The document discusses the use of big data analytics to combat fraud during the holiday season, highlighting significant financial losses incurred by merchants, banks, and online retailers. It outlines the challenges of existing data architectures and presents solutions, including the deployment of modern data analytics tools and methodologies for fraud detection. Additionally, it covers the speakers' expertise, the agenda of the presentation, and the importance of leveraging existing data sources and architectures to enhance fraud prevention capabilities.

1. Fight Fraud with Big Data
Analytics this Holiday Season
© 2013 Datameer, Inc. All rights reserved.

2. View Full Recording
View the full recording of this webinar at:
http://info.datameer.com/SlideshareFighting-Fraud-this-Holiday-Season.html

3. Fight Fraud with Big Data
Analytics this Holiday Season
© 2013 Datameer, Inc. All rights reserved.

4. About our Speakers
Karen Hsu (@Karenhsumar)
– Karen is Senior Director, Product Marketing
at Datameer. With over 15 years of
experience in enterprise software, Karen
Hsu has co-authored 4 patents and worked
in a variety of engineering, marketing and
sales roles.
– Most recently she came from Informatica
where she worked with the start-ups
Informatica purchased to bring data quality,
master data management, B2B and data
security solutions to market.
– Karen has a Bachelors of Science degree
in Management Science and Engineering
from Stanford University.

5. About our Speakers
• John Kreisa (@marked_man)
– A veteran from the enterprise
marketing industry John has worked
worked on products at every level of
the IT stack from the depths of
storage through to the insight of
business intelligence and analytics.
Currently John leads partner and
strategic marketing initiatives at open
source leader Hortonworks who
develops, distributes and supports
Apache Hadoop.

6. Fight Fraud with Big Data
Analytics this Holiday Season
© 2013 Datameer, Inc. All rights reserved.

7. Agenda
• Current challenges
• What to look for in a solution addressing
fraud
• Demo
• Q&A

8. Challenges
Merchants paying
$200-250B in fraud
losses annually
Banks and Financial
Organizations
losing $12-15B
annually
eTailers lost $3.5B
to online fraud
Over 20B
credit card
transactions
annually

9. Face of Fraud is Changing
HELLO
my name is
$5.15
$3.95
$4.10
$4.15
$4.55
$3.22
greg
7-ELEVEN
POS Reports
Location Data
Transactions
Authorizations

10. APPLICATIONS
Challenges with Existing Data Architecture
Custom
Applica4ons
Business
Analy4cs
Packaged
Applica4ons
DATA
SYSTEM
2.8
ZB
in
2012
85%
from
New
Data
Types
RDBMS
EDW
MPP
REPOSITORIES
15x
Machine
Data
by
2020
40
ZB
by
2020
SOURCES
Source: IDC
Exis4ng
Sources
(CRM,
ERP,
Clickstream,
Logs)
© Hortonworks Inc. 2013

11. What to Look For in a Fraud
Analytics Solution
© 2013 Datameer, Inc. All rights reserved.

12. Big Data Analytics Lifecycle
1. Integrate
Identify
Use Case
4. Visualize
2. Prepare
3. Analyze
Modern Day Architecture
Deploy

13. Define!
▪ Use Cases
ROI and TCO Methodology
" Customer Analytics
" ROI customer metrics"
" Operational Analytics
" ROI and TCO calculator"
" Legacy Modernization
" Fraud and Compliance
Funnel
Optimization
Behavioral
Analytics
Fraud
Prevention
EDW
Customer
Optimization
Segmentation
Increase
Customer
conversion by 3x
Increase
Revenue by 2x
Identify $2B in
potential fraud
98% OpEx
savings$1M+
CapEx savings
© 2013 Datameer, Inc. All rights reserved.
Lower Customer
Acquisition
Costs by 30%

14. Polling question 1
© 2013 Datameer, Inc. All rights reserved.

15. Polling Question
What use cases are looking at or implementing
today?
▪ Profiling and segmentation
▪ Product development and operations optimization
▪ Cross-sell / up-sell
▪ Campaign management
▪ Acquisition and retention
▪ EDW optimization
▪ Fraud and compliance
▪ Other

16. Integrate!
Codeless Integration
Big Data Management
"
Reuse existing DB views and SQL"
"
Data Partitioning"
"
50+ Datameer connectors, plug-in API"
"
Data Retention policies"
© 2013 Datameer, Inc. All rights reserved.

