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FRONT END DETECTION IN HRA’S PROGRAMS

FRONT END DETECTION IN HRA’S PROGRAMS. James Sheehan Chief integrity officer/executive deputy commissioner Saratu Grace Ghartey ACTING Deputy Commissioner NYC Human Resources Administration Investigations, Revenue & Enforcement. NYC Human Resources Administration.

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FRONT END DETECTION IN HRA’S PROGRAMS

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  1. FRONT END DETECTION IN HRA’S PROGRAMS James Sheehan Chief integrity officer/executive deputy commissioner Saratu Grace Ghartey ACTING Deputy Commissioner NYC Human Resources Administration Investigations, Revenue & Enforcement

  2. NYC Human Resources Administration • Headed by Commissioner Robert Doar • Over 12,000 employees • Largest Social Services Department in the nation • Provides social services to the neediest populations of New York City: • 3.0 million Medicaid recipients • 1.8 million SNAP recipients • 350,000 Cash Assistance recipients

  3. Investigations, Revenue & Enforcement Administration The investigative/recovery arm of HRA 1,200 investigators, analysts & support staff Conducts eligibility reviews/data analytics Detects and investigates fraud Recoups and recovers overpayments Cost avoidance/deterrence

  4. Investigations, Revenue & Enforcement Administration Results of Investigations • Administrative Actions • Recommend application rejection • Sanctions (Intentional Program Violations) • Case closings/Rebudgeting • Criminal Prosecutions • Monetary Recoveries & Recoupments • Civil Litigation

  5. Cash Assistance Front Door Bureau of Eligibility Verification • In-person review of Cash Assistance applicants • Office interviews and field visits conducted • Worker-oriented software aggregates collateral database results to inform the interview • Nearly 300,000 recommendations annually • 1,100 appointments/day • Significant identification of ineligibility

  6. SNAP Front Door Supplemental Nutrition Assistance Program (SNAP) Front End Review (SNAP-FER) • Went live on July 1st, 2013 • Filters identify subset of higher risk applications for additional investigative review • Reviews include verification of: • Identity and address • Income • Household composition Results through July 18th • Over 1,000 recommendations • 20% “Deny” recommendations

  7. Medicaid Front Door Medicaid: Medicaid Integrity Investigation Program (MIIP) • Pilot began last year • Identifies 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 reviewed • 12,000+ investigations conducted • 1,400+ luxury vehicle matches • 11,000+ property matches • 9,700+ cases rejected for a cost avoidance value of $52 million

  8. Guiding Principles • Use data meaningfully • Creating case filters • Building predictive models • Measuring outcomes • Prevention is better than cure—keep the fraud from ever getting into the program • Close collaboration with key partners is paramount—Family Independence Administration (HRA SNAP and Cash Assistance Operations)

  9. NEED FOR FRONT-END ANALYTICS REVIEW OF AUTOMATED APPLICATIONS • Future of social services programs • Automated applications • Limited or no face-to-face interaction with front line staff • Loss of practical expertise and local knowledge of front-line staff in assessing applicants • Movement from merit workers to contract staff • No original documents (or front-line staff copy of original documents presented) • internet access for all-anonymous or public access devices • Limited preservation of electronic communications

  10. WHY ARE ON-LINE APPLICATIONS THE FUTURE OF SOCIAL SERVICES PROGRAMS? Convenience for applicants-particularly working families Benefits costs are federal, administration costs are local and state Opportunity for reduced head count and space requirements We have not made effective, data based case for face-to-face interaction between worker and recipient Banks did this twenty years ago-techniques and systems exist

  11. 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 behavior • FEMA applications

  12. WHAT DO WE NOT KNOW ABOUT OUR APPLICANTS? 15% of economy is “informal” or cash 35-50% of cash economy is unreported >10% of New York City adult benefit enrollments use SSNs never before seen in LexisNexis database Adults who do not file tax returns “off-the-books” form entry for income verification

  13. PROGRAM RISKS 4.4 million residents, 9 million public health care cards outstanding (2011) British Columbia Ministry of Health Services Ontario -300,000 more public health care cards than people (2006 Auditor General report)

  14. WHAT WE CAN LEARN FROM BANKS AND CREDIT CARDS Identity verification (new accounts) Transaction tests ($ thresholds, patterns, locations ) Front end identity questions Prompt telephone and IM contact on fraud risk identification Transaction verification Scripted interviews and answers (e.g., “there has been a security breach on your account.”) Close and replace account promptly Someone is watching Reduced reliance on prosecution

  15. OTHER ISSUES IN AUTOMATED PROGRAM INTEGRITY medical, benefits tourists and affiliated providers and entities Whistleblower cases Identity fraud Messaging-websites, application communications (e.g., where do you put the certification?) Hotlines Successful cases

  16. 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.pdf Hard to connect to the “Data Sources Hub”

  17. Best Practices & Lessons Learned • Best approaches are flexible approaches • Fraud is a moving target • The front-end tool must be adaptable • Small number of predictive variables • Use your data and analytics tools • Front-end tools are data collection devices • Refine your methods • Find new fraud profiles • Use your Subject Matter Experts • Investigative staff know the patterns and appropriate followup

  18. The Future of Front End Loss of numbers, experience, expertise, and local knowledge of front line staff and offices is inevitable Increased automation through tools such as applicant identity verification quizzes Increased use of data analytics to uncover additional fraud patterns Continuous improvement of existing models through more and better data

  19. The Future of Front End Loss of numbers, experience, expertise, and local knowledge of front line staff and offices is inevitable Increased automation through tools such as applicant identity verification quizzes Increased use of data analytics to uncover additional fraud patterns Continuous improvement of existing models through more and better data

  20. The Future of Front End Loss of numbers, experience, expertise, and local knowledge of front line staff and offices is inevitable Increased automation through tools such as applicant identity verification quizzes Increased use of data analytics to uncover additional fraud patterns Continuous improvement of existing models through more and better data

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