modeling public records fraud detection• claim edits, usual and customary charges, and network...

Using Predictive Modeling and Public Records in Fraud DetectionClint Fuhrman

National DirectorGovernment Healthcare

Taxpayer Dollars Are Under Attack

Opportunities

Eliminate the “Pay and Chase”

status quo by looking to other industries, private sector for

successful approaches and technologies

– Identity Proofing/Identity Management –

Financial Services, Banking– Predictive Claims Analytics –

Property and Casualty Insurance– Social Network Analysis –

Intelligence and Law Enforcement

• Greater focus on the individuals and entities in the program

• Are beneficiaries enrolling who they claim to be? • Have they disclosed all assets, income, correct state of residence, etc?• What are the true backgrounds of the practitioners, officers, agents, etc?• What is the risk profile of a provider based on background, associations, etc.?• What significant events are occurring between enrollment periods?

CMS Center for Program Integrity (CPI) “National Fraud Prevention Program”

focused

on prevention and detection that is integrated, risk‐based, and measurable; four areas

of focus: Provider Screening; Predictive Modeling; Data Integration; Case Management

The Many Faces of Fraud

Over 80% of all suspected fraud cases involve provider fraud.

FALSIFICATION OF INFORMATIONFALSIFICATION OF INFORMATION

QUESTIONABLE PRACTICESQUESTIONABLE PRACTICES

OVERUTILIZATIONOVERUTILIZATION

Note: Lists are not comprehensive.

False coding,altered claims

Upcoding, unbundling,

cost‐shifting,

prescribing practices,

clustering, underutilization,

invalid places of service,

non‐contracted providers

Medically unnecessary diagnostics,

high frequency of office visits,

unnecessary durable medical equipment,

inappropriate diagnosis procedures

Government

Background Screening

Collections

Legal

InsuranceInsurance

Financial Services

Who are you?

Where are you?

Who are you related to, and how?

How much of a risk do you present?

Identity Analytics Health Care

We assess the risks and opportunities associated with people,

businesses and assets.

Data

250M+ unique

individuals

1B unique business

contacts

Analytics

30M transactions/hr

<500 millisecond avg

search response time

~34 Terabytes in use

ComputingReal time analytics Scores

to support customer

workflow for remote

transactionsScores around individual

risk/ opportunity

Linking34 billion public

records

1 million documents

added every day

36,000 legal,

business, news

sources

Overview of a Data Aggregation/Risk Solution Provider

Health Care Solutions for Commercial Payers 5

ENTITY RESOLUTION

LINK ANALYSIS

CLUSTERING ANALYSIS

COMPLEX ANALYSIS

PUBLIC RECORDS

PROPRIETARY DATA

NEWS ARTICLE

UNSTRUCTURED

RECORDS

STRUCTURED

RECORDS

Utilizing Advanced Technology to Establish Identity and Risk

Health Care Solutions for Commercial Payers

Claims Analytics

Presentation Title

• Early detection of fraud, waste and abuse• Prioritized results with fewer false positives, which enable

more efficient use of investigative resources

• Alerts concerning adverse changes in the status of individuals or entities accessing benefits or networks

• Lower claims losses, better cash flow and higher ROI than

traditional “post‐payment only”

methods

• Consistent control over risk, quality and costs thanks to automated provider screening and monitoring

• Confidence in knowing that the right providers are being paid for the appropriate services on the appropriate members

Reducing Risk: Advantages of Enterprise Solutions

Analytics: The Value of Tips vs. FWA Software

0%

10%

20%

30%

40%

50%

60%

70%

Tips Data Analysis

Percent of Respondents

Most volume

Most savings


Predictive Modeling Adds a Score Plus More

TwoSanctions

Criminal Record

SignificantEdits

Plus More

Bankruptcy

Sample Model Score: 985

Copyright © 2011 LexisNexis. All rights reserved.

Fraud Prevention: Predictive Claims Analytics

Claim EditsClaim Edits

Provider DataProvider Data

Diagnosis DataDiagnosis Data

Treatment DataTreatment Data

Internal (P

ayer) D

ata

External Data

Claims Fraud IdentificationClaims Fraud Identification

“Provider of Interest”

Identification

“Provider of Interest”

Identification

Subrogation IdentificationSubrogation Identification

Social Network AnalyticsSocial Network Analytics

And more…And more…

Edits

Public Records Data

Sanctions Data

Fee Schedules

PREDICTIVE MODELING

TEXT MINING

BUSINESS RULES

IDENTITY MATCHING

TEXT SEARCH

SOCIAL NETWORK ANALYTICS

VISUALIZATION

DATA SMART ORDER

USER INTERFACE

REPORTING ENGINE

SCORING ENGINE

DATA MART

DATA EXCHANGE

FUNCT

IONAL CO

MPO

NEN

TSSTRU

CTURA

L COMPO

NEN

TS


Stopping abusive and fraudulent claims prior to payment will allow customers to devote

more resources to providing care to members.

