sas fraud framework for insurance€¦ · overview . sas fraud framework is an . end-to-end ....

Company Confidential - For Internal Use OnlyCopyright © 2015, SAS Insti tute Inc. Al l r ights reserved.

SAS FRAUD FRAMEWORKFOR INSURANCE

OVERVIEW, CLOUD ANALYTICS, & CASE STUDIES

SAS FRAUD & SECURITY INTELLIGENCE PRACTICEDAN DONOVAN, PRINCIPAL SOLUTIONS ARCHITECT


SAS FRAUD FRAMEWORK FOR

INSURANCE®OVERVIEW

SAS Fraud Framework is an end-to-end technology infrastructure that integrates advanced fraud detection, alert

management and investigation tools.

Industry specific solutions enrich the framework allowing a more targeted and effective value-based solution for the customer.

Single, integrated platform built on SAS End-to-end transactional, entity, product and network centric

monitoring Portfolio of industry specific solutions Multiple deployment options Scalable to national level data volumes

Detect, prevent and manage Fraud & Financial Crime across your enterprise

GovernmentSolutions

Banking Solutions

InsuranceSolutions

HealthcareSolutions

SAS Fraud Framework for Fraud

& Financial Crime

Copyr i g ht © 2014, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

ANALYST VALIDATION

• Aite Group complimented SAS for its superior analytics and called SAS, “one of the best-kept secrets among enterprise fraud solution providers.” (2014)

• Forrester ranked SAS as the #1 leader in Enterprise Fraud Management, calling SAS a “true power tool” (2013)

• SAS Fraud Framework won the Innovation in Action award from leading insurance strategic advisory firm Strategy Meets Action (2012)

• Celent named CNA as a model insurer for its use of the SAS Fraud Framework for detecting suspicious insurance claims and networks (2013)

• Gartner places SAS in the leaders quadrant for Data Integration, Data Quality, and Business Intelligence and Analytics (2012/13) Market share leader with 24% of Insurance Fraud customers


INSURANCE®


CUSTOMER CASE STUDY POV RESULTS & PROJECTED SAS FRAUD FRAMEWORK ROI

2014


CLAIMS FRAUD DETECTION

CHALLENGES• Increase suspicious claim

detection rate while minimizing false positives and maintaining staffing levels

SOLUTIONSAS® Fraud Framework for Insurance • Hybrid approach deployed

with models for each line of business and organized fraud networks

BUSINESS IMPACT

60 viable organized fraud network investigations identified in 9 months.

Over US$4.2M in tangible savings to date.

““SAS gave us another tool to detect fraud that was flying under the radar. We can sort out legitimate claims more quickly, and we save money by not paying for fraudulent claims."

”


CROSS CARRIER FRAUDCONSORTIUM

CHALLENGES Increased organised cross carrier fraud

SOLUTION SAS® Fraud Framework for Insurance Alerts on cross-carrier frauds and organized suspicious

networks

METHODOLOGY• SAS Data Management capability enables integration of

50,000 motor claims per annum from multiple insurers• Advanced analytics identify suspicious cross-carrier frauds

and organized criminal networks

BUSINESS IMPACT

Identified 96 ‘hard core’ cross-carrier cases worth €5M per annum

Additional 2,000+ high quality cases to send to insurers


INSURANCE FRAUD DETECTION

In the first six months identified 1,161 insurance fraud cases worth more than CZK 62M.

Improved suspicious claim detection rate by 58%.

CHALLENGES Replace existing

underperforming solution Improve data quality and

fraud detection results

SOLUTION SAS® Fraud Framework for

Insurance Used for motor, property and

life lines of business Able to exchange information

with other insurers

““The decision to choose SAS was a good one… giving us a real boost in savings and efficiency. We’ve even uncovered some major organized fraud groups."

”Maya Mašková, CAEInternal Audit Manager

BUSINESS IMPACT


CARRIER’S METRICSSAS MODEL METRICS: DETECTION EFFICIENCY IMPROVEMENTAcceptance Rate: SAS Model – 74%• Top 5% scored Enterprise Claims • Identified through a population of 3x less non-SIU claims• Increased efficiency through less false positives

33% Accept

Rate

Approx. 900 Claims Referred PIP Claims

261 Claims Accepted

300 Claims

Carrier

SAS ResultsSAS Model

Carrier Current State Results

221 Claims Accepted not previously identified

74% Accept

Rate

Efficiency Gap

Time value associated with less false positives - rededicating resources to higher value tasks

MI & KY PIP Data 2010 - 2012

MI & KY PIP Data 2010 - 2012

Copyr i gh t © 2014 , SAS Ins t i tu te Inc . A l l r i gh ts res erved .