17. Prepare and Analyze!
Interactive Data
Preparation
Interactive + Smart
Analytics
Transparency +
Governance
" JSON, XML, URL-specific
" 250+ built-in functions"
" Visual data lineage"
" Automated machine learning"
" Complete audit trail"
" SmartSampling "
" Metadata catalog"
functions
" Multi-column joins, unions"
© 2013 Datameer, Inc. All rights reserved.

18. Visualize!
Visualization Anywhere
Visual Discovery
" Infographic or dashboard"
" Machine Learning algorithms"
" Run on tablets and smart phone devices"
© 2013 Datameer, Inc. All rights reserved.

19. Deploy!
Security
Scheduling
Monitoring
" LDAP / Active Directory "
" Dependency triggers"
" Monitoring system, jobs,
" Role based access control"
" Data synchronization"
" Support for Kerberos"
" External scheduling integration"
performance, throughput"
" Error handling"
" Log management"
© 2013 Datameer, Inc. All rights reserved.

20. APPLICATIONS
Modern Data Architecture Enabled
Custom
Applica4ons
Business
Analy4cs
Packaged
Applica4ons
DEV
&
DATA
TOOLS
SOURCES
DATA
SYSTEM
BUILD
&
TEST
OPERATIONAL
TOOLS
RDBMS
EDW
MANAGE
&
MONITOR
MPP
REPOSITORIES
Exis4ng
Sources
(CRM,
ERP,
Clickstream,
Logs)
© Hortonworks Inc. 2013 - Confidential
Emerging
Sources
(Sensor,
Sen4ment,
Geo,
Unstructured)
Page 20

21. 3
Requirements for Hadoop Adoption
Requirements for Hadoop’s Role
in the Modern Data Architecture
Integrated
Interoperable with
existing data center
investments
Key Services
Skills
Platform, operational and
data services essential for
the enterprise
Leverage your existing
skills: development,
operations, analytics
© Hortonworks Inc. 2013 - Confidential
Page 21

22. Requirements for Enterprise Hadoop
1
2
3
Key Services
Platform, Operational and
Data services essential
for the enterprise
OPERATIONAL
SERVICES
AMBARI
HBASE
CORE
PIG
SQOOP
LOAD
&
EXTRACT
Skills
PLATFORM
SERVICES
Integrated
MAP
REDUCE
NFS
TEZ
YARN
WebHDFS
KNOX*
HIVE
&
HCATALOG
HDFS
Enterprise Readiness
High Availability, Disaster
Recovery, Rolling Upgrades,
Security and Snapshots
HORTONWORKS
DATA
PLATFORM
(HDP)
Engineered with existing
data center investments
OS/VM
© Hortonworks Inc. 2013 - Confidential
FLUME
FALCON*
OOZIE
Leverage your existing
skills: development,
analytics, operations
DATA
SERVICES
Cloud
Appliance
Page 22

23. Requirements for Enterprise Hadoop
3
Leverage your existing
skills: development,
analytics, operations
Integration
DEVELOP
ANALYZE
2
Skills
Platform, operational and
data services essential
for the enterprise
OPERATE
1
Key Services
COLLECT
PROCESS
BUILD
EXPLORE
QUERY
DELIVER
PROVISION
MANAGE
MONITOR
Engineered with existing
data center investments
© Hortonworks Inc. 2013 - Confidential
Page 23

24. Familiar and Existing Tools
3
Leverage your existing
skills: development,
analytics, operations
Integration
DEVELOP
ANALYZE
2
Skills
Platform, operational and
data services essential
for the enterprise
OPERATE
1
Key Services
COLLECT
PROCESS
BUILD
EXPLORE
QUERY
DELIVER
PROVISION
MANAGE
MONITOR
Interoperable with existing
data center investments
© Hortonworks Inc. 2013 - Confidential
Page 24

