Detect and prevent fraud, waste and abuse with data mining

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Detect and prevent fraud, waste and abuse with
data mining
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Todays agenda

• Examine the impact of fraud, waste and abuse
• Data mining for detection and prevention
• Data mining in action case studies

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For the purpose of todays seminar

• Fraud, waste and abuse includes
• Illegal practices
• Waste
• Payment error
• Non-Compliance
• Incorrect billing practices

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The impact of fraud, waste and abuse
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The impact of fraud

• GAO cited 19.1billion in improper Government
  payments in 17 major programs for fiscal year
  1998. GAO, Financial Management Increased
  Attention Needed to Prevent Billions in Improper
  Payments, October 1999
• Medicare 12.6 Billion
• Supplemental Security Income 1.6 B
• The Food Stamp Program 1.4 B
• Old Age and Survival Insurance 1.2 B
• Disability Insurance 941 Million
• Housing Subsidies 847 Million
• Veterans Benefits, Unemployment Insurance and
  Others 514 Million

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The impact of fraud

• An IRS audit of returns claiming the EIC (Earned
  Income Credit) for tax year 1994 found 4.4
  billion in overpayments out of 17.2 billion in
  total claims. A follow-up study by the IRS
  determined that, even after the implementation of
  compliance reforms, the error rate was still at
  least 20 percent of all EIC claims filed. GAO,
  Major Management Challenges and Program Risks
  Department of the Treasury, January 1999.
• From 1994 through 1998, defense contractors
  returned a total of roughly 4.6 billion in
  overpayments. GAO, DOD Contract Management
  Greater Attention Needed to Identify and Recover
  Overpayments, July 1999
• Federal Employees Health Benefits Program, is
  estimated to consume as much as 1.8 billion a
  year in waste, fraud, and abuse--Office of
  Personnel Management IG, Most Serious Management
  Problems Office of Personnel Management, 1
  December 1999

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A pervasive problem...

• Medicare, Medicaid and other essential public
  health programs
• Entitlement and subsidy payments
• Tax collection
• Procurement
• Food stamps
• Child welfare benefits
• Workers compensation
• Unemployment benefits
• Any benefit payment

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And it is set to continue...

• Total public sector transaction volumes now
  exceed 2 trillion
• Forrester Research

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The impact if fraud goes undetected

• Much needed programs and services are
  under-funded
• Billions of dollars are lost
• Countless man-hours spent on investigative and
  auditing efforts yielding little in recoupments

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Data mining for detection and prevention
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Data mining defined

• The process of discovering meaningful new
  relationships, patterns and trends by sifting
  through data using pattern recognition
  technologies as well as statistical and
  mathematical techniques.
• - The Gartner Group

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Matching known fraud/non-compliance

• Which new cases are similar to known cases?
• How can we define similarity?
• How can we rate or score similarity?

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Anomalies and irregularities

• How can we detect anomalous or unusual behavior?
• What do we mean by usual?
• Can we rate or score cases on their degree of
  anomaly?

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Data mining is not

• Blindapplication of analysis/modeling
  algorithms
• Brute-force crunching of bulk data
• Black box technology
• Magic

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How do you mine data?

• Use the Cross Industry Standard Process for Data
  Mining (CRISP-DM)
• Based on real-world lessons
• Focus on business issues
• User-centric interactive
• Full process
• Results are used

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Techniques used to identify fraud

• Predict and Classify
• Regression algorithms (predict numeric outcome)
  neural networks, CRT, Regression, GLM
• Classification algorithms (predict symbolic
  outcome) CRT, C5.0, logistic regression

• Group and Find Associations
• Clustering/Grouping algorithms K-means,
  Kohonen, 2Step, Factor analysis
• Association algorithms apriori, GRI, Capri,
  Sequence

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Techniques for finding fraud

• Predict the expected value for a claim, compare
  that with the actual value of the claim.
• Those cases that fall far outside the expected
  range should be evaluated more closely

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Techniques for finding fraud
Decision Trees and Rules

• Build a profile of the characteristics of
  fraudulent behavior.
• Pull out the cases that meet the historical
  characteristics of fraud.

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Techniques for finding fraud
Clustering and Associations

• Group behavior using a clustering algorithm
• Find groups of events using the association
  algorithms
• Identify outliers and investigate

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Fraud detection using CRISP-DM

• Provides a systematic way to detect fraud and
  abuse
• Ensures auditing and investigative efforts are
  maximized
• Continually assesses and updates models to
  identify new emerging fraud patterns
• Leads to higher recoupments

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Data mining in action Fraud, waste and
abusecase studies
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How can data mining help?

• Payment error prevention
• Billing and payment fraud
• Audit selection

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Payment Error Prevention
The US Health Care Finance Administration needed
to isolate the likely causes of payment error by
developing a profile of acceptable billing
practices and...
used this information to focus their auditing
effort
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Payment error prevention solution

• Clementine
• Using audited discharge records, built profiles
  of appropriate decisions such as diagnosis coding
  and admission
• Matched new cases
• Cases not matching are audited

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Payment error prevention results

• Detected 50 of past incorrect payments
  resulting in significant recovery of funding lost
  to payment errors
• PRO analysts able to use resultant Clementine
  models to prevent future error

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Billing and payment fraud
The US Defense Finance and Accounting Service
needed to find fraud in millions of Dept of
Defense transactions and...
Identified suspicious cases to focus
investigations
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Billing and payment fraud solution

• Clementine
• Detection models based on known fraud patterns
• Analyzed all transactions scored based on
  similarity to these known patterns
• High scoring transactions flagged for
  investigation

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Billing and payment fraud results

• Identified over 1,200 payments for further
  investigation
• Integrated the detection process
• Anomaly detection methods (e.g., clustering) will
  serve as sentinel systems for previously
  undetected fraud patterns

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Audit selection
The Washington State Department of Revenue needed
to detect erroneous tax returns and...
Focused audit investigations on cases with the
highest likely adjustments
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Audit selection solution

• Clementine
• Using previously audited returns
• Model adjustment (recovery) per auditor hour
  based on return information
• Models will then score future returns showing
  highest potential adjustment

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Audit selection results

• Maximizes auditors time by focusing on cases
  likely to yield the highest return
• Closes the tax gap

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Data mining - key to detecting and preventing
fraud, waste and abuse

• Learn from the past
• High quality, evidence based decisions
• Predict
• Prevent future instances
• React to changing circumstances
• Models kept current, from latest data

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Announcing
http//www.spss.com/dataminingsummit/
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