New York’s Fraud Analysis and Selection Team (FAST)

Leveraging Business Analytics to Stop Refund Fraud

Nonie Manion Executive Deputy Commissioner

Federation of Tax Administrators Annual Meeting June 9, 2014

Overview

§  Snapshot of the NYS Tax Department

§  Evolution of business analytics

§  Analytics + skilled personnel = scheme detection

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Snapshot - Personnel

4,300 employees § 2,900 in Albany § 1,400 in district offices

Audit Tax Processing Enforcement Tax Administration Agency Administration Property Tax Services

37%

22%

22%

6% 4%

7%

Snapshot - Returns

Income (11.7 million)

Withholding (8.6 million)

Sales (1.8 million)

MCTMT (0.4 million)

Corp (1.3 million)

Other (1.1 million)

47%

35%

7%

5% 4% 2%

24.9 million returns processed in 2013

Snapshot - Collections

Other Local Taxes

NYC Income Tax

Local Sales and Use

Other State Taxes

Corporation and Business

Sales, Excise and User

Personal Income $40.2B

$13.9B

$7.6B

$2.0B

$14.2B

$8.5B

$1.0B

Taxes Collected by DTF in the 2012-13 Fiscal year ($87.4 Billion Total)

State taxes collected

$63.6 Billion

Local taxes collected

$23.8 Billion

All figures represent net collections for period; excludes Stock Transfer Tax (fully eligible for rebate)

Case Identification & Selection System (CISS)

CISS uses predictive intelligence and analytics to determine when to process a refund or set it aside for further review

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As  a  result,  we  automa.cally  review  each  of  the  10  million  PIT  returns  filed  annually  –  before  refunds  are  issued.  

Evolution of Business Analytics

CISS Phase 1- Refunds

§  began in 2003

§  saves $370M annually

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CISS – Refunds

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REFUND   SCREENING   AUDIT  LETTER   FRAUD  STOP  

Department Action

CISS (Case Identification and Selection System) Tax Return

• Taxpayer  Filing  Histories  

• Employer  Info  • 3rd  Party  Data  

DTF  Data  Warehouse  

Applies  business  rules  that  may  indicate  condi.ons  common  to  ques.onable  or  fraudulent  returns  

Predic.ve  models  are  applied  to  return  data  to  calculate  the  probability  of  fraud  

Return  is  classified,  given  a  score  for  poten.al  fraud  or  audit  selec.on,  then  ranked  against  other  returns  

Workflow  rules  applied  to  suggest  the  appropriate  ac.on  to  be  taken  

1 2 3 4

Volume of Returns •  10 million returns filed •  6M refund requests •  $7B in refunds paid out

Fraudulent Filings •  fictitious withholding •  fictitious business expenses/losses •  exaggeration of itemized deductions

Processing System •  all returns processed •  business rules •  validation to third party information

CISS •  predictive model •  updated by business during season •  scored and routed in workflow

Role of CISS

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Accurate refunds paid quickly

Questionable refunds stopped or audited

RESULT PROBLEM SOLUTION

Evolution of Business Analytics

CISS Phase 2 – Withholding

§  Began in 2008

§  Saves $45M annually

CISS Phase 3 – Collections

§  Began in 2009

§  Increased collection of $80M annually

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Fraud Analysis & Selection Team (FAST)

§  CISS limited in its ability to ID patterns among multiple PIT returns

§  In response, we created FAST

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Mission FAST is dedicated to applying analytics to incoming personal income tax filings for the purpose of uncovering and stopping fraud schemes quickly, ensuring the integrity of New York State’s tax program and protecting taxpayer dollars.

Marrying state-of-the-art technology with well-trained human resources

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FAST (Fraud Analysis and Selection Team)

The data attributes of questionable returns are assigned codes using Advanced Query Tool (AQT).

Reports are created using Statistical Analysis System (SAS), stored on SQL servers, and then accessed through Excel spread sheets.

The team reviews the SAS reports using statistical models that look for unusual commonalities shared between multiple returns, an indication of possible fraud.

The additional data from AQT is input back into CISS, making it easier to detect incoming fraudulent returns in the future.