Stopping abusive and fraudulent claims prior to payment will allow customers to devote

more resources to providing care to members.

Claim ArrivesClaim Arrives

License and sanctions data,criminal history, sexual offender,

etc.

• Claim edits, usual and customary charges, and network pricing

agreements stop only part of the impact of fraud, waste and

abuse on healthcare payers• Data‐driven analytics can produce additional claim edits that can

significantly supplement the current claims adjudication process• Claim‐level scoring can:

• enhance identification of claims post‐pay for audit and

potential recoveries• be tuned for use in pre‐pay to stop the most egregious

abuses before payment is made• Business rules, monitoring for specific treatment codes, and

rules for claim routing pre‐pay or post‐pay improves workflow

• Claim‐level edit and scoring results can be supplemented by the

identification of providers who consistently bill outside of

normal patterns and practice• While some providers are relatively easily identified, others

exhibit much more subtle patterns that are nonetheless abusive• Identifying these more subtle patterns can provide benefit to:

• In some cases, the SIU• In some cases, the claim audit team, and• In some cases, the network management team

• Problem providers can also have their bills returned before payment to have medical records attached

Analytics for Claims Processing Workflow


Claim Continues in

Adjudication

Claim Continues in

Adjudication

Fraud is hidden in a sea of valid

claims

Without Anything

Fraud is concentrated

and prioritized for review and

mitigation

With Predictive Modeling

Fraud

FraudPotential

High

Low

CLAIM NUMBER

SUSPICION SCORE

144618 993138514 991143949 989145594 988148531 986152506 983152787 982146937 981157651 976141970 973152271 970138703 969149491 968139439 963158952 950149319 948152602 945

Fraud Prevention: Claim Scoring Using Predictive Models

Predictive analytics provides a score for each claim, policy, etc., allowing activity to be

concentrated on areas that have the highest probability of financial return

Some fraud is captured but much is missed

With Rules

Create the target richenvironment

• Models can help identify problem providers early that would

not have been identified by other methods• Looking at thousands of attributes about a provider or a

claim to find a data pattern that makes a robust prediction• Models use:

•Diagnostic codes•Treatment codes•Provider types•Date stamps

• Identify treatment patterns associated with diagnoses that

are characteristic of known problem providers and flag other

providers that exhibit similar treatment patterns

Provider Models


Algorithms

• Supervised vs. Unsupervised Learning• Have a specific outcome in historic data• Do not have an outcome “cluster”

like together

• Decision Trees• Accurate, conceptually “understandable”, non‐linear, non‐parametric,

robust with outliers, missing data, automatic interaction terms

• Neural Nets• Work best with pre‐transformed “smooth”

data• Difficult training time• Black Box

• Regression• Most established/widely used algorithm• Works well, but doesn’t have some of the advantages of trees• Works much better on linear data


Social Network Analytics


Challenges Facing Health Care Enterprises

Disparate data is spread across separate physical locationsScale of data. BIG Data is getting BIGGER.Adding relationships exponentially expands the size of the BIG Data analytics challenge.LexisNexis has leveraged parallel‐processing computing platforms and large scale graph analytics for a over a decade.

17

Technology advances are enabling a more proactive response

The emergence of open-source massive parallel- processing computing platforms opens new opportunities for enterprises to increase the agility and scale of solutions focused on addressing fraud and abuse.

– Effectively ingest and integrate massive volumes of disparate data.

– Process and Analyze exponentially faster than traditional databases.

Large Scale Graph analytics, generally thought to be the domain of companies like Google, offer new variables that provide relationship context between events, exposing patterns and outliers that otherwise would be hidden.

– Can be applied to many other many areas beyond network analysis and social graph analysis, such as epidemiology and mathematics.

– Suited to revealing well organized fraud networks hidden within BIG Data and generating actionable results.

18

•

Graph Analysis

‐

Twitter uses Graph Analysis to help

the site determine who’s connected to

whom in the Twittersphere.

‐

Google uses Graph Analysis to power

its PageRank feature.

‐

LexisNexis uses Graph Analysis to

resolve Identities and establish

relationships

•

Social Network Analysis

‐

Graph Analysis that specifically focuses

on graphs built on social relationships.

Graphic Analysis and Social Network Analytics

19

http://www.lexisnexis.com/

Mixes First Party data with Public and Third Party data sourcesAdds fidelity to existing entitiesAdds new linkages into the analysisAds new entities into the analysisExposes ring leaders and brokers that don’t directly participate

Addition of External Data

Trends in Social Network Analytics

20

Reliance on “Created”

Data

Transform “straw” into “gold”• Process numerous discrete data points

into high‐value dataAdvanced Linking Technology • Resolve numerous names, addresses,

phones, and other info into a “Person

ID”• Better accuracy than other resolution

techniques• Resilient to name, address, and other

info changes (i.e. stable over time)Improves detection, simplifies processing, makes results easier to understand

Trends in Social Network Analytics

21

Powered by massive parallel‐processing open‐

source computing platforms.

