afraud%anthology% - government finance …anthology% experienceperspecvve%//...

92
Erin Ballou, Metropolitan Development Housing Agency Shauna WoodyCoussens, BKD Kevin Huffman, Comptroller of the Treasury Moderator: Speakers: Monday, June 1, 2015 3:35 – 4:50 1.5 CPE A FRAUD ANTHOLOGY

Upload: ngodiep

Post on 20-Apr-2018

213 views

Category:

Documents


1 download

TRANSCRIPT

Page 1: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Erin  Ballou,  Metropolitan  Development  Housing  Agency    Shauna  Woody-­‐Coussens,  BKD    Kevin  Huffman,  Comptroller  of  the  Treasury    

Moderator:    

Speakers:    

Monday,  June  1,  2015  3:35  –  4:50  1.5  CPE  

A  FRAUD  ANTHOLOGY  

Page 2: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

experience  perspecVve  //    

CPAs  &  ADVISORS  

Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE Managing Director – Forensics & Valuation Services

Page 3: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

1   • Fraud  Trends  

2   • Big  Data  &  AnalyAcs  

3   • ApplicaAons  in  Government  Orgs.  

4   • Plan  for  GeJng  Started  

PresentaVon  Map  

3  //  experience  perspecAve  

Page 4: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

   Fraud  Trends  

Page 5: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

5  //  experience  perspecAve  

Page 6: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

6  //  experience  perspecAve  

Page 7: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

7  //  experience  perspecAve  

Page 8: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

8  //  experience  perspecAve  

Page 9: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Why  Government  Orgs.  may  Experience  Fraud  

o  Too  much  reliance  on  audits  to  catch  fraud  –  Most  common  detecAon  methodologies  

•  #1:    Tips  =  42.4%  •  #2:    Management  review  =  16.0%  •  #3:    Internal  audit  =  14.1%  •  #4:    By  accident  =  6.8%  •  #7:    External  audit  =  3.0%  

o More  “service”  driven  versus  “profit”  driven  –  Staff  are  seen  as  being  there  to  serve  the  public  good  

o  Greater  culture  of  trust  

9  //  experience  ideas  

Page 10: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

   Big  Data  &  AnalyVcs  

Page 11: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

 …processes  &  ac1vi1es  designed  to  obtain  &  evaluate  data  to  extract  useful  informa1on  and  answer  strategic  ques1ons...    

DefiniVon  of  Data  AnalyVcs  

11  //  experience  perspecAve  

Page 12: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

o Answer  quesVons  through  use  of  analyVcal  so_ware  – As  simple  as  Excel  

•  Filter    •  Sort  

– As  complex  as  you  want  to  make  it  •  ACL  •  IDEA  •  Sequel  

Data  Ana-­‐YOU  WANT  US  TO  DO  WHAT??  

12  //  experience  perspecAve  

Page 13: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Data  AnalyVcs  –  Common  Challenges  

o Existence  of  useful  data  o Data  quality  o Ownership  of  data  o OrganizaVonal  culture  o Lack  of  experVse  &  personnel  o Volume  of  data  available  

13  

Page 14: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

ApplicaVons  in  Government  Sector  

     

Page 15: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

o  Fraud  happens  in  government  orgs.  the  same  way  it  occurs  in  the  private  sector  

–  Do  have  a  tendency  to  see  more  corrupAon  schemes  due  to  large  government  contracts  

o  Lots  of  people  do  not  like  taxes  and  may  not  always  approve  of  how  their  taxes  are  spent.    So  it  is  easy  for  some  to  jusVfy  bilking  government  orgs  

o  A  government  org.  is  not  a  “person”;  therefore,  fraud  o_en  seen  as  a  vicVmless  crime  

Fraud  SuscepVbility  of  Government  Orgs.  

15  //  experience  perspecAve  

Page 16: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

o CorrupVon                36.2%  

o Billing                    19.1%  

o Non-­‐Cash                17.7%  

o Payroll                  15.6%  

o Expense  Reimbursement      12.8%  

Top  5  Fraud  Schemes  in  Government  Orgs.  

