detecting healthcare vendor fraud using data analysis
DESCRIPTION
Webinar series from FraudResourceNet LLC on Preventing and Detecting Fraud Using Data Analytics. Recordings of these Webinars are available for purchase from our Website fraudresourcenet.com This Webinar focused on fraud detection using data analytic software (Excel, ACL, IDEA) FraudResourceNet (FRN) is the only searchable portal of practical, expert fraud prevention, detection and audit information on the Web. FRN combines the high quality, authoritative anti-fraud and audit content from the leading providers, AuditNet ® LLC and White-Collar Crime 101 LLC/FraudAware. The two entities designed FRN as the “go-to”, easy-to-use source of “how-to” fraud prevention, detection, audit and investigation templates, guidelines, policies, training programs (recorded no CPE and live with CPE) and articles from leading subject matter experts. FRN is a continuously expanding and improving resource, offering auditors, fraud examiners, controllers, investigators and accountants a content-rich source of cutting-edge anti-fraud tools and techniques they will want to refer to again and again.TRANSCRIPT
Detecting Healthcare Vendor Fraud Using Data
Analysis
April 17, 2013
Special Guest Presenter:Katrina Kiselinchev, CIA, CPA, CFE, CFF
Copyright © 2013 FraudResourceNet™ LLC
Copyright © 2013 FraudResourceNet™ LLC
About Peter Goldmann, MSc., CFE
President and Founder of White Collar Crime 101
Publisher of White‐Collar Crime Fighter
Developer of FraudAware® Anti‐Fraud Training Monthly Columnist, The Fraud Examiner,
ACFE Newsletter
Member of Editorial Advisory Board, ACFE
Author of “Fraud in the Markets”
Explains how fraud fueled the financial crisis.
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About Jim Kaplan, MSc, CIA, CFE
President and Founder of AuditNet®, the global resource for auditors
Auditor, Web Site Guru
Internet for Auditors Pioneer
Recipient of the IIA’s 2007 Bradford Cadmus Memorial Award.
Author of “The Auditor’s Guide to Internet Resources” 2nd Edition
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About Katrina Kiselinchev, CPA, CIA, CFE, CFF
President of Inclusivitie, We Do “SMART”
Integration Partner with IDEA
Experience across industries, audit, fraud, and value
Achieved Value of $5+ Million & ROI of 2000+%, which
included using IDEA’s SMART Analyzer .
Adjunct Professor at Georgia State University
Specific Fraud in Fixed Asset e.g. Mischaracterized Fixed
Asset v. Expense to Increase Bottom Line.
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This webinar and its material are the property of AuditNet® and FraudAware®. Unauthorized usage or recording of this webinar or any of its material is strictly forbidden. We will be recording the webinar and you will be provided access to that recording within five business days after the webinar. Downloading or otherwise duplicating the webinar recording is expressly prohibited.
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Disclaimers
5
The views expressed by the presenters do not necessarily represent the views, positions, or opinions of FraudResourceNet LLC (FRN) or the presenters’ respective organizations. These materials, and the oral presentation accompanying them, are for educational purposes only and do not constitute accounting or legal advice or create an accountant‐client relationship.
While FRN makes every effort to ensure information is accurate and complete, FRN makes no representations, guarantees, or warranties as to the accuracy or completeness of the information provided via this presentation. FRN specifically disclaims all liability for any claims or damages that may result from the information contained in this presentation, including any websites maintained by third parties and linked to the FRN website
Any mention of commercial products is for information only; it does not imply recommendation or endorsement by FraudResourceNet LLC
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Learning Objectives
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Who are the Most Common Dishonest Healthcare Providers
New Fraud Risks Under Obamacare
Case Studies: Ways Vendors Rip Off Hospitals, Clinics and Other Providers
How Insiders Collude with Vendors to Embezzle Funds, Divert Inventory and Steal Confidential Medical Data
Red Flags of Healthcare Billing Schemes, Sham Vendor Frauds, Drug Diversion Schemes and Other Hugely Costly Scams
Proven Data Analytics for Detecting Indicators of Healthcare Fraud
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Agenda
Introduction
The Auditor’s Role
Fraud State of the Union
Most Common Dishonest Healthcare Providers
IMA Bleeding Company Case
Planning, Data Gathering & Software Introduction
Discovery with DA: Is There Fraud at IMA Bleeding
Red Flag Detection & Collusion
Finding Healthcare Fraud & Next Steps
New Fraud Risks Under ObamaCare
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The Auditor’s Role
IPPF Standard 1210.A3
Internal auditors must have sufficient knowledge of…available technology
based audit techniques to perform their assigned work
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IIA Guidance – GTAG 13
Internal auditors require appropriate skills
and should use available technological
tools to help them maintain a successful
fraud management program that covers
prevention, detection, and investigation.
