fico insurance fraud webinar april 2011
TRANSCRIPT
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This presentation is provided for the recipient only and cannot bereproduced or shared without Fair Isaac Corporation's express consent.
2011 Fair Isaac Corporation.
Prevent Claims Fraud withAdvanced AnalyticsUncover new fraud analytics that traditional methods simply cannot find
Scott HorwitzSenior Director, Insurance Solutions
Derek DempseySenior Analyst
April 20th, 2011
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FICO has Seen this Before and Understands theValue of Fighting Fraud, Starting Over 20 years Ago
US Credit Card Fraud before and after the introduction
of Comprehensive Fraud Management
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Year
B
asis
Points
Introduction of Comprehensive Fraud Management
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Beyond Better DetectionWhat we are hearing in the marketplace
Pressures on Profitability in Current Financial Environment
General strategies around containing costs both claims andoperational
Will consumers under financial strain be more apt to commit fraud?
Increasing Medical Costs
How to differentiate between normal cost increases and fraudulentactivity?
Regulatory Compliance
Prompt claim payment requirements
Fraud Prevention Program requirements
Operational Concerns
Driving efficiencies into the process
Pre-payment assessments
Increase post-payment ability to drive collections and recoveries
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Beyond Better DetectionWhat we are hearing in the marketplace
Automating Claims Processing
Insurers are adopting automated methods to efficiently process themillions of claims in a timely manner
As a result of automating claims management, there is lessopportunity for review.
Many claims are paid that should be due to fraud, abuse or billing
errors Without an analytic fraud detection program, more automation
creates opportunities for talented fraudsters to sneak through.
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FICO Insurance Fraud SolutionsKey Benefits
Helps insurers detect more fraud waste, and abuse dollars,
with the same or fewer resources, while settling good claimsquickly and efficiently:
Provides a much higher detection rate than rules-based systems byutilizing predictive analytics
Identifies previously unknown patterns of fraud and
suspicious behavior Identifies more high risk claims with lower false-positives than rules
or query-based systems.
Identifies waste, abuse and systemic (policy) issues.
Helps insurers prioritize and allocate the right resources tothe right claims based on the level of fraud risk and the potentialfor savings.
Helps insurers identify fraud earlier in the claims adjudicationprocess, rather than retrospectively, once the majority of
payments have been made.
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OldPrevented
Activity
Old Investigationand Claim
Mgmt
Early Detection BenefitIllustrative Example
Time (weekly)
First Investigation
High
SuspicionL
evel
Low
Threshold
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OldPrevented
Activity
Old Investigationand Claim
Mgmt
Early Detection BenefitIllustrative Example
Time (weekly)
First Investigation
Early Detection BenefitHigh
SuspicionL
evel
Low
Fraud Detectedby FICO Tools
New First Investigation
Threshold
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OldPrevented
Activity
Old Investigationand Claim
Mgmt
Early Detection BenefitIllustrative Example
Time (weekly)
First Investigation
Early Detection BenefitHigh
SuspicionL
evel
Low
New Investigationand ClaimMgmt
Early Detection Benefit
Fraud Detectedby FICO Tools
New First Investigation
NewPrevented
Activity
Threshold
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FICO Insurance Fraud SolutionsComprehensive Approach
Predictive analytics predict likelihood of waste, fraud and abuseon a claim or provider
Detection can be conducted throughout the claims processprepayment and post-payment
Detection Review Investigation
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Comprehensive ApproachDetection
Companies start from different places
Rules
Documented vs. Anecdotal
Automated vs. Manual
Analytics
Predictive
Reporting
Link Analysis
Internal vs. Industry database reviews
Reporting
Integration
Part of the overall process or ad hoc
Goal is to understand the strengths of where an organization iscurrently and how to build improvements going forward
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Insurance
Fraud andAbuse
Predictive AnalyticsDepth of Detection
Predictive analyticsenable the efficientdetection of more
types of fraud
Predictive analyticsenable pre-payment
fraud detection to stopthe outflow of moneyon fraudulent claims
Newly emergingschemes
Previously unknownpatterns
Subtle, complexcases
Early detection
Rank-ordering
Traditional Rules BasedSolutions Detect Fraud at
This Level Only
Simple schemes
and billing errors
Known fraud andabuse schemes
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Predictive AnalyticsUnique challenges in Fraud Assessment
Supervised vs. unsupervised models
Supervised Unsupervised
Use tags todifferentiate
Fraud
Learnpatterns
identifyaberrance
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FICO Predictive AnalyticsDetection: Variables
Models collect, organize and process data to create behavioral features
QuotationDatabase
DerivePowerfulVariables
PolicyholderDetails
ClaimMaster
Payments
ExternalData
.....