16  

Page 17: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

CorrupVon  

17  //  experience  perspecAve  

Page 18: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

CorrupVon  

o An  employee    misuses  his  or  her  influence  in  a  business  transacVon  in  a  way  that  violates  his  or  her  duty  to  the  employer  in  order  to  gain  a  direct  or  indirect  benefit    

o In  most  businesses,  the  most  common  form  of  corrupVon  is  the  payment  of  kickbacks  to  related  to  purchases  

18  //  experience  perspecAve  

Page 19: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Red  Flags  for  CorrupVon  o Off-­‐book  fraud,  so  very  hard  to  detect  

–  Payments  o_en  do  not  go  through  the  organizaAon’s  accounAng  records  

–  Payments  o_en  paid  in  cash  o  Look  for  “behavioral”  red  flags  

–  Rapidly  increasing  purchases  from  one  vendor  –  Excessive  purchases  of  goods  and  services  –  Too  close  of  a  relaAonship  with  a  vendor  

19  //  experience  perspecAve  

Page 20: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

o Compare  order  quanVty  to  opVmal  reorder  quanVty  

o Compare  purchase  volumes/prices  from  like  vendors  

o Compare  quanVVes  ordered  and  received  o Check  for  inferior  goods  (#  of  returns  by  vendor)  

o Unstructured  data  review  (read  suspected  fraudster’s  email….)  

 

Data  AnalyVcs  for  CorrupVon  

20  //  experience  perspecAve  

Page 21: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Billing  Schemes  

21  //  experience  perspecAve  

Page 22: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

o Fraudster  creates  false  support  for  a  fraudulent  purchase,  causing  the  organizaVon  to  pay  for  goods  or  services  that  are  nonexistent,  overpriced  or  unnecessary  –  Invoicing  via    shell  company  –  Invoicing  via  an  exisAng  vendor  

•  False  invoicing  for  non-­‐accomplice  vendors  •  Pay-­‐and-­‐return  schemes  

– Personal  purchases  with  organizaAon’s  funds  

Billing  Schemes  

22  

Page 23: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

o Vendor  asribute  analysis    o Trending  of  vendor  acVvity  

o IdenVficaVon  of  “high  risk”  payments  

o Unstructured  data  analyVcs  

Red  Flags/Data  AnalyVcs  for  Billing  Schemes  

23  //  experience  perspecAve  

Page 24: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Vendor  Asribute  Analysis  –  Employee  /Vendor  Matching  

24  //  experience  perspecAve  

Page 25: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Data  Mining  -­‐  Vendor  Trending  Analysis  

Vendor: JLM Plumbing Authorized: Janice L. McPhearson

Test phase

Acceleration as confidence

builds

Getting Greedy

Page 26: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

High  Risk  Vendor  Asributes  

26  //  experience  perspecAve  

Page 27: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Matching  Asributes Employee  ID

First  Name

Middle  IniVal Last  Name Vendor  ID Name City State

 Total  Payments  

Address 131313131 Beth E Davis D58468431 Davis  Designs Anytown MO                      5,768   Address,  TIN 687431598 George R Davis

RelaVonship  Analysis  

27  //  experience  perspecAve  

Page 28: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Vendor  (A)    Shauna’s  Design  Company,  123  5th  Street,  Anytown,  MO  (Total  Payments  =  $84,337)  

 Employee  (B)  

 Shauna  Woody,  4300  Oak  Street,  Anytown,  MO  

Proximity  Analysis  

28  //  experience  perspecAve  

Page 29: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Latent  SemanVcs  

To:    Vendor  Rep  From:    Employee  -­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐  Thank  you  for  the  “gi_”  –  I’m  so  excited!  It  looks  great  in  my  driveway!    I  can’t  wait  to  take  it  out  on  the  open  road!  My  neighbors  are  soooo  jealous!    

