DATA ANALYTICS IN FRAUD PREVENTION
DURBAN, AUG 24TH 2014
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AGENDA
1.
2.
3.
4.
5.
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Introductions
General Concepts around Fraud ,Waste, and Abuse
Overview of the SFF
Screenshots
Questions/Discussion
INTRODUCTIONS
Chris McAuley, Director, Security & Intelligence Practice
Chris.McAuley@SAS.com
+44 7747 100189 (m)
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GENERAL CONCEPTS ON FWA
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HEALTHCARE FRAUD: WHO HAS THIS PROBLEM?
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HEALTHCARE FRAUD: HOW BIG IS THE PROBLEM?
“…potential losses to healthcare fraud and corruption between
€30-100 billion across Europe”
“…estimates conservatively that $68 billion (3%) is fraud”
“…approximately €180 billion euros or 6 percent of global health
care spending is lost to fraud each year”
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HEALTHCARE FRAUD: WAIT…THERE’S MORE??
Fraud
Abuse?
Waste?
Corruption?
Estimates of 20% – 30% total FWAC in health care
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HEALTHCARE FWA: SO THESE ARE DIFFERENT, RIGHT?
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Fraud
Abuse
Waste
Corruption
FRAUD, WASTE, & ABUSE
A CONTINUUM
Waste
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Abuse
Fraud
FRAUD, WASTE, & ABUSE
NEFARIOUSNESS SCALE
Abuse
Waste
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TM
Fraud
FRAUD, WASTE, & ABUSE
HOWEVER, IN TERMS OF [€£$] TO THE SYSTEM…
Abuse
Fraud
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Waste
FRAUD, WASTE, & ABUSE
WE NEED TO FOCUS ON ALL OF IT
Waste
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Abuse
Fraud
COMBATING FWA: HOW DO WE DEAL WITH IT?
Sometimes prosecutions are in order:
• Criminal organizations
• Doctors committing true fraud
• Grievously offending doctors
• In other words – for Fraud
What if companies do not want to prosecute?
• Bad PR
• Bad for customer retention
• Legal action not possible
• What do they do about waste and abuse?
• Want to develop alternative strategies for identifying and dealing with
doctors who are engaging in aberrant behavior (as opposed to fraud)
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COMPREHENSIVE COST-CONTAINMENT STRATEGY
Provider
FWA
Adherence to
Guidelines
Quality of Care
Hospital
FWA
Cost
Containment
Member
FWA
Contract
Negotiation
Policy
Modification
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Pharmacy
FWA
EXAMPLES: PROVIDER FRAUD AND ABUSE
Overutilization
Billing for services not medically warranted, to receive insurance payments, or falsifying diagnosis to
justify medically unnecessary procedures.
Upcoding
Using a code for a more expensive treatment than what was performed.
False Claims
Billing for services not performed or supplies not provided.
Unbundling
Improper submission of separate claims for services that should be combined under a global fee.
Billing for Non-Covered
Treatments
Billing for non-covered treatment as though they were covered treatment (e.g. experimental not
covered by insurance plan).
Fraudulent Dates of
Service
Falsifying the date to avoid contract limitations on eligibility or payment maximums.
Waiver of Co-pay
Waiving coinsurance or deductible to accept insurance as payment in full, and then inflating charges
to insurer.
Free Medical Service
Free service to patient, then billed to insurer, to entice ongoing other treatments.
Kickbacks
Providers receiving cash payments in exchange for driving business to certain ancillary providers
(e.g. labs).
Phantom Providers
Unlicensed providers posing as physicians.
Misrepresenting Medical
Records
Falsifying the medical records to justify services that were not provided or not warranted.
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EXAMPLES: MEMBER FRAUD
False Claims
Patient misrepresents services and submits false claims for reimbursement.
Collusion
Patient and provider collude to submit false claims, typically with provider
returning portion of reimbursement to patient for cooperation.
Speculation
Patient has multiple individual health insurance policies without revealing
other coverage and collects on all.
Application Fraud
Misrepresentation of material statements on application for insurance in order
to obtain coverage that would be denied or modified.
