Post on 05-Jul-2020
Session Title Goes Here
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SNAP Recipient Trafficking Meets Data AnalyticsSuzy Cole, SNAPMike McKenzie, Wisconsin DHSSteve Lowe, Washington DSHS
49th Annual ISM ConferenceSeptember 2016
SNAP Basics
• What is it? • How is it set up?
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Congress
Food and Nutrition
Act
FederalRegulations and Policy
Retailers
States
Recipients- Applications, Eligibility,
and Benefits- Fraud Investigations
- Program Disqualifications
Fraud Happens
• Not all Fraud is Created Equal
$$
– Trafficking is one type of SNAP fraud • How do we improve the outcomes with
limited resources?
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Our 3-Part Data Analytics Project
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BPR
Analytics
Implementation
Data-driven approach to detecting and preventing trafficking
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Objectives & Benefits
Increase effectiveness of fraud detection efforts
Identify likely predictors of trafficking
Identify new insights and emerging trends
Support prioritization of referrals and investigations
Individual Attributes & Behaviors
Relationships Between Individuals or Groups
Analyze Data to Identify Trafficking
Behaviors & Characteristics
Prepare Data in Integrated Datamart
Receive State and Federal Data
State Data
Federal Data
Integrated Datamart
Analytics Model Examples
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Determines the influence of the most important factors in order to assign a risk value to each recipient case.
Identifies individual characteristics from eligibility information and transaction behaviors that were statistically different for traffickers
Identifies interaction between different entities, such as common individuals, organizations, dates & times
Identifies patterns, natural groupings or ways to classify data so that it leads to better understanding of data. Assists anomaly detection.
Digit Analysis by Benford’s Law identifies certain patterns of transaction amounts associated with trafficking stores
Benford’s Law
Univariate AnalysisMultivariate Analysis & Model Build
Social Network Analysis
Clustering Analysis
Important Notes about Data Analytics
Data security is paramount Lead Lists are a Starting Point Validation Requires: Knowledge of the population Thorough Investigations
No Disqualifications without Due Process Goal is to find ways to best use limited
resources to improve outcomes
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Examples of State Projects as a Result of this Process
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Client Education & Trafficking Deterrence
Identified New Trafficking Trends
Improved Investigative
Process
Establishment of New Investigation
Methodology
Next Up
• Mike McKenzie – Wisconsin DHS
• We will leave time for questions at the end
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Wisconsin Department of Health Services
Office of the Inspector GeneralFraud Investigation, Recovery &
Enforcement Section
Moving Forward in Our Recipient Integrity Efforts
Presented by Mike McKenzie
∗ Prior to creation of OIG in 2011 Wisconsin was not investigating DQ Retailer referrals from FNS
∗ Trafficking team started developing a process in April 2013
∗ By June 2013 we were investigating our first referrals
∗ As of September 2014 we had investigated 1200 cases and suspended 156 individuals from SNAP for intentional program violation
Disqualified Retailer Referrals
Late 2014:∗Circuit Court decision overturns
IPV decision by our Division of Hearings and Appeals
∗We can’t continue to pursue DQ retailer referrals for recipient unless we can support our allegations with better data
Disqualified Retailer Referrals
But the timing is good…
Disqualified Retailer Referrals
∗Wisconsin selected from Midwest Region ∗Strong support for program integrity efforts∗Contract with Accenture∗Project provided a Business Process Review
and Data Analytics Proof of Concept∗POC was focused on trafficking
FNS Anti-fraud Project
∗This predictive model reflects analysis of recipient data, EBT transactions, and investigations data.
∗Assigns a label to each case:∗ “Has characteristics of known
traffickers” or ∗ “Does Not have characteristics
of known traffickers”.
FNS Anti-fraud Project
Pros:∗Model is capable of analyzing
multiple variables together, such as using eligibility and spending behaviors as inputs.
∗Allows for the analysis to be updated as trafficker’s behavior changes.
∗More “scientific”
FNS Anti-fraud Project
Cons:∗complete investigation is still a
critical process in preparing a case;
∗We currently have to depend on Accenture to “scrub” the data and provide lead lists
∗More “scientific”
FNS Anti-fraud Project
Next Steps∗Testing the new process now on
DQd store∗Pre-hearing conference∗Administrative Hearing
∗Retailers that are also recipients∗Did they report their income?∗Stocking shelves with SNAP
benefits?
FNS Anti-fraud Project
For more information contact:michael.mckenze@wisconsin.gov
THANK YOU!
