Transcript analytics - SAS Support | SAS

SAS Global Forum 2009
Marty Ellingsworth (iiA)
The views expressed by the presenter does not necessarily represent
the views, positions, or opinions of ISO.
1
Overview
• Analytic Environment
• About ISO
• Analytics Framework
– Ecosystem
– Innovation process
– Data opportunities
– Sample Problem
• What’s next – Good to Great
2
Business Environment
Why things are becoming so data driven.
The Market
 Electronic connectivity is expected
 Touch point knowledge is anticipated
 Personalized service is assumed
 Ease of doing business is desired
 Low tolerance for not learning
Each Company
 Define, attract, retain, and grow “good” customers
 Match offering to customer
 Improve ‘customer facing processes’
 Reduce expenses while building skills
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General Organizational Overview
An information business focused on risk taking.
Make. Sell. Serve.
Sales and Distribution
Underwriting
Risk Selection and Pricing
Portfolio Management
Premium Adequacy
Billing and Collections Management
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Producer Segmentation
Market Planning
Revenue Forecasting
Cross sell and Up sell
Retention and Profitability
Claims
Payment Accuracy
Claim Collaboration
> Fraud Detection
> Subrogation
> Risk Transfer
> 3rd Party Deductible
> Reinsurance Recoverable
Analytic Value Effort Framework
Reporting = “Having the data”
Timeliness and accuracy
Reports and Tables
Surfacing data with agility
Descriptive Analyses = “Seeing the data”
Scorecards / Measurements
Profiles and Exceptions
Segmentation
Analytic Modeling = “Knowing the data”
Understand Trends
Evaluate Business Practices
Choice Models and “What ifs”
Predictive Analytics = “Acting on the data”
Informed decision-making
Actionable Information Engines
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ISO’s Strategy
Better
Analytics
Better
Data
Best
Customer
Decisions
Better
Decision
Support
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property/casualty insurance
mortgage lending
healthcare
government, and
human resources.
ISO Family Of Companies
Domus
Systems
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Strategic Space (2008+)
Assets
Data
Risk
Hazards
Losses
Analytics &
Decision
Support
DATA
LOSS PREDICTION
RISK SELECTION & PRICING
FRAUD DETECTION & PREVENTION
LOSS QUANTIFICATION
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Next?
Government
Mortgage
Lending
P&C
Insurance
Healthcare
Enterprise
Risk Mgmt
Employment
Decisions
COMPLIANCE & REPORTING
World-Class Staff
We have more than 400 individuals with advanced degrees,
certifications, and professional designations in such fields as:
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Actuarial science
Data management
Mathematics
Statistical modeling and
predictive analytics
• Operations Research
• Economics
• Chemical, environmental,
electrical, and other
engineering disciplines
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Healthcare
Soil mechanics
Geology
Remote sensing
Meteorology
Atmospheric and climate
science
• Oceanography
• Applied physics
• Many other disciplines
ISO Family Of Companies
Domus
Systems
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Emerging Value in the Enterprise
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What way can we create value together?
What are we already doing?
What’s working / not working?
Some ideas on next steps
The iiA Role
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Critical Success Factors
• Technical Expertise
– in Statistical Modeling, Data Mining, and Data
Management
• Intimate Market Awareness
• Strong Coordination
– with other company units
– Underwriting, Loss Control, Claims, Sales/Agents
• Senior Executive Commitment and Support
• Access to Data
• Project selection and execution
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Golden Rule of Analysis
Your product is not computers,
application software systems,
user interfaces or database connections
Your product is reliable information that
helps answer compelling business questions.