Page 30: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Latent  SemanVcs  

To:    Employee  From:    Vendor  Rep  -­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐  Think  nothing  of  it,  you  deserve  a  treat  every  now  and  then  for  all  you’ve  done  for  us.  

Page 31: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Evasiveness Vagueness Tension, Nervousness

Page 32: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

EmoVonal  Tone  of  Overall  Department  

   

32  //  experience  clarity  

Page 33: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Non-­‐Cash  

33  //  experience  perspecAve  

Page 34: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Non-­‐Cash  Fraud  Schemes  

o Any  scheme  in  which  an  employee  steals  or  misuses  non-­‐cash  assets  of  the  vicVm  organizaVon  – Employee  steal  inventory  from  a  warehouse  or  storeroom  

– Employee  extracts  customer’s  personal  and  account  informaAon  from  a  database  and  then  sells  that  data  –  idenAty  the_  

– Employee  steals  employer’s  compeAAve  data  and  supplies  it  to  a  compeAtor  

•  Common  when  employees  change  employers  

34  //  experience  clarity  

Page 35: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Non-­‐Cash  Fraud  Schemes  

–  Inappropriate  usage  of  organizaAon  assets  •  O_en  computers  or  so_ware  

– Conflict  of  interest  resulAng  in  personal  benefit  

35  //  experience  clarity  

Page 36: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Red  Flags  for  Non-­‐Cash  Schemes  

o Shrinkage  in  inventory/supplies  o Employees  who  frequently  visit  the  office  a_er  hours  

o Missing  tools,  equipment,  office  supplies,  etc.  o Missing,  altered,  or  unmatched  supporVng  documents  

o Employees  borrowing  office  supplies,  tools  or  equipment  

 

36  //  experience  clarity  

Page 37: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Data  AnalyVcs  for  Non-­‐Cash  Schemes  

o Automated  monitoring  of:  – Online  transacAons  (monetary  and  non-­‐monetary)  and  inquiries  

– The  date,  Ame  and  source  of  online  access,  especially  if  the  system  can  be  accessed  from  a  WAN  or  the  Internet  

– Report  generaAon  and  downloading,  including  operaAonal  and  custom  reports  or  queries,  especially  those  containing  student  or  account  informaAon  

37  //  experience  clarity  

Page 38: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Data  AnalyVcs  for  Non-­‐Cash  Schemes  

o Automated  monitoring  of:  – Cell/camera  phone  and  flash  drive  use  as  compared  with  company  policies  and  guidelines,  including  area  restricAons  

– Accessing  of  company  and  external  websites  by  the  employee  

– E-­‐mails  sent  and  received  and  aoachment  sizes  – Telephone  use,  including  use  of  restricted  phone  numbers  

38  //  experience  clarity  

Page 39: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Payroll    

39  //  experience  perspecAve  

Page 40: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

o Ghost  employees  – FicAAous  employees  entered  into  payroll  system  

o Terminated  employees  – Terminated  employees  remain  on  payroll  system  

o Duplicate  payroll  o Overpayment  schemes  

– Higher  pay  rates,  inflated  hours,  unauthorized  bonuses  

Payroll  Schemes  

40  //  experience  perspecAve  

Page 41: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

o  Look  for  lack  of:  –  Bank  accounts  for  electronic  payments  –  Home  addresses  and  phone  numbers  –  Holiday  leave,  vacaAon  or  sick  leave  –  Benefit/tax  deducAons  

o  Also  look  for  –  Duplicate  SSNs  –  Duplicate  bank  account  numbers  –  Duplicate  home  addresses  –  PO  box  addresses  –  Payments  a_er  terminaAon  

Red  Flags/Data  AnalyVcs  for  Payroll  Schemes  

41  

Page 42: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Expense  Reimbursements  &  Purchasing  Cards  

Page 43: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Expense  Reimbursement/P-­‐Cards  

o Any  scheme  in  which  an  employee  makes  a  claim  for  reimbursement  or  ficVVous  or  inflated  business  expenses  – Employee  files  fraudulent  expense  report,  claiming  personal  travel,  nonexistent  meals,  etc.    

