BIG Data & Hadoop Applications in Finance

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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.

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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 CharacteristicsFlexible

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




Why SkillSpeed?

Course Curriculum

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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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