Shutting downfraud, waste,and abuseMoving from rhetoric to real solutions ingovernment benefit programs

Shutting down fraud, waste, and abuseAbout the authorsPeter ViechnickiPeter Viechnicki is a strategic analysis manager and data scientist with Deloitte Services LP, wherehe focuses on developing innovative public sector research using geospatial and natural languageprocessing techniques. Follow him on Twitter @pviechnicki.William D. EggersWilliam Eggers Eggers leads public sector research for Deloitte. His new book, Delivering onDigital: The Innovators and Technologies that are Transforming Government, will be published inJune 2016. His commentary has appeared in dozens of major media outlets including the New YorkTimes, Wall Street Journal, and the Chicago Tribune. He can be reached at [email protected] oron twitter @wdeggers.Brien LorenzeBrien Lorenze is a principal in the Regulatory, Forensics & Compliance practice of DeloitteTransactions and Business Analytics LLP and the Advisory global public sector leader. He is recognized for his industry knowledge, application of information technology to complex challenges, andas a leader in applying analytics to monitor/detect money laundering, fraud, and sanctions evasion.Michael GreeneMichael Greene is a senior manager and data scientist with Deloitte Consulting LLP. He focuses onhelping public- and private-sector organizations solve complex issues with predictive analytics andbehavioral science.James GuszczaJames Guszcza is the US chief data scientist for Deloitte Consulting LLP. He is the author of dozensof articles on analytics, including “The last-mile problem: How data science and behavioral sciencecan come together.”Dan OlsonDan Olson, CFE, a senior manager with Deloitte & Touche LLP, has worked for over 20 years inhealth care fraud examination following five years in auditing and compliance. Olson serves asa content specialist in the design and deployment of health care fraud, waste, and abuse predictive models and predictive analytics. Among his accomplishments, Olson has authored five healthcare white papers and testified before Congress regarding recommendations to identify health carefraud, waste, and abuse.

Moving from rhetoric to real solutions in government benefit programsContentsIntroduction 2Seeing fraud, waste, and abuse clearly 4A holistic approach to waste and fraud reduction 8A roadmap to increased program integrity 16The path ahead 18Endnotes 191

Shutting down fraud, waste, and abuseIntroductionFRAUD, waste, and abuse.” A simple Googlesearch returns about 35 million mentions ofthis term.It’s not surprising. For decades, our political leaders have promised to cut fraud, waste,and abuse from government spending, butsomehow the problems persist, draining billions—some estimates would say trillions1—oftaxpayer dollars.In the 2015–2016 election season alone,several presidential candidates have madecutting fraud, waste, and abuse a key part oftheir platforms. Blue-ribbon commissions andbipartisan panels from California to Wisconsinhave vowed to tackle the problem.2 None of2these, however, have managed to cool the hotrhetoric around the topic.Or rewind to 2012, the year in whichPresident Barack Obama asked Vice PresidentJoe Biden to spearhead a “campaign to cutwaste” that would “hunt down and eliminatemisspent tax dollars in every agency.”3 Thegoal: Restore citizen trust in government.4During the 1996 presidential campaign,Senator Bob Dole mentioned governmentwaste at least 33 times, and promised to fundhis proposed tax cuts with a scalpel: “There’senough waste in the government to give youthe tax cut, enough waste, enough fraud,enough abuse, enough people flying aroundthe world ”5In 1982, Ronald Reagan asked investigators to “work like tireless bloodhounds” to“root out inefficiency.”6 Calling fraud, waste,and abuse “the byproduct of mismanagement,”Reagan said, “Our management improvements,together with the tremendous accomplishments of our Inspectors General, are a one-twopunch taking steam out of the waste and fraudthat was eroding faith in our government.”And way back in 1949, President Trumandirected ex-President Herbert Hoover toorganize 300 men and women to seek wastein what Hoover called “the most formidableattempt yet made for independent review ofthe Executive Branch.” Such investigations, henoted, happened periodically at least “since theTaft Administration.”7Yet despite decades of pledges, campaigns,and thick reports, the challenge remains. TheGovernment Accountability Office recentlyannounced it found 137 billion in improperpayments in 2015, an increase of 31 billion injust two years.8Politicians have been promising to win thewar on fraud, waste, and abuse for about as