– Employee  purchases  personal  items  and  submits  and  invoice  to  employer  for  payment  

– Employee  purchases  goods/services  for  inappropriate  uses  and  charges  to  employer  for  payment  

 

43  //  experience  clarity  

Page 44: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Red  Flags  for  Expense  Reimbursement  /P-­‐Card  Schemes  

o  Expenses  exceed  what  was  budgeted  or  prior  years  totals  o  Expenses  claimed  on  days  employee  did  not  work  o  Purchases  that  do  not  appear  to  be  business  related  o Minimal  or  non  existent  support  for  requests  o  Altered  receipts  o  Unusual  or  excessive  reimbursements  to  one  employee  o  Submised  receipts  are  consecuVvely  numbered  o  Expenses  in  round  dollar  amounts  o  Expenses  just  below  receipt  submission  threshold  

 

44  //  experience  clarity  

Page 45: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

o IdenVfy  transacVons  on  weekends,  holidays  or  while  employee  is  on  vacaVon  

o IdenVfy  split  transacVons  in  which  a  large  purchase  are  split  into  smaller  transacVons  just  under  approval  threshold  

o IdenVfy  unusually  high  or  frequent  expense  reimbursement/p-­‐card  usage  

o IdenVfy  expenses  in  round  dollar  amounts  

Data  AnalyVcs  for  Expense  Reimbursement/P-­‐Card  Schemes  

45  //  experience  perspecAve  

Page 46: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

TransacVons  in  Round  Amounts  

46  //  experience  perspecAve  

Page 47: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

   

47  //  experience  perspecAve  

Page 48: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

High  Risk  Merchants  

48  //  experience  perspecAve  

Page 49: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

   Leveraging  Data  in  Your  OrganizaVon  

Page 50: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Data  AnalyVcs  –  A  Guide  to  ApplicaVon  1.   Build  a  profile  of  potenVal  risks  

•  What  are  your  highest  risk  business  processes?  •  What  frauds  could  occur  in  those  processes?  •  What  would  red  flags  for  fraud  look  like  in  those  business  

processes?  

2.   IdenVfy  data  available  to  help  test  for  potenVal  fraud  •  IdenAfy  and  define  specific  fraud  risks  to  be  tested  •  For  each  risk,  idenAfy  and  define  data  requirements,  data  

access  processes  and  analysis  logic  

3.   Develop  procedures  &  analyze  data  •  Start  with  relaAvely  simple  tests  and  then  add  more  complex  

analysis  building  a  library  of  specific  tests  •  This  is  not  tesAng  a  sample,  it  is  tesAng  the  POPULATION  

50  

Page 51: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Data  AnalyVcs  –  A  Guide  to  ApplicaVon  4.   Make  analysis  results  understandable  

•  Try  to  answer  one  quesAon  at  a  Ame    

5.   Does  analysis  result  address  the  idenVfied  fraud  risk?  •  If  not,  go  back  to  step  #3  and  refine  •  Are  there  addiAonal  tests  that  are  needed  

6.   Perform  invesVgaVon  of  anomalies  or  unexpected  paserns,  as  appropriate  

51  

Page 52: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Paper-­‐based  &  limited  electronic  tesVng    

(Sampling)  

Data  AnalyVcs  (100%  coverage,  ad  hoc  

electronic  tesVng)  

ConVnuous  AudiVng  (Automated  analyVcs,  

100%  coverage)  

Reactive Proactive Responsiveness

52  //  experience  perspecAve  

Page 53: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Closing  Thoughts  

Page 54: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

o AnalyVcs  does  not  tell  the  whole  story  o It  tells  you  where  to  start  looking  o Creates  efficiency  in  your  review  process  o Once  you    understand  your  data  and  your  environment,  you  can  automate  your  analyVcs  to  repeat  on  schedule  