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Predictive Modeling Projects you should do
Loss Control
Cost Avoidance
Fraud Prevention
Property Inspections
Assess Work sites
Re-underwriting
Automate Manual Work
Appetite Qualification
Underwriting Guides
Redundant Processes
Vendor Sourcing
Spend Analysis
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Cash-flow
Opportunity
Better Decision
Making
Subrogation
Credit to Loss
Third Party Deductible
Premium Audit (Comm)
Account Identification
Audit Ordering
Insured to Value (PI)
Risk Selection
Renewal (Attrition)
New (Acquisition)
Cross-sell & Up-sell
Portfolio Management
Broker/Agent Profiles
Medical Management
Litigation Management
Large Loss Reserving
Improved Collaboration
Roles in the analytic process
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Predictive Modeling Staff Portfolio Challenge
Predictive Model Development Group
Identified Concerns
• Limited Resources
–
–
People – need to train
Recruiting/retaining
–
Decision on whether and/or how to audit
–
Need to show value of audit process ROI
• Limited Time
• Limited Funds
• More work than people
• Pressures
–
Time, turnaround, goal attainment
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More price competition
Less U/W accuracy
More “oops” moments reveal themselves
• Identify "best bang for buck"
• Measure of Project’s value/success
• Market getting softer (turning)
Key need is to efficiently allocate scarce resources to
optimize your efforts across the Insurance Value Chain
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innovation
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7 SOURCES OF INNOVATION IMPULSES
(Drucker)
INTERNAL
1.
2.
3.
4.
unexpected event
contradiction
change of work process
change in the structure of industry or market
EXTERNAL
5. Demographic changes
6. Changes in the world view
7. New knowledge
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# 7. New knowledge
• Based on convergence or synergy of various
kinds of knowledge, their success requires,
high rate of risk
– Thorough analysis of all factors. identify the “missing
elements” of the chain and possibilities of their
supplementing or substitution;
– Focus on winning the strategic position at the market.
the second chance usually does not come;
– Entrepreneurial management style. Quality is not what is
technically perfect but what adds the product its value for
the end user
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What’s in ‘analysis’?
•Information Theory
•Database Management
•Visualization
•High Performance
Computers
ANALYTICS
•Applied Statistics
•Algorithms
•Machine Learning
•New Techniques
•More/Better Data
•FEEDBACK
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Why text works – academic origins…
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Improve the Quality of Knowledge
Transform Knowledge Up the Value Taxonomy
Capability
Expertise
Knowledge
Information
Data
Sensory
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Types of Capabilities
Actuarial
Statistical analysis
Visualization
Geospatial
Text mining
New Data
Better Data
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The Role of Synergy
• Synergy means that the whole is more
•
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than the sum of the parts.
Synergy leads to:
1. Increased customer and shareholder value
2. Strategic focus in the management process
3. Efficient operating costs
4. Savvy investment through collaboration
5. Serendipitous Opportunities
Expect the Unexpected
Creating Successful Innovations
Results:
Success to Failure Rates
– Trend Following
1:3
– Need Spotting
2:1
– Market Research
4:1
– Solution Search
7:1
– Serendipity
13:1
Serendipity => Taking advantage of unplanned opportunity
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Source: Expect the Unexpected,
The Economist Technology Quarterly, September 2003
Types of Data and the Data Opportunity
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Structured data
Semi-structured data
Unstructured data
Text
Pictographic
Graphics
Multimedia
Voice
Video
Geospatial
Multi-Spectral
Climatologic
Atmospheric
What to learn from Structured Data
Significant pre-processing of raw data is needed
to create useful informational features.
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Repeatable Patterns
Trends, Seasons, Cycle
Propensities, Likelihood
Causation and Interaction
Ratios between Dollars and Distances
Stakeholder Behavior
Unlikely Occurrences
Proximity of stakeholders
Ownership interests of stakeholders
Data Fusion and Learning is the key
to successful Data Mining
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Deriving Data = Power
Depending on the target variable, there are many factors that may be
relevant for modeling.
