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Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
SAS FRAUD FRAMEWORK FOR INSURANCE
MORE INFORMATION
Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
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
Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
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!
Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
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
Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
FRAMEWORK-BASED
APPROACH END-TO-END SOLUTION
Data
• Structured & Unstructured Data Sources
• Batch or real time processing
• Data Cleansing
• Data Integration
• Variable Extraction & Sentiment Analysis with Text Mining
Detection
• Business Rules
• Anomaly Detection
• Advanced Predictive Models
• Watch Lists
• Social Network Analysis
• Network-level analytics
• Hybrid Technology
Reporting
• Advanced Ranking Technology
• Easy to use web based interface
• Advanced Query of integrated data
• Full business intelligence reporting capability
• Claim system integration
Administration
• Self administered
• Custom alert queues
• Alert suppression & routing rules
• Workflow analysis
• Direct integration with Case Management
Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
LEVERAGING SAS HYBRID APPROACH TO SCORE TRANSACTIONS,
ENTITIES, AND NETWORKS ACROSS MULTIPLE ORGANIZATIONS
Analytic
Decisioning
Engine
Automated
Business Rules
Anomaly
Detection
Predictive
Modeling
Text
Mining Database
Searches
Social
Network
Analysis
FRAUD ANALYTICS USING A HYBRID APPROACH FOR FRAUD DETECTION
Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
AUTOMATED
BUSINESS RULES KNOWN PATTERNS
• 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”
Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
DATABASE
SEARCHING KNOWN FRAUD
• 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?
Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
ANOMALY
DETECTION UNKNOWN PATTERNS
• 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
Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
PREDICTIVE
MODELING COMPLEX PATTERNS
• Base: uses confirmed fraud cases
• Use historical behavioral information of
known fraud to identify suspicious
behaviors similar to previous fraud
patterns
• Result – fraud risk score
• Include multiple modeling techniques,
such as regression analysis,
generalized linear models, decision
tree, neural networks etc.
Fraud Scores
Predicted
Fraud Scores
Claims # of previous
investigations
Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
SOCIAL NETWORK
ANALYSIS ASSOCIATIVE LINK PATTERNS
• 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
Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
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
Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
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
Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
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
Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
MAKING LIFE
EASIER
Final
Analysis &
Summary
Decision
to
Proceed?
Analysis
of
Findings
Combine &
Synthesize
Information
Rank &
Prioritize
Results
Query
Various
Systems
Establish
Search
Parameters
Final
Analysis &
Summary
Decision
to
Proceed?
Analysis
of
Findings
Analytical Value-Add
Combine &
Synthesize
Information
Rank &
Prioritize
Results
Query
Various
Systems
Establish
Search
Parameters
Framework-Based Predictive Analytics
“What used to take me most of a day, now takes 10 minutes.”
“It completely streamlines where we need to go.” -SIU Analyst
Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
CUSTOMER STORY CNA (US)
Customer Quote
We have an excellent partnership with SAS. They took the time to meet with us and truly understand the nuances of CNA so that we could build effective predictive models for each line of our business
Tim Wolfe, SIU Director
Business Problem
• Detect and prevent fraud in four separate commercial
lines of business
• Optimally direct its investigation resources on cases with
higher likelihood of fraud
Results
• $2.1m in fraud recovery / prevention within the first 9
months of implementation
• Detection and investigation of 15 potentially fraudulent
provider networks – four times what CNA anticipated
Solution
• SAS Fraud Framework for Insurance
Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
WHY SAS?
More suspicious cases identified
• Including both previously undetected fraudulent networks and extensions to already identified
fraud
Reduction in false positive rates
• Significant improvement in ‘quality’ of suspicious cases past for investigation
Improved investigation efficiency
• Each referral taking 1/2 – 1/3 the time to investigate using SAS’ link analysis visualization
“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
“What used to take me most of a day, now takes 10 minutes.’’ SIU Manager, Major Tier 1 USA Insurer
“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
Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
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
Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d . www.SAS.com
THANK YOU