One  Tool  in  the  Toolbox  

54  

Page 55: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Thank  you  

FOR  MORE  INFORMATION  //  For  a  complete  list  of  our  offices  and  

subsidiaries,  visit  bkd.com  or  contact:  

Shauna  Woody-­‐Coussens,  CFE//  Managing  Director  

[email protected]  //  816.701.0250  

Page 56: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Fraud in Tennessee Trends and preventive measures

Kevin B. Huffman, CPA, CGFM, CFE Investigative Audit Manager Financial and Compliance Unit

Page 57: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

   

Page 58: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Fraud in Tennessee  o Who  does  it  impact?  

Page 59: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Fraud in Tennessee  

o What  we  do:  –  InvesAgate  maoers  of  fraud,  waste,  and  abuse  in  governments  or  agencies  receiving  government  funding.      

– How  are  we  noAfied?        Audits,  hotline  calls,  fraud  reporAng  forms,  other  agencies,  CPA  contracted  auditors,  Aps  

Page 60: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Fraud in Tennessee  o What  we’ve  been  working  on?  

– During  FYE  6/30/14,  we   released  17   invesAgaAve  reports   and   leoers   revealing   losses   of   at   least  $713,051   due   to   fraud   and   cited   an   addiAonal  $189,107  of  quesAoned  costs  due  to  waste  and/or  abuse.    

Page 61: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

2013  Schedule  of  Cash  Shortages  and  Other  Thefts    

     

Beg/Forward  Balance  

2013  Increase  

2013  Decrease  

2013  Year  End  Balance  

Counties   $        563,373   $      449,624   $      (237,775)   $          775,222  

Municipalities   400,824   441,909   (53,948)   788,785  

Internal  School  Funds   35,332   9,691   (765)   44,258  

Utility  Districts   2,100   210,600   (3,246)   209,454  

Housing  Authorities   255,121   187,539   (416,531)   26,129  

Other  Govt.  Entities   949,000   3,570,247   (4,431,957)   87,290  

       Totals   $  2,205,750   $  4,869,610   $  5,144,222   $  1,931,138  

Unaudited  Entities   N/A   38,025   N/A   N/A  

     Total  Increase   $  4,907,635  

Page 62: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

2013  Reported  Fraud  by  Area  

Grand  Division   Reported  Fraud  

West        $          462,675  

Middle                3,665,398  

East                      779,562  

     Total        $    4,907,635  

Page 63: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Fraud in Tennessee  o What  we  typically  see…  

o Lack  of  qualified  competent  staff    

– Lack  of  management  oversight  – Lack  of  internal  controls  – “…I  trust  my  people”  

Page 64: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Fraud in Tennessee  o Who  steals  in  our  governments?    

– Fraud  Triangle  –  RaAonalizaAon;  Opportunity;  Pressure  

– 10/10/80  Rule  

Page 65: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Fraud in Tennessee  

o What  you  should  take  away  from  this  presentaAon:  

–  Recognizing  your  risks  

–  Think  about  some  addiAonal  internal  controls  

–  Trust,  but  also  verify  

Page 66: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE
Page 67: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Fraud in Tennessee  

o Recognizing  your  risks:  – According  to  Federal  Law  Enforcement:  

•  Employee  the_  is  one  of  the  fastest  growing  crimes  •  Ten  Ames  the  value  of  street  crimes  ($40  billion  each  year)  

•  Nearly  one-­‐third  of  all  employees  commit  some  degree  of  the_  

Page 68: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Fraud in Tennessee  o Recognizing  your  risks  -­‐    o Employee  the_  takes  many  forms:  

– Voiding  receipts;  adjusAng  accounts  =  stealing  cash  

– FabricaAng  invoices;  ficAAous  payees  =  stealing  thru  disbursements  

– Use  of  gov’t  equipment  for  personal  use  – The_  of  Ame;  “borrowing  funds”  

Page 69: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Fraud in Tennessee  o Recognizing  your  risks:  