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FICO Predictive AnalyticsDetection: Variables
QuotationDatabase
DerivePowerfulVariables
PolicyholderDetails
ClaimMaster
Payments
ExternalData
.....
Dynamic Profiles
Time to ReportClaim
Age of Car
Residential Status
Type of Accident
Variable N
Once variables are derived, they are used to build a complete profile for thetarget entity (the entity being scored) to describe the behavior of the entity.
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FICO Predictive AnalyticsDetection: Variables
QuotationDatabase
DerivePowerfulVariables
PolicyholderDetails
ClaimMaster
Payments
ExternalData
.....
Dynamic Profiles
Time to ReportClaim
Age of Car
Residential Status
Type of Accident
Variable N
Scores andReasons
Predictive
Models
The models simultaneously blend and combine these profile variables/features
to produce a fraud-score
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FICO Predictive AnalyticsComplex Multi-dimensional Profiles
Dynamic ProfileProviders
Members
Claims
Dental
Pharmacy
Ratio of X-raysto exams
$ billed vs. peers
Procedure mix vs.peers
Max single-dayactivity
Profile Variable N
DerivePowerfulVariables
Scores andReasons
PredictiveModels
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Rules-based vs Predictive Analytics Approach
Predictive Analytics are objective rather thenjudgemental.
Assume that the past is our best guideto the future.
The main features of analytic methods are:
Combine multiple data elements.
Data-driven. Use derived variables to make data
more effective.
Produce a single fraud risk score or rankordering.
Can be combined with strategy rules for
optimum effectiveness If score > 700 and claim type = theft
then refer for investigation.
If score > 400 and location =Edinburgh then refer to FraudQueue X.
A fraud detection approach forinsurance claims based solely on ruleshas some advantages but also severaldrawbacks. These include:
Advantages:
Easily understood
Adjustable by the business user
Disadvantages:
Low detection rates
Easily avoided by fraudsters
Detect only what is already known
Optimum Fraud Detectionapproach combines bothanalytics and rules
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Information value is based on the predictiveness of a characteristic.
The larger the information value, the more predictive the characteristic
Model Development Enables the Evaluation ofIndividual Key Variables (predictors)
Information Value
Time to Report 0.251
Physical Damage 0.133
Insured Value of
Car
0.042
ILLUSTRATIVE
High level of prediction capability
Low level of prediction capability
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And The Correlation of Key Variables to Identifythe Most Predictive Information
MarginalContribution
TotalInformation Value
Time to Report
Physical Damage
0.029 0.280
Time to Report
Insured Value of Car
0.012 0.269
High Correlation Level
Low Correlation Level
ILLUSTRATIVE
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Auto Fraud ModelSample Fraud Types - Organized Activity
Staged Accident
All parties collude in staged accident.
Policyholder is implicated.
Multiple exposures/third party claims.
Insurer liability through both first party AND third party.
Induced Accident Policyholder is victim of ring.
Typically policyholder is induced to hit rear of fraudsters vehicle thusbeing liable for all damages.
Multiple exposures/third party claims typically.
Signatures for each type of fraudulent activity are learned by themodel. This enables rapid identification to quickly identifypotential cases before payments on the claims are made.
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Fraud Signatures
The multiple variables that define the claim profile provide
signatures that define fraud types.
Description
Accident Type:= Insured hit TP
Damage type:= Rear end
Incident location type:= Roundabout Loss description:= None
No. of exposures:= 4
TP passenger injury claims:= 2
TP driver injury claim:= Yes
TP vehicle damage claim:= Yes
100% liability for insured party claimant
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22 2011 Fair Isaac Corporation.
Link AnalysisVisualize claim connections
Link Analysis attempts to generate networks of claims that match
through specific entities
The notion is that claims connected to a suspicious claim mightalso be suspicious
The degree to which two claims are connected is a function of
the strengths between their connections.
Claims can be linked through multiple link types including:
Name
Address
Vehicle Registration Number
Phone number
Fax number
Email address
Professional (Garage, Hire company, Medical Facility, Chiropractor, etc)
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Link AnalysisFocused Queries
A Steve Wonnacott is involved in two claims one of which has been
identified as suspicious or fraudulent (claim highlighted in pink)
Are the two Steve Wonnacott nodes the same person?
The system checks if thetwo Steve Wonnacotts
share the same details(address, phone number,etc). Here they do not.
However, the two SteveWonnacotts do haveaddresses that are veryclose.
