1. Business Intelligence & Analytics
Susan Meyer, MasterCard Network Products
April 22, 2013
Business Intelligence & Analytics
Career Paths and Industry Trends

2. Agenda Introduction The BI Job Market Analytical Processes
Data Science Roles
Analytics Products & Services
Analytical Platforms
January 30, 2013

3. Data Crunchers In Demand
Data Scientist
Deemed “the sexiest job of the 21st century” by Harvard Business Review, data scientists bridge the gap between the skills of a statistician, a computer scientist and an MBA.
Salaries vary from $110,000 to $140,000.
January 30, 2013

4. Data Mining Job Prospects
Gartner says worldwide IT spending will increase 3.8 percent in 2013 to reach $3.7 trillion, and that excitement for big data is leading the way.
By 2015, 4.4 million jobs will be created to support big data.
Over 90-percent of the NCSU Class of 2013 have received one or more offers of employment, and over 80-percent have accepted new positions. The average base salary reached an all-time high of $96,900, an increase of nearly 9% over the Class of 2012.
January 30, 2013

5. A Brief Overview of Data Mining
Innovation
Business Question
Technologies
Data Collection (1960’s)
“What was total revenue in the past 5 years?”
Mainframe computers, tape backup
Data Access (1980’s)
“What were unit sales in New England last March?”
RDBMS, SQL, ODBC
Data Warehousing (1990’s)
““What were unit sales in New England last March?
Drill down to Boston”
OLAP, multi-dimensional databases, data warehouses
Data Mining
(Today)
“What’s likely to happen to Boston sales next month and why?”
Advanced algorithms, massively parallel databases, Big Data
January 30, 2013

6. Business Intelligence & Data Mining Services
Descriptive
Dashboards
Process mining
Text mining
Business performance management
Benchmarking
Predictive
Predictive analytics
Prescriptive analytics
Realtime scoring
Online analytical processing
Ranking algorithms
Authentication
These functions are highly inter-related and fall on a continuum
January 30, 2013

7. CRISP-DM: Data Mining Methodology is Highly Iterative
Up to 60% of the work effort in a major data mining project is typically related to data preparation and cleansing
Be prepared for the unexpected when working with real-world data
ftp://ftp.software.ibm.com/software/.../Modeler/.../CRISP-DM...
January 30, 2013

8. Data Scientists Work in Teams
Many major organizations in St. Louis are actively using data mining in their core line of business
Job Categories
Business Analyst
Data Analyst
Data Engineer
Data Scientist
Marketing
Sales
Statistician
January 30, 2013

9. Statistical Business Analyst
January 30, 2013

10. Statistical Programmer
January 30, 2013

11. Data Integration Developer
January 30, 2013

12. Predictive Modeler
January 30, 2013

13. A Typical Product Developer / Data Scientist Role
Job Details
Facebook is seeking a Data Scientist to join our Data Science team. Individuals in this role are expected to be comfortable working as a software engineer and a quantitative researcher. The ideal candidate will have a keen interest in the study of an online social network, and a passion for identifying and answering questions that help us build the best products.
Responsibilities
Work closely with a product engineering team to identify and answer important product questions
Answer product questions by using appropriate statistical techniques on available data
Communicate findings to product managers and engineers
Drive the collection of new data and the refinement of existing data sources
Analyze and interpret the results of product experiments
Develop best practices for instrumentation and experimentation and communicate those to product engineering teams
Requirements
M.S. or Ph.D. in a relevant technical field, or 4+ years experience in a relevant role
Extensive experience solving analytical problems using quantitative approaches
Comfort manipulating and analyzing complex, high-volume, high-dimensionality data from varying sources
A strong passion for empirical research and for answering hard questions with data
A flexible analytic approach that allows for results at varying levels of precision
Ability to communicate complex quantitative analysis in a clear, precise, and actionable manner
Fluency with at least one scripting language such as Python or PHP
Familiarity with relational databases and SQL
Expert knowledge of an analysis tool such as R, Matlab, or SAS
Experience working with large data sets, experience working with distributed computing tools a plus (Map/Reduce, Hadoop, Hive, etc.)
January 30, 2013

