fico insurance fraud webinar april 2011

8/6/2019 FICO Insurance Fraud Webinar April 2011

1/33

1

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


2/33

2 2011 Fair Isaac Corporation.

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

0

2

4

6

8

10

12

14

16

18

20

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Year

B

asis

Points

Introduction of Comprehensive Fraud Management


3/33


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


4/33


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.


5/33


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.


6/33


OldPrevented

Activity

Old Investigationand Claim

Mgmt

Early Detection BenefitIllustrative Example

Time (weekly)

First Investigation

High

SuspicionL

evel

Low

Threshold


7/337 2011 Fair Isaac Corporation.

OldPrevented

Activity


Mgmt


Time (weekly)

First Investigation

Early Detection BenefitHigh

SuspicionL

evel

Low

Fraud Detectedby FICO Tools

New First Investigation

Threshold



OldPrevented

Activity


Mgmt


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



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



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



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



Predictive AnalyticsUnique challenges in Fraud Assessment

Supervised vs. unsupervised models

Supervised Unsupervised

Use tags todifferentiate

Fraud

Learnpatterns

identifyaberrance



FICO Predictive AnalyticsDetection: Variables

Models collect, organize and process data to create behavioral features

QuotationDatabase

DerivePowerfulVariables

PolicyholderDetails

ClaimMaster

Payments

ExternalData

.....




QuotationDatabase


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.




QuotationDatabase


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



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


Scores andReasons

PredictiveModels



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



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



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



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.



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


22/33


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)


23/33


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.


24/33


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.


25/33


FICO Insurance Fraud SolutionsComprehensive Approach


Detection can be conducted throughout the claims processprepayment and post-payment


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


26/33


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


27/33


FICOTM Insurance Fraud SolutionsComprehensive Approach


Detection can be conducted throughout the claims process

prepayment and post-payment


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


28/33


Comprehensive Fraud SolutionOperational View


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


29/33


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


30/33


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


31/33


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


32/33


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


33/33

This presentation is provided for the recipient only and cannot bed d h d ith t F i I C ti ' t

THANK YOU

fico insurance fraud webinar april 2011

Documents