business intelligence & analytics

©2013 MasterCard.Proprietary and Confidential

Career Paths and Industry Trends

Business Intelligence & Analytics

April 22, 2013Susan Meyer, MasterCard Network Products


• Introduction • The BI Job Market • Analytical Processes • Data Science Roles• Analytics Products & Services• Analytical Platforms

Agenda

January 30, 2013


Data Crunchers In Demand

January 30, 2013

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.

http://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century/ar/1


• 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.

Data Mining Job Prospects

January 30, 2013


http://www.thearling.com/

A Brief Overview of Data Mining

January 30, 2013

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




Descriptive• Dashboards• Process mining• Text mining• Business performance

management• Benchmarking

Business Intelligence & Data Mining Services

January 30, 2013

Predictive• Predictive analytics • Prescriptive analytics• Realtime scoring• Online analytical

processing• Ranking algorithms• Authentication

These functions are highly inter-related and fall on a continuum


CRISP-DM: Data Mining Methodology is Highly Iterative

January 30, 2013

• 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...


Data Scientists Work in Teams

January 30, 2013

http://www.datasciencecentral.com/

Job Categories• Business Analyst • Data Analyst • Data Engineer • Data Scientist• Marketing • Sales • Statistician

Many major organizations in St. Louis are actively using data mining in their core line of business

http://www.datasciencecentral.com/

http://careers.analytictalent.com/jobs/browse/category/business

http://careers.analytictalent.com/jobs/browse/category/data_management

http://careers.analytictalent.com/jobs/browse/category/software_engineering

http://careers.analytictalent.com/jobs/browse/category/math

http://careers.analytictalent.com/jobs/browse/category/marketing_mgr

http://careers.analytictalent.com/jobs/browse/category/sales_business_development

http://careers.analytictalent.com/jobs/browse/category/statistics


Statistical Business Analyst

January 30, 2013


Statistical Programmer

January 30, 2013


Data Integration Developer

January 30, 2013


Predictive Modeler

January 30, 2013


A Typical Product Developer / Data Scientist Role

January 30, 2013

Job DetailsFacebook 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.

ResponsibilitiesWork closely with a product engineering team to identify and answer important product questionsAnswer product questions by using appropriate statistical techniques on available dataCommunicate findings to product managers and engineersDrive the collection of new data and the refinement of existing data sourcesAnalyze and interpret the results of product experimentsDevelop best practices for instrumentation and experimentation and communicate those to product engineering teams

RequirementsM.S. or Ph.D. in a relevant technical field, or 4+ years experience in a relevant roleExtensive experience solving analytical problems using quantitative approachesComfort manipulating and analyzing complex, high-volume, high-dimensionality data from varying sourcesA strong passion for empirical research and for answering hard questions with dataA flexible analytic approach that allows for results at varying levels of precisionAbility to communicate complex quantitative analysis in a clear, precise, and actionable mannerFluency with at least one scripting language such as Python or PHPFamiliarity with relational databases and SQLExpert knowledge of an analysis tool such as R, Matlab, or SASExperience working with large data sets, experience working with distributed computing tools a plus (Map/Reduce, Hadoop, Hive, etc.)


• University of Missouri – St. Louis • Northwestern University • University of California – San Diego • University of California – Irvine • North Carolina State University

Advanced Training

January 30, 2013

Case Study: Realtime Scoring Systems


MasterCard, NAC Announce ‘Real World' Plan to Address April 19 Chip Liability Shift

January 30, 2013

• All Maestro transactions will be blocked at ATMs that averaged no more than one Maestro transaction per month in 2012. 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.


US Maestro Cross-Border ATM Fraud is a Localized Problem: FY 2012 Trends

November 9, 2012

8 671 13341997266033233986464953125975663873017964 $-

$10,000

$20,000

$30,000

$40,000

$50,000

$60,000

$70,000

$80,000 • Fraud losses per ATM range from zero to thousands

Source: MasterCard Fraud Mart

Ranking of US ATM Fraud $ Losses for Maestro XR

96.9%

3.1%

$0 Fraud >$0 Fraud• Only 8,242 of the 264,516 US ATMs that

processed cross-border Maestro traffic saw any fraud (3.1% of total)

US ATM Count

Mae

stro

XR

$U

SD

Los

ses

Only a small number of ATMs experience high Maestro XR fraud losses


Expert Monitoring Solutions: Data Mining in Action

Acquirer Issuer

Network Access

Point

Intelligent, real-time transaction monitoring interface routes transactions to the appropriate value-added service

Fraud Rule Manager

Data Analytics

Fraud Decisioning Platform(EMS iPrevent, Silver Tail, and MasterCard technology)

Fraud Rules Engine(IBM iLog BRMS technology)

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.

Transaction Blocking

Prepaid ATM Monitoring

ATM Acquirer Monitoring

EMS Fraud Scoring Issuers

EMS Fraud Scoring

Merchants

EMS Compromise

Accounts

Web Session Threat Index Merchants

Auth IQ Platform


Fraud Rule Manager for ATM: Detection Rate Performance Results

, TDR=Transaction Detection Rate, VDR=Value Detection Rate

MasterCard Model Performance Report, Mar 2013

Inter-Regional ATM Performance, All Brands

3:1 TFPR 5:1 TFPR

64.54%73.19%

62.48%72.01%

TDR VDR

March 5, 2013

Results for the blind test months of Nov – Dec 2012


Fraud Rule Manager for ATM: Financial Performance Results

At the 5:1 TFPR threshold:

• 72% of the $USD fraud liability is blocked

• 1.94% of genuine Maestro cross-border transactions are blocked

Genuine Blocked Txns vs. Genuine ATM Txns Retained

MasterCard FRM ATM Model Performance Report, Mar 2013

March 5, 2013

98%

2%

Genuine Count RetainedGenuine Count Blocked

ROI analysis has shown the fraud loss avoidance far outweighs lost revenue on the small percentage of genuine transactions blocked


MasterCard Fraud Data Mart

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

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)

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%

The design of the Fraud Data Mart and analytic innovations led to submission of three pending patents and 2012 Technology Lever IT Transformation Award


Analytics

• Modeling• Ad Hoc Query &

Reporting• Diagnostic

Analytics• Optimization to

provide alternative scenarios

Data Management

• Extraction and Manipulation of Data

• Data Quality• Data preparation,

summarization and exploration

Detection

• Continuous Monitoring

• Alert Generation Process

• Real-time Decisioning

• Balance between risk and reward

Alert Management

• Social Network Investigation

• Alert Disposition• Case

Management Integration

STREAM IT - SCORE IT - STORE IT

Case Investigation

• Workflow & Doc Management

• Intelligent Data Repository

• Continuous Analytic Improvement

• Dashboards & Reporting

How Major Financial Institutions Use SAS

SAS Fraud Management - End-To-End Value Chain


Gartner Magic Quadrant for Business Intelligence Platforms

January 30, 2013

• IT — 38.9%

• Business user — 20.8%

• Blended business and IT responsibilities — 40.3%

BI Platform Decision Makers:


• Your questions?

Q&A

January 30, 2013

business intelligence & analytics

Documents

data preparation

data mining methodology

data mining job prospectspage

confidential2013 mastercard

continuum2013 mastercard

major data mining project

realworld data ftp

big data2013 mastercard