The document outlines a comprehensive fraud framework developed by SAS for the insurance industry, addressing the rising sophistication and complexity of insurance fraud schemes that result in billions of dollars in losses annually. It details various analytical techniques and approaches for fraud detection, including predictive modeling, anomaly detection, and social network analysis, aimed at improving the efficiency and accuracy of fraud investigations. The framework also emphasizes the importance of leveraging advanced analytics to enhance detection rates and reduce false positives in insurance claims.

1. SAS FRAUD FRAMEWORK FOR INSURANCE
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
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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!
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4. 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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5. 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
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6. 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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7. AUTOMATED
KNOWN PATTERNS
BUSINESS RULES
• Automates manual processes
• Operationalize traditional “red flags” or
suspicious loss indicators
• Effective regardless of adjuster
training or experience level
• Administered by business
• Catch suspicious claims that would
“fall through the cracks”
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8. DATABASE
KNOWN FRAUD
SEARCHING
• Match against data already held on file
• Known customer
• Watch or Hot-list
• Match at household level
• ‘Supplier’ watch list
• Doctors, treatment centres, garages,
agents, lawyers etc.
• Country insurance industry co-
operatives
• Other external databases
• Data protection issues?
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9. ANOMALY
UNKNOWN PATTERNS
DETECTION
• Use when no known target exists
• Examine current behavior to identify
outliers and abnormal transactions that
are somewhat different from ordinary
transactions
• Include univariate and multivariate
outlier detection techniques, such as
peer group comparison, clustering,
trend analysis, and so on
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10. PREDICTIVE
COMPLEX PATTERNS
MODELING
• Base: uses confirmed fraud cases
Fraud Scores
• Use historical behavioral information of
known fraud to identify suspicious Predicted
behaviors similar to previous fraud Fraud Scores
Claims # of previous
investigations
patterns
• Result – fraud risk score
• Include multiple modeling techniques,
such as regression analysis,
generalized linear models, decision
tree, neural networks etc.
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11. SOCIAL NETWORK
ASSOCIATIVE LINK PATTERNS
ANALYSIS
• Detect unexplained relationships
• Data Linking Analysis
• Nodes = individuals, policies, claims,
addresses, telephone numbers, repairers
(garages), medical providers, lawyers,
employees, bank accounts etc.
• Links
• Scoring: Rule and analytic-based
• Modeling techniques
• Sequence analysis
• Path analysis
• Fuzzy matching
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12. TEXT MINING UNSTRUCTURED PATTERNS
• Up to 80% of insurer data is
unstructured text
• Adjuster notes
• Call centre logs etc.
• Configurable parsing, tagging, and
extracting of free text for use in fraud
analytics
• Combine quantitative and qualitative
data with text analysis to improve
predictions
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13. CASE MANAGEMENT
• Single portal for holistic view of fraud –
can see both current and historical
cases
• Enables Investigation Unit to:
• Manage investigation workflows
• Attach documents and digital files
• Record exposures and losses
• Utilize dashboards and management
reporting
• Track operational performance
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14. FINANCIAL CRIMES
MONITOR
• Logically manage your rules, models
and alerts for investigators
• Maintain simple or complex routing
and suppression rules
• Manage analytical table, project,
scenario and scenario group
relationships
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15. 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
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16. 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
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17. 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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18. 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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19. THANK YOU
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