This document discusses fraud and risk in the context of big data. It begins by defining big data and providing examples of how large companies like Walmart and Facebook handle massive amounts of data daily. It then discusses different types of fraud that can occur, such as credit card fraud and fraud on social media. Finally, it discusses risk management and how credit and market risk analytics are used to analyze past data to predict future risks. In summary, the document outlines the opportunities and challenges of using big data for fraud detection and risk management.

1. Fraud and Risk in Big
Data
Data Engineering and Cloud Computing Department
Umma Khatuna Jannat
Reva University
SRN:R16MDC06

2. Outline
1. Introduction
2. What is Big Data
3. Fraud
4. Risk

3. Introduction
Big Data may well be the Next Big Thing in the IT world.
Big data burst upon the scene in the first decade of the 21st century.
The first organizations to embrace it were online and startup firms. Firms like Google,
eBay, LinkedIn, and Face book were built around big data from the beginning.
Like many new information technologies, big data can bring about dramatic cost
reductions, substantial improvements in the time required to perform a computing task, or
new product and service offerings.

4. Big Data
‘Big Data’ is similar to ‘small data’, but bigger in size
but having data bigger it requires different approaches:
Techniques, tools and architecture
an aim to solve new problems or old problems in a better way

5. Big Data
Walmart handles more than 1 million customer transactions every hour.
Face book handles 40 billion photos from its user base.
Decoding the human genome originally took 10 years to process; now it can be achieved
in one week.

6. Fraud
Fraud as a Crime: Fraud is a generic term, and embraces all the multifarious means that
human ingenuity can devise, which are resorted to by one individual, to get an advantage
by false means
Corporate Fraud: Corporate fraud is any fraud committed by, for, or against a business
corporation.
Management Fraud: Management fraud is the intentional misrepresentation of
corporate or unit performance levels

7. Fraud
One of the most common forms of fraudulent activity is credit card fraud.
Social media and mobile phones are forming the new frontiers for fraud.

8. Risk
It would be an understatement to say that risk management is data-driven
The two most common types of risk management are credit risk management and market
risk management.
Credit risk analytics focus on past credit behaviors to predict
Market risk analytics focus on understanding the likelihood that the value of a portfolio
will decrease due to the change in stock prices, interest rates, foreign exchange rates.

9. Marketing Operations Bankers CEOs
• Next Best Action
• Recommended
Interventions
• Lifestyle Yield Management
• Seasonal Personal Impact
• Theft Profiling
• Fraudulent Transaction
Identification
• Remote Shutdown
• Site Monitoring
• Recommended Interventions
• Risky Customer Profiling
• Call Center Monitoring
• Churn Scoring
• Payment System Errors
• Money Laundering
prevention
• Compliance
• Data Entry Intervention
?
Personalization of offers &
banking experience
Risk Reduction &
ComplianceCustomer Churn PreventionFraud Detection
Areas of Opportunity for Financial Analytics

10. Big Data Challenges

11. Fraud Detection Reference Architecture
Apps data
from devices
News and
other alerts
Solution UX
Provisioning API (Pull)
User Profile Information
Stream Processors
Analytics &
Machine Learning
Business
Integration
Connectors
and
Gateway(s)
User Recent Activity Store
Gateway
Data Lake
Gateway
App Backend
Data Path
Optional solution component
Main solution component
Thin Client
Presentation & Business
Connectivity
Data Processing, Analytics and ManagementDevice Connectivity
Personal
mobile
devices
Trades
and/or
transactions
Business
systems

12. Benefits of Big Data
Newest research finds that organizations are using big data to target customer-centric
outcomes, tap into internal data and build a better information ecosystem.
Big Data is already an important part of the $64 billion database and data analytics
market