1.
SAS FOR CLAIMS FRAUD
MORE INFORMATION
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2.
GLOBAL INSURANCE CLAIMS FRAUD
• US Insurance Information Institute estimate $30 billion losses annually; about 10% incurred losses and loss
adjustment expenses
• FBI estimate costs $40+ billion per annum; costing between $400 and $700 in extra premiums
• Insurance Council of Australia estimates that between 10 and 15% of insurance claims across of lines exhibit
elements of fraud
• Swedish Association estimate that 5 to 10% of claims include fraud
• ALFA estimate that fraud 15% of claims paid, or 4-8% of premiums collected equating to €2.5bn per annum
• ABI estimates that undetected fraud = £2.1bn adding about £50 to average premium
• South Africa Insurance Crime Bureau estimate that 30% of short term insurance claims include fraud
• Swiss Insurance Association estimate that 10% of claims paid are fraudulent
• German Insurance Association estimates that fraud costs circa €4bn per annum
C op yr i g h t © 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .

3.
THE SHIFTING LANDSCAPE OF INSURANCE FRAUD
Insurance fraud is on the rise & today’s schemes are:
• Increasingly sophisticated
• More agile
• Higher velocity
• Cross industry
• Influenced by regulatory & political climate
Yesterday’s methods are insufficient
to address today’s fraud risk!
C op yr i g h t © 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .

4.
CHALLENGES TO
DEALING WITH DATA
FRAUD DETECTION
A good fraud detection solution must:
 Integrate data from multiple disparate sources like claims,
underwriting, human resources, billing/payment systems and 3rd party
sources
 Match identities across all data sets
 Address data quality issues like misspellings, input errors, typos,
missing data, acronyms, shorthand and jargon
 Leverage unstructured data text data sources like claims notes and
service logs
 Provide transparency & adaptability to quickly respond to changing
fraud threats
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5.
CHALLENGES TO
TRANSPARENCY
FRAUD DETECTION
Push Pull
vs.
 Reliance on  Advanced
rules / red flags detection methods
 Inconsistent  Consistent
 First-come,  Optimal
first-served prioritization
C op yr i g h t © 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .

6.
CHALLENGES TO
LEGACY SIU PROCESS
FRAUD DETECTION
 Multi-claims organized frauds may be difficult for individual adjusters to
identify
 Organizational structures may be inadequate
 Relationships are increasingly important…and complex
 Business rules are marginally effective
 Supervised predictive models can be biased toward single-claim fraud
detection
 Distinction between fraud vs. abuse
C op yr i g h t © 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .

7.
BUSINESS ANALYTICS AND FRAUD DETECTION
 Allows insurers to identify ‘suspicious cases’
 Works underneath the insurers existing processes
 Does not replace expertise of claims team members but ensures cases
are not missed
 Allows insurers to detect fraud by multi-dimensions
 Case-by-case
 Repeat
 Organised rings
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8.
FRAMEWORK-BASED
END-TO-END SOLUTION
APPROACH
Data Detection Reporting Administration
• Structured & • Business Rules • Advanced Ranking • Self administered
Unstructured Data • Anomaly Detection Technology • Custom alert queues
Sources • Easy to use web
• Advanced • Alert suppression &
• Batch or real time Predictive Models based interface routing rules
processing • Advanced Query
• Watch Lists • Workflow analysis
• Data Cleansing of integrated data
• Social Network • Direct integration
• Data Integration Analysis • Full business with Case
• Variable Extraction intelligence Management
• Network-level reporting capability
& Sentiment analytics
Analysis with Text • Claim system
Mining • Hybrid Technology integration
C op yr i g h t © 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .

9.
SAS FRAUD
FRAMEWORK FOR PROCESS FLOW
INSURANCE
Operational Exploratory
Data Sources Data Analysis &
Alert Generation Process
Transformation Business Alert SAS® Social
Fraud Rules Administration Network
Data Analysis
Staging
Network
Providers Rules
Analytics
Anomaly Network
Detection Analytics
Members
Predictive
Modeling
Facilities
Alert Management &
BI / Reporting
Intelligent Learn and
Fraud Repository Improve
Claims Cycle Case Management
C op yr i g h t © 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .

10.
FRAUD ANALYTICS USING A HYBRID APPROACH FOR FRAUD DETECTION
Text
Mining
Database
Predictive Searches
Modeling
Anomaly
Detection
Automated Analytic
Business Rules Decisioning
Engine
Social
Network
Analysis
LEVERAGING SAS HYBRID APPROACH TO SCORE TRANSACTIONS,
ENTITIES, AND NETWORKS ACROSS MULTIPLE ORGANIZATIONS
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11.
WHY SAS? MAKING LIFE EASIER
Establish Query Rank & Combine & Analysis Decision Final
Search Various Prioritize Synthesize of to Analysis &
Parameters Systems Results Information Findings Proceed? Summary
Framework-Based Predictive Analytics Analytical Value-Add
“What used to take me most of a day, now takes 10 minutes.”
“It completely streamlines where we need to go.”
-SIU Analyst
C op yr i g h t © 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .

12.
CUSTOMER STORY CNA (US)
Business Problem
• Detect and prevent fraud in four separate commercial
lines of business
Customer Quote • Optimally direct its investigation resources on cases with
higher likelihood of fraud
We have an excellent
partnership with SAS.
They took the time to Solution
meet with us and truly
understand the nuances • SAS Fraud Framework for Insurance
of CNA so that we could
build effective predictive
models for each line of
our business Results
• $2.1m in fraud recovery / prevention within the first 9
Tim Wolfe, SIU Director months of implementation
• Detection and investigation of 15 potentially fraudulent
provider networks – four times what CNA anticipated
C op yr i g h t © 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .

13.
WHY SAS?
 More suspicious cases identified
• Including both previously undetected fraudulent networks and extensions to already identified
fraud
“We discovered that 5% of its claims pay-outs were fraudulent, and these can now be
corrected and prevented in the future."
Assistant General Manager, Market Leader, Southern Europe
 Reduction in false positive rates
• Significant improvement in ‘quality’ of suspicious cases past for investigation
“84% of the claims flagged as possibly fraudulent, turned out to be fraud. A 69 % uplift in
suspicious claim detection compared with the old system.."
SIU Manager, Major Tier 1 USA Insurer
 Improved investigation efficiency
• Each referral taking 1/2 – 1/3 the time to investigate using SAS’ link analysis visualization
“What used to take me most of a day, now takes 10 minutes.’’
SIU Manager, Major Tier 1 USA Insurer
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14.
MORE
INFORMATION
• Contact information:
Stuart Rose, SAS Global Insurance Marketing Director
e-mail: Stuart.rose@sas.com
Blog: Analytic Insurer
Twitter: @stuartdrose
• White Papers:
Combatting Insurance Claims Fraud
Insurance Fraud Race
• Research:
State of Insurance Fraud Technology
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15.
THANK YOU
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