Moving from rhetoric to real solutions in government benefit programslong as we’ve had voters. But the disappointing outcomes suggest they still haven’t found astrategy that works.9This study sidesteps the tired rhetoric topresent a realistic, proven approach for reducing fraud, waste, and abuse, and debunks somecommon myths along the way. Our approachborrows from commercial leading practicesto approach fraud at an enterprise level, whilealso incorporating new methods from socialscience. If the private sector’s experience is anyguide, the fixes we propose here won’t happenovernight, but the progress they offer could begame-changing.We urge agencies to cut across silos and usenew tools and techniques, such as predictiveanalytics, behavioral economics, and collectiveintelligence, to reduce system-wide vulnerabilities. Redesigned systems can reduce thechances of wasting funds in the first place.By creating an ecosystem in which the incentives of all stakeholders align to prevent fraud,waste, and abuse, the government can begin todrain the sources of a perennial problem.Before diving deeply into these new solutions, however, it’s important to first understand the nature of the challenge—fraud,waste, and abuse in government benefitprograms—and the ways in which they haveendured despite decades of effort.3

Shutting down fraud, waste, and abuseSeeing fraud, waste, andabuse clearlyBERRI Davis has a daunting mission. She’sone of the directors of a program integrityteam at the Government Accountability Office(GAO), charged with auditing all federallyfunded benefits programs, including enormous programs such as Medicaid and SocialSecurity.10 Davis and her colleagues work withfederal agencies and states to improve programintegrity by identifying vulnerabilities andrecommending fixes.Davis’s team, with its bird’s-eye view of billions of dollars wasted or stolen annually frombenefits programs, faces a task that could easilybecome overwhelming. Fortunately, she andher GAO colleagues have an important asset:They can rely on almost 10 years’ worth of datato assess the size and scope of improper payments in various programs.11The 2002 Improper Payments InformationAct (IPIA) requires federal agencies to measure and report on improper payment rates intheir benefits programs.12 In response, agencieshave developed methods such as the Centersfor Medicare & Medicaid Services’s (CMS’s)Payment Error Rate Measurement (PERM)program.13 Data produced by PERM andsimilar programs over the past 10 years showthe fluctuations in improper payments14 whichthemselves reflect the dynamic nature of fraud,waste, and abuse.Our analysis of the data produced underIPIA, together with interviews with numerousfederal and state integrity officers, reveals thelandscape of fraud, waste, and abuse in benefitsprograms. With these data, we can examine theextent and scope of losses as well as trends andrecovery amounts.4The size of the challengeThe Congressional Research Service estimates that the federal government allocatednearly 2.1 trillion for mandatory expenditures in 2014, mostly for benefits programs.15How much of that enormous sum was lost tofraud, waste, and abuse? For 2015, the WhiteHouse estimated a loss of 137 billion throughimproper payments.16Some expenditures, such as those for healthprograms, may be particularly prone to fraud,waste, and abuse. The most rigorous availableassessments of overall waste in health spendinghave placed it in the range of 30 percent.17Of course, fraud implies intention—adeliberate act. Many other improper paymentsrepresent waste and error. If a doctor’s officebills a higher-level procedure code without therequired documentation, it does not necessarily mean that it was intentional.Improper payments data aren’t designedto measure fraud directly because they can’tassign or assess intention. Claims analysiscan identify repeated trends and patterns thatappear suspicious. To identify fraud, intentneeds to be established—which moves beyondtraditional claims analysis and involves ahuman element to confirm the behavior thatwas exhibited.Better reporting boostsimproper payment numbersAt first glance, the improper paymentsfigures tell a dismal story. Total improperpayments reported by government benefitsprograms rose from 38 billion in 2005 to 137 billion in 2015, a 197 percent increase ininflation-adjusted dollars over 10 years.18