3 1 2 4

DTF  Data  Warehouse  

CISS (Case Identification and Selection System)

 Melissa  Data  

(third  party  service)  

provides validation data for addresses listed on the

return

Tax Return Data

BAD RETURNS STOPPED

Volume of Returns •  10 million returns filed •  6M refund requests •  $7B in refunds paid out

Fraudulent Filings •  fictitious withholding •  fictitious business expenses/losses •  exaggeration of itemized deductions

Refundable Credit Schemes •  fictitious wages •  fictitious business income/loss •  illegitimate dependents

Processing System •  all returns processed •  business rules •  validation to third party information

CISS •  predictive model •  updated by business during season •  scored and routed in workflow

FAST Builds on CISS

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Accurate refunds paid quickly

FAST •  identify, stop, and prevent •  detect suspicious patterns or

similarities between returns •  real time stops/changes to rules

and selection

Questionable refunds stopped or audited

Schemes stopped

RESULT PROBLEM SOLUTION

FAST Return on Investment

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* year to date

Next Step

§  Representatives from all business units §  Data analytics and predictive modeling

•  improving business processes •  maximizing efficiency •  increasing voluntary compliance

§  Fraud prevention •  automating pattern analysis & refund stops •  provide FAST team members with tools

-  SPSS modeler -  additional data sources

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Tax Analytics Solution Center (TASC)

Fraud Prevention Evolution

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Case Identification and Selection System

(CISS)

§  Cost: $3.8 Million

§  Now $350+ Million per year

Fraud Analysis and Selection Team (FAST)

§  Limited cost

Tax Analytics Solution Center

(TASC)

§  Center of Excellence

§  Department-wide expertise

§  Expanded analytics 10 years over $2 B $150 Million 1 year

2004 2013 2014

The Compliance Continuum

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§  FAST shut down a Bronx preparer who falsely claimed over $7M in refunds that either •  claimed the exact same credits •  or fictitious business income for seniors

§  Pled guilty to felony charges

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Case Study

Tax Preparer Filing Fraudulent Returns

Case Study

§ We ran a report on debit card refunds over $5,000 •  business losses (for EITC “sweet spot”) •  similar names (Eastern European) •  same neighborhood (Brighton Beach area of Brooklyn)

§  Audits revealed fraudulent claims by home attendants, health aides, and street vendors

§  Documentation requested but not provided §  Refunds denied – bills issued for those who

owed tax

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Home Attendant Business Losses

FAST Finds Schemes by Identifying …

§  Same paid preparer

§  Common IP address

§  Physical/mailing address

§  Bank accounts

§  Similar refund amounts claimed

§ Other return commonalities

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Tools Used for Detecting Fraud

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IP Address

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% S

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10.204.184.144 707 0 177 0.2 22 54 21 80 63 22 9 3 0 40 85 5 2 3 1 12 35.46 2 1 2

173.3.169.133 694 14 379 0.5 52 37 10 80 81 37 15 7 0 46 90 9 2 3 2 18 42.57 5 3 5

184.77.191.13 663 0 27 0 0 100 0 100 100 4 0 0 0 88 100 86 1 0 0 0 0 0 0

67.244.2.101 562 17 424 0.7 66 28 4 82 79 54 31 9 0 60 89 5 1 11 7 9 26.06 7 6 8

§  SAS Enterprise Guide §  Specialty reports

•  weekly PIT complete report •  IPA stats

•  preparer stats •  individual reports

Tools Used for Detecting Fraud (cont.)

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Row Labels Count of MAIL_LN_2_ADR 426  NEW  KARNER  RD   138  

701  5TH  AVE  APT  BSMT   66  

678  5TH  AVE  #  1   57  

213  NAGLE  AVE  APT  5A   43  

2502  AVENUE  D  APT  E8   41  

TB  REFUND  IDA  PARK  RING  RD   39  

701  5TH  AVE  BSMT   35  

2485  MORRIS  AVE  APT  3G   31  

PO  BOX  1802   25  

1086  KELLY  ST  APT  4B   22  

275  SUNRISE  HWY   22  

2569  ADAM  CLAYTON  POWELL  JR  BL   19  

701  5  AVE  APT  BSMT   18  

507B  E  163RD  ST  #  123/BASICS   17  

17  OAK  HILL  RD   17  

PO  BOX  1065   16  

160  W  KINGSBRIDGE  RD  APT  1A   16  

§  Pivot Tables

Tools Used for Detecting Fraud (cont.)

§  Referrals •  from criminal investigations

• Taxpayer Contact Center

§  Supplemental Information • LexisNexis

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