dental insurance fraud detection with predictive modeling soa spring health meeting may 29, 2008...
TRANSCRIPT
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Dental Insurance Fraud Dental Insurance Fraud DetectionDetection
With With
Predictive ModelingPredictive ModelingSOA Spring Health Meeting
May 29, 2008
Jonathan Polon FSA
www.claimanalytics.com
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• Intro to Predictive Modeling
• Supervised vs Unsupervised Learning
• Modeling Procedures
• Dental Insurance Fraud Detection
• Examples of Atypical Dentists
AgendaAgenda
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Introduction to Introduction to Predictive ModelingPredictive Modeling
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A Part of Everyday LifeA Part of Everyday LifeHave you used a predictive model
today?
•Mail sorting
•Credit card processing
•Credit scores
•Weather forecasting
•Grocery shopping
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What is Predictive What is Predictive ModelingModeling
• Harnesses power of modern computers to find hidden patterns in data
• Used extensively in industry
• Many possible uses in insurance:
•Claim management•Pricing•Reserving•Fraud detection
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Predictive Modeling Predictive Modeling ToolsTools
Some common techniques
• Generalized linear models
• Neural networks
• Genetic algorithms
• Random forests
• Stochastic gradient boosted trees
• Support vector machines
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SupervisedSupervisedVsVs
Unsupervised Unsupervised LearningLearning
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Supervised LearningSupervised Learning
• In the training dataset, there is a known outcome associated with each record
• Learning objective: build a model that can accurately estimate the outcomes from the predictor variables for each record
• Eg, Sports Betting: predict winners based on observations from past games
• Commonly referred to as predictive modeling
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Unsupervised LearningUnsupervised Learning
• In the training dataset, there is not an outcome associated with any record
• Learning objective: self-organization or clustering; finding structure in the data.
• Eg, Grocery Stores: define types of shoppers and their preferences
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ModelingModelingDentalDental
ProceduresProcedures
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Why Model Procedures?Why Model Procedures?
There’s more to diagnosis than category
• within categories, severity varies
• similarities can exist between procedures of different categories
• how do we extract more information?
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Scoring ProceduresScoring Procedures
Create a series of metrics for each procedure
• some relative values from 0 – 10
•Some absolute values
• for example:- unbundling - specialized- major problem - emergency
• allows every procedure to be compared to every other procedure
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Scoring Procedures - Scoring Procedures - ExampleExample
Caries, trauma,
pain control
Pulpotomy, molars,
emergency
Root canals, 3 canals,
calcified
Category Restorative
Endodontics
Endodontics
Specialized
0 0 8
Major work
5 8 10
Emergency No Yes No
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Dental Fraud DetectionDental Fraud DetectionUsingUsing
Unsupervised LearningUnsupervised Learning
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Research ProjectResearch Project
• 200,000 dental claim records from 2004
• General practitioners in Ontario
• Applied modern pattern detection techniques to identify dentists with atypical claims histories
• Research paper: Dental Insurance Claims: Identification of Atypical Claims Activity
http://www.actuaries.ca/members/publications/2007/207033e.pdf
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Traditional ToolsTraditional Tools
Rule-based
Strong at identifying claims that match known types of fraudulent activity
Limited to identifying what is known
Typically analyze at the level of a single claim, in isolation
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Pattern Detection ToolsPattern Detection Tools
• Analyze millions of claims to reveal patterns– Technology learns what is normal and what is
atypical – not limited by what we already know
• Categorize each claim, reveals dentists with a high percentage of atypical claims– Immediately highlights new questionable behaviors
– Finds new large dollar schemes
– Also identifies frequent repetition of small dollar abuses
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Methodology – 3 Methodology – 3 PerspectivesPerspectives1. By Claim e.g. Joe Green’s semi-annual
check-up
2. By Tooth e.g. all work done in 2004 on Joe’s bicuspid by Dr. Brown
3. By General Work e.g. all general work (exams, fluoride, radiographs, scaling and polishing) done on Joe in 2004 by Dr. Brown
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Methodology – 2 Methodology – 2 TechniquesTechniques1. Principal components analysis
2. Clustering
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PCA Cluster
Claim by claim Tooth by tooth General work
Methodology – SummaryMethodology – Summary
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Principal Components Principal Components AnalysisAnalysis•Allows reduction of multi-dimensional data to lower dimensions while maximizing amount of information preserved
•Reducing to 2 dimensions allows data points to be plotted on a chart
•Powerful approach for identifying outliers
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PCA: Simple ExamplePCA: Simple ExampleP C A: S am p le D ata
-6
-4
-2
0
2
4
6
-6 -4 -2 0 2 4 6
X -Ax is
Y-A
xis
Lots of variance between points around both axes
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PCA: Simple ExamplePCA: Simple ExampleP C A: Ro ta te Axes 45 o
-6
-4
-2
0
2
4
6
-6 -4 -2 0 2 4 6
X -Ax is
Y-A
xis
Create new axes, X’ and Y’: rotate original axes 45o