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Totals: Household Income
Trends: Rate of Medical Bill Increases
Ratios: Claims/Premium, Target/Median
Friction: Level of inconvenience, ratio of rental to damage
Sequences: Lawyer-Doctor, Auto-Life Policy
Circumstances: Minimal Impact Severe Trauma
Temporal: Loss shortly after adding collision
Spatial: Distance to Service, proximity of stakeholders
Logged: Progress Notes, Diaries,
• Who did it, When, “Why”
Deriving Data = Power (Cont’d)
Depending on the target variable, there are many factors that may be
relevant for modeling.
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Behavioral: Deviation from past usage, spike buying
Experience Profiles: Vendor, Doctor, Premium Audit
Channel: How applied, How reported, Service Chain
Legal Jurisdiction: Venue Disposition, Rules
Demographics: Working, Weekly wage, lost income
Firmographics: Industry Class Code Vs Injuries Claimed
Inflation: Wage, Medical, Goods, Auto, COLA
Gov’t Statistics: Crime Rate, Employment, Traffic
Other Stats: Rents, Occupancy, Zoning, Mgd Care
Extraction Engines
Identify and type language features
Examples:
People names
Company names
Geographic location names
Dates
Monetary amount
Phone numbers
Others… (domain specific)
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Building Chronologies can be very useful
Process flow and cash flow are traceable.
Date of First Report
of Injury:
Employer Insurer
Date of 1st
Payment
Date of
Return to Work
Date Claim
Re-Open
Date of
Injury
Date of 1st
Treatment
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Date Accepted Date of MMI
or Denied
or P & S
Date Claim
Closed
Date Claim
Re-Closed
Roll up and roll down the data for the
proper level of analysis.
Claim System
Claim File
$x,xxx.xx
Payments
Medical Payments
Indemnity Payments
Expense Payments
Reserves
Bill Review Vendor
Medical Bill Review Systems
Bill Record
Bill Line Item Detail
Reduction Reasons
Charged versus Paid
• Bill Review Rule
• Fee Schedule
• U&C Repricing
• PPO Discount
• Other Savings
Bill Review Rule Reasons
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See for yourself ---The importance and relevance of text
Accident: 170824130 - Employee Injured In Fall From
Second-Floor Decking
Inspection
Open Date
SIC
Establishment Name
127366367
07/29/1996
1521
xxxxxxxxxxxxxxxxxxxxxxxx
Employee
of themeans
second
deckingin
ofuse.
a newly
not
tied off,#1
norwas
wereatop
any other
of floor
fall protection
constructed home, connecting frame work for a wall. He fell 18
ft 6had
in.,not
sustaining
injuries
that required
hospitalization.
He
been trained
in working
from elevated
work surface
Employee #1 was not tied off, nor were any other means of fall
protection
use.
had
not been
trained
in working
from an
the
companyindid
notHe
have
a written
safety
program,
and
elevated work surface, the company did not have a written
safety program,
regular
inspections were not performed.
regular
inspectionsand
were
not performed.
Keywords:
decking, fall, tie-off, untrained, work rules, fall
protection, construction
Inspection
1 12736636
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Age
Sex
29
M
Degree
Nature
Occupation
Hospitalize
d injuries
Cut/Lacerati
on
Carpenters
Source: U.S. Department of Labor Occupational Safety & Health Administration
Accident
Report Detail Accident Investigation Summaries (OSHA-170 form) which result from OSHA accident inspections
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GeoSpatial layers
Location Analyst taps into ISO GIS Repository:
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TeleAtlas Dynamap 2000 Files (includes a Roadbase,
Landmarks, Water bodies, etc.)
Zip Code Boundaries
State/County/Municipal Boundaries
Census boundaries: Track > Block Group > Block
Aerial Imagery – DigitalGlobe/GlobeXplorer
All LOCATION GIS Layers
FireLine and historical wildfire burn perimeters
ISO statistical data and related analytics (ZIP-level)
CAP Index Crime Information
USGS Topography
US Census Demographics
Government promulgated natural catastrophe and historical
weather layers
Coastlines
US Labor Statistics
Custom datasets (e.g., customer portfolios/individual risks)
County Tax Assessor data, for 75M homes
Flood Information Mapping
Current weather conditions/current wildfire activity feeds
What can help?