INSURANCE®

SAS FUTURE STATE VS CUSTOMER CURRENT STATEACCEPTED REFERRALS AND INVESTIGATION ASSIGNMENTS

480

288

19210934 20

0

100

200

300

400

500

Bodily Injury No Fault Med Pay0

100

200

300

400

500

ACC

EPTE

D R

EFER

RAL

S

All Casualty Referrals

SAS Accepted Referrals Current State Accepted Referrals

LOB Total SIUFTE

INVPer Mo.

BI 480 5 8

NF 288 3 8

MP 192 2 8

Total 960 10 8

LOB Total SIUFTE

INVPer Mo.

BI 109 5 2

NF 34 3 1

MP 20 2 .5

Total 163 10 1.3

Current State Accepted Referral Assignments

Future State SAS Accepted Referral Assignments



INSURANCE®

3 YEAR HISTORICAL DATA - CUSTOMER CURRENT STATE ACCEPTED REFERRALS, IMPACTED REFERRALS, AND ANNUAL SAVINGS

1009

306 192248 51 38

0

200

400

600

800

1000

Bodily Injury No Fault Med Pay0

200

400

600

800

1000

AC

CE

PTE

D R

EFE

RR

ALS

IMPA

CTE

D R

EFE

RR

ALS


Current State Referrals Accepted Current State Referrals Impacted

3yr Total Savings$324,320

3yr Total Savings$3,010,784

3yr Total Savings$107,929



INSURANCE®

ANNUALIZED PROJECTED SFFI DERIVED ROI FOR CASUALTY FRAUD DETECTION (USING CUSTOMERS CURRENT STATE AVERAGE $ SAVINGS BY COVERAGE TYPE)

960 960 960 960 960 960960 864 768 672 576 480

$8,147,904$7,333,114

$6,518,323

$5,703,533

$4,888,742

$4,073,952

$0

$2,000,000

$4,000,000

$6,000,000

$8,000,000

100% 90% 80% 70% 60% 50%01002003004005006007008009001000

FIN

ANC

IAL

BEN

EFIT

IMPACT RATIO

ALER

TS


Accepted Alerts Impacted AlertsTotal Financial Benefit PIP Financial BenefitBI Financial Benefit MP Financial Benefit

LOB Total SIUFTE

INVPer Mo.

$/COV

BI 480 5 8 $12,000

NF 288 3 8 $6,398

MP 192 2 8 $2,840


CARRIER’S METRICSSAS MODEL METRICS: DETECTION EFFICIENCY IMPROVEMENTSSAS Model Acceptance Rate – 81%808 Previously Unidentified Suspicious Claims20% Increase in Acceptance Rates

81% Accept

Rate

1,000 PIP Claims Evaluated

808 Claims

Accepted

433 Claims Referred

SAS Model

Carrier Current State Results

Carrier

SAS Results

268 Claims Accepted

62% Accept

Rate

Efficiency Gap

Time value associated with less false positives -rededicating resources to higher value tasks

NY NFMP Data 2011 - 2013

NY NFMP Data 2011 - 2013


PLUGGING PREMIUM LEAKAGEUSING ANALYTICS TO PREVENT UNDERWRITING FRAUD


UNDERWRITING FRAUD PERSONAL AUTO

60% of policies contain underreported annual mileage

Youthful drivers are not reported in 20 to 30% of applicable policies

Garaging location is misreported on up to 14% of applications resulting in average rating errors as high as 30% of premium

Average auto premium leakage is 10% of written premium

http://www.iso.com/Research-and-Analyses/ISO-Review/The-Black-Gold-of-Underwriting-Quality-Data-Enables-Meticulaous-Pricing-and-improved-Rating-Integ.html

http://www.iso.com/Research-and-Analyses/ISO-Review/The-Black-Gold-of-Underwriting-Quality-Data-Enables-Meticulaous-Pricing-and-improved-Rating-Integ.html


UNDERWRITING FRAUD PERSONAL AUTO

0

5000

2009 2010 2011 2012

NICB Questionable Claims Reported

Premium Avoidance Application Misrepresentation


UNDERWRITING FRAUD KNOWN SCHEMES

FrontingParent becomes main driver of car to reduce premium – daughter/son is named driver but reality is that they are the main driverSurvey in UK by Co-operative Insurance (2010) found that • 41% of parents deliberately lie when filling out policy applications• 61% would do so in the future

Flipping/GaragingChanging the address that the car is kept at• Student – parental home vs. student residence• Two house owners - city vs. rural • PO Boxes



ManipulationChanging certain rating factors to reduce premium• Where car kept overnight• Miles driven per annum• Any claims in last x years• Any convictions in last x years• Date of birth• Occupation• Education• Period license held etc.