14. Advanced Training University of Missouri – St. Louis
Northwestern University
University of California – San Diego
University of California – Irvine
North Carolina State University
January 30, 2013

15. Case Study: Realtime Scoring Systems

16. MasterCard, NAC Announce ‘Real World' Plan to Address April 19 Chip Liability Shift
All Maestro transactions will be blocked at ATMs that averaged no more than one Maestro transaction per month in This represents approximately 80 percent of U.S. ATMs.
Fraud Rule Manager will block Maestro transactions at low-activity ATMs and will decline potentially fraudulent transactions at the remainder of ATMs.
A complementary Fraud Control Shield program will be rolled out across Maestro card-issuing FIs in Europe, using similar metrics to identify and decline potential fraudulent ATM transactions on the issuer side.
January 30, 2013

17. US Maestro Cross-Border ATM Fraud is a Localized Problem: FY 2012 Trends
Ranking of US ATM Fraud $ Losses for Maestro XR
Fraud losses per ATM range from zero to thousands
Maestro XR $USD Losses
Only a small number of ATMs experience high Maestro XR fraud losses
US ATM Count
Only 8,242 of the 264,516 US ATMs that processed cross-border Maestro traffic saw any fraud (3.1% of total)
Source: MasterCard Fraud Mart
November 9, 2012

18. Expert Monitoring Solutions: Data Mining in Action
May 13, 2018
Expert Monitoring Solutions: Data Mining in Action
We leverage one infrastructure with layers of technology to route the transaction from the network to the appropriate technology platform for the value-added service.
EMS Fraud Scoring Issuers
EMS Fraud Scoring Merchants
EMS Compromise Accounts
Web Session Threat Index Merchants
Fraud Rule Manager
Transaction Blocking
Prepaid ATM Monitoring
ATM Acquirer Monitoring
Fraud Decisioning Platform
(EMS iPrevent, Silver Tail, and MasterCard technology)
Fraud Rules Engine
(IBM iLog BRMS technology)
Data Analytics
Auth IQ Platform
Intelligent, real-time transaction monitoring interface routes transactions to the appropriate value-added service
Acquirer
Issuer
Network
Access
Point

19. Fraud Rule Manager for ATM: Detection Rate Performance Results
May 13, 2018
Fraud Rule Manager for ATM: Detection Rate Performance Results
Inter-Regional ATM Performance, All Brands
Results for the blind test months of Nov – Dec 2012
MasterCard Model Performance Report, Mar 2013
, TDR=Transaction Detection Rate, VDR=Value Detection Rate
March 5, 2013

20. Fraud Rule Manager for ATM: Financial Performance Results
May 13, 2018
Fraud Rule Manager for ATM: Financial Performance Results
Genuine Blocked Txns vs. Genuine ATM Txns Retained
At the 5:1 TFPR threshold:
72% of the $USD fraud liability is blocked
1.94% of genuine Maestro cross-border transactions are blocked
ROI analysis has shown the fraud loss avoidance far outweighs lost revenue on the small percentage of genuine transactions blocked
MasterCard FRM ATM Model Performance Report, Mar 2013
March 5, 2013