The system uses the conceptof salience to assist. If namesare unusual they are morelikely to be related thancommon names such as Smithor Jones.
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Link AnalysisFocused Queries
Hamid Ahmad lives at thesame address as ShaikAhmad
Shaik is involved in asuspicious claim
There are other Hamid Ahmadinstances are they in factthe same individual?
A more detailed investigationof the claims shows that all
these participants are from thesame location where a fraudring is operating. The claimsrefer to similar types ofincident and all these claimsshould be investigated.
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25 2011 Fair Isaac Corporation.
FICO Insurance Fraud SolutionsComprehensive Approach
Predictive analytics predict likelihood of waste, fraud and abuseon a claim or provider
Detection can be conducted throughout the claims processprepayment and post-payment
Detection Review Investigation
Assigns high risk claims or providers to the right investigator
Guides reviewers to the right next action, based on fraud score,
reason codes, and claim data
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FICO Insurance Fraud SolutionsReview
Utilize detection results and business
rules to assign the high risk claims orentities to the right resource.
Guides investigators to the right nextaction, based on:
Likelihood of fraud (rank ordered by
fraud score) Reason codes
Control the score thresholds forinvestigation
Actions can be based on industrybest practices and/or tailored to yourbusiness processes
Thresholds and associated actionsare set based on detection results
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27 2011 Fair Isaac Corporation.
FICOTM Insurance Fraud SolutionsComprehensive Approach
Predictive analytics predict likelihood of waste, fraud and abuseon a claim or provider
Detection can be conducted throughout the claims process
prepayment and post-payment
Detection Review Investigation
Assigns high risk claims or providers to the right investigator
Guides reviewers to the right next action, based on fraud score,reason codes, and claim data
Manages tasks and activities surrounding the investigation
Provides claim history and data to support investigation process
Final outcome is output to claims management system
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Comprehensive Fraud SolutionOperational View
Detection Review Investigation
Claim In
Through
Processing
System
Auto
Healthcare
Other
LOB
WC
Customer
Update
Adjudication
BusinessRules
Score
Update
Adjudication
Data Center Claims Fraud Data
EffectiveDetection
EfficientReview
Case
ManagementReports
Link Analysis
Decision Optimization
What actions are takenonce detected?Predictive
Analytics
Claims Operations Environment
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UK Auto InsurerBackground
Top 5 UK Motor Insurer
Over 900 million GBP in annual premium Almost 2 million policyholders
Well-funded claims fraud department
Challenges
Static rules based on previously known fraud activity Large percentage of claims reviewed not fraudulent
Difficult to manage claims review workloads
Fraud department Director of Claims Initiated a search for solutions to more effectively handle fraud
Constructed file of information to be reviewed by 2 different solutions Designed Pilot Project
1 year of claims data
Approximately 200,000 claims
Approximately 250 Million GBP of claim payments
About 1,500 claims already identified as fraudulent or suspicious
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UK Auto InsurerResults
FICO given all claims for 2008 with no designation of fraud
Returned results of analytic scoring models FICO sent the worst scoring 1,000 claims
Insurer reviewed the top 200 scoring claims 85 matched Fraud claims already identified amounting to 250,000 GBP
Increased Fraud ring suspects by 33%
Determined that 18 claims should have been referred Value of the results determined to be an additional 350,000 GBP in
worst 200 claims
Over 100% increasein amount of fraud detected in 0.1% of totalclaims
Updating models now based on results of first review
Showing greater predictive power
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Protect against fraud, abuse and waste
Highmark is one of the largest health insurers in the United States with 28 million members, andprocesses over 200 million health, dental, vision, Medicare, and pharmacy claims.
BUSINESS NEED:
Highmark needed to target fraud, waste and abuse (FWA) more aggressively, to combineprepayment claims scoring with retrospective provider analysis.
Did not wish to add any technology, turn investigators into programmers, or hire additional ITresources.
STRATEGY
Highmark implemented FICO Insurance Fraud Manager, using predictive analytics to
identify and manage claims FWA
Highmark selected FICO based on analytic capabilities
FICO trained the models based on Highmarks historical data.
Models analyze hundreds of data points and relationships simultaneously
Models spot unusual or suspicious care and billing patterns
FICO provides an environment for investigation and management
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Highmark - Approach
Results:
Highmark identified substantial savings in their use of FICO Insurance Fraud
Manager:
In first 7 months found over 330 new pursuable cases over and above casesidentified through other methods.
Calculated Identified Savings ROI of 9:1 in just over 6 months of use
Found that the identified savings from 18% of cases exceeded operationalcosts of a single month.
Gained insight on medical policies that need modification
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