Ghost BrokingThe "ghost brokers" operate through websites or small ads offering cheap insurance. They target young motorists who face rocketing premiums and communities where their national language is not the first language and where there is a lack of understanding about the way the insurance industry works.• In some cases they issue completely fictitious policies. • In other cases they apply to genuine insurance companies for cover on the customer's

behalf, but alter personal details such as age and address which would otherwise push up the cost.


UNDERWRITINGFRAUD

DIRECT SALES FRAUDUSE CASE

The ‘www’ is becoming a common new business channel for many insurers offering:

• Low cost distribution channel• 24x7 interaction• ‘Not sold at’ experience for customers• Ability to change prices, coverage rapidly etc.

This removal of the personal interface has increased the ability of customers to manipulate the information they put into the new business web site to either

• Reduce premium• Be accepted for the risk


DIRECT SALES FRAUD USE CASE

Source: UK Institute of Actuaries, May 2010

Example of quote manipulation

Driver

Quote Driver Age Garaging Claims NCD Zip Code Premium ($)

%

1 IOD 24 Road 1 0 55425 $3,425 100%

2 IOD 24 Road 0 0 55425 $2,960 86%

3 IOD 24 Road 0 2 55425 $2,168 63%

4 2 named 24/51 Road 0 2 55425 $2,890 84%5 2 named 51/24 Road 0 5 55425 $1,026 30%

6 2 named 51/24 Garaged 0 5 55425 $926 27%

7 2 named 51/24 Garaged 0 5 55424 $435 13%

If Quote #7 is actually bound by this insurer, $2,990 of premium is potentially lost due to rate evasion!


UNDERWRITING FRAUD INSURANCE FRAUD TECHNOLOGY USAGE

• 95% of survey respondents currently deploy anti-fraud technology.

• Less than half use technology for non-claims functions, such as underwriting fraud.


PLUGGING THE LEAKS CURRENT APPROACH

Point of Sale Focused• Agent diligence• Pre-fill technology• Public records matching/cross-checking• Rules based fraud detection• Manual underwriting review

How do we move beyond this approach?


PLUGGING THE LEAKS

LIFE OF POLICY APPROACH

HOLISTIC VIEW OF SALES AND UNDERWRITING FRAUD

USEABLE DATA IS GENERATED AT ALL THESE POINTS

POLICY LIFECYCLE

Detect, prevent and manage fraud across life of policy


DATA COLLECTED AT NEW BUSINESS AUTO

INSURED PERSONS• Title• Name• Date of birth• Marital status• Occupation• Type of licence• Period license held• Email address• Telephone number• House name/number• Zip Code• Do you own your home• How long have you lived at current address• When do you want cover to start• Do any children under 16 live in your house• Do any children over 16 live in your house• Additional Drivers• Have you made any claims in the past 3 years• Have you had any convictions in the past 5 years• Total number of cars at your home

Vehicle• Car Registration• Type of vehicle• Year• Make• Model• VIN• Personal or business use• Estimated annual mileage• Estimated car value• Estimated purchase date• No Claims Discount entitlement• Have you or any of the drivers ever had

insurance cancelled by an insurer



Insured Persons• Title• Name• Date of birth• Marital status• Occupation• Type of licence• Period license held• Email address• Telephone number• House name/number• Zip Code• Do you own your home• How long have you lived at current address• When do you want cover to start• Do any children under 16 live in your house• Do any children over 16 live in your house• Additional Drivers• Have you made any claims in the past 3 years• Have you had any convictions in the past 5 years• Total number of cars at your home



Fronting



Insured Persons• Title• First name• Date of birth• Marital status• Occupation• Type of licence• Period license held• Email address• Telephone number• House name/number• Zip Code• Do you own your home• How long have you lived at current address• When do you want cover to start• Do any children under 16 live in your house• Do any children over 16 live in your house• Additional Drivers• Have you made any claims in the past 3 years• Have you had any convictions in the past 5 years• Total number of cars at your home



