, open source data warehouse. It provides powerful and rapid analytics on petabyte
scale data volumes.
◦ True MPP architecture and features that meet mandatory requirements of enterprise-class data warehousing
◦ Allow AMZ market-leading power and scalability on commodity hardware.
◦ affordably ride the curve of hardware advances and enjoy the simplicity and flexibility of a private cloud environment
◦ Can get market leading load performance (10X faster than peer data warehouses) with comprehensive ELT transformation capabilities
to enterprise-class high availability/disaster recovery
◦ Can use Specific customer experience analytical packages (ClickFox and Merced) towards data analysis
 Setting up a predictive analytics software
◦ Alpine Miner as a Predictive analytics software
 Alpine Miner on the Greenplum database provides a fully integrated environment for statistical transformation and modeling
methods for data analysis, modeling and scoring, with true scalability and top processing speed.
 As a completely scalable in-database solution, these processes can be built entirely within the Alpine Miner interface, and then
executed directly where the data resides, with no limitations on size or complexity
 With Alpine Miner, business and data analysts can flexibly and efficiently conduct end-to-end knowledge discovery and predictive
analytics—including data preparation, data transformation, data modeling and data scoring.
 All the models built with Alpine Miner are automatically stored in the database and can be published or deployed directly within the
database at the press of a button, further assuring data reliability and integrity and accelerating model integration with business
applications.
 The Alpine Miner architecture cuts weeks to months from the process because it reduces unnecessary data movement, and supports
better data governance and data-mining process standardization.

18.  Source: http://david.portnoy.us/comparison-of-
mpp-data-warehouse-platforms/

19.  Lack of opportunity to use
Possible solutions
◦ Increase partner, merchants and network
◦ Don’t sell cards to such geographies
 Lack of knowledge and faith
Possible solutions
◦ Invest in customer training
◦ Showcase your security and compliances
 Already owns multiple credit cards
Possible solutions
◦ Analyze list of open lines of credit

20.  The low performance in the products can be due to various reasons.
◦ Product Design, Pricing, Customer segment etc.
 Improvement Approaches
◦ This can be improved by developing a service delivery strategy. For Ex. “Mobile Wallet”.
◦ Mobile banking: Offers the convenience of banking anytime, anywhere.
◦ Mobile payments: Enables customers to send money to any mobile phone number.
◦ Mobile payments–contactless: Allows customers to save time with tap-and-go mobile payment service.
◦ Mobile marketing: Provides consumers with exclusive promotions, coupons, and alerts based on their current
location.

21.  Market segment mismatch:
 The product was not designed for this type of customer.
 Provide a tailored product based on the historic data.
 Customer-specific mismatch:
 The unique needs of the customer may be causing some friction points even though
the customer is in the product’s target market.
 Reach out to the customer asking feedbacks on the current offerings.
 Geographical mismatch:
 The customer may be based in a particular country or region with regulations or local
business norms that create unique challenges.
 Find out features at a region level, which features are used most and which are not.

22.  Banking industry : Data Landmine with tremendous capabilities
 Mix of flagship products & open-source gives technical flexibility with unbounded scope: Essential for the
right architecture and design
 Case Study
◦ 2 key objectives – Maximize active credit card users, Reduce Customer Churn
◦ High level architecture followed by a snapshot of proposed solution to meet the objectives
◦ Major Tasks for improvement
 Challenges
◦ Transforming the approach to do business with Big Data.
◦ Align all layers of the organization understand and leverage the benefits out of Big Data.
◦ Hire, nurture and retain fresh talent to develop and build the new system.
◦ Sustain business though the transformation period ranging from 5-10 years on an average.

23.  http://wikibon.org/wiki/v/Financial_Comparison_of_Big_Data_MPP_Solution_and_
Data_Warehouse_Appliance
 http://david.portnoy.us/comparison-of-mpp-data-warehouse-platforms/
 http://www.emc.com/collateral/hardware/white-papers/h8072-greenplum-
database-wp.pdf
 http://datascienceseries.com/assets/blog/GREENPLUM_Analyzing_customer_beha
vior-web.pdf
 http://www.emc.com/collateral/campaign/global/forums/presentations/ny-
future-data-warehousing.pdf
 http://www.ciosummits.com/media/pdf/solution_spotlight/alpine_china-citic-
bank.pdf
 http://datascienceseries.com/partners/partners/alpine-data-labs
 http://www.predictiveanalyticstoday.com/top-predictive-analytics-software/