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PCA: Simple ExamplePCA: Simple Example
In the new axes, very little variance around Y’Y’ contains little “information”
P C A: Ro ta ted Axes
-6
-4
-2
0
2
4
6
-8 -6 -4 -2 0 2 4 6 8
X '-Ax is
Y'-
Ax
is
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PCA: Simple ExamplePCA: Simple Example
Can set all Y’ values to 0 – ie, ignore Y’ axisResult: reduce to 1 dimension with little loss of info
P C A: Red u ce to 1 D im en s io n
-6
-4
-2
0
2
4
6
-8 -6 -4 -2 0 2 4 6 8
X '-Ax is
Y'-
Ax
is
♦ Original
● Revised
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PCA: Identifying Atypical PCA: Identifying Atypical DentistsDentists
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PCA: Identifying Atypical PCA: Identifying Atypical DentistsDentists
1. Begin at the individual transaction level
2. Determine the average transaction for each dentist
3. Graph quickly isolates dentists that are outliers
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Dentist Average 90th percentile
PCA: Identifying Atypical PCA: Identifying Atypical DentistsDentists
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PCA: SummaryPCA: Summary
1. Visualization allows for easy and intuitive identification of dentists that are atypical
2. Manual investigation required to understand why a dentist or claim is atypical
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Clustering Techniques
•Categorization tools
•Organize claims into several groups of similar claims
•Applicable for profiling dentists by looking at the percentage of claims in each cluster
•We apply two different clustering techniques: K-Means and Expectation-Maximization
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Clustering ExampleClustering Example
Clustering ExampleClustering Example
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Example: Clusters found – by Example: Clusters found – by ClaimClaim 1. Major dental problems
2. Moderate dental problems
3. Age > 28, minor work performed
4. Work includes unbundled procedures
5. Minor work and expensive technologies
6. Age < 28, minor work performed
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Clusters: Identifying Atypical Clusters: Identifying Atypical DentistsDentists
1. Begin at the individual claim level
2. Calculate the proportion of claims in each cluster – in total, and for each dentist
3. Isolate dentists with large deviations from average
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Clusters: Identifying Atypical Clusters: Identifying Atypical DentistsDentists
Proportion of General Work by ClusterK-Means Clustering
0%
20%
40%
60%
80%
100%
% C
laim
s X
Average 19% 2% 6% 20% 6% 9% 21% 17%
Dent A 0% 0% 0% 0% 0% 49% 0% 51%
1 2 3 4 5 6 7 8
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Clustering Techniques: Clustering Techniques: SummarySummary1. Clustering provides an easy to
understand method to profile dentists
2. Unlike PCA, clustering tells why a given dentist is considered atypical
3. Effectively identifies atypical activity at the dentist level
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Pilot Project ResultsPilot Project Results
• PCA identified 68 dentists that are atypical
• Clusters identified 182 dentists that are atypical
• 36 dentists are identified as atypical by both PCA and Clusters
• In total, 214 of 1,644 dentists are identified as atypical (13%)
• Billings by atypical dentists were $2.5 MM out of $16.1 MM billed by all dentists (15%)
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ExamplesExamplesOfOf
Atypical DentistsAtypical Dentists
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Proportion of General Work by ClusterK-Means Clustering
0%
20%
40%
60%
80%
100%%
Cla
ims
X
Average 19% 2% 6% 20% 6% 9% 21% 17%
Dent A 0% 0% 0% 0% 0% 49% 0% 51%
1 2 3 4 5 6 7 8
Analysis: Dentist A is far beyond norms in clusters 6 and 8; both indicate high charges in ‘general dentistry’
What we discovered: Each of Dentist A’s patients is being billed for at least 30 minutes of polishing and 45 minutes of scaling
Dentist ADentist A
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Dentist BDentist B
Proportion of Teeth by ClusterK-Means Clustering
0%
20%
40%
60%
80%
100%%
Cla
ims
X
Average 22% 9% 5% 13% 19% 25% 8%
Dent B 5% 20% 21% 0% 0% 0% 54%
1 2 3 4 5 6 7
Analysis : Dentist B is far beyond norms in cluster 7 (age > 19, major work)
What we discovered: Frequent extractions and anesthesia Dentist B looks like an oral surgeon, but is a general practitioner
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Dentist CDentist C
Proportion of General Work by ClusterEM Clustering
0%
20%
40%
60%
80%
100%%
Cla
ims
X
Average 23% 77%
Dent C 66% 34%
1 2
Analysis: Very high proportion in Cluster 1, suggesting many high-ticket visits
What we discovered: First, Dentist C performs an inordinate amount of scaling (average of 1 hour p.p.). Second, Dentist C has emergency examinations with atypically high frequency
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Dentist MDentist M
Analysis: Dentist M has a disproportionate amount of work beyond the 90th percentile of work by all dentists on all teeth
What we discovered: Dentist M appears to utilize lab work very heavily
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More Atypical DentistsMore Atypical Dentists
• Frequent use of panoramic x-rays
• Frequent use of nitrous oxide – including with procedures rarely associated with anesthesia
• Large number of extractions, often using multiple types of sedation
• Very high proportion of claims for crowns and endodontic work
• Individual instruction on oral hygiene provided with very atypical frequency
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Dental Fraud SummaryDental Fraud Summary
• Highly effective in identifying dentists with claim portfolios significantly different from the norm
• Enables experts to quickly identify and focus on those dentists with atypical claims activity
Pattern detection:
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Questions?Questions?