Moving from rhetoric to real solutions in government benefit programsFigure 1. Growth in total program outlays tracked and number of programs reporting improper 00420052006200720082009Total outlays (in trillion)201020112012201320140Number of programsSource: US Office of Management and Budget, Office of Federal Financial Management improper payments dataset, payment dataset; Government Accountability Office, Government-wide estimates and use of death datato help prevent payments to deceased individuals, March 16, 2015, p. 1,; and GovernmentAccountability Office, Government-wide estimates and reduction strategies, July 9, 2014, p. 1, Deloitte University Press DUPress.comBut much of the apparently sharp increaseactually resulted from two factors: One, moreagencies are finally reporting their improperpayments accurately (figure 1)19 and two, analytic techniques are getting better at detectingunderlying problems that lead to improperpayments. As Carolyn Yocom, a GAO colleague of Berri Davis, notes: “Not all increasesin improper payments are bad news, as anincreased rate can be due to agencies’ improvements in measuring improper payments andtaking steps to combat them.”20Despite some gaps, our store of improperpayments data is becoming more comprehensive every year.21Many programs are still establishing theirprocedures for estimating unnecessary expenditures. But we can finally begin to understandthe scope of the problem.And it’s clear that improper payment ratesremain staggeringly high, particularly forbig-ticket benefits programs. Figure 2 provides improper payment amounts for sevenlarge programs tracked under the ImproperPayments Act. Together, these seven programslost more than 115 billion through improperpayments in 2015 alone.Fraud is dynamicBecause the nature of fraud itself is changing, program integrity officials at the GAOaren’t expecting victory any time soon. And3,000 miles away from the capital, a pair ofrecent high-profile cases have revealed sometroubling trends.Jamie Ralls and Ian Green are certifiedauditors with the Oregon Secretary of State.They and a dedicated team of about 70 colleagues are on the lookout for fraud, chargedwith confirming that the state’s tax dollars arebeing spent for their intended purpose. Theiroffice has audit authority over the entirety ofOregon’s 69 billion budget, including morethan 21 billion in federal funds.22The recent exposure of a massive foodstamp fraud ring in Klamath Falls, Oregon,shows just how quickly the problem of benefitsfraud and abuse can change.The first hints that something was amiss inKlamath Falls came in 2012, when an Oregon5

Shutting down fraud, waste, and abuseFigure 2. Improper payments in large benefits programs, 2005–2015Total outlays in2015 (in billion)Improper paymentrate in 2015Total improper paymentsin 2015 (in billion)Medicare Earned Income Tax Credit(EITC)65.623.8%15.6Medicare Advantage (Part C)148.69.5%14.1Old Age, Survivors andDisability Insurance (OASDI)862.70.6%5.0Supplementary SecurityIncome (SSI)56.58.4%4.8Unemployment Insurance (UI)32.910.7%3.5ProgramSource: Office of Management and Budget, Office of Federal Financial Management improper payments dataset.Department of Human Services caseworkerheard from a food-stamp recipient that thelocal market Carniceria Mi Pueblo was makingfraudulent sales to beneficiaries in return forcash payouts.23 State officials began examiningfood-stamp transactions from the Carniceriaand noticed a series of red flags. These smallscale infractions led to a criminal ring thatlaundered an estimated 20,000 each month infood-stamp benefits and had links to Mexicandrug cartels. Two years later, police arrested65 people in connection with the case.24 Theensuing headlines generated opinions rangingfrom approval for public officials’ hard work tocondemnation for the two years it took themto act.25Oregon anti-fraud officials probably alsotook notice of a 2013 case of Medicaid fraudin neighboring California, which highlightedhow would-be fraudsters are joining forces toform criminal networks. The network in question stole millions of dollars from California’sMedi-Cal program by convincing nonaddictedresidents of group homes to participate inaddiction therapy sessions.26 The participantsreceived cash, cigarettes, and snacks, while thestate kept paying providers for the sessions.Following this discovery, scrutiny of organized6crime networks became an integral part ofMedi-Cal’s integrity efforts.Both cases make it clear that dishonestactors are applying more sophisticated methods to perpetrate fraud. In fact, would-befraudsters continually probe benefits systemsto identify vulnerabilities—and then move toexploit them.27For states such as Oregon and federalagencies as well, the inevitable move to digitaltechnologies has created new opportunitiesfor fraud, including large-scale identity theft.In 2012, some 12.6 million Americans becamevictims of identity theft, and 46 percent ofthese cases involved government documentsor benefits fraud.28 Benefits fraud is oftencombined with identity theft, providing astrong economic incentive for thieves to stealidentifying information.As benefits administration shifts frombrick-and-mortar locations to web-basedtransaction systems, interactions betweenbeneficiaries and administrators become lesspersonal, creating more space for small acts ofdishonesty; it’s easier to lie to a computer thanto a person. Psychologist Dan Ariely has calledthis “the personal fudge factor.”29

