innovations in detecting suspicious claims 1 measure, manage, & reduce risk sm

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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


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


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


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


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


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.


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


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.


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%)


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


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 NotesText 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


R I SK SM

Unique Insights in TextUnique 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


R I SK SM

Better Detection with Text Mining

All Claims (Fraud Rate

2%)

# Clmts > 1 (5%)



Clmt Vehicle = Older-American

(70%)

Clmt Vehicle = Older-Japanese

(45%)

Clmt Vehicle = Newer (10%)


(7%)

Insd Driver = Male

(3%)

Highway Exit = Y(15%)

No EMR = Y(50%)

# Clmts = 1 (1%)

Low Impact = Y

(5%)

Exaggerated Treatment = Y

(40%)


MINING NETWORK DATA


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


R I SK SM

Querying Claim Networks

ISO’s NetMap tool for link analysis and visualization


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


Network Measures Add Value

All Claims (Fraud

Rate 2%)

# Clmts > 1

(5%)




American (70%)


Japanese (45%)

Clmt Vehicle = Newer (10%)

Density = High

(80%)

Density = Med

(40%)

Density = Low(2%)


(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



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


Feedback and Questions

• Send feedback to: – Janine Johnson– +1.415.276.4105– e-mail: [email protected]

mailto:[email protected]

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