Fronting

Garaging



Insured Persons• Title• First name• Date of birth• Marital status• Occupation• Type of licence• Period license held• Email address• Telephone number• House name/number• Zip Code• Do you own your home• How long have you lived at current address• When do you want cover to start• Do any children under 16 live in your house• Do any children over 16 live in your house• Additional Drivers• Have you made any claims in the past 3 years• Have you had any convictions in the past 5 years• Total number of cars at your home



Fronting

Garaging

Manipulation


DATA COLLECTED LIFE OF POLICY AUTO

Insured Persons• Title• Name• Date of birth• Marital status• Occupation• Type of licence• Period license held• Email address• Telephone number• House name/number• Zip Code• How long have you lived at current address• Do you own your home• Do any children under 16 live in your house• Do any children over 16 live in your house• Additional Drivers• Have you made any claims in the past 3 years• Have you had any convictions in the past 5 years• Total number of cars at your home


insurance cancelled by an insurerAll of this data is subject to change throughout the life of the policy


UNDERWRITING FRAUD LIFE OF POLICY DATA CAPTURE

AUDITS

POLICY CHANGES POLICY

RENEWALCLAIMS

These are all opportunities to capture data that can be compared a

These are all opportunities to capture data that can be compared against the original application and current policy within an

automated fraud scoring process


INTERNAL FRAUD


INTERNAL FRAUD WHAT IS CONSIDERED INTERNAL ‘FRAUD’?

• Happens in most insurers and banks• Role of internal auditors / internal security

• Examples• Reopening claims• Inventing claims on ‘safe’ policies• Life/Annuity account take over• Self serving applications• Procurement Fraud

• Part of organized criminal network• Working alone

Intervention strategy is key to success

The staff member is colluding

with a provider or

vendor

The staff member is

stealing money from an elderly customer

The staff member is re-opening claims and funnelling

money to friend/family

accounts

The staff member is

abusing expense

accounts or company

credit cards

The staff member is

stealing policy

premiums


SFFI FOR INTERNAL FRAUD

INTERNAL FRAUD IS AN INCREASING CHALLENGE

Collusion with external parties

Lone individuals

Financial losses

Reputational Risk

Former insurance adjuster to pay victim

$165,000Former Insurance claims associate Fariborz Romeo Rahrovi, 40, was sentenced in King County Superior Court to 12 months of work release and ordered to pay his victim $165,000 in restitution.

On Nov. 21, 2014, he pleaded guilty to two counts of theft, criminal conspiracy, and money laundering related to the theft of an accident victim’s $525,000 insurance settlement. Rahrovi worked with Seattle private law attorney Edward Joseph Callow on the scam, the Washington State Insurance Commissioner’s Office said in a statement.

Insurance agent theft of premium spurs alert to consumers in MississippiCommissioner of Insurance Mike Chaney and the Mississippi Insurance Department are alerting consumers that there may be issues with policies purchased through the Mid-Delta Insurance Agency in Indianola, Mississippi and its representative, Randal Ray Henson.Earlier today Mr. Henson was arrested by the Sunflower County Sheriff’s Department and charged with false pretense. The charges are related to Mr. Henson’s failure to forward insurance premium payments on to insurance carriers. The investigation was a joint operation between the Sunflower County Sheriff’s Department, Sheriff James Haywood and the Mississippi Insurance Department.Randal Ray Henson surrendered his insurance producer’s license to the Mississippi Insurance Department in 2012.Anyone who purchased a policy from Mid-Delta Insurance Agency or Mr. Henson needs to be aware that their premium payment may not have been sent to their insurance carrier.

Insurance salesman allegedly copped to $4 million scam in a fake suicide note.Even his own knowledge of the industry couldn't help a Enumclaw, Wash. life insurance salesman get away with an alleged multi-million-dollar insurance scheme. Federal prosecutors say Aaron Travis Beaird not only pilfered $2 million from his clients' accounts but convinced a family friend to buy a $2 million life insurance policy, then claimed the man died in order to cash it, the Seattle Post Intelligencer reports.When police investigated, Beairdallegedly parked his car by a bridge and stuffed a suicide note on the dash with an admission of his crime. He signed it 'Travis the scam man.'Beaird was later found very much alive in late June. He's been charged with two counts of wire fraud and awaits trial.

