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Innovations in Detecting Suspicious Claims. MEASURE, MANAGE, & REDUCE RISK. SM. Agenda. Impact of insurance fraud Resisting fraud effectively Building fraud detection solutions Keep up with changing scams Maximize value from structured data Business rules Predictive modeling - PowerPoint PPT Presentation
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Innovations in Detecting Suspicious Claims
M E A S U R E , M A N AG E , & R E D U C E RISKSM
M E A S U R E , M A N A G E , & R E D U C E R I SK SM
Agenda• Impact of insurance fraud• Resisting fraud effectively• Building fraud detection solutions– Keep up with changing scams– Maximize value from structured data• Business rules• Predictive modeling
– Leverage textual data assets– Exploit claim networks
Why Focus on Fraud?• It is a big problem– of personal injury claims contain elements of
fraud1
– $50 to $100 of policyholder premiums go to pay fraudulent claims2
• It is widespread– Fraudsters operate across touch points and verticals– New entrants driven by the economy
• It keeps changing and morphing!
26%
1 2001 study conducted by the Insurance Bureau of Canada2 http://www.infoassurance.ca/en/preventing/automobile/fraud.aspx
M E A S U R E , M A N A G E , & R E D U C E R I SK SM
Resisting Fraud Effectively
• Corporate culture– Fighting fraud must be a core responsibility– Organizational measurements must be aligned
• e.g., fraud investigation impact on cycle time
• Effective process– Effective antifraud training programs– Well-defined processes for detection, referral, and investigation– Integration with technology/solutions
• Systematic fraud detection solutions– Best-in-class solutions that evolve to stay current– Multiple techniques to cover different angles and types of data
M E A S U R E , M A N A G E , & R E D U C E R I SK SM
Building Fraud Detection Solutions
UnderstandFraud red flags, schemes, and
scams
BuildSystematic fraud
detection mechanisms
ScoreProcess to score claims for fraud
potential
ReferBusiness thresholds
to refer claims to SIU
EvaluateSIU investigation and feedback on evolving scams
1
2
34
5
M E A S U R E , M A N A G E , & R E D U C E R I SK SM
Example Scams• Staged auto accidents
– Swoop-and-squat – Car in front of you stops suddenly– Wave-on – claimant indicates it is safe for you to merge or pull out of a parking space, but then runs into you
• Repair shop scams– Airbag fraud – bill for new airbags but replace with stolen or salvaged– Burying the deductible – inflate estimates to make insurer pay the deductible (collusion with insured)
• Owner give-ups – Owners report their used car stolen and then set it on fire. Total loss ensures insurance pays off the entire car loan• Auto glass fraud – Bill for a windshield replacement when only a chip repair was done
– Soliciting glass claims
M E A S U R E , M A N A G E , & R E D U C E R I SK SM
Scams Change and Evolve
• Increasing PIP fraud• Rise in property
scams (e.g., hail)• Effects of the new
economy– Auto give-ups– Glass claims
Fraud costs in Ontario top those in other parts of the country… according to panelists at an RBC Insurance roundtable on fraud.
Those costs represent an estimated $1.3 billion of $9 billion in premiums in the province, the insurance executives noted during the July 28 [2010] discussion…
The average cost of a claim in Ontario rose from $30,000 in 2005 to $53,000 in 2009, according to Insurance Bureau of Canada (IBC) data. That’s markedly more than average claims costs in Alberta ($3,689) or Nova Scotia ($5,904).
M E A S U R E , M A N A G E , & R E D U C E
R I SK SM
Changing Scams
Source - NICB ForeCAST Report - 3Q Referral Reason Analysis (Ann Florian, Strategic Analyst )
USING STRUCTURED DATA
M E A S U R E , M A N A G E , & R E D U C E R I SK SM
Structured Data in Claim Systems
• Policy details– Insured details (age, sex, etc.), # of years insured,
policy inception date, etc.• Loss details– Date and time of loss, location of loss, details of vehicles
involved in loss, etc.• Claimant details– # of claimants, injuries, treatment dates and amounts
• Representation– Attorneys involved (if any), date of engagement, etc.
M E A S U R E , M A N A G E , & R E D U C E R I SK SM
Business Rules: SIU ScorecardRed Flag / Indicator Points
Insured reports accident did not happen 100
Informant notifies carrier of suspected fraud 100
Unexplained inconsistent damages 100
Indication that the accident was a setup 100
Claim reported more than 20 days after loss 40
Minor impact 30
Loss within 90 days of a new policy 20
Multiple injured claimants 30
Unrelated claimants with same doctor 25
Unrelated claimant with same attorney 25
Treatment started over 15 days after injury 30
Claimant had another BI claim 40
1. For each claim, determine indicators that apply
2. Add the corresponding points
3. If total points > 99, refer to SIU
Scoring & Referral
M E A S U R E , M A N A G E , & R E D U C E R I SK SM
Predictive/Statistical Modeling• Supervised models– If target flag (suspicious/not-suspicious) tags are available
on a historical body of claims– Many model forms available• Naïve Bayes models • Decision trees• Logistic regression• Neural network classifiers• Etc.