Moving from rhetoric to real solutions in government benefit programsGovernment agencies can’t buytheir way out of the problemThe current fiscal climate means that statesand federal agencies cannot respond to rapidlychanging trends in fraud and abuse simplyby increasing funding for fraud prevention.30Public budgets for prevention and enforcementare either flat or declining. Budgets for auditorsand inspectors general are particularly vulnerable, since they’re often viewed as partisanpolitical targets, with little recognition for theirpositive returns on investment.31Instead, those fighting fraud will have toinnovate within their existing budgets. To besuccessful, their efforts will require a moreholistic approach, one that spans the enterprise of government and employs new toolsand techniques, from predictive analytics anddeep learning to behavioral “nudges” andcollective intelligence.7

Shutting down fraud, waste, and abuseA holistic approach to wasteand fraud reductionTHERE’S no single solution to the problemof fraud, waste, and abuse. Because theproblems are complex and evolving quickly,any effective solution must be both multifaceted and agile.Fortunately, 20 years of successful fraudreduction in the private sector has shown thatprogram vulnerabilities can be mitigated withan enterprise approach that combines retrospective and prospective approaches, predictive analytics, and adaptive techniques such asmachine learning and randomized controlledtrials. (Figure 3 illustrates such a system.) Fivestrategies in particular are critical: Make data collection central to anti-fraudand waste strategies Create a learning system to respond to everchanging threats Emphasize prevention to get the best returnon effort Use “choice architecture” toencourage compliance Share intelligence to reduceintentional fraudMake data integration central toanti-fraud and waste strategiesMost readers aren’t terribly excited byextensible database architectures or enduringdata-sharing agreements. But these are justthe sorts of features needed to plug holes inbenefits programs. Due in part to small butsignificant improvements in data sharing and8data matching, improper payment rates in theSupplemental Nutrition Assistance Program(SNAP) have been pushed down from a high of6.0 percent in 2007 to its lowest level ever, 3.2percent, in 2014.32Data collection is critical to the preventionof fraud, waste, and abuse. It should begin withthe identification of relevant data sources and arobust process for acquiring data and compiling them into a dynamic data warehouse.Such data might include information aboutapplicants and current beneficiaries; information from other government systems aboutthe same individuals; and data on current andpast claims. Such data may come from externalsources: other benefits programs, other agencies and states, and even social media.As these data are assembled, the system acquires enough information to makeinformed decisions about incoming claims,applications, and other transactions throughrisk-scoring.Create a learning system torespond to ever-changingthreatsAdaptive enterprises learn from interactionwith data and humans, continuously reconfiguring in pursuit of better outcomes. Thisadaptive strategy is crucial to effective programintegrity platforms.All too often, leaders think of programintegrity systems as a fixed defense, like a wall.But a Great Wall can be scaled; a Maginot Linecan be avoided. Fixed obstacles are fixed targets. That’s not optimal defense. Instead, thinkof the fight against fraud as a chess match.