As such, all audit professionals — not just
IT audit specialists — are expected to be
increasingly proficient in areas such as
data analysis and the use of technology to
help them meet the demands of the job.
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Professional Guidance
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Fraud State of the Union
Source: 2012 ACFE Report to the Nations
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Fraud State of the Union
Source: 2012 ACFE Report to the Nations
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Healthcare Overview
The prevention, treatment, and management of
illness and the preservation of mental and physical
well‐being through the services offered by the
medical and allied health professions.
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Polling Question 1
Frequency of Healthcare Cases in the Latest ACFE “Report to the Nation”
A. 6%
B. 30%
C. 15%
D. 2%
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Most Common Dishonest Providers
Durable Medical Equipment (DME)
Pharmaceutical Companies
Third Party Billing Companies
Ambulance Service Providers
Diagnostic Laboratories
Organized Crime
Pharmacies
MD’s and other care providers
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Large Amounts of Data Identify Red Flags Faster Increased Coverage Determine Trends Continuous Analysis Process Improvement
Data Analytics for Fraud:Tools You Can Use
Small Amounts of Data Delay in Identifying Red Flags Decreased Coverage Harder to ID Trends
Data Analytics Traditional Auditing
Show First View of IDEA
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Healthcare Fraud: The Big Picture
Healthcare Fraud ranges between $125 billion and $175 billion annually in Healthcare
(Thomson Reuters).
Every $2 million invested in fighting health‐care fraud returns $17.3 million in recoveries, court‐ordered
judgments, plus bogus claims that weren’t paid and other anti‐fraud savings. (National Health Care Anti‐Fraud Association,
2008) Source: www.fbi.gov
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Vendor Fraud SchemesReminding Yourself What Exists
Bid Rigging – A commercial contract is promised to one party even though for the sake of appearance several other parties involved.
Price Fixing ‐ agreement between participants on the same side in a market to buy or sell a product, service, or commodity only at a fixed price to control pricing.
Market Division ‐ Competitors divide markets among themselves. In such schemes, competing firms allocate specific customers or types of customers, products, or territories among themselves.
Vendor Masking ‐ tricks e.g. shell companies to hide their true identity.
Inside Job ‐ current or former employees collude with vendor to approve and/or pay overstated or false invoices for personal gain.
False Claims – billing for more expensive equipment and billing for supplies never received
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Bribery – involves bribes either directly or indirectly e.g. masking through classification type in order to gain service / business. Corruption PerceptionIndexes are good source for countries.
Kickbacks – typically external sources provide kickback for selection of services e.g. kickback.
Vendor Fraud SchemesReminding Yourself What Exists
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Flying Under the Radar ‐ criminals avoid detection by using “proven”
techniques for blending in with legitimate invoices, vendors and payments.
Organized Crime Billing Schemes ‐ Sophisticated groups of criminals take a savvy, organized approach to defrauding your company. Medicare Examples of Increased Fictitious Companies
Health care fraud, waste and abuse
add roughly 3‐10 percent to all
healthcare spending.
ABC News Video
Healthcare Vendor Fraud Schemes
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Healthcare Vendor Fraud Schemes
Provider Utilization Rates > Peers
Unlikely or Unrelated Procedures
Above Average Patient Volume and/or Procedures
Unbundling Procedures for Billing for + Reimbursement
Patient Name Does Not Equal Insured’s Name
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Case Study: IMA BLEEDING
Initial Contact:
IMA Bleeding Co. gets an employee tip that they should look at two vendors that recently got approved for Medicare Billing. The tipster states there is massive overbilling going on for non‐eligible expenses.
“Concerns about Unnecessary Diagnostic Tests & Kickbacks”
“We Need a Data Analysis Expert… .. Massive Data”
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Polling Question #2
For Every $2 Million Invested in Fighting Healthcare Fraud, How Much is Estimated to Be Returned?
A. $17 million
B. $68 billion
C. $5 Million
D. $100K
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IMA Bleeding Co
No Pre‐Screening of New Co. Existence for Medicare Billing
No Comparison of Vendor Address v. Employees
No Comparison of Managed Care Rates v. Billings
No Comparison of Budget v. Spend
.