M E A S U R E , M A N A G E , & R E D U C E
R I SK SM
Decision Tree for Fraud Detection
All Claims (Fraud Rate
2%)
# Clmts > 1 (5%)
Insd Driver = Female (10%)
Insd Vehicle = Luxury
(25%)
Clmt Vehicle = Older-
American (70%)
Clmt Vehicle = Older-
Japanese (45%)
Clmt Vehicle = Newer
(10%)
Insd Vehicle = Non-Luxury
(7%)Insd Driver =
Male (3%)
# Clmts = 1 (1%)
= Refer to SIU = Alert adjuster = Settle claim
TEXT MINING FOR ADDITIONAL LIFT
M E A S U R E , M A N A G E , & R E D U C E
R I SK SM
NO PROP DMG FOR INS AND CLMT AS COLL HIT WAS LOW. CLMT CLAIMS INJ FROM AX AND TRTD W CP AND PT EXTENSIVELY. TX APPEARS EXAGGERATED.
Text Mining Adjuster NotesIT APPEARS THAT THIS WAS A LOW-IMPACT COLLISION WHERE THE INSURED’S FOOT SLIPED OFF THE BRAKE, AND SHE ROLLED INTO THE REAR OF THE CLAIMANT. THIS IS CONSSTENT WITH THE FACT THAT THERE WAS NO PROPERYT DAMAGE CLAIM MADE TO THE CLAIMANT VEHICLE. UNDER THE CIRCUMSTANCES, HOW THE CLAIMANT COULD HAVE SUSTAINED SUCH SEVERE SHOULDER INJURIES AS A RESTRAINED DRIVER APPEARS RATHER SUSPECT.
Low Impact Exaggerated Treatment
Questionable Injuries
M E A S U R E , M A N A G E , & R E D U C E
R I SK SM
Unique Insights in Text
• “Structurized” data– Structured fields created with codes/values extracted using
text mining, e.g.:• Near Highway Exit = Y/N• Low Impact = Y/N
INSD R/E CLMT VEH WHEN IT BRAKED SUDDENLY NEAR HIGHWAY EXIT. INSD THINKS SPEED OF TRAVEL ABOUT 25 MPH. INSD SUFFERED AIRBAG BURNS. MULTIPLE CLMTS IN VEHICLE WERE INJ BUT WAIVED AMBULANCE.
Insured R/E Claimant
Near Highway Exit
No EMR and/or Ambulance Waived
M E A S U R E , M A N A G E , & R E D U C E
R I SK SM
Better Detection with Text Mining
All Claims (Fraud Rate
2%)
# Clmts > 1 (5%)
Insd Driver = Female (10%)
Insd Vehicle = Luxury (25%)
Clmt Vehicle = Older-American
(70%)
Clmt Vehicle = Older-Japanese
(45%)
Clmt Vehicle = Newer (10%)
Insd Vehicle = Non-Luxury
(7%)
Insd Driver = Male
(3%)
Highway Exit = Y(15%)
No EMR = Y(50%)
# Clmts = 1 (1%)
Low Impact = Y
(5%)
Exaggerated Treatment = Y
(40%)
= Refer to SIU = Alert adjuster = Settle claim
MINING NETWORK DATA
M E A S U R E , M A N A G E , & R E D U C E R I SK SM
Industry Data: ISO ClaimSearch®
• Workers Compensation• Automobile Liability• Medical Payments• Personal Injury Protection• Auto Medical Payments• Homeowner’s Liability• General Liability• Disability• Personal Injury• Employment Practices• D&O / E&O• Fidelity and Surety
Casualty
>170 Million
Property• Homeowners• Farm Owners• Fire• Allied Lines• Commercial• Ocean Marine• Inland Marine• Burglary and Theft• Credit • Livestock
>36 Million
• Theft Claims• Theft Conversions• Vehicle Claim System
(damage estimates from vendors)• Shipping & Assembly• Salvage Records• Impound Records• Export Data• International Salvage and
Thefts
Auto
>395 MillionInsurers representing 93% of direct written premium, National Insurance
Crime Bureau, and law enforcement agencies
M E A S U R E , M A N A G E , & R E D U C E
R I SK SM
Querying Claim Networks
ISO’s NetMap tool for link analysis and visualization
M E A S U R E , M A N A G E , & R E D U C E
R I SK SM
Characterizing Network Measures
ORA (Organizational Risk Analyzer) from the Center for the Computational Analysis of Social and Organization Systems at CMU
Centrality
Density
Betweenness
M E A S U R E , M A N A G E , & R E D U C E R I SK SM
Network Measures Add Value
All Claims (Fraud
Rate 2%)
# Clmts > 1
(5%)
Insd Driver = Female (10%)
Insd Vehicle = Luxury (25%)
Clmt Vehicle = Older-
American (70%)
Clmt Vehicle = Older-
Japanese (45%)
Clmt Vehicle = Newer (10%)
Density = High(80%)
Density = Med(40%)
Density = Low(2%)
Insd Vehicle = Non-Luxury
(7%)
Insd Driver = Male (3%)
Highway Exit = Y
(15%)
No EMR = Y(50%)
# Clmts = 1 (1%)
Low Impact = Y
(5%)
Exaggerated Treatment = Y
(40%)
= Structured data
= Text-mined data
= Network data
= Refer to SIU = Alert adjuster = Settle claim
M E A S U R E , M A N A G E , & R E D U C E R I SK SM
Summary
• Undetected fraud impacts the bottom line• Effective fraud detection requires
– Corporate focus– Process and training– Effective tools and solutions
• Good solutions exist, but there is more to come– Cross-vertical fraud detection– New data sources (LPR data, cell phone data, etc.)– Geospatial data and technology– More innovations with predictive modeling, text mining, and
network mining
M E A S U R E , M A N A G E , & R E D U C E R I SK SM
Feedback and Questions
• Send feedback to: – Janine Johnson– +1.415.276.4105– e-mail: [email protected]