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Keeping Fraud Out of the Public Assistance Programs

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James SheehanChief integrity officer/executive deputy commissionerSaratu Grace GharteyACTING Deputy CommissionerNYC Human Resources AdministrationInvestigations, Revenue & Enforcement
FRONT END DETECTION IN HRA’S PROGRAMS
NYC Human Resources Administration
Headed by Commissioner Robert DoarOver 12,000 employeesLargest Social Services Department in the nationProvides social services to the neediest populations of New York City:3.0 million Medicaid recipients1.8 million SNAP recipients350,000 Cash Assistance recipients
Investigations, Revenue & Enforcement Administration
The investigative/recovery arm of HRA1,200 investigators, analysts & support staffConducts eligibility reviews/data analyticsDetects and investigates fraudRecoups and recovers overpaymentsCost avoidance/deterrence
Investigations, Revenue & Enforcement Administration
Results of InvestigationsAdministrative ActionsRecommend application rejectionSanctions (Intentional Program Violations)Case closings/RebudgetingCriminal ProsecutionsMonetary Recoveries &RecoupmentsCivil Litigation
Cash Assistance Front Door
Bureau of Eligibility VerificationIn-person review of Cash Assistance applicantsOffice interviews and field visits conductedWorker-oriented software aggregates collateral database results to inform the interviewNearly 300,000 recommendations annually1,100 appointments/daySignificant identification of ineligibility
SNAP Front Door
Supplemental Nutrition Assistance Program (SNAP) Front End Review (SNAP-FER)Went live on July 1st, 2013Filters identify subset of higher risk applications for additional investigative reviewReviews include verification of:Identity and addressIncomeHousehold compositionResults through July 18thOver 1,000 recommendations20%“Deny” recommendations
Medicaid Front Door
Medicaid: Medicaid Integrity Investigation Program (MIIP)Pilot began last yearIdentifies higher risk cases found with assets suggesting income (either income generating-e.g., rental property, or requiring income to support-luxury vehicles)Over 315,000 cases reviewed12,000+ investigations conducted1,400+ luxury vehicle matches11,000+ property matches9,700+ cases rejected for a cost avoidance value of $52 million
Guiding Principles
Use data meaningfullyCreating case filtersBuilding predictive modelsMeasuring outcomesPrevention is better than cure—keep the fraud from ever getting into the programClose collaboration with key partners is paramount—Family Independence Administration (HRA SNAP and Cash Assistance Operations)
NEED FOR FRONT-END ANALYTICS REVIEW OF AUTOMATED APPLICATIONS
Future of social services programsAutomated applicationsLimited or no face-to-face interaction with front line staffLoss of practical expertise and local knowledge of front-line staff in assessing applicantsMovement from merit workers to contract staffNo original documents (or front-line staff copy of original documents presented)internet access for all-anonymous or public access devicesLimited preservation of electronic communications
WHY ARE ON-LINE APPLICATIONS THE FUTURE OF SOCIAL SERVICES PROGRAMS?
Convenience for applicants-particularly working familiesBenefits costs are federal, administration costs are local and stateOpportunity for reduced head count and space requirementsWe have not made effective, data based case for face-to-face interaction between worker and recipientBanks did this twenty years ago-techniques and systems exist
WHAT DO WE KNOW ABOUT AUTOMATED APPLICATIONS
Anonymity/reduced identification breeds fraud risk:What people will do in the dark (psych research)Earned income tax credit (GAO reports)Driver behavior vs. pedestrian behaviorFEMA applications
WHAT DO WE NOT KNOW ABOUT OUR APPLICANTS?
15% of economy is “informal” or cash35-50% of cash economy is unreported>10% of New York City adult benefit enrollments use SSNs never before seen in LexisNexis databaseAdults who do not file tax returns“off-the-books” form entry for income verification
PROGRAM RISKS
4.4 million residents, 9 million public health care cards outstanding (2011) British Columbia Ministry of Health ServicesOntario -300,000 more public health care cards than people (2006 Auditor General report)
WHAT WE CAN LEARN FROM BANKS AND CREDIT CARDS
Identity verification (new accounts)Transaction tests ($ thresholds, patterns, locations )Front end identity questionsPrompt telephone and IM contact on fraud risk identificationTransaction verificationScripted interviews and answers (e.g., “there has been a security breach on your account.”)Close and replace account promptlySomeone is watchingReduced reliance on prosecution
OTHER ISSUES IN AUTOMATED PROGRAM INTEGRITY
medical, benefits tourists and affiliated providers and entitiesWhistleblower casesIdentity fraudMessaging-websites, application communications (e.g., where do you put the certification?)HotlinesSuccessful cases
EXCHANGE APPLICATION 3.5ATTESTATION REQUIRED
“The Exchange has the ability to conduct verifications pursuant to 42 CFR 155 subpart D and is able to connect to data services, such as the Data Sources Hub and other sources as needed”http://cciio.cms.gov/resources/files/hie-blueprint-081312.pdfHard to connect to the “Data Sources Hub”
Best Practices & Lessons Learned
Best approaches are flexible approachesFraud is a moving targetThe front-end tool must be adaptableSmall number of predictive variablesUse your data and analytics toolsFront-end tools are data collection devicesRefine your methodsFind new fraud profilesUse your SubjectMatterExpertsInvestigative staff know the patterns and appropriatefollowup
The Future of Front End
Loss ofnumbers, experience, expertise, and local knowledge of front line staff andoffices is inevitableIncreased automation through tools such as applicant identity verification quizzesIncreased use of data analytics to uncover additional fraud patternsContinuous improvement of existing models through more and better data
The Future of Front End
Loss ofnumbers, experience, expertise, and local knowledge of front line staff andoffices is inevitableIncreased automation through tools such as applicant identity verification quizzesIncreased use of data analytics to uncover additional fraud patternsContinuous improvement of existing models through more and better data
The Future of Front End
Loss ofnumbers, experience, expertise, and local knowledge of front line staff andoffices is inevitableIncreased automation through tools such as applicant identity verification quizzesIncreased use of data analytics to uncover additional fraud patternsContinuous improvement of existing models through more and better data

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Keeping Fraud Out of the Public Assistance Programs