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© 2010 IBM Corporation August 10, 2010 Fraud and Forensics: New Techniques, Better Results National Association of State Auditors, Comptrollers, and Treasurers
© 2010 IBM Corporation Fraud, waste, and abuse costs governments hundreds of billions in revenues each year Medicaid fraud increasing throughout state, but economy forcing cutbacks in investigations… losses could be $2 billion a year. Naples (FL) Daily News, 1/4/2009Medicaid fraud increasing throughout state, but economy forcing cutbacks in investigations… losses could be $2 billion a year. Naples (FL) Daily News, 1/4/2009 Medicare & Medicaid Tax & Revenue Workers Compensation Unemployment Insurance Food & Nutrition Programs …the FTB has pegged Californias tax gap associated with the PIT and CT at $6.5 billion annually. California Legislative Analysts Office, Budget …workers compensation fraud is the fastest growing insurance scam in the nation. Today, 10 cents out of each premium dollar is wasted on fraud. Connecticut Conference of Municipalities, 11/12/2008 …sophisticated shell games are costing Michigan's unemployment trust fund up to an estimated $95 million a year.. Michigan Unemployment Insurance Agency Every year, food stamp recipients exchange hundreds of millions of dollars in benefits for cash instead of food with retailers across the country... Lincoln (NC) Tribune, 10/21/2006 Improper Payments …agencies reported a total improper payment estimate of about $55 billion for fiscal year US Government Accountability Office, 1/23/2008
© 2010 IBM Corporation Date provided to OMB by the Federal Departments and Agencies. Fraud, waste, and abuse costs governments hundreds of billions in revenues each year
© 2010 IBM Corporation The problem of fraud and abuse has process, organization, technology, and analytics dimensions Fraud & Abuse Process OrganizationAnalytics Process Dimension Are proper business controls and procedures in place to detect and deter fraud and abuse? Do laws and policies constrain fraud related processes? Are fraud detection and investigation processes optimally aligned to organizational goals? Organization Dimension Are there organizational barriers to implementing an effective fraud detection and investigation program? Is there a single focal point (person or team) that is responsible for fraud and abuse activities? Are key performance indicators in place that measure and promote excellence in fraud and abuse pursuit? Analytics Dimension What detection analytics, if any, are in place? Do detection analytics allow for both immediate and retrospective analysis? Are analytics used to control and optimize fraud workload? Technology Technology Dimension What technologies are used to support fraud detection and investigation? Does fragmented data create challenges in having a complete picture of behavior? Does technology support measurement and reporting of fraud exposure?
© 2010 IBM Corporation Predictive Models Who is behaving well? Which entities are likely to behave badly in the future? What are the indicators that an entitys behavior is getting better over time? Worse over time? Data Mining & Clustering What are patterns of non-compliant (and criminal) behavior that I dont know about? If I catch a bad entity, how can I find out who else is behaving like that? Are there groups of entities who behave the same way? Which entities are behaving differently than others (in a suspicious way)? How good or bad is a entity behaving, relative to other entities? What is normal behavior? Outlier Detection Analytics are transforming how governments are tackling fraud, waste, and abuse
© 2010 IBM Corporation New York State Department of Taxation and Finance –Income tax refund fraud and abuse –Debt collection North Carolina Department of Health and Human Services –Medicaid fraud detection Case Studies
© 2010 IBM Corporation 5,000 total employees Approximately $60 billion collected annually (2009) Highly sophisticated taxpayer population (most Fortune 500 companies have a presence in New York) Wide range of taxpayers (demographic and cultural) New York State Department of Taxation and Finance
© 2010 IBM Corporation The project objective was to build a system to enhance existing audit case selection methods for fraud detection of pre-processed Returns The Questionable Refund Detection unit wanted… A better way to identify questionable returns To question suspect returns before issuing refunds To improve collect ability of audit cases To issue refunds in a timely manner To make program management more flexible To leverage investments in data warehousing and business intelligence technologies To scientifically predict good audit candidates utilizing return filing patterns, case history, and other external indicators To improve their ability to detect new areas of fraud New York State Department of Taxation and Finance