–  It  can  happen  to  you!    If  you  don’t  put  the  proper  controls  in  place,  it’s  not  a  maoer  of  “if”  something  will  happen…but  “when”  

– Fraud  has  a  common  thread  –  it  happens  regardless  of  the  size  of  your  gov’t  

– Once  you  lose  the  public  (taxpayer)  trust,  it’s  difficult  to  get  it  back…  

Page 70: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Fraud in Tennessee  o Recognizing  your  risks:  o Things  we  rouAnely  hear  –  

“I  trust  my  people…wouldn’t  hire  them  if  I  didn’t”    “I’ve  been  here  for  years  and  there’s  never              

 been  a  problem”    “We  don’t  handle  much  money…we’re  small”    

Page 71: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Fraud in Tennessee  o Recognizing  your  risks:  

o Once  fraud  occurs  –    – Taxpayers  have  a  right  to  know…  – EnAre  operaAon  is  scruAnized…  – Report  is  public…  

Page 72: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Fraud in Tennessee  o Think  about  some  addiAonal  internal  controls:  

–  Internal  Controls  =  safeguards  

– #1  Control  =  Tone  at  the  Top!  

– Government  leader  =  #1  control  

Page 73: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Fraud in Tennessee  

o Think  about  some  addi1onal  internal  controls  

o Tone  at  the  Top  –  some  things  to  think  about  –  – Lead  by  example  (many  examples  here)  

•  Make  expectaAons  of  employees  clear  •  Hold  them  accountable  •  The_  of  any  amount;  “fudging”  on  Ame;  “borrowing”  is  never  allowable/tolerated  regardless  of  who  the  employee  is…  

Page 74: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Fraud in Tennessee  

o Think  about  some  addiFonal  internal  controls  

o Tone  at  the  top  (cont.)  •  Hire  qualified,  experienced  employees  •  Find  ways  to  improve  employee  morale  •  Let  employees  know  it’s  a  TEAM  effort  •  Be  approachable  -­‐  Create  an  environment  where  employees  feel  comfortable  coming  to  you  at  any  Ame  to  voice  concerns  or  issues  

Page 75: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Fraud in Tennessee  

o Think  about  some  addi1onal  internal  controls  

o Famous  quote  from  military  general  –      “The  day  my  soldiers  stop  coming  to  me  with  their  quesVons  or  problems  is  the  day  they  think  I  either  don’t  care  or  can’t  help  them  anymore…either  way  it’s  a  failure  of  my  leadership”  

Page 76: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Fraud in Tennessee  

o Think  about  some  addi1onal  internal  controls  

Do  you  know  all  the  cash  collecVon  points  in  your  gov’t?    How  o_en  are  deposits  made?  

 What  are  your  procedures?    Do  ALL  your    employees  know  the  procedures?    Are  they  in  wriVng?    SVck  to  them!      

Page 77: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Fraud in Tennessee  

o Think  about  some  addi1onal  internal  controls  Keep  cash  on  hand  to  a  minimum  (Do  you  know  all  the  cash  on  hand  amounts  across  your  gov’t?)  

Are  there  delays  in  deposits?    Why  are  there  delays?  Should  be  made  daily  if  possible…    Consider  security  cameras  

Page 78: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Fraud in Tennessee  

o Think  about  some  addiFonal  internal  controls    Let  employees  know  it’s  a  team  effort  to  watch  over  operaAons  of  the  office…  

 -­‐Review  what  you  sign    -­‐Require  two  signatures    -­‐Separate  duAes  (have  an  employee        outside  the  approval  process  review    invoices)  

Page 79: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Fraud in Tennessee  

o Think  about  some  addiFonal  internal  controls  

o Make  it  inconvenient  and  difficult  to  commit  fraud…  – RouAnely  review  voids  or  other  adjustments  to  accounts  

– Write-­‐offs  require  management  approval  –  employees  involved  in  receipAng  s/n/b  wriAng-­‐off  accounts…  

