1. Sergei Ananyan, Ph.D. Healthcare Fraud Detection through Data Mining Your Knowledge Partner TM
(c) Megaputer Intelligence

2. Your Knowledge Partner TM
Problem
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3. Problem US annual healthcare expenditures - $1.3 trillion
Fraud detection through DM
Problem
US annual healthcare expenditures - $1.3 trillion
Fraudulent transactions - $100 billion
About one dollar in every ten is spent to pay for fraud

4. Problem Typical healthcare fraud schemes include
Fraud detection through DM
Problem
Typical healthcare fraud schemes include
Billing for services not actually performed
Falsifying a patient's diagnosis to justify procedures that aren't medically necessary
Misrepresenting procedures performed to obtain payment for non-covered services, such as cosmetic surgery
"Upcoding" — billing for a more costly service than the one actually performed
"Unbundling" — billing each stage of a procedure as if it were a separate procedure
Accepting kickbacks for patient referrals
Waiving patient co-pays or deductibles and over-billing the insurance carrier or benefit plan (involves both the provider and the patient)

5. Fraud detection through DM
Problem
Fraudulent activities CAN be discovered by professional investigators through in-depth manual analysis
Yet, due to huge volume of patients, providers and transactions involved, human investigators can analyze only a small portion of available data
This makes the task of fraud detection very difficult and results in the majority of fraud activities going undetected for extended periods of time

6. Challenge Overwhelming volumes of data
Fraud detection through DM
Challenge
Overwhelming volumes of data
15,000 diagnoses
20,000 procedures
40,000 providers
1,000,000 patients
150,000,000 transactions per year
Investigators need automated fraud detection tools

7. Fraud detection through DM
Automated analysis?
It is hard to separate fraud from errors - and often even from normal practices
Automated tools can only catch most suspicious cases and bring them to attention of human investigators
Final determination of fraud is a result of thorough and possibly very non-trivial investigation by human analysts
Knowing what cases to focus their attention on, can dramatically increase effectiveness of human investigators

8. Your Knowledge Partner TM
Automated fraud detection approaches
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9. Approaches Rule-based approach Data Mining approach Hybrid approach
Fraud detection through DM
Approaches
Rule-based approach
Data Mining approach
Hybrid approach
New
Future

10. Rule-based systems Are based on Some systems are available (IBM, ViPS)
Fraud detection through DM
Rule-based systems
Are based on
Documenting knowledge acquired from domain experts: storing signatures of known fraud schemes as a collection of business rules
Using the developed business rules to comb historical data and discover inconsistencies
Some systems are available (IBM, ViPS)
BUT
Can catch only a small portion of bad transactions matching ALREADY KNOWN fraud schemes
Do not help in discovering new fraud schemes
Are quite costly to purchase and maintain
Cause investigators to seek more powerful tools

11. Data Mining approach New generation of self-learning tools
Fraud detection through DM
Data Mining approach
New generation of self-learning tools
Are based on
Determining typical “normal” courses of treatment of patients with different diagnoses from the analysis of huge volumes of historical data
Detect and red-flag anomalous deviations from the norm
Output
Lists of the most suspicious providers, patients and transactions for further manual investigation
Can catch and help understand even unknown fraud schemes

12. Data Mining system CAN discover previously unknown fraud schemes
Fraud detection through DM
Data Mining system
CAN discover previously unknown fraud schemes
Provides tools for the visualization and interactive manipulation of the obtained results
Requires minimum knowledge from domain expert
Is cheap to maintain

13. Future: hybrid systems
Fraud detection through DM
Future: hybrid systems
A Data Mining-based fraud detection system enriched with known domain-specific business rules can provide the best results
Merging two approaches will require good domain expertise
The resulting system can be used for real-time fraud prevention
Both Rule-based and Data Mining tools can only suggest suspicious cases, which have to be further scrutinized by professional investigators

14. Your Knowledge Partner TM
Developed data mining solution
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15. DM system for fraud detection
Fraud detection through DM
DM system for fraud detection
Megaputer MediCop™ - first dedicated Data Mining system for Fraud Detection
Discovers and helps understand situations involving previously unknown fraud schemes
Carries out an objective, data-drive and bias-free analysis
Requires minimal intervention and maintenance from human analysts
Provides interactive data visualization and aggregation simplifying further in-depth analysis of red-flagged data by human investigators
Can further improve its performance by incorporating rules holding background knowledge coming from a domain expert – hybrid approach

16. Data Mining solution Megaputer MediCop™
Fraud detection through DM
Data Mining solution
Megaputer MediCop™
Was developed in a project with Medicaid Agency of the State of Missouri and Medstat
Determines “anomalous” transactions, patients and providers based on the analysis of data only
Demonstrated good accuracy in capturing “bad” providers (over 65%)
Is currently being enriched with additional self-learning fraud detection scenarios

