You can view the full presentation of this webinar here: http://info.datameer.com/Slideshare-Fighting-Fraud-this-Holiday-Season.html
In 2012, retailers lost $3.5 billion in revenue to online fraud. These losses spike by a substantial estimated 20% during the holiday season.
Join Datameer and Hortonworks in this webinar to learn how Big Data Analytics can be used to identify new fraud schemes during peak fraud season.
In this webinar, you will learn about:
current challenges in identifying fraud
what to look for in a big data solution addressing fraud
how big data analytics can identify credit card fraud
best practices
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.
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
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
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
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.
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