25. APPLICATIONS
Requirements for Enterprise Hadoop
Custom
Applica4ons
Business
Analy4cs
Packaged
Applica4ons
Integrated with
DEV
&
DATA
TOOLS
Applications
BUILD
&
DATA
SYSTEM
Business Intelligence,
TEST
Developer IDEs,
Data Integration
SOURCES
3
OPERATIONAL
TOOLS
RDBMS
EDW
MANAGE
&
Systems
MONITOR
MPP
Data Systems & Storage,
Systems Management
REPOSITORIES
Platforms
Integration
Exis4ng
Sources
Engineered with Lexisting
(CRM,
ERP,
Clickstream,
ogs)
data center investments
© Hortonworks Inc. 2013 - Confidential
Emerging
Sources
(Sensor,
Sen4ment,
Geo,
Unstructured)
Operating Systems,
Virtualization, Cloud,
Appliances
Page 25

26. DATA
SYSTEM
APPLICATIONS
Datameer in the Modern Data Architecture
DEV
&
DATA
TOOLS
OPERATIONAL
TOOLS
RDBMS
EDW
HANA
MPP
SOURCES
INFRASTRUCTURE
Exis4ng
Sources
(CRM,
ERP,
Clickstream,
Logs)
© Hortonworks Inc. 2013 - Confidential
Emerging
Sources
(Sensor,
Sen4ment,
Geo,
Unstructured)
Page 26

27. Demonstration 1
© 2013 Datameer, Inc. All rights reserved.

28. Identifying Potential Fraud
How much has been spent at
a vendor?
Is that spend normal?
Were there transactions…
When a credit card stolen?

29. Identify Outliers in Transactions
1. Calculate average and standard deviation
for each category
2. Identify outliers in all transactions
Transaction
Amount
-
Category
Average
> 2*
Std Dev of
Category

30. Demonstration 2
© 2013 Datameer, Inc. All rights reserved.

31. Fraud and Data Mining on Hadoop
Clustering
Column Dependencies
Decision Tree
Recommendations

32. Demonstration 3
© 2013 Datameer, Inc. All rights reserved.

33. Predictive Modeling and Datameer
Model Building
Model Deployment
Integration / Execution
PMML
Datameer Server
PMML
PMML
PMML
(models)
(models)
(models)
UPPI

34. Predictive Modeling and Fraud
1. Bring in model
2. Apply function data to get likelihood
transaction is fraudulent

35. Next Steps:
More about Datameer and Big Data
www.datameer.com
Get started on with Datameer and Hortonworks
http://hortonworks.com/hadoop-tutorial/datameer/
Contact us:
John Kreisa jkreisa@hortonworks.com
Karen Hsu khsu@datameer.com
Page 35

36. Polling Question
What part of webinar did you find the most useful?
▪ Use cases
▪ Tool ease of use of setup comparison
▪ Tool quality comparison
▪ Best practices
▪ Demonstration

37. Q&A

38. Best Practices
© 2013 Datameer, Inc. All rights reserved.

39. Calculating ROI is a process

40. Apply ROI to Multiple Projects
Project 3
Project 2
Project 1
Hardware
Savings
Software
Savings
Productivity
Business
Benefit

41. Calculating Return
Benefits
-
Costs
=
Return
Identify Fraud
Hardware
$$$
Improve Marketing
Software
Time
Increase Sales
Integration
Flexibility
Improve Product
People
Increase Conversion
Operations
Lower IT expenses
Logistics

42. Universal Plug-In Overview
Features and Model Types
The Plug-in delivers a wide range of predictive analytics for high performance scoring, including:
• Decision Trees for classification and regression
• Neural Network Models: Back-Propagation, Radial-Basis Function, and Neural-Gas
• Support Vector Machines for regression, binary and multi-class classification
• Linear and Logistic Regression (binary and multinomial)
• Naïve Bayes Classifiers
• General and Generalized Linear Models
• Cox Regression Models
• Rule Set Models (flat decision trees)
• Clustering Models: Distribution-Based, Center-Based, and 2-Step Clustering
• Scorecards (including reason codes)
• Association Rules
• Multiple Models: Model ensemble, segmentation, chaining and composition
It also implements the a data dictionary, missing/invalid values handling and data pre-processing.
42