http://www.seattlepi.com/local/article/Feds-Life-insurance-salesman-faked-his-own-3686886.php%23ixzz20Ld9xMTT

Corruption Cases by Region

35

Industry of Victim Organization

36

Duration of Fraud Schemes

37

Initial Detection of Occupational Frauds

38

Median Loss and Median Duration by Detection Method

39

Effectiveness of Controls

40


BENEFITS VALUE FOR THE BUSINESS

Reduced reputational risk through more accurate identification of high risk employees

Reduced exposure to operational risk and fraud committed by employees• More suspicious cases identified• Fraud identified earlier• Reduction in false positive rates• Improved investigation efficiency

Extensible solution• SAS Fraud Framework is extensible to provide best of breed detection of other

fraud types

Reducing losses,

protecting reputation

Copyr i g ht © 2014, SAS Ins t i tu t e Inc . A l l r ights reser ve d . sas.com

OBRIGADO!

http://www.sas.com/

http://www.sas.com/

Copyr i g ht © 2014, SAS Ins t i tu t e Inc . A l l r ights reser ve d . sas.com

APPENDIX

http://www.sas.com/

http://www.sas.com/


Analytic Decisioning

EngineAutomated Business Rules

Anomaly Detection

Predictive Modeling

Text Mining

Database Searches

Social Network Analysis

Business rule (example): A

claim is suspicious if it is submitted a few days before a policy ends

Anomaly detection

(example): Looking for a claim that has a very low damage

to injury ratio

Text mining (example): Similar suspicious accident descriptions occurring across multiple claims

Database Searches

(example): Looking for

matches across the lists of suspicious bodyshops or

medical providers

Predictive modelling (example): Number of previous investigations on

the network may be input to the predictive model of a suspicious claim

SNA (example): Looking for organised groups of people staging multiple suspicious accidents


INSURANCE®UNIQUE HYBRID APPROACH TO ANALYTICS



INSURANCE®HYBRID APPROACH HELPS ACROSS THE SPECTRUM OF FRAUD

Average Fraud Deliberate Fraud Organized Criminal GangsInsurance Rules

Database Searching

Anomaly Detection

Advanced Analytics

Social Network Analysis

Text Mining


Investigations


INSURANCE®OUR APPROACH

Operational Sources

Policies

Investigations

Watch-lists

Claims

Alert Management & Reporting

GUI for self-administration

Data updates

Case Management

Actions taken

Data Management

Ingest

Cleansing

Enrichment

Quality analysis

Entity resolutionSocial networks generation

Potential Fraud Risk

Suspicious alerts for Investigation

Additional sources

Fraud Detection

Alert Generation

Intelligence Repository

Intelligence updates

http://www.google.co.uk/url?sa=i&rct=j&q=&esrc=s&frm=1&source=images&cd=&cad=rja&docid=GF1oTeXLalr-gM&tbnid=nheaB4pBMWi-OM:&ved=0CAUQjRw&url=http://forum.sedty.com/t243808.html&ei=kuF3UceALtKg0wXq4IGABA&bvm=bv.45580626,d.d2k&psig=AFQjCNFe5JRX6VdToRnI9UrHLtHdSBlxQg&ust=1366897417991866





CLOUD ANALYTICS SOLUTION SAS FRAUD FRAMEWORK FOR INSURANCE

Out of the box

• Data model & ETL• Data quality & entity

resolution routines• Fraud detection scenarios• Alert queue user interface• Social network configuration• Detailed user & management

reports• Cloud Hosted or

Remote Managed Service

Key Benefits

• Faster: Customers can be up and running in less than 4 months.

• Affordable: Cloud model lowers operating costs and offers pricing flexibility.

• Powerful: Leverages same underlying SFF technology as on-premise deployment.

Modules Available

• Personal Auto Claim

• Personal Auto Underwriting

• Personal Auto Provider

• Broker


SFFI CLOUD ANALYTIC SOLUTION

SFFI DELIVERY OPTIONS & CLIENT BENEFITS

DELIVERY OPTIONS SAS Cloud hosted or RMSS Integrates hardware, hosting, software, delivery, and services in a single

contract, for a single fixed price Additional services can be negotiated by SSOD as a fixed price or T&M

CLIENT BENEFITS Faster, more powerful and economically viable solution with proven results Access to experienced SIU and Insurance Industry domain expertise and

business consulting to assist with operationalizing the analytics solution Less reliance on internal IT resources RMSS - Manage everything but the hardware Can be positioned as OpEx rather than CapEx



INSURANCE®WE CAN TAKE THE INSURER ON A JOURNEY

Valu

e

SAS Fraud Framework Capability

• The SAS Fraud Framework runs on a single platform (SAS)

• Capability can therefore be developed over time

• Strategic investment

• It isn’t a “big bang theory”

Manual Detection

Rules

Predictive Models

Fraud Network Analysis

Anomaly Detection


DOMAIN EXPERTS SAS INSURANCE FRAUD RESOURCES

James Ruotolo – 15 years insurance SIU experience. Former Director of SIU Strategic Operations for The Hartford.