© 2010 IBM Corporation CASE STUDY – State of New York Predicting tax compliance The Case Identification and Selection System (CISS) applies business rules and predictive models to categorize and score returns nightly and identifies the next best case for audit selection. In addition, a separate web based portal provides screening and resolution of cases. Solution Benefits Challenge New York wanted to enhance current audit case selection methods for detection of audit issues at the time a return is processed. Specific audit programs include Earned Income Credit, Dependent Child Care Credit, Itemized Deductions, Wage/Withholding, and Identity Theft. $889 million increase in revenue in the first five years Increased screener and auditor productivity Enhanced taxpayer correspondence Improved audit program management 9
© 2010 IBM Corporation 100 employees in Program Integrity Unit Approximately $14 billion in annual paid claims (2009) $25 million in recoupment letters issued annually North Carolina Department of Health and Human Services
© 2010 IBM Corporation Solution components: IBM Fraud Analysis Center InfoSphere Identity Insight IBM Global Business Services: Business Analytics and Optimization IBM Software Group: Lab Services I think we are going to save tens of millions of dollars. – Beverly Perdue, Governor State of North Carolina The Need: This large state social services agency faces a significant exposure to healthcare fraud and abuse. The current business process and technology used to fight fraud, waste, and abuse in Medicaid is ineffective – producing only around $25 million annually in recoveries. This leakage, combined with a significant state budget deficit, motivated the state to aggressively pursue cost takeout projects. The Solution: The state implemented a comprehensive health analytics solution. This solution examines claims for suspicious patterns of behavior, quickly identifying providers and recipients for investigation. In additional, the solution identifies organized criminal rings and collusive behaviors by uncovering suspicious relationships among providers and between providers and recipients. Benefits: $60m - $100m in recoupments in a 12 month period (expected) CASE STUDY – State of North Carolina Detecting and pursuing Medicaid fraud
© 2010 IBM Corporation Claim Data Identified $140M = 18% in suspect claims Personal Care Services ($86M of $555M = 15%) Schemes identified within PCS include: High payments per patient, high home health aid visit utilization Billing for services on Sundays/Holidays that may not have been rendered Out of sequence billing Durable Medical Equipment ($55M of $235M = 23%) Schemes identified within DME suppliers include: Expensive orthotics for amputees Respiratory equipment unbundling North Carolina Department of Health and Human Services
© 2010 IBM Corporation PCS Analysis Results North Carolina Department of Health and Human Services
© 2010 IBM Corporation DME Investigation - Provider – Andy Griffith Medical Equipment Andy Griffith Medical Equipment claims reveal the following patterns: –Claim submission delay which may indicate bill fabrication –Many patients with only 1 visit or service and over $5,000 charged –High reimbursement per patient in traction equipment, beds and power wheelchairs –Billing high charges for only a short window of time – appears to be a store front scheme –Relationship analytics discovered that these providers are related to Andy Griffith Medical Equipment: , , each or these DME suppliers exhibit the same behaviors –Biilling for items for the same patients in the following pattern: 1 DME supplier bills the wheelchair 2 months later, another of the suppliers bills repair and additional accessories
© 2010 IBM Corporation CASE STUDY – Predicting the next best case and technique for debt collections The Collections Optimizer applies business rules and predictive models to prioritize the next best debt for collection. The solution will also create a customized collection technique map for each individual collection case, rescoring as new events occur. Solution Benefits (expected) Challenge New York assigns collections cases based on dollar value and uses a standard series of techniques to pursue payment. This approach has led to a substantial backlog of collections cases. As new events occur that affect collectability, cases are not reprioritized. As a result, collectors may spend time on cases that have low probability of collection. $100 million increase in revenues collected over a 3 year period Substantial reduction in backlog of delinquent debts owed to the state 15 New York State Department of Taxation and Finance
© 2010 IBM Corporation What lessons can you take away from these case studies? 1.We are in the early adopter phase of sophisticated, real-time analytics in government 2.Learn more about these methods and technologies –Helps to provide proper oversight –Can recommend proactively in your audits 3.Analytics uses math and statistics to examine your existing data sources smarter, faster, and better 1.Not an exercise in magical data sources 2.Privacy concerns (if any) can be overcome 4.Using analytics is plain ol good government –Great approach for helping to close budget gaps and be responsible stewards of public funds –Can enable positive ROI in same budget cycle –Allows you to do more with the same or less –Sentinel effect 5.Adopt analytics in YOUR audits and day-to-day work 6.Ways to get started –Pick a business risk and jump in –Government Commission –Fraud Club
© 2010 IBM Corporation Frieda Yueh (914) Shaun Barry (516) Contact Information
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