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Software Overview & Introduction
Set Working Folder
Import Data
Check Control Totals
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Client Communication:Planning for Healthcare Fraud
Walk‐through of Entity Approvals, Contracts, Rates, and Service Types
Procure‐to‐Pay Cycle
Complete Audit Plan with Data Analysis Incorporated
Include Different Types Alongside Consideration of Fraud
Comparisons to Similar Services & Pricing
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Data Gathering for Data Analysis
Request All Meta Data Fields & FilesBillings, Employees, Budgets, & Rates
Microsoft Excel 97-2003 Worksheet
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Polling Question 3
What Are Three Steps to Start Data Analysis?
A. Set Working Folder, Import, Check Control Totals
B. Set Working Folder
C. Set Working Folder, Import, & Begin Tests
D. Import & Begin Tests
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Discovery with Data Analysis:Is There Fraud at IMA?
Initial Audits to Perform:
Vendor Locations
Address v. Employee
Stripping Address
Contracts:
Billings v Rates
Billings v Patients
Billings v Budgets
Duplicates
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Discovery with DA: Spend
Audits to Perform:
Vendor Address Match
Summarize by Billings
Strip Addresses in Both Files
Join Files by Address (c)Red Flag #1: Three Vendors Match Employee Addresses
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Polling Question 4
Which Tests Should IMA Run Initially
to Identify Red Flags?
A. Billings v. Rates, Patients, Budgets, and Duplicates
B. Recalculate Depreciation
C. COGS Transfer to Sale
D. Only Address v. Employee
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Discovery with DA: Duplicates
Red Flag #2: Numerous Duplicates, Odd Amounts & Quantities
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Discovery with DA: Billings v. Rates, Patients, Budgets
Red Flag #3:Drug A Mismatch on Rates
Red Flag 4:No Match on Patient 125
Red Flag 5:Spend v. Contract Overages
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Total Issues Found & Next Steps
1) Complete Summary of Data & Findingsa) Total by Each Type of Discrepancyb) Aggregate Discrepancy
2) Determine Additional Tests to Run & Perform3) Discuss Findings with Owner Initially & Recommendations4) Complete Interviews with Employees 5) Review Gaps & Control Deficiencies & Provide Recommendations 6) Recommend Management Review for Cost v Benefit & Implement ASAP7) Determine if Client has Fraud Insurance Rider. If not, recommend.8) Contact Insurance Carrier (if applicable) & Begin Claim Process.9) Contact Authorities & File Report.10) Determine if Prosecution is Viable e.g. Dollar Amount, Management, etc.11) Complete Report (See ACFE.com) 12) Complete Supporting Doc for Insurance Co. & Authorities
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ObamaCare Fraud Concerns
The Obama Administration’s bonus system for insurers participating in Medicare Advantage, the private‐market alternative to the government’s traditional health insurance program for the elderly, has been criticized
by Republicans as a political giveaway and by the Government Accountability Office as illegal.
The government rates every Advantage plan from one to five stars, depending on how they perform on a range of tests including measures of patient care, what proportion of members receive flu shots, and
measures of customer satisfaction such as complaints to Medicare. More stars are better.
Since 2011, when the bonuses first became available, the average star rating for plans that offer drug coverage — the most popular type — has
increased from 3.18 to 3.66
Legitimate or Not? Manipulation to Get Bonuses…
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Polling Question 5
Which of the following is/are Red Flag(s)/ Finding(s) Identified in IMA Bleeding?
A. Improper Depreciation CalculatedB. No Match On Patient 125, Duplicates, Budget & Pricing OveragesC. Terminated Employees Receiving PaychecksD. No Match on Patient 125 Only
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Questions?
Any Questions?Don’t be Shy!
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Thank You!
Website: http://www.fraudresourcenet.com
Jim KaplanFraudResourceNet™
800-385-1625 [email protected]
Peter GoldmannFraudResourceNet™
Katrina KiselinchevInclusivitie, LLC832-236-4778
[email protected]://www.inclusivitie.com
Copyright © 2013 FraudResourceNet™ LLC
Coming Up….
Anti-Fraud Professionals’ Role in Building an Anti-Fraud Culture, April 23, 2013
Detecting, Preventing and Auditing for Fraud Using Excel, May 7, 2013.
Details at: http://www.fraudresourcenet.com