Jim Hulett – 22 years insurance & SIU experience. Former SIU AVP at The Hartford and Multi-Claim Investigations Manager at State Farm.

Dennis Toomey – 22 years SIU, Claims and Data Analytic experience. Former National Director for Liberty Mutual’s Agency Market SIU.

Investment in Insurance fraud domain resources:

Dan Donovan – 20 years SIU & claims experience. Former Assistant Director for Liberty Mutual Personal Markets SIU and National Director of No Fault claims.

Ricardo Saponara – 13 years insurance experience. An Actuary and former Chief Risk and Underwriting Officer for Mondial Assistance Brazil.

Michelle Bergeron – 2009 IASIU Analyst of the Year. 5 years as the SIU Analytics Manager at Esurance. 5 years as an SIU Analyst at Geico.


CLAIMS WORKFLOW INTERACTION – FRAUD EXAMPLE

Guidewire Extract

LegacyData

3rd PartyData Explore

SimulatePrototype

Visual Analytics

Alert Interface

SAS Analytic Accelerator

Claim Data

Score &

LinkLink

Nightly Batch

Claim Milestone

Alerts

SingleCases

Promote Scenarios

SAS Fraud Framework for Insurance

Real Time Scoring EngineBatch Scoring Engine

Complex Cases

GUIDEWIRE – SAS COMBINED VALUE

PROPOSITIONBusiness workflow & scoring trigger

rules defined within Guidewire

New information added to claim file, i.e. providers, participants can trigger a rescore


PERSONAL LINES CARRIER VALIDATION

RESULTSTOTAL DISPOSITION RESULTS

74%

26%

Total Accept vs. Total Decline

Accept Decline

49%25%

7%19%

Total Dispositions by Type

Accept SIU Provider EvalMedical MGMT Decline

Total Alerts Validated = 300


PERSONAL LINES CARRIER VALIDATION

RESULTSACCEPTED ALERTS WITH MULTIPLE DISPOSITIONS

Accept

Accept + SIU Provider Eval

Accept + Medical Management

Accept +

Alerts with Multiple DispositionsAccept Accept + SIU Provider Eval Accept + Medical Management Accept + Both

65%

2%14%

20%

Total Alerts “Accepted” = 147



INSURANCE®

HISTORICAL NFMP CLAIM AND REFERRALSPOLICY STATE = NEW YORK

Claim & Referral Counts (Policy State = New York)

Claim Volume

NFMPExposures per Claim

SIU Referral Volume

SIU Referral

Rate

SIU Referrals Accepted

SIU Acceptance

Rate

SIU Referral Acceptance

Rate

Total # Referrals Impacted

Total # closed

files

Impact Ratio

Total Financial Benefit

Financial Benefit per

ClaimNY NFMP

(2011) 8,822 1.29 162 1.84% 97 1.10% 60% 52 84 62% $1,157,932 $22,268

NY NFMP(2012) 8,568 1.27 136 1.59% 96 1.12% 71% 45 88 51% $592,734 $13,172

NY NFMP(2013)* 6,853 1.29 135 1.97% 75 1.09% 56% 38 90 42% $758,608 $19,963

Total 24,243 1.29 433 1.79% 268 1.11% 62% 135 262 52% $2,509,275 $18,587

*2013 represents partial year data through 10/15/2013


VALIDATION RESULTS FOR CLAIM MODEL (1000 TOTAL)

Accepted: 808; 81%

Declined: 182; 18%

Bill Management:

10; 1%

CLAIM MODEL DISPOSITION RESULTS


INSURANCE®

167 185 187 176 49

2717

10 28 39 51 8

2333

0%10%20%30%40%50%60%70%80%90%

100%

99th 98th 97th 96th 95th 90th 75th

CLAIM MODEL DISPOSITION RESULTS(BY PERCENTILE)

