BIG Data & Hadoop Applications in Finance

Slide ‹#›© 2015 BlueCamphor Technologies (P) Ltd. www.skillspeed.com
Applications of BIG Data in
Finance

Introduction to BIG Data
Big data is the term for a collection of data sets so
large and complex that it becomes difficult to
process using on-hand database management tools
or traditional data processing applications
Retail industries generate extremely high volumes of
data due to high churn of transactions & inventory.
These companies can utilize BIG Data insights to
ensure higher profitability.

Applications of BIG Data in Finance
• Recommend Investment and Stocks
• Improved Compliance and Reporting System
• Fraud Detection
• Customer Segmentation Analysis
• Credit Risk Assessment
• 360-Degree Customer Service

BIG Data Applications in Finance
Get Started with BIG Data & Hadoop
Recommend Investment and Stocks
With a Big Data platform, stock market traders and investment portfolio managers can process
vast amounts of unstructured data to identify the best companies in which to invest.
Unstructured public information like company news, product reviews, supplier data and price
list changes can be processed en masse as Big Data, thus, producing mathematical models that
help traders decide which stock to buy or sell.
Improved Compliance and Reporting System
BIG Data has revamped the compliance & reporting process from a company as well as
compliance perspective. It ensures that ALL transactions, swaps & financial exchanges are
documented. Nothing goes un-noticed.

Fraud Detection
BIG Data scans through all financial transactions taking place in an organization, this ensures
the real-time detection of fraudulent activities. Any alterations to ‘normal behavior’ are
instantly caught & scrutinized.
This ensures absolute protection of ALL transactions.
Customer Segmentation Analysis
Segmentation powers contextual marketing, which always results in greater returns compare
to broad-spectrum marketing. BIG Data enables the classifications of customers into various
segments based on consumer behavior across multiple touch-points including website activity
& call-center interactios.
Existing customers can also be broken down into segments based on transactional data.

Credit Risk Assessment
Big Data enables greater accuracy in analyzing customer credit score. This is a very crucial
metric as it determines the lending practices for a particular customer. BIG Data can analzye
customer deposit-expenditure cycles and answer the question, how secure is it to lend money
to this individual? Or, what should be the credit spending limit?
Thus, BIG Data enables sharper credit risk assessment.
360-Degree Customer Service
You can only satisfy your customers when you know their exact requirements. BIG Data
enables you to track data regarding consumer behavior across social platforms, expenditure
cycles, website activity & support interaction. When all this is combined, you can create better
customer service experiences. And, this is the key to customer retention.

Current Examples of Big Data Application
American Express
 This is an excellent use of BIG Data to ensure customer loyaly & retention. American Express has launched an
application which provides its user with exclusive contextual discounts. For example, if they see you’ve been
purchasing products on Amazon, they’ll provide you with a personalized offer. Not only this, they also provide
discounts based on geographic proximity from relevant retail outlets. This is a win-win situation, customers get a
better experience & retailers get more business. This can also compel merchants on other systems to shift to
American Express.
Citi
 Citi Bank created an application to generate financial insights for large instituions. This allowed them to present
their clients with real-time access to key financial data-points. For example, they can track expenditure-revenue-
profit cycles in an instant. Not only this, Citi went one step ahead and integrated predictive analytics into this
applications.

© 2015 BlueCamphor Technologies (P) Ltd.
How Are Insights Derived from BIG
Data?
Say Hello to Hadoop Development :)

Introduction to Hadoop
Apache Hadoop is a framework that allows the distributed processing of large data sets across clusters of
commodity computers using a simple programming model.
It is an Open-Source Data Management technology with extensive storage and distributed processing.
Hadoop
Characteristics
Flexible
Reliable
Economical
Scalable Get Started with BIG Data & Hadoop

Why Should You Learn Hadoop?
• Hadoop is a modern BIG Data processing framework which utilizes distributed computing clusters.
• It can process, manage and store unstructured data with absolute ease.
• Due to its scalability & effectiveness, companies are heavily adopting Hadoop.
• It has become the standard for storing, processing and analyzing big data.
• BIG Data & Hadoop professionals are in extremely high demand.
• This is the first step to becoming a data scientist or data architect.

Reports – 2015
Here are some of the other Reports that convey the same message:
• “It is expected that Hadoop and Big Data Analytics will grow to around $13.9 billion from 2012 to 2017.”
(Markets and Markets Research Report)
• “The Data market currently with the fastest growth are Hadoop and NoSQL software and services.”
(Technology Research Organization, Wikibon)
• “The Big Data market will likely hit $23.8 billion with a 31.7% rise per year.” (IDC Report)
• “Almost 90% organizations have embarked on Hadoop related projects and thus Hadoop skills are in huge
demand.” (According to the Big Data Executive Survey 2013)
• “Companies hiring Hadoop professionals Analytics professionals obtain a 250 percent hike in their
salaries.” (According to Analytics Industry Report)
Gartner had predicted, “Hadoop will be in most advanced analytics products by 2015.” By far and large, this
prediction has turned out to be true. Hadoop is “The Technology” to harness Big Data

Predictions for 2015
According to the Forrester Report:
• A new data economy will arise, Hadooponomics. Due to its vast data prowess, enterprise adoption of
Hadoop will become compulsory. All those companies, which were unsure of adopting Hadoop till 2014,
are bound to adopt it in 2015
• The surge in demand for Hadoop job professionals will be fulfilled by Java professionals & other software
engineers who will upgrade their skills
• The creation of SQL-on-Hadoop options will further enhance usability & adoption
• Due to the introduction of Yarn (Version 2.0), the usage of Hadoop will go beyond analytics. It will
transform into an application platform giving birth to new frameworks in the Hadoop ecosystem
• New Hadoop sources will emerge from organizations like Oracle, HP, Tibco and SAP. Vendors such as Red
Hat, VMware and Microsoft will include Hadoop in their operating Systems

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Course Topics
Module 1
Introduction to Big
Data and Hadoop
Module 2
HDFS Internals, Hadoop
Configurations and
Data Loading
Module 3
Introduction to Map
Reduce
Module 4
Advanced Map Reduce
Concepts
Module 5
Introduction to Pig
Module 6
Advanced Pig and
Introduction to Hive
Module 7
Advanced Hive
Concepts
Module 8
Extending Hive and
HBase Introduction
Module 9
Advanced HBase and
Oozie Introduction
Module 10
Project Set-up
Discussion

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BIG Data & Hadoop Applications in Finance

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