Page 80: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Fraud in Tennessee  

o Think  about  some  addiFonal  internal  controls  Establish  separate  cash  drawers  for  each  employee…  o Do  not  share  passwords  o Keep  the  office  secure  o Perform  random  “audits”  in  your  office  of  employees  cash  drawers  

o Understand  the  accounAng  so_ware  you  use  

Page 81: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Fraud in Tennessee  

o Think  about  some  addiFonal  internal  controls  

o Official,  prenumbered  receipts  should  always  be  used  

o Preferably,  an  employee  outside  the  receipAng  process  should  balance  the  day’s  transacAons  

o More  than  one  employee  should  verify  the  deposit  amounts  

Page 82: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Fraud in Tennessee  

o Think  about  some  addiFonal  internal  controls  

o Keep  money  contained  in  the  office  in  which  it  was  collected  –    –  If  it  leaves  the  office  and  is  taken  to  another  locaAon  for  deposit,  receipts  and  signatures  should  be  generated  on  the  exchange.  

Page 83: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Fraud in Tennessee  

o Think  about  some  addi1onal  internal  controls  o Rotate  duVes  in  your  office  o Never  a  good  idea  to  share  keys,  cash  drawers,  or  deposit  bags  

o Consider  invesVng  in  locking  money  bags,  and  cash  drawers  

o Keep  an  inventory  of  valuable  items  in  your  office  

 

Page 84: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Fraud in Tennessee  

o Think  about  some  addi1onal  internal  controls  

o Be  mindful  of  loop  holes  in  your  operaVons  – ConAnuously  monitor  your  employees  and  other  operaAons  under  your  control  

– Tighten  up  as  needed  

Page 85: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Fraud in Tennessee  u  Trust,  but  also  verify  

Provide  management  oversight  -­‐  

u  Monthly  reports  –  Review  them  and  ask  ques1ons  

u  Annual  audits  –  If  you  have  findings,  ask  quesAons  and  correct  them  

Page 86: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Fraud in Tennessee  u  Trust,  but  also  verify  

o If  you’re  a  gov’t  leader  on  a  Board  –  – Know  your  mission  –  how  do  you  want  it  carried  out?  

– Know  the  policies  and  procedures  – Require  organizaAonal  ethics  

Page 87: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Fraud in Tennessee  u  Trust,  but  also  verify  

If  on  a  Board  (cont…)  o Hold  management  accountable  –    

–  If  there  are  3,000  customers  and  bills  are  $150  a  month,  revenue  should  be  approximately  $450,000…  

•  Require  answers…quesAon  reports  and  audits    Review  budgets  closely  and  ask  quesAons!  

Page 88: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Fraud in Tennessee  

o QuesVons  

Page 89: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Fraud in Tennessee  

Page 90: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Fraud in Tennessee  

Page 91: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Fraud in Tennessee  

o Contact  InformaAon:  Kevin  Huffman  Desk  –  615.401.7843  [email protected]    Comptroller’s  Web  Site  also  has  a  list  of  manuals  and  procedures  for  ciVes  and  counVes  www.comptroller.tn.gov    

Page 92: AFRAUD%ANTHOLOGY% - Government Finance …ANTHOLOGY% experienceperspecVve%// CPAs(&(ADVISORS(Detecting Fraud with Data Analytics Shauna Woody-Coussens, CFE

Please  provide  feedback  on  the  session  

o Quick  Text  Feedback  1.  Step  1  -­‐  Text  “GFOA”  to  22333  2.  Step  2  -­‐  Did  the  session  meet  your  expectaAons  

for  being  high  quality  and  relevant  to  your  job?  •  Exceeded  ExpectaAons–  Text  “T11EXC”  •  Met  ExpectaAons  –  Text  “T11MET”  •  Did  Not  Meet  –  “T11NOT”    

o To  provide  more  detailed  evaluaVon  on  the  session  or  full  conference  to  go  www.gfoa.org/evals