21. MasterCard Fraud Data Mart
May 13, 2018
MasterCard Fraud Data Mart
A set of platforms, software tools, and DW data dedicated to support MasterCard’s Fraud detection and prevent efforts.
Massive
120 TB Netezza Platform
70 TB Used
50 TB Free
Multi-sourced
Daily transactional inputs
Core DW (3+ current-year copy of Authorization/Clearing/ Debit/Chargeback/Retrieval Requests)
Risk (All Fraud/ADC)
EMS (3+ years of Supplemental data)
AMS (Stop List)
Usage
65 Users
Data Analytics, Profiling and Predictive Modeling (SAS)
Production Batch Processing
289 Million LTV Accounts updated weekly
Supports Business Rules (IBM iLog)
Daily / Weekly Batch Feeds to Expert Monitoring Systems
Internal Reporting
Features
Online Q4 2010
3+ year historic view
Unique PAN Proxy/Un-proxy for each vendor.
IBM Pure / Netezza analytics tools
Transaction Life Cycle
Transaction Life Cycle*
(Match Rates)
Clearing to Auth.
- US: 97%/ Non US: 92%
Fraud to Auth.
- US: 97%/ Non US: 80%
Fraud to Pin Debit - 97%.
Fraud to Clearing - 85%
MasterCard’s data advantage is proprietary and fifteen years in the making
Data in and of itself is not usable; what MasterCard does makes it usable and actionable
Insights at the speed of consumer behavior
Let me explain:
First, whenever an electronic payment message is received by a bank or a network, it contains information of uneven quality. Fields may be missing. Names may be confusing—what the consumer knows as Standard Hardware may be called SH1234 in the message. So we apply 700,000 constantly updated and tested automated rules, supplementary data sources, and sometimes human sweat to cleanse the data, supply missing information, and aggregate it into chains no matter what they’re called in the transaction message. We also geo-code it so aggregate it geographically.
Then, we store it, and we’ve been storing it for _______ in a warehouse with more than _________terabytes of data, the ___________ data repository in the world. That gives us an historic view of behavior—which is better than a snapshot view. Masses of data that can’t be retrieved rapidly and accurately is useless. We’ve built a retrieval capability that enables us to retrieve a _______ data set in __________ minutes, which would take most companies _________ to get. When we warehouse, use, and retrieve data, we do it under the strictest privacy and security standards. MasterCard’s commitment to privacy and protection requirements go above and beyond and starts with the fact that we never have personal identifiable information in our warehouse—in fact we never receive it when we get the transaction
Finally, we transform the data into actionable insights in the form of reports, indexes, benchmarks, behavioral variables, models, scores, forecasting, and econometrics that can be sold as such by our information services business or leveraged by our consulting and implementation services to deliver superior insights and results
And because the data is a byproduct of our core business, the MasterCard data advantage can be leveraged across all three Advisors services—information, consulting, and implementation, at a low marginal cost.
The design of the Fraud Data Mart and analytic innovations led to submission of three pending patents and 2012 Technology Lever IT Transformation Award

22. Stream It - Score It - Store It
How Major Financial Institutions Use SAS
SAS Fraud Management - End-To-End Value Chain
Data Management
Extraction and Manipulation of Data
Data Quality
Data preparation, summarization and exploration
Analytics
Modeling
Ad Hoc Query & Reporting
Diagnostic Analytics
Optimization to provide alternative scenarios
Detection
Continuous Monitoring
Alert Generation Process
Real-time Decisioning
Balance between risk and reward
Alert Management
Social Network Investigation
Alert Disposition
Case Management Integration
Case Investigation
Workflow & Doc Management
Intelligent Data Repository
Continuous Analytic Improvement
Dashboards & Reporting
SAS provides an end-to-end technology infrastructure for detecting, preventing and managing financial crimes across various business lines. This framework includes components for detection, alert and case management, along with category-specific workflow, content management and advanced analytics.
The long-term goal of is to establish a framework for enterprise-wide
deployment of resources, including both material and human assets. This
framework should make it possible to:
• Gather and cross-match relevant data from all product lines, organizational
units and geographic regions of your organization.
• Analyze this data to “connect the dots” and spot large-scale fraud attacks early in
their life cycle.
• Plan and execute focused countermeasures to combat large-scale attacks.
There are two key business drivers that are causing organizations to give serious attention to an enterprise-wide strategy. These are:
• Increased effectiveness. The ability to look at the issues holistically across the
enterprise and identify large-scale threats early in their development,
and mount effective countermeasures while there is still time for them to have maximum impact.
• Increased efficiency. The ability to leverage investments in data, tools and
personnel in an economic environment where every organization and function is being asked to “do more with less.”
Advanced analytics, configurability, data management and reporting/dashboards are its key differentiators.
Stream It - Score It - Store It

23. Gartner Magic Quadrant for Business Intelligence Platforms
BI Platform Decision Makers:
IT — 38.9%
Business user — 20.8%
Blended business and IT responsibilities — 40.3%
January 30, 2013

24. Q&A
Your questions?
January 30, 2013