Integration of data with other frauds
Bridging to new data sources
Smarter transformation of data
Text Mining – expose information
GIS Platform – geospatial elements
Graph mining – highlight social networks
Grid computing – diagonal scaling
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P&C Personal Lines Situation
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Market Demand - Opportunity
• Top carriers control large markets
– E.g., Personal Auto – Top 25 carriers hold over 80% of
market (over $120B of a total market >$160B)
– Strong motivation to –
• “Protect” market share
• Grow against stiff odds
• Predictive analytics has gained senior
leadership attention as a mechanism to –
– Execute risk-based pricing and segmentation
– Create competitive/strategic differentiation
– Generate operational efficiencies
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Indication of Increased Competition
Number of Companies writing Personal Auto Insurance in the US
600
500
400
300
1/3 of companies gone
in 12 years
200
100
0
1980
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1985
1990
1995
2000
2005
Indication of Increased Competition
Consolidation of Auto Insurance Markets
100%
Market Share
90%
Top Carrier
Group
80%
Top 10
Top 25
Top 50
70%
60%
50%
1995
40
2000
2005
2007
Below 50 now
has only 9%
for remaining
280 groups
How Analytics Fuel Competition
My Book of Business
(Actual Cost per Policy)
My Rate
(Average)
Total
Revenue
$600
$800
$1000
$800
$2400
$600
$800
$1000
$900
$1800
$600
$800
$1000
$1000
$1000
If your competitor has advanced analytics,
your book and your profitability are vulnerable
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Predictive Analytics for the
Community Environment
The Environment
is the Exposure
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In Depth for Auto Weather
Component
Environmental
Model Loss Cost
by Coverage
Coverage
Frequency
×
Severity
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Frequency
Severity
Causes of Loss
Frequency
Traffic
Generators
Traffic
Composition
Weather
Traffic
Density
Experience
and Trend
Sub Model
Neural Net
Weather
RBF
Weather
Temperature
Scale
Clusters
& Other
Summaries
Weather
Precipitation
Scale
Neural Net
Weather
MLP
Data Summary
Variable
Weather
Summary
Variables
Raw Data
35 Years of
Weather Data
Combining Environmental Variables
at a Particular Garage Address
• Individually, the geographic variables
have a predictable effect on accident rate
and severity.
• Variables for a particular location could
have a combination of positive and
negative effects.
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Techniques Employed in Variable
Reduction
• EDA (Exploratory Data Analysis) –
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univariate analysis, transformations, known
relationships
Statistical Techniques – greedy
selection, machine learning techniques
Sampling – cross validation, bootstrap
Sub models/data reduction – neural
nets, splines, principal component analysis,
variable clustering
Spatial Smoothing – At various
distances and/or with parameters related to
auto insurance loss patterns
Breakthroughs in Personal Auto Analytics
Factors Affecting Auto Loss Experience
• Weather:
– Measures of snowfall,
• Traffic Generators:
– Transportation hubs
rainfall, temperature
– Shopping centers
• Traffic Density and Driving
– Hospitals/medical centers
Patterns:
– Entertainment districts
– Commute patterns
• Experience and trend:
– Public transportation
– ISO loss cost
usage
– State frequency and
• Traffic Composition:
severity trends from ISO
lost cost analysis
– Size of vehicles
– Age and cost of vehicles
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ISO Risk Analyzer ®
Personal Auto Framework
ISO Risk Analyzer
Input
Rating Plan
State
Environmental Risk
Module:
Weather, Street, Businesses,
Traffic Density, Driving Patterns
etc
Address
Vehicle Age
& Symbol
Vehicle Risk Module:
VIN
Class
Refined Points Module
Territory
Weight, Engine Size, etc.
Credit Module (optional)
Limits &
Deductibles
No Change
Special
Adjustments
No Change
Policy Risk Module
Interactions of all indicators
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State
Personal
Identifiers
Address,
Drivers,
Vehicles
What has the impact been?