Moving from rhetoric to real solutions in government benefit programsGovernments must deploy their advantagesand strengths against their opponents’ disadvantages and weaknesses.Twenty years of best practices in privatesector fraud prevention show that perpetualunpredictability is the best defense againstbenefits fraudsters.33 The goal is to modifydefenses so fast that adversaries are continually playing catch-up. The more you changethe game, the more the fraudsters’ costs goup, and the more your costs go down. Maybethey’ll move on to a different target.A system that can learn from its own experiences requires an offline component that collects outcomes and uses them to manage andupdate rule bases and statistical models usingnew training data (figure 3). Business rulesand anomaly detectors are updated based onincoming transactions as well as external information. Such a system is adaptive because itlearns both from the effects of its own actionsand from external data, including the wisdomof crowds.This knowledge base can be used to assigna risk score to each incoming transaction.Transactions that match a normal risk profileare processed with little manual intervention.Those that stand out in some way are assigneda high risk score and sent for further investigation or immediate action.Successful systems also measure theeffectiveness of each action taken, and adjustactions based on those results. A/B testingof potential interventions, discussed below,can reveal which ones have more impact ondesired outcomes, such as more accuratebeneficiary data.Emphasize prevention to get thebest return on effortMany public benefits programs approachfraud, waste, and abuse with a “pay-and-chase”model. They focus on clawing back moneypaid out on fraudulent claims after the fact,and pay less attention to the potentially morelucrative categories of waste and error. We saymore lucrative because fraud typically accountsA prevention-focused strategycan be doubly lucrative:Prevention saves not just thecost of overpayments, but alsothe cost of the chase.for a third or less of all improper payments.Deliberate fraud in the unemployment insurance program, for instance, constitutes 28percent of overpayments.34 Fraudulent recipient claims in SNAP account for just 10 percentof overpayments.35 And potentially fraudulentclaims in California’s Medi-Cal fee-for-serviceprogram made up 37.8 percent of all erroneous payments in 2011 ( 473 million of 1.25billion), which themselves comprised 6.05percent of all FFS payments.36Traditionally, states and federal agenciesthus have approached program integrity reactively: pay first, notice an error later (whetherfraudulent or not), and then attempt to “chase,”or recover, the funds. By doing so, they’re missing the low-hanging fruit: the prevention ofimproper payments in the first place. It’s wellknown among program integrity professionalsthat prevention is much more cost-effectivethan after-the-fact recovery.37Prevention identifies and vets cases in whichan erroneous payment is likely. A preventionfocused strategy can be doubly lucrative:Prevention saves not just the cost of overpayments, but also the cost of the chase. Potentialfraudsters, moreover, are often discouragedfrom committing fraud and abuse if they knowtheir behavior is being watched.Prevention, of course, isn’t a completesubstitute for pay-and-chase. Retrospectivestrategies such as forensic investigations are anintegral part of a comprehensive approach toprogram integrity, and should be used alongside preventive strategies.Here’s how such an approach would work.All transactions—such as applications orbenefits claims—are recorded in a centralwarehouse. These records power the system’s9

Shutting down fraud, waste, and abuseFigure 3. Flexible and holistic program integrity platform designLTELE INVITECLLCO1CEIGENENTERPRISE(COLL OUT14EC SITIV DEE I SONT UREL CELIG SENCE)USER2YC SA MG TEL E YSS3DI N ATATEGRATIONEVENTS4RULMO ESSC DE LOR SESPL ANAAT LYFO TICRM5101Adopt an enterprise-wide perspectiveTake a holistic view of fraud, waste, and abuserisk across the ecosystem of internal andexternal stakeholders.2Work with legacy systemsIntegrate into existing information systems andtechnical architectures, without requiring thosetechnologies to be rebuilt or reconfigured.3410Integrate data to increase collective intelligenceFuse structured and unstructured data streams frominternal operations, accounting, and communicationssystems. Then select third-party data providers andother external claim and non-claim data sourcesto produce enterprise-view insight into accounts,individuals, and relationships.Apply the right analytics at the right timeDissect and understand transactions and events in nearreal time by applying sophisticated business rules andadvanced analytics, including cognitive technologies,predictive models to diagnose fraud patterns andprofiles, anomaly detection to flag suspicious behavior,and social network analysis to uncover fraud rings,collusion, and kickback schemes.5Score resultsCalculate data-driven fraud risk scores based onthe aggregation of all business rules and models toprofile transactions, events, and activities to facilitatedownstream review, investigation, or interventionas necessary.6Prioritize resultsRoute the scored results for each transaction intofunctional workflow streams. Low-risk transactionsare processed in the regular course of business;transactions marked as requiring further investigationare channeled to investigative teams for analysis.High-risk transactions are channeled for various typesof countermeasures.7Leverage specialistsInvestigate the prioritized flagged transactions withforensic methods and protocols, resulting in either arecommendation to process the transaction normally orstop it entirely.8Request additional informationEnrich the investigative results by accessingsupplemental and explanatory information from theintegrated data repository.6