21DSHS | Services and Enterprise Support Administration | Office of Fraud and Accountability ● SEPTEMBER 2016
Data AnalyticsSNAP Trafficking Meets Data Analytics
IT Solutions Management (ISM) Conference Phoenix, Arizona September 2016
Steve LoweSenior Director, Services and Enterprise Support Administration Office of Fraud and Accountability
Getty Images/iStock
22DSHS | Services and Enterprise Support Administration | Office of Fraud and Accountability ● SEPTEMBER 2016
Washington StateA pioneer in data analytics
Thurston
Grays Harbor
Mason
Jefferson
Clallam
Whatcom
San Juan
Island
Kitsap
Skagit
Snohomish
King
Pierce
LewisPacific
Wahkiakum Cowlitz
Clark
Skamania
Yakima
Klickitat
Kittitas
ChelanDouglas
Okanogan Ferry Stevens Pend Oreille
Grant
Benton
Franklin
Walla Walla
Adams
Lincoln Spokane
Whitman
Garfield
Columbia
Asotin
23DSHS | Services and Enterprise Support Administration | Office of Fraud and Accountability ● SEPTEMBER 2016
DSHS SecretaryPatricia Lashway (Acting)
Services and Enterprise Support
Administration
Office of Fraud and Accountability
Research and Data Analysis Division
WASHINGTON STATE
Office of the GovernorJay Inslee, Governor
Getty Images, iStock
24DSHS | Services and Enterprise Support Administration | Office of Fraud and Accountability ● SEPTEMBER 2016
Housing and Urban
Development Public Housing
Authority
School Outcomes Preschool – College
Internal
Arrests Charges
Convictions
Incarcerations
Community Supervision
Dental ServicesMedical Eligibility Medicaid, State OnlyHospital Inpatient/ OutpatientManaged Care Physician ServicesPrescription Drugs
Hours
Wages
Housing AssistanceEmergency ShelterTransitional HousingHomeless Prevention and Rapid Re-housing Permanent Supportive Housing
Public HousingHousing Choice VouchersMulti-Family Project-Based Vouchers
External
Administrative Office
of the Courts
Employment Security
DepartmentDepartment
of CorrectionsWashington State Patrol
Department of Commerce
Health Care Authority
WASHINGTON STATEDepartment of Social and Health Services
Integrated Client Databases
Nursing Facilities
In-home Services
Community Residential
Functional Assessments
Case Management
Community Residential Services
Personal Care Support
Residential Habilitation Centers and Nursing Facilities
Medical and Psychological Services
Training, Education, Supplies
Case Management
Vocational Assessments Job Skills
Child Protective Services
Child Welfare Services
Adoption
Adoption Support
Child Care
Out of Home Placement
Voluntary Services
Family Reconciliation Services
Institutions
Dispositional Alternative
Community Placement
Parole
Food Stamps
TANF and State Family Assistance
General Assistance
Child Support Services
Working Connections Child Care
DSHS Juvenile
Rehabilitation
DSHS Economic Services
DSHS Aging and Long-
Term Support
DSHS Developmental
Disabilities
DSHS Vocational
Rehabilitation
DSHS Children’s Services
Child Study Treatment Center
Children’s Long-term Inpatient Program
Community Inpatient Evaluation/ Treatment
Community Services
State Hospitals State Institutions
Assessments
Detoxification
Opiate Substitution Treatment
Outpatient Treatment
Residential Treatment
DSHS Behavioral Health and Service
IntegrationMental Health and Substance Abuse Services
Education Research Data
Center
De-identified
Births
Deaths
Department of Health
Washington State Social and Health Services Integrated Client DatabasesEstablished and Maintained by the DSHS Research and Data Analysis Division
JULY 9, 2015
25DSHS | Services and Enterprise Support Administration | Office of Fraud and Accountability ● SEPTEMBER 2016
Criminal caseload at the height of the backlog (Nov 2015)
• 4,198 cases were open:
Example
16%
Severe Mental Health Problems
1%
Dead
1%
Alzheimer’s or Similar Form
of Dementia
<1%
Over 75 Years or Under 18
<1%
Developmental Disability
<1%
Adult Protective Service Cases
26DSHS | Services and Enterprise Support Administration | Office of Fraud and Accountability ● SEPTEMBER 2016
Local Housing Agency Investigations
Example
27DSHS | Services and Enterprise Support Administration | Office of Fraud and Accountability ● SEPTEMBER 2016
Web tool identifies fraud cases by location
Example
. . . by DSHS Region . . . by Legislative District
. . . or by County, Zip Code or Age
28DSHS | Services and Enterprise Support Administration | Office of Fraud and Accountability ● SEPTEMBER 2016
USDA and Washington State
Example
Fraud Cases of Store Owners on Benefits
STORE DATA
CLIENTDATA
29DSHS | Services and Enterprise Support Administration | Office of Fraud and Accountability ● SEPTEMBER 2016
Precautionary Tales
Prioritization
Predictive Analysis
Future Plans
30DSHS | Services and Enterprise Support Administration | Office of Fraud and Accountability ● SEPTEMBER 2016
uestions?CONTACT:
Steve Lowe 360.664.5767Senior Director, Office of Fraud and Accountability
DSHS Services & Enterprise Support AdministrationLoweSM@dshs.wa.gov
QUESTIONS?Steve LoweSenior Director, Office of Fraud and AccountabilityWashington DSHS Services & Enterprise Support ManagementLoweSM@dshs.wa.gov
Michael McKenzieChief, Fraud Investigation, Recovery and Enforcement SectionOffice of the Inspector GeneralWisconsin Department of Health Servicesmichael.mckenze@wisconsin.gov
Suzy ColeProgram Analyst, Program Accountability and Administration DivisionSupplemental Nutrition Assistance ProgramSusan.M.Cole@fns.usda.gov
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Post Session Housekeeping
• RATE this session in the conference app• To download the conference app search for APHSA Events
• VISIT the vendors in the Exhibit Hall during Breakfast and the Networking Breaks to learn more about the solutions presented throughout the day.
• DONATE to Childhelp – the ISM Technology for a Cause campaign to raise $10,000 to help stop child abuse in Arizona and across the country.
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See you again soon
THANKS FORCOMING