Graph \ Network 3 Billion derived public data

relationships between people merged with

risk indicators.

Graph Analytics examine up to 20 billion data

points to create variables that allows for

predictive analysis incorporating relationship

context and associated risk.

Targets fraud across all sectors including Health

Care, Financial Services and Government.

Targeting fraud using large‐scale graph analytics

22

http://10.239.225.1:8010/

On June 6, 2008, the Department of Justice announced the arrest of Felcoranenda Estudillo

on charges of defrauding Medicare of approximately $12 million in an elaborate scheme

involving home health care services and kickbacks for referrals of patients who were not

eligible for services.

Estudillo was a registered nurse and operated Wescove Home Health Services from her

home in West Covino, CA. Her husband, Oscar Estudillo, owned the business, as well as

several others that used the same home address as their base. Mrs. Estudillo is the only

person named in the indictment, but records show her husband was

the legal owner of the

business.

The link analysis chart on the following slide was constructed to show the complex array of

relationships among Estudillo, her husband, and the varied business they own and operate.

Businesses were linked to the Estudillos that were not reflected

in the indictment.

The identities linked to the Estudillo’s in following slide have been masked but are an

accurate representation of the relationships revealed by the link analysis.


23


24

A top insurer flagged 7 claims as “collusion claims”

Using carrier data alone, we found a connection between 2 of the

7 claims.

Fraud Detection: Social Network Analytics

25

Collusion in Louisiana AFTER Advanced Linking Technology is Applied

Assigned unique IDs to all parties and HPCC added 2 additional degrees of relative data

Family 1

Family 2

Showed 2 family groups interconnected on the 7 original claims plus linked to 11 more.

Fraud Prevention: Social Network Analytics

26

Proof of Concept – NY Office of Medicaid Inspector General


Applied social network analytics to information provided by

the State of New York and public data supplied to identify

relationships between a group of New York Medicaid

recipients living in high‐end condominiums located within the

same complex and any links those individuals might have to

medical facilities or others providing care to New York

Medicaid recipients.

Purpose of Proof of Concept

28

• Derived Public Data Relationships are built from +/‐

50 terabyte data base

for the entire U.S. population. This is used to build a large scale network map

of the Medicaid Recipients and everyone associated within 2 degrees.

• Patented algorithms used to cluster the network map and generate

statistics

to measure every cluster.

• Graph is queried for the clusters with the most significant statistics.

• For each cluster, if all these recipients are connected..How many of them are living in expensive residences, owned expensive property or drive expensive cars?How many recipients are contacts of medical businesses?How many medical businesses are associated with any of the people in the cluster?How many are currently receiving benefits?

Methodology

29

What is the list of preferred expensive vehicles?

Make Description # Owned Make Description # Owned

Mercedes-Benz 46 Chevrolet 2

Lexus 41 Hummer 2

BMW 27 Jeep 2

Infiniti 13 Nissan 2

Acura 9 Toyota 2

Lincoln 8 Aston Martin 1

Audi 7 Bentley 1

Land Rover 7 Cadillac 1

Porsche 6 GMC 1

Jaguar 5 Honda 1

Mercedes Benz 3 Volkswagen 1

Saab 3 Volvo 1

City Walk Sample Vehicle Statistics

30

Name Deeds Held Name Deeds HeldHudson Eight 78 Mike Greem 21Hudson Five 74 Scott Hill 21Hudson First 73 Betty Donaway 21Hudson Nine 65 Al Clark 19Harry Anderson 45 Dave Miller 17Hudson Ten 41 Mark Walker 16Hudson Seven 39 Mike Smith 16Home Nationwide 33 Val Edwards 15

Hudson Three 33 Eric Garcia 14Brian Smith 28 Dane Young 14Alan Stevens 25 Bill Moore 14Chris Doe 24 Karen Carter 14Sophie Davis 23 Casey Baker 14Washington Mutual 23 Art Nelson 14Fleet Mortgage Co. 21 Cathy Parker 13

Dominant buyers and sellers at City Walk

Property Deed Reference Counts for City Walk

31

Cluster Visualization

32

A Comprehensive Approach

Presentation Title

A Layered Solution with Fresh Insights

Business Rules

Predictive Modeling

Identity Match/ Claims Watch Intelligent Data Retrieval

Severity Analysis

External Evaluators

Medical Claims

Payer Watch

List

ProviderBill

ProviderBill

Prepayment

ClaimFocusSM

Evaluation

Prepayment

ClaimFocusSM

Evaluation

PAY

Contributory

Data

IDV &

Authentication

EvaluateEvaluate

Appropriate Claims Handling Process

Appropriate Claims Handling Process

Claims Data

History

Policy Data

Provider Billing

History

Medical Bill

Detail

Analytics Processing

Yes

No

Bringing it All Together

Clint Fuhrman

National Dir, Government Healthcare

LexisNexis Risk, Inc.

202‐503‐6639

Thank You!

36

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modeling public records fraud detection• claim edits, usual and customary charges, and network...

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