Moving from rhetoric to real solutions in government benefit programs912PH12 Tailor solutionsOne size does not fit all. Adapt the program integritysolution to the specific organization, strategy, priorities,and risk factors; business segments and product orservice lines; and geographies.13 Build the solution in stepsDesign and forge the program integrity solutionincrementally, beginning with pilot project efforts, riskidentification exercises, tools assessment, and datasource profiling initiatives—building toward a fullinstallation when needed.11E2IN14. Enrich the system’s knowledge through outsidedata sourcesThese may include identity resolution, outcomes ofprevious cases, and siloed administrative records.VESTIGATIONALE3MASR5432SUPHOitASiesNior11 Scale solutionsCalibrate the program integrity solution to thestructure, which may involve multiple locations anddiverse operations or a single group focused onone department.10 Get smarterOptimize the business rules, advanced analytics, andscoring models over time by factoring in actual results,newly identified risks and updated intelligence, andchanges to organizational strategy, policy, and controls.Collective intelligence increases as the system learnsfrom experience.13PrTake actionProtect the agency from identified fraud risk andloss with definitive countermeasures, includingblocking fraudulent transactions before assets arecompromised, intervening via soft notices, initiatingformal investigations and providing evidence forcriminal prosecution, and closing policy loopholes andimplementing front-end edits to mitigate future risk.S1PICURGENTIOUS5432A1BNURGENTORMAL8GVODR E S2GENT43REINVQUIREES STIG FUAT RTHION OATNRYVEGEAN IC YCH POLOECRPRFT ESSO TICONTOSUECTIONK IONOC TB L S ACNRA11

Shutting down fraud, waste, and abuse“forensic” capability, allowing investigators tolook at the record and learn what actions weretaken, by whom, and when. When the systemnotices an erroneous or fraudulent paymenthas been made, the investigative unit can becalled into action to retrieve the funds, as intraditional pay-and-chase.But the system is also prospective, because itcreates a knowledge base about prior transactions and outcomes, which allows for predictions about future transactions.Two additional strategies discussed next,behavioral science and collective intelligence,can further enable governments to be proactive in tackling fraud, waste, and abuse. Ratherthan wait for tips from hotlines, data can identify “hot spots” of waste and fraud and applybehavioral science interventions to preventthem before payments are made.38Use “choice architecture” toencourage complianceFraud control efforts are made more difficult by a nonintuitive but important statistical phenomenon called the “false positivesparadox.” (See sidebar, “The problem of falsepositives.”) When a population produces alow rate of fraud, even a highly accurate frauddetection system will yield a surprisingly highshare of false positives.For this reason, even highly accurate fraudclassification algorithms carry a degree ofinherent risk. Given the likelihood of falsepositives, you simply can’t automatically accusea flagged individual.Advances in statistical modeling, however,can help mitigate the false positives paradox.“Soft-touch” behavioral tactics are particularly well suited to the ambiguous nature ofalgorithmically generated fraud indications.THE PROBLEM OF FALSE POSITIVESImpressive accuracy in a predictive model doesn’t always lead to actionable intelligence. To illustrate, consider ahypothetical type of fraud with a 2 percent prevalence—or “base rate”—in the overall population. In other words,about 20 out of each 1,000 cases sampled at random are expected to involve this type of fraud.Next, suppose a data scientist—call him Dr. Keyes—has built a statistical fraud detection algorithm (or “fraudclassifier”) that is 95 percent accurate.39 With this level of accuracy, he would be the envy of his peers. Finally,suppose this algorithm has flagged Mr. Neff as a suspected fraudster. What’s the probability that Neff is actually afraudster? Perhaps surprisingly, the answer is considerably lower than 95 percent.To understand this, let’s return to our hypothetical expectation of 20 fraudsters in a population of 1,000. Keyes’salgorithm’s 95 percent accuracy rate implies that the model could correctly identify 19 of 20 cases of fraud. Butit also implies that the model will flag an expected 49 of the remaining 980 cases as fraudulent (0.05 x 980 49). Neff therefore could be either one of the 19 true positives or one of the 49 false positives. Thus the so-called“posterior probability” that Neff is in fact a fraudster is only 28 percent.The model does provide useful intelligence: One would sooner investigate Neff than an individual not flagged bythe model. But in practical terms, his flagging remains an ambiguous indicator of wrongdoing.This ambiguity becomes a bigger problem when fraud detection is scaled to larger samples. Consider, for example,California’s Medicaid program, Medi-Cal. In 2011, Medi-Cal’s fee-for-service program processed 26,472,513claims.40 Medi-Cal reported that 4.1 percent (49 of 1,168) of sampled claims were potentially fraudulent in 2011,the latest year for which data were available at the time of publication. Extrapolated to the 26 million claimsprocessed during that quarter, more than 1 million of t

Dan Olson, CFE, a senior manager with Deloitte & Touche LLP, has worked for over 20 years in health care fraud examination following five years in auditing and compliance. Olson serves as a content specialist in the design and deployment of health care fraud, wast