Applied Analytics for Fraud Detection
Lessons Learned in Healthcare
October 26, 2009 • Mandalay Bay • Las Vegas, Nevada
Agenda
 Aetna – Special Investigation Unit
 Lessons Learned
 Questions & Answers
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Applied Analytics for Fraud
Detection
The scope of Healthcare fraud and abuse is estimated at 3% to 5% of
Healthcare spending
Business Challenges
Cost of Healthcare had risen much
faster than general inflation.
$40
Health claim volume surpassed 1
million daily.
$30
Reduced support for large scale civil
litigation.
Increased attention from regulators,
legislators and customers.
$35
Millions
$25
$20
$15
$10
$5
$0
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Perpetrators became more
sophisticated and schemes more
diverse.
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1997 - 2008
Business process automation
reduced opportunity for traditional
healthcare fraud detection (referrals).
Applied Analytics for Fraud
Detection
Aetna’s Special Investigation Unit (SIU) investigators had few system
tools and relied completely upon referrals to initiate investigations
Business Environment
Detection
 Minimal pro-active data analysis was
possible.
 Pre-payment claim review was not “granular”
and depended upon staff with conflicting
incentive models.
 No “investigator-friendly” analysis tools were
available.
 IT support was overburdened with custom
requests and general purpose tools.
 Data warehouse support was limited.
 No regular reporting of pre-payment results
was available making reporting of financial
results difficult.
 Pay & chase recovery was expensive and
adversarial
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Prevention
Applied Analytics for Fraud
Detection
Reporting,
Education &
Compliance
Investigation
Recovery
The SIU Information Management team had implemented a data
analytics, entity profiling solution, and set about integrating technology
with improved business processes to achieve industry leading results
Business Goals
 Introduce proactive health fraud detection while maintaining
a “zero tolerance” policy for referrals.
 Consistently promote high quality cases for investigation based
on “outlier” billing, practice and treatment behavior.
 Improve prevention (pre-payment results) by reducing “false positives” and
increasing the specificity of claim review triggers to match behavior profiling
capabilities.
 Reduce “erroneous” overrides of fraud warnings during adjudication.
 Reduce the “IT bottleneck” that limited the number of analyses that could be
provided to investigators in a timely manner.
 Leverage investments in data warehousing and business intelligence technologies
 Balance case generation across 4 investigative teams and numerous specialties
 Detect fraud in product areas not previously supported by proactive detection.
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Applied Analytics for Fraud
Detection
SIU Healthcare Anti-Fraud Process Model
1997 - 2008
$40
$35
$30
Millions
$25
$20
$15
Core SIU Functions
$10
$5
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$0
Detection
Prevention
Reporting,
Education &
Compliance
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Investigation
Recovery
Detection – Proactive data analysis identifies
providers of interest based on behavior within
peer group
Investigation – Thoroughly explore all of a
provider’s billing/practice behavior, not just a
single issue
Recovery – Work directly with providers to
understand/educate and/or pursue recovery
Reporting, Education & Compliance –
Operational and financial results reporting;
mandatory anti-fraud training for employees,
business associates and delegates; mandated
state and federal fraud reporting
Prevention – Pre-payment claim review and
routing avoids fraudulent payments
Applied Analytics for Fraud
Detection
Collaborating with investigators and clinical experts, an integrated
business/technology solution was created utilizing specialty focused behavior
models to improve pre-payment prevention and introduce self-service
investigator analytics
Solution Approach
 A process model was developed to integrate & leverage analytics in each core SIU
function (detection, investigation, prevention, recovery and reporting/compliance)
 Profile models were developed/updated for each provider specialty and each
investigative team is served by the Fraud Intelligence Team (FIT), composed of both
technical and investigative staff.
 Auto data extract was implemented to integrate FAMS analytics with Aetna’s
comprehensive enterprise data warehouse.
 Over 75 data analyses were implemented for investigator “self-service” execution
during subsequent case investigation (including on-demand, provider report cards).
 Claim system enhancements were implemented to support automated pre-payment
denial, protected claims routing & review and behavior-based fraud edit alerts.
 A proof-of-concept technology pilot of real time pre-payment fraud detection was
successfully conducted
 Pre-payment review staff (prevention) were realigned with the SIU organization.
 Once editing was improved to reduce false positives, edit override was further
restricted.
 Fraud transaction inventory and prevention metrics/tracking were added to the
weekly and monthly dashboard reports.
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Applied Analytics for Fraud
Detection
Collaborating with investigators and clinical experts, an integrated
business/technology solution was created utilizing specialty focused behavior
models to improve pre-payment prevention and introduce self-service
investigator analytics
Solution Approach (continued)
 Profile models were developed/updated for each provider specialty and each
investigative team is served by the Fraud Intelligence Team (FIT), composed of both
technical and investigative staff.
Outlier Analytics Examples:
 Phantom provider detection (volume/address detection)
 Provider/Member collusion detection (member model)
 Drug seeking behavior (member model)
 Upcoding/Creative coding, ER coding abuse
 Concurrent procedure loading
 Rx duplicate billing (Specialty Rx)
 Condition-based profiles (e.g. “Low back pain”)
 Claim system enhancements were implemented to support automated pre-payment
denial, protected claims routing & review and behavior-based fraud edit alerts.
Examples:
 Point-of-entry denial
 Automated reduced calculation
 Downstream risk assessment
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Applied Analytics for Fraud
Detection
The integration of analytics-based detection and investigation support with an
effective anti-fraud investigative model has resulted in SIU process
improvements, and significant ROI
Business Results ~ 1997 vis-à-vis Today
 Pre-Payment SIU claim reductions & denials ~ $6M  $157M
 Fully automated pre-payment denials ~ $0  $30M
 SIU Recoveries ~ $10M  $5.4M
 New investigations ~ 3,200  2,700
 Analytics derived investigations ~ 5  > 400
 Self-service SIU analyses ~ 2,200  28,800
 SIU FTE staff ~ 23  107
 False Positive rate (claim level) ~ 90%+  < 50%
 SIU staff ROI ~ (not measured)  >17:1
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•
Sr. executive awareness of SIU value to Aetna
•
Plan sponsor awareness of Aetna diligence protecting their funds
•
SIU participation in strategic direction of healthcare products
Applied Analytics for Fraud
Detection
*Source: Aetna, Inc. Special Investigations Unit
“Top 10” Lessons Learned
Applied Analytics for Fraud Detection
10. Understand the solution’s organizational impact early on in the project
9. Business process modeling is not a waste of time
8. Leading edge is not always the bleeding edge
7. In the end, the technology is the easy stuff (it’s all just 1’s and 0’s)
6. There are 37 ways to spell Brooklyn and the warehouse has them all
5. “There’s gold in them thar hills”
4. The ROI calculation for improved fraud detection is a “no-brainer”, but project
funding for anything that combines compliance and operations is a “non
starter”!
3. Technology is just as effective for black hats as it is for the “good guys”
2. “You can’t make this @#$# up!”
1. Data Analytics is not a project, but a process that keeps evolving
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Applied Analytics for Fraud
Detection
Questions & Answers
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Applied Analytics for Fraud
Detection
Contact Information
Benjamin Wright, Manager,
SIU Information Management
Special Investigation Unit, Aetna Inc.
860-751-4886
[email protected]
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Applied Analytics for Fraud
Detection