Download Fraud Detection in Healthcare

Fraud Detection in Healthcare
MBF’s successful
approach through
SAS South Pacific
Data Mining
Discovery Centre
Jolie Reichel
Research Manager - Risk Management
Medical Benefits Fund of Australia Limited
Background
• MBF is Australia’s largest privately owned health
insurance company, covering 1.6 million Australians
• In 1999-2000, MBF received over $AUD 1 billion in
contributions
• Hospital Benefits Paid - $AUD 619 million
• Ancillary Benefits Paid - $AUD 237 million
– Optical Claims Paid - $AUD 39 million
The Mission
• 77% of all Optical Claims are
processed via Autoclaim, an
automated claims processing
system
• These transactions are matched
against our system to perform
eligibilty checks
• As all family members are
represented on one card, “Limit
Surfing” can occur
• How can we detect this?
The Strategy
• Identify test cases of “Limit Surfing” through standard
investigation methods
• Extract transaction-level detail from the Claims
Database
• Call in the Experts
– Extensive vendor selection process!
– Contacted SAS South Pacific Data Mining Discovery Centre
– Used MBF domain experts and SAS technical staff to develop
categories and measures
Optical Project
•Mined a 200 MB file of national optical data
covering 2 years successfully
•All project criteria were met, namely:
– Discovery of aberrant optical cases uncovered
by Risk Management
– Knowledge transfer to Risk Management staff
•Also uncovered a significant amount of additional
suspicious cases than using previous methods
Optical Project –
Data Manipulation
• Pre processing claims
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A sample of two years data
Need to shape data into a
suitable format for mining
Fraud pattern was supplied
All transactions are rolled up to
membership level
New summary variables were
created to measure key
indicators of fraudulent activity
Optical Project – SEMMA process
Optical Project (1)
• Sample transformation
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The target variable is set
Analysed to establish frequency
and significance of variables.
Irrelevant variables were
removed
Distribution graphs highlight that
fraud represents 1.08% of all
memberships
Stratified sampling enabled these
“rare events” to be well
represented in our modelling
dataset - approx. 10,000
memberships
Optical Project (2)
• Explore
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Distribution graphs of each
characteristics
Explore patterns in the data
through visualisation tools
Verify correlations between
characteristics
Optical Project (3)
• Modify
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Missing values modified to
produce meaningful results
Created new variables from
existing ones
Skewed variables further
transformed
Data Partitioning performed to
remove “overtraining” of data
Automated variable selection
Optical Project (4)
• Model
•
Tested Decision Tree as input
variable selector for further
modelling
•
Tested Logistic Regression,
Neural Networks & Decision
Trees modelling
• Assess
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Logistic Regression produced
best results
•
Identified a multiple of up to 23
times previously identified fraud
cases
Lift Chart
•By applying this
model and
targeting the top
5% of likely
fraudulent
services, MBF can
potentially obtain
an improvement
of 800% over
random audits
Lift value of 9 if the top 5%
percentile is examined
Data Mining at MBF now
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Since the Optical Project, MBF has applied
data mining techniques across other health
areas
MBF has also implemented data mining
techniques and models to develop business,
product & system rules & controls
The Risk Management team is supported by
our own IT infrastructure, including a
dedicated data mining server
Data Mining - Impact Assessment
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Since implementation in November 2000, MBF
achieved an ROI of over 200% within six months
Full year forecasted savings of over $AUD 2 million
through Fraud Detection alone
Flow-on effects through product design and
enhancement to control aberrant behaviour at the
source