fight fraud with big data analytics

© 2013 Datameer, Inc. All rights reserved.

Fight Fraud with Big Data Analytics this Holiday Season

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View the full recording of this webinar at:

http://info.datameer.com/Slideshare-Fighting-Fraud-this-Holiday-Season.html

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.

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.

Agenda •  Current challenges •  What to look for in a solution addressing

fraud

•  Demo •  Q&A

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

H E L L O my name is

greg 7-ELEVEN

$4.10

$3.22 $4.55

$5.15 $4.15

$3.95

Location Data Transactions Authorizations POS Reports

Face of Fraud is Changing

© Hortonworks Inc. 2013

Challenges with Existing Data Architecture AP

PLICAT

IONS

DATA

SYSTEM

REPOSITORIES

SOURC

ES

Exis4ng Sources (CRM, ERP, Clickstream, Logs)

RDBMS EDW MPP

Business Analy4cs

Custom Applica4ons

Packaged Applica4ons

Source: IDC

2.8 ZB in 2012

85% from New Data Types

15x Machine Data by 2020

40 ZB by 2020


What to Look For in a Fraud Analytics Solution

Big Data Analytics Lifecycle

1. Integrate

3. Analyze

4. Visualize 2. PrepareIdentify

Use Case Deploy

Modern Day Architecture


▪ Use Cases " Customer Analytics " Operational Analytics " Legacy Modernization " Fraud and Compliance

ROI and TCO Methodology "  ROI customer metrics""  ROI and TCO calculator"

Funnel Optimization

Behavioral Analytics

Fraud Prevention

EDW Optimization

Customer Segmentation

Increase Customer

conversion by 3x Increase

Revenue by 2x Identify $2B in potential fraud

98% OpEx savings$1M+

CapEx savings

Lower Customer Acquisition

Costs by 30%

Define!


Polling question 1

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


Codeless Integration " Reuse existing DB views and SQL"" 50+ Datameer connectors, plug-in API"

Integrate!Big Data Management " Data Partitioning"" Data Retention policies"


Interactive Data Preparation

" JSON, XML, URL-specific functions

" Multi-column joins, unions"

Interactive + Smart Analytics

"  250+ built-in functions"

"  Automated machine learning"

"  SmartSampling "

Transparency + Governance

"  Visual data lineage"

"  Complete audit trail"

"  Metadata catalog"

Prepare and Analyze!


Visualization Anywhere "   Infographic or dashboard"

"   Run on tablets and smart phone devices"

Visualize!

Visual Discovery "   Machine Learning algorithms"


Scheduling "  Dependency triggers"

"  Data synchronization"

"  External scheduling integration"

Monitoring "  Monitoring system, jobs, performance, throughput"

"  Error handling"

"  Log management"

Deploy!Security "  LDAP / Active Directory "

"  Role based access control"

"  Support for Kerberos"

© Hortonworks Inc. 2013 - Confidential

Modern Data Architecture Enabled

APPLICAT

IONS

DATA

SYSTEM

REPOSITORIES

SOURC

ES


RDBMS EDW MPP

Emerging Sources (Sensor, Sen4ment, Geo, Unstructured)

OPERATIONAL TOOLS

MANAGE & MONITOR

DEV & DATA TOOLS

BUILD & TEST

Business Analy4cs

Custom Applica4ons



Integrated Interoperable with existing data center investments Skills

Leverage your existing skills: development, operations, analytics

Requirements for Hadoop Adoption

Key Services Platform, operational and data services essential for the enterprise

3 Requirements for Hadoop’s Role in the Modern Data Architecture


1

Integrated Engineered with existing data center investments

Key Services Platform, Operational and Data services essential for the enterprise Skills Leverage your existing skills: development, analytics, operations

2

3

Requirements for Enterprise Hadoop

OS/VM Cloud Appliance

PLATFORM SERVICES

CORE

Enterprise Readiness High Availability, Disaster Recovery, Rolling Upgrades, Security and Snapshots

HORTONWORKS DATA PLATFORM (HDP)

OPERATIONAL SERVICES

DATA SERVICES

HDFS

SQOOP

FLUME

NFS

LOAD & EXTRACT

WebHDFS

KNOX*

OOZIE

AMBARI

FALCON*

YARN

MAP TEZ REDUCE

HIVE & HCATALOG PIG HBASE



1

Integration Engineered with existing data center investments

Key Services Platform, operational and data services essential for the enterprise

Skills Leverage your existing skills: development, analytics, operations

2

3 DE

VELO

P AN

ALYZE

OPE

RATE

COLLECT PROCESS BUILD

EXPLORE QUERY DELIVER

PROVISION MANAGE MONITOR


Familiar and Existing Tools

1 Key Services Platform, operational and data services essential for the enterprise

Skills Leverage your existing skills: development, analytics, operations

2

DEVE

LOP

ANAL

YZE

OPE

RATE

COLLECT PROCESS BUILD

EXPLORE QUERY DELIVER

PROVISION MANAGE MONITOR

Integration Interoperable with existing data center investments 3


APPLICAT

IONS

DATA

SYSTEM

REPOSITORIES

SOURC

ES


RDBMS EDW MPP


OPERATIONAL TOOLS

MANAGE & MONITOR

DEV & DATA TOOLS

BUILD & TEST

Business Analy4cs

Custom Applica4ons



Integration Engineered with existing data center investments 3

Integrated with Applications Business Intelligence, Developer IDEs, Data Integration

Systems Data Systems & Storage, Systems Management

Platforms Operating Systems, Virtualization, Cloud, Appliances


Datameer in the Modern Data Architecture

APPLICAT

IONS

DATA

SYSTEM

SOURC

ES

RDBMS EDW MPP


HANA

OPERATIONAL TOOLS

DEV & DATA TOOLS


INFRASTRUCTURE


Demonstration 1

Identifying Potential Fraud

How much has been spent at a vendor? Is that spend normal?

Were there transactions… When a credit card stolen?

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


Demonstration 2

Fraud and Data Mining on Hadoop

Clustering Column Dependencies

Decision Tree Recommendations


Demonstration 3

Model Deployment Integration / Execution

Model Building

Datameer Server

UPPI

PMML (models) PMML (models) PMML (models)

PMML

Predictive Modeling and Datameer

Predictive Modeling and Fraud

1. Bring in model

2. Apply function data to get likelihood transaction is fraudulent

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 [email protected] Karen Hsu [email protected]

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


Best Practices

Calculating ROI is a process

Apply ROI to Multiple Projects

BusinessBenefit

SoftwareSavings

HardwareSavings Productivity

Project 1

Project 2

Project 3

Calculating Return

Costs ReturnBenefits - =

Hardware

Software

Operations

People

Integration

Identify Fraud

Increase Sales

Improve Marketing

Increase Conversion

Improve Product

Lower IT expenses

$$$

Time

Flexibility

Logistics

Universal Plug-In Overview Features and Model Types

42

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.

fight fraud with big data analytics

Technology

data quality

data services essential

big data analytics lifecycle1

master data management

fraud analytics solution

datameer connectors

analytics hortonworks

fight fraud