Identity Theft
Member has SSN or Benefits ID stolen for purposes of someone else
receiving insurance benefits, or sells or “rents” their ID to another for an
access fee.
Disability Fraud
Patient misrepresents the nature or extent of a disability or misrepresents
loss of income to obtain higher benefits.
Doctor Shopping
Member keeps looks to many doctors for the same services (often narcotics)
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EXAMPLES: WASTE
Using brand name drugs when an alternative generic is available
Using a second line drug when a first line drug is indicated
Consistently selecting a surgical option when non-surgical options are available
and effective
Preventative prescription of a drug when not indicated
Using high intensity diagnostic tools when a lower intensity tool is available
Over-utilization of laboratory testing when it is not necessary
Under-utilization of laboratory testing leading to disease progression
Under-treatment of a disease early in onset leading to more severe disease
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NOT JUST DOCTORS
Radiological
Centers
Infusion
Centers
Nursing
Homes
Doctors
Dialysis
Centers
Chiropractors
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Transportation
Services
Personal
Care
Assistants
Optometrists
Hospitals
Medical
Equipment
Suppliers
Pharmacies
Home
Health
Care
Adult
Foster
Care
Substance
Abuse
Clinics
Podiatrists
Dentists
Laboratories
OVERVIEW OF THE SFF
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SAS FRAUD
END-TO-END SOLUTION FOR HEALTH INSURANCE
FRAMEWORK
Data
It’s all in the prep
• Structured &
unstructured data
Sources
• Batch or real time
processing
• Data cleansing
• Data integration
• Advanced network linking
• Text mining
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Detection
Unique hybrid approach
• Business rules
• Anomaly detection
• Predictive models
• Text mining
• Database searches
• Social network analysis
Investigation
Taking action
• Layered alert ranking
• Tailored workflow
• Easy to use web based
interface
• Advanced query of
integrated data
• Claim system integration
• Case management
integration
Management
Self administered
• Alert suppression
• Modification of rules
• Model management
• Champion challenger
• Alert queue management
• Workflow analysis
• MI reporting
SAS FRAUD
UNIQUE HYBRID APPROACH TO ANALYTICS
FRAMEWORK
Anomaly
detection
(example):
Providers that have
volumes or intensity
far above their
peers
Predictive modelling (example):
Number of previous investigations on
the network may be input to the
predictive model of a suspicious claim
Text mining (example):
Harnessing call center data
Database
Text
Searches
Mining
Predictive
Modeling
Anomaly
Detection
Business rule
(example): A
claim is
suspicious if it is
submitted for a
person of the
wrong gender
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Automated
Business Rules
Analytic
Decisioning
Engine
Database
Searches
(example):
Looking for
matches
across the lists
of sanctioned
providers or
death master
files
Social Network Analysis
SNA (example): Looking for a number of
similar connected actors
INDUSTRY BEST PRACTICE
USING HYBRID ANALYTICS FOR FRAUD DETECTION
Enterprise Data
Employer
Medical
Data
Procedure
Claims
Provider /
Member
Known
Bad Lists
For known patterns
For unknown patterns
For complex patterns
Rules
Anomaly Detection
Predictive Models
Rules to surface
known fraud
behaviors
Algorithms to surface
unusual (out-of-band)
behaviors
Examples:
• Inaccurate eligibility
information
Referral
• Daily provider billing
exceeds possible
3rd Party
Data
• CPT up-coding
• Value of charges for
procedure exceeds
threshold
For unstructured data
Network Analysis
Text Mining
Identify attributes of
known fraud
behavior
Associative discovery
thru automated link
analysis
Leverage unstructured
data elements in
analytics
Examples:
Examples:
Examples:
Examples:
• Abnormal service
volume compared to
similar providers
• Like patterns of
claims as
confirmed known
fraud
• Provider/claimant
associated to known
fraud
• Claim/call center
notes high-lighting
key fraud risks (e.g.,
policy questions)
Payments
• Unlicensed or
Suspended Provider
For associative linking
• Ratio of $ /
procedure exceed
norm
• # patients from
outside surrounding
area exceeds norm
• Provider behavior
similar to known
fraud cases
• Like provider/
network growth rate
(velocity)
• Linked members
with like suspicious
behaviors
• Suspicious referrals
to linked providers