• Major innovations in an historically static
rate plan
• Increased competition
• Profitable growth for adopters of
advanced analytics
• Hunger for the next innovation
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Good to Great
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What was Not Working
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Infrastructure impacting work productivity
Constant appetite for more “computing” capacity
Limited ability to process large datasets
Need to build core capabilities –
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Data access
Leveraging multiple modeling methodologies
Geo-spatial analysis
Managing and maintaining multiple versions of models
Text analytics (e.g. cause of loss and entity extraction)
Identity resolution
ISO Search and Retrieve information
• Remote team collaboration is cumbersome
• Critical KSA’s sometimes ‘outside’
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Next Generation iiA Systems
Analytics Platform
– Hardware
• Exploring a single large analytics server or a grid solution that
ties together many commodity processors
• either solution will be a true client/server analytics
– Software – SAS Enterprise Miner
• Industry standard predictive analytics software suite
• Will increase analyst productivity as well as the quality of the
final models and documentation
• Analytics Data Store
– Goal: Professional management of the data used by iiA for model
development and production model scoring
– Characteristics
• Professional
• Scalable
• Well-documented
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Highlights of the Proposed Solution
• GRID computing infrastructure
– Allows “diagonal” scalability
• Add higher-capacity machines to grid to support future growth
• Protects and increases “life-span” of investment in hardware
– Holy grail of scalable, adaptive, on-demand computing
• SAS EnterpriseMiner
– Full-function, grid-enabled data mining platform
• Extensive suite of data processing and modeling methodologies
– One of two top Analytics products in the market
– Industry-tested stability and reliability – wide usage
• SAS JMP Visual BI
– Powerful visualization and visual data exploration software
• SAS Model Manager
– Seamless management of models – assessing new models,
archiving old models, and deploying/using current models in
production
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Highlights of the Proposed Solution
• Benefits of choosing SAS
– ISO is a long-standing SAS customer (since 1982)
• Can leverage loyalty discounts
• Known vendor with proven value to ISO
• Additional discounts obtained in other SAS licenses (e.g.,
Mainframe)
– SAS is the most common platform in the industry
• Easier to find candidates with SAS/Eminer knowledge and
experience
– SAS offers comprehensive training (compared to other
competitors)
• Easier to keep staff on the cutting-edge of new modeling
methodologies and business applications
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Grid Processing Improves Speed &
Capacity
Increasing Number of Users & Jobs
Increasing Job Size
Optimize the Efficiency and Utilization of Computing Resources
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SAS Enterprise Miner – Parallelized Workload
Balancing
Parallel Processing Reduces Time to Results
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Key Benefits of Infrastructure Investment
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Stable, high-availability platform
Increased bandwidth for simultaneous users
One platform offering multiple tools/methods
Build models quicker and fail faster for better models
Visualization capabilities will significantly reduce data
exploration timelines
• Model assessment and comparison capabilities built-in
– no separate coding necessary
• Significant risk mitigation in model maintenance and
archiving
• Data warehousing capability will shorten the cycle on
re-use of data in other initiatives
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Summary
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Centralized, shared environment
Dynamic resource allocation to meet peak demand
Policies and prioritization for use of resources
Run large more complex analysis
De-couple applications from infrastructure
Ease maintenance of computing infrastructure
Improve price/performance with commodity hardware
Scale out cost effectively as needs grow
CONCLUSION
Why things are becoming so data driven.
Why are we really here…
More data-savvy Executives
Why will we be back here next year…
Ever improving analytic solutions
Industry, Third party, and Government Data
Structured, Unstructured, and Location Data
Faster, Cheaper, Better – Processors,
Storage, & Tools
Growing Skill Sets of Staff and Vendors
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Marty Ellingsworth
[email protected]
The views expressed by the presenter does not necessarily represent
the views, positions, or opinions of ISO.
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