17. Typical users Health insurance companies HMOs
Fraud detection through DM
Typical users
Health insurance companies
HMOs
Medicaid and Medicare agencies

18. Your Knowledge Partner TM
Fraud Detection through data mining
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19. Steps in fighting fraud
Fraud detection through DM
Steps in fighting fraud
Fraud Detection
Investigation of red-flagged cases
Distilling rules for transaction monitoring
PolyAnalyst can automate the analysis and significantly simplify the steps of
Fraud Detection – MediCop module
Investigation – Text OLAP and Link Analysis modules

20. Transaction Monitoring Fraud Detection system
Fraud detection through DM
Methodology
Automated
Transaction Monitoring
System 3
Bad transactions
Automated
Fraud Detection system
based on Data Mining 1
MediCop™
Distill new rules for
detecting and preventing
suspicious transactions
in real time
Enrich with domain-
specific business rules 2
Investigate and detect
exploited fraud scheme
Text OLAP™
Catch fraudsters and
recover the money

21. Your Knowledge Partner TM
Fraud Detection:
MediCop™
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22. Fraud Detection - MediCop
Fraud detection through DM
Fraud Detection - MediCop
MediCop™
Determine the “norm”
for different situations
and identify deviations
from the norm
Raw data
Distinguish and rank most
anomalous patients for
different groups of
diagnoses, ages, etc.
Identify anomalous
providers and their profiles
Suspicious providers
Combine obtained results
with relevant business
considerations

23. MediCop output List of most anomalous patients for various diagnoses
Fraud detection through DM
MediCop output
List of most anomalous patients for various diagnoses

24. MediCop output List of most anomalous providers
Fraud detection through DM
MediCop output
List of most anomalous providers

25. MediCop output Groups of providers sharing many anomalous patients
Fraud detection through DM
MediCop output
Groups of providers sharing many anomalous patients

26. Fraud detection through DM
MediCop output
Visualizing anomalous patients, providers and procedures

27. Fraud detection through DM
MediCop output
Different view of the same results – supports drill-down

28. Fraud detection through DM
MediCop drill-down
Drilling down to get a summary for anomalous provider

29. MediCop drill-down List of anomalous patients for this provider
Fraud detection through DM
MediCop drill-down
List of anomalous patients for this provider

30. MediCop drill-down List of anomalous procedures for this provider
Fraud detection through DM
MediCop drill-down
List of anomalous procedures for this provider

31. Fraud detection through DM
MediCop drill-down
Drilling down to get a summary for anomalous patient

32. Fraud detection through DM
MediCop drill-down
Distribution of procedures between different providers

33. Fraud detection through DM
MediCop drill-down
List of diagnoses and anomalous providers for a patient

34. Your Knowledge Partner TM
Fraud Investigation:
Text OLAP™
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35. Fraud detection through DM
Fraud investigation
Megaputer Text OLAP engine allows the user to directly answer numerous questions:
What is the profile of a considered provider?
Which providers perform unusual procedures for patients with a particular diagnosis?
Do these providers perform unusual procedures for patients with other diagnoses?
Which providers tend to perform more expensive procedures given the same patient diagnosis?
What is the distribution of providers the patient had visited during the last year?

36. Fraud detection through DM
Text OLAP drill-down
Seeking providers performing unusual procedures for patients with a selected diagnosis

37. Your Knowledge Partner TM
Fraud Investigation:
Association analysis
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38. Fraud detection through DM
Association analysis
Helps reveal and visually display groups of related providers sharing significant numbers of patients

39. Your Knowledge Partner TM
Fraud Investigation:
Link Analysis
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40. Fraud detection through DM
Link Analysis
Visualize correlations between providers, patients, diagnoses and procedures

41. Your Knowledge Partner TM
Benefits
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42. Result Dozens of millions of transactions
Response prediction
Result
A short list of the most suspicious transactions and providers
Fraud
Dozens of millions of transactions
Automated focusing on a dramatically reduced number of records representing the most suspicious cases

43. Fraud detection through DM
Benefits
Dramatic cost reduction and increase in quality and speed of the analysis compared to manual processing
Objective data-driven analysis and solid foundation for further in-depth analysis
Discovery of even unknown fraud schemes
Automated monitoring of known problems and timely discovery of newly developing issues
Utilization of 100% of available data
Easy and cheap to maintain solution
Analytical foundation for future expansion in other fraud detection areas

44. Your Knowledge Partner TM
Further Questions?
Call Megaputer at
(812)
or write
120 W Seventh Street, Suite 310
Bloomington, IN USA
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