Accepted Rejected


PROJECTED IMPACTED EXPOSURE ON SAS ACCEPTED CLAIMS FROM THE 3-YEAR POC DATA (POLICY STATE = NEW YORK)


INSURANCE®

Percentile # Claims w/o SIU

Accept Rate

# Claims Accepted

Impact Rate

# Claims Impacted

Benefit / Claim

Loss Avoidance

99 179 94% 168 78% 131 $36,030 $4,721,414

98 208 88% 182 44% 80 $13,221 $1,058,745

97 219 82% 180 61% 110 $14,104 $1,548,669

96 221 76% 169 43% 73 $9,060 $658,414

95 73 89% 65 62% 40 $9,097 $366,615

90 50 54% 27 51% 14 $14,119 $195,385

75 50 34% 17 40% 7 $9,000 $61,200

Total 1000 81% 808 56% 455 $18,944 $8,610,443

• Total Loss Avoidance estimate of $8,610,443 represented by the 808 claims accepted (out of 1,000) during the POC validation within the top 10% of scored claims.

CustomerCurrent State3 Year Loss Avoidance$2,509,275


PROJECTED IMPACTED EXPOSURE ON ALL POC CLAIMS WITHIN THE TOP 10% FROM THE 3-YEAR POC DATA (POLICY STATE = NEW YORK)


INSURANCE®

Percentile # Claims w/o SIU

Accept Rate

# Claims Accepted

Impact Rate

# Claims Impacted

Benefit / Claim

Loss Avoidance

99 179 94% 168 78% 131 $36,030 $4,721,414

98 208 88% 183 44% 80 $13,221 $1,063,835

97 219 82% 180 61% 110 $14,104 $1,548,669

96 221 76% 169 43% 73 $9,060 $658,414

95 239 89% 213 62% 132 $9,097 $1,200,287

90-94 1159 54% 626 51% 321 $14,119 $4,529,033

Total 2226 69% 1539 55% 847 $16,206 $13,721,653

• Total Loss Avoidance estimate of $13,721,653 represented by all claims within the top 10% of scored claims.

CustomerCurrent State3 Year Loss Avoidance$2,509,275


INSURANCE FRAUD BRAZIL DPVAT 2012


INSURANCE FRAUD BRAZIL LIFE & PERSONAL ACCIDENT 2012


INSURANCE FRAUD BRAZIL CARGO 2012


INSURANCE FRAUD BRAZIL HOME/HOUSING 2012


SAS FRAUD FRAMEWORK FOR INSURANCECLOUD ANALYTICS


SAS CONFIDENTIALITY

This document is the confidential and proprietary property of SAS Institute Inc. It does contain approaches and content that is proprietary to SAS and shall not be disclosed in whole or in part to any third parties.


APPENDIX


POTENTIAL FRAUD EXPOSURE IMPACT SCENARIOSPotential benefit scenarios related to increased detection, acceptance and impact capabilities


INSURANCE®

Most likely scenarios based on model experience

Anticipated impact of recommended modeling approach is between $11.1 to $20.1 million for claim impacted through the detection and investigation process• Impact assumptions based 2013 annual

results of 298,526 annual auto claims with an average paid of $3,737 per claim. Actual impact of investigations most likely bigger based on the types of exposures investigated


CARRIER’S METRICSQuestionable Provider Identification: Results• 2 States Enterprise PIP Exposure - 148 questionable providers linked to $2.7 million in claim payments• SAS population linked to 2.6X more questionable provider exposure, or 162% increase from Carrier’s population• Identified 46 questionable providers not previously identified • SAS population represented a 150% increase of exposure to the providers identified in Carrier’s SIU

investigations• Opportunity to close provider gaps through surfacing flagged providers through UI

18% of quest.

Provider exposure

$2.7 million

46 Questionable Providers not previously identified:linked to $334K payments

$485 thousand

$1.3 million

Enterprise Exposure to Questionable Providers in 2 states combined PIP claims:4166 claims from the time period 2010 – 2012

Carrier

SAS results

SAS Model

Carrier’s results

150% increase in mutual identified provider exposure

261 PIP Claims

221 PIP Claims not previously identified

49% of quest. provider exposure

162% increase provider exposureidentification

oppo

rtun

ity oppo

rtun

ity

sas fraud framework for insurance€¦ · overview . sas fraud framework is an . end-to-end ....

Documents