• Collusive network of
providers & referrals
• Static data elements
(e.g., address) used
for linking suspicious
activity
• Integration of rich
case file information
Hybrid Approach
Proactively applies combination of all approaches at entity and network levels
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INDUSTRY BEST PRACTICE
USING HYBRID ANALYTICS FOR FRAUD DETECTION
Internationally Developed IP
Rules
Employer
Medical
Data
Procedure
Claims
Provider /
Member
Known
Bad Lists
Network Analysis
Text Mining
Identify attributes of
known fraud
behavior
Associative discovery
thru automated link
analysis
Leverage unstructured
data elements in
analytics
Examples:
Examples:
Examples:
Examples:
• Abnormal service
volume compared to
similar providers
• Like patterns of
claims as
confirmed known
fraud
• Provider/claimant
associated to known
fraud
• Claim/call center
notes high-lighting
key fraud risks (e.g.,
policy questions)
Anomaly Detection
Predictive Models
Rules to surface
known fraud
behaviors
Algorithms to surface
unusual (out-of-band)
behaviors
Examples:
• Inaccurate eligibility
information
Payments
Referral
• Unlicensed or
Suspended Provider
• Daily provider billing
exceeds possible
3rd Party
Data
• CPT up-coding
• Value of charges for
procedure exceeds
threshold
• Ratio of $ /
procedure exceed
norm
• # patients from
outside surrounding
area exceeds norm
• Provider behavior
similar to known
fraud cases
• Like provider/
network growth rate
(velocity)
• Linked members
with like suspicious
behaviors
• Suspicious referrals
to linked providers
• Collusive network of
providers & referrals
• Static data elements
(e.g., address) used
for linking suspicious
activity
• Integration of rich
case file information
Hybrid Approach
Proactively applies combination of all approaches at entity and network levels
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SAS FRAUD
EFFICACY AND THE HYBRID APPROACH
FRAMEWORK
FRAUD
Additional
variables further
this benefit even
more
Analytics enhance fraud detection,
improving the accuracy as well as
finding cases near impossible in a
manual process
If you examine 50% of the
population, you would expect to
find 50% of the fraud
█ Advanced analytics
via Hybrid
█ Advanced analytics
without Hybrid
█ RANDOM
POPULATION
If the accuracy of detection doubles by using a hybrid approach, an investigation team would be able
to find twice the amount of fraud with the same number of referrals!
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SAS FRAUD
IMPROVED IDENTIFICATION, QUALITY, AND EFFICIENCY
FRAMEWORK
Detection
Investigation
Capability
Enhanced scoring model
with network attributes and
scores incorporated
Visual representation of data
from multiple systems in one
single environment
Outcome
Increase in volume &
quality of fraud detected
Increased efficiency during
fraud investigations
Benefit
Increased fraud saving
Improved operational saving
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SAS FRAUD
OUR APPROACH
FRAMEWORK
Operational
Sources
Policies
Data
Management
Alert Generation
Fraud Detection
Alert Management
& Reporting
Ingest
GUI for selfadministration
Cleansing
Enrichment
Claims
Investigations
Investigations
Quality analysis
Entity resolution
Social networks
generation
Intelligence Repository
Potential
Fraud Risk
Suspicious alerts
for Investigation
Watch-lists
Additional
sources
Intelligence updates
Data updates
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Case Management
Actions taken
SCREENSHOTS?
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SFF SCREENSHOTS – ALERTS VIEW
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SFF SCREENSHOTS – DRILL INTO ALERT
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SFF SCREENSHOTS – DRILL INTO ALERT
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SFF SCREENSHOTS – SNA
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SFF SCREENSHOTS – SNA
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SFF SCREENSHOTS – SNA
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SFF SCREENSHOTS – SNA
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SFF SCREENSHOTS – SNA
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SFF SCREENSHOTS – DASHBOARDS
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SFF SCREENSHOTS – DASHBOARDS
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SFF SCREENSHOTS – ALERT VIEW
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QUESTIONS?
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