business intelligence & analytics
Post on 12-Feb-2016
111 Views
Preview:
DESCRIPTION
TRANSCRIPT
©2013 MasterCard.Proprietary and Confidential
Career Paths and Industry Trends
Business Intelligence & Analytics
April 22, 2013Susan Meyer, MasterCard Network Products
©2013 MasterCard.Proprietary and Confidential
• Introduction • The BI Job Market • Analytical Processes • Data Science Roles• Analytics Products & Services• Analytical Platforms
Agenda
Page 2January 30, 2013
©2013 MasterCard.Proprietary and Confidential
Data Crunchers In Demand
Page 3January 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.
©2013 MasterCard.Proprietary and Confidential
• 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
Page 4January 30, 2013
©2013 MasterCard.Proprietary and Confidential
http://www.thearling.com/
A Brief Overview of Data Mining
Page 5January 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
©2013 MasterCard.Proprietary and Confidential
Descriptive• Dashboards• Process mining• Text mining• Business performance
management• Benchmarking
Business Intelligence & Data Mining Services
Page 6January 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
©2013 MasterCard.Proprietary and Confidential
CRISP-DM: Data Mining Methodology is Highly Iterative
Page 7January 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...
©2013 MasterCard.Proprietary and Confidential
Data Scientists Work in Teams
Page 8January 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
©2013 MasterCard.Proprietary and Confidential
Statistical Business Analyst
Page 9January 30, 2013
©2013 MasterCard.Proprietary and Confidential
Statistical Programmer
Page 10January 30, 2013
©2013 MasterCard.Proprietary and Confidential
Data Integration Developer
Page 11January 30, 2013
©2013 MasterCard.Proprietary and Confidential
Predictive Modeler
Page 12January 30, 2013
©2013 MasterCard.Proprietary and Confidential
A Typical Product Developer / Data Scientist Role
Page 13January 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.)
©2013 MasterCard.Proprietary and Confidential
• University of Missouri – St. Louis • Northwestern University • University of California – San Diego • University of California – Irvine • North Carolina State University
Advanced Training
Page 14January 30, 2013
Case Study: Realtime Scoring Systems
©2013 MasterCard.Proprietary and Confidential
MasterCard, NAC Announce ‘Real World' Plan to Address April 19 Chip Liability Shift
Page 16January 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.
©2013 MasterCard.Proprietary and Confidential
US Maestro Cross-Border ATM Fraud is a Localized Problem: FY 2012 Trends
Page 17November 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
©2013 MasterCard.Proprietary and Confidential
Expert Monitoring Solutions: Data Mining in Action
Page 18
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
©2013 MasterCard.Proprietary and Confidential
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
Page 19March 5, 2013
Results for the blind test months of Nov – Dec 2012
©2013 MasterCard.Proprietary and Confidential
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
Page 20March 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
©2013 MasterCard.Proprietary and Confidential
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%
Page 21
The design of the Fraud Data Mart and analytic innovations led to submission of three pending patents and 2012 Technology Lever IT Transformation Award
©2013 MasterCard.Proprietary and Confidential
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
©2013 MasterCard.Proprietary and Confidential
Gartner Magic Quadrant for Business Intelligence Platforms
Page 23January 30, 2013
• IT — 38.9%
• Business user — 20.8%
• Blended business and IT responsibilities — 40.3%
BI Platform Decision Makers:
©2013 MasterCard.Proprietary and Confidential
• Your questions?
Q&A
Page 24January 30, 2013
top related