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Fraud Forum 2001

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					Latest trends in serious and
  organised identity fraud


              Gareth Jones
 Fraud Products Director - Experian Ltd
    gareth.jones@uk.experian.com
             0115 992 2101
Latest trends in serious and
organised identity fraud
• The methodology used by the fraudsters with
  reference to case examples
• The impact of the fraud in terms of value of loss
  and spread of victims
• Good practice in the management of mass-multiple
  fraud cases of this sort
• Gaps in the fraud detection process that could be
  improved upon
• Opportunities for fraud prevention
• Taking care of the victim
Latest trends in serious and
organised identity fraud
• The methodology used by the fraudsters with
  reference to case examples
• The impact of the fraud in terms of value of loss
  and spread of victims
• Good practice in the management of mass-multiple
  fraud cases of this sort
• Gaps in the fraud detection process that could be
  improved upon
• Opportunities for fraud prevention
• Taking care of the victim
Personal identity fraud
• Impersonation
      Current address
      Previous address
      Deceased
• False identity
      False identity fraud
• Account take-over
      Transaction fraud
Examples
Three short case studies
1. Fraudulent placement of data
      See Credit Today crime supplement article
2. Fraudulent acquisition of data
      Direct to Consumer
3. Fraudulent acquisition of data
      Recent NHTCU success story
      We supported the prosecution of these
       fraudsters - pleaded guilty, total 15.5 years
       imprisonment
Case Study - empty house fraud
• West African fraud ring
      Minimum of eight involved
      6 men 2 women
• IT literate - 4 had degrees, 3 of those IT degrees
• Persistent offenders - on bail for other frauds
• London area
• Access to good forgers
• Formed companies to launder proceeds
      Properly formed, CCL, IC registered,
       Accommodation addresses
• Business premises to operate front company
Case Study - empty house fraud
• Identification of addresses - mailing lists for empty
  properties
• Identify previous occupant - Electoral Register - no
  adverse - no access to premises
• Forged proofs of identity / address
• Strategy for collecting mail - re-direction to forwarding
  address, use of accommodation address
• Broaden identity - employment / bank / telephone
  details
• Face to face application for banking facility - inexact
  impersonation
• Broaden relationship and fully utilise all facilities
• Dispose of incriminating evidence
Latest trends in serious and
organised identity fraud
• The methodology used by the fraudsters with
  reference to case examples
• The impact of the fraud in terms of value of loss
  and spread of victims
• Good practice in the management of mass-multiple
  fraud cases of this sort
• Gaps in the fraud detection process that could be
  improved upon
• Opportunities for fraud prevention
• Taking care of the victim
Quantity and cost of identity fraud -
nationally
• UK assessment of ID
  fraud
• Part of Government
  Entitlement Card
  White Paper
• Full exploration of
  identity fraud issues,
  costs and views of
  disparate market
  sectors
Total £680m
What you won’t hear!
• Significant percentage of identity fraud is prevented
• CIFAS statistics:
      2002 - 75%
      2003 - 90%
• Deceased impersonation:
      2001 - 5,000
      2002 - 9,000
      2003 - estimate 16,000
Identifying likely victims:
Bin raiding research
• Anecdotal evidence of activity in Nottingham area
• National survey of local authorities revealed
  significant majority received complaints
• Initiated arm’s-length research (MEL) with
  involvement of Police and Nottingham City Council
Our sample
• 1 week’s waste from 5 different areas using
  MOSAIC socio-economic profiling
      A: High income                   (94)
      C: Blue collar owners            (75)
      D: Low rise council              (65)
      F: Victorian low status          (89)
      H: Stylish singles               (91)
• Waste from each sample household collected
  separately
Sorting and analysis
• Each bag opened separately
• Documents filtered
• No personal data was
  recorded
• Type and condition of each
  document listed
• All filtered documents
  destroyed as confidential
  waste
Headline results
• Rubbish bags contained
      no relevant material 14%
      full name and address 72%
      telephone number 6%
      date of birth 2%
      card number 20%: of those with expiry date 80%
      signatures 13%
      bank account information 27%
      utility bills 16%
      official letters 25%
Summary of findings
• Stylish singles at greatest risk
      throw away more sensitive information; more
       people per household
• Full name and address easy to find
      no one group at greater risk
• Rare attempts made to destroy material (8%
  attempted - mostly unsuccessful)
      attempts usually limited to card numbers
• Card number and bank account information
  common
      easy to combine material to put together a
       picture of a person’s life
Communicate findings:
PR objective
• Intention of research was
      to provide evidence
       of need for action
      to formulate a nation-
       wide fraud prevention
       message
      to work initially with
       BBC Hard Cash and
       then other media to
       broadcast message
Latest trends in serious and
organised identity fraud
• The methodology used by the fraudsters with
  reference to case examples
• The impact of the fraud in terms of value of loss
  and spread of victims
• Good practice in the management of mass-multiple
  fraud cases of this sort
• Gaps in the fraud detection process that could be
  improved upon
• Opportunities for fraud prevention
• Taking care of the victim
Good practice
• Have a fraud strategy
• Review and follow plan
• Identify point of failure and address it
• Look for other unrelated cases
• Adopt an ethical approach - involve the Police
• Identify the victims - consumers and business
• Dry run consumer literature
• Match resources to levels of outward
  communication
• Have a PR plan, scripts, statements
Latest trends in serious and
organised identity fraud
• The methodology used by the fraudsters with
  reference to case examples
• The impact of the fraud in terms of value of loss
  and spread of victims
• Good practice in the management of mass-multiple
  fraud cases of this sort
• Gaps in the fraud detection process that could be
  improved upon
• Opportunities for fraud prevention
• Taking care of the victim
Gaps in fraud prevention process /
Prevention opportunities
• Victims suddenly credit active
• Miss-match date of birth to actual age
• Miss-match date of birth to ‘stage’ of forename
• Not confirmed BT at current address
• Re-direction on at current
• Forgeries - same serial numbers
• Deceased file matches
• Employment - new business, accommodation
  address
Other inferences within an
application
• Applicant profile - do they fit the product?
• Amount of facility - highest possible amount
• Day? Time? - just before closing
• Duplicates - at address level
• Early extension - new products sold
• E-mail - free ISP - can check IP to area
Prevention - relevance of new
channels
• Online / Batch / Retrospective reporting
• Internet channel opened up online fraud prevention
  services to SME’s
• Made business more competitive, requirement to
  improve SLA’s
• Impact - trade off between advances in fraud
  prevention and expectations of improvements in the
  business process
• Risk is that natural tension between these
  competing parts of a business can lead to
  ‘accidents’
Tension between prevention and production



                                    Parity Zone
                        High Risk
                        Business
Fraud Prevention




                                Low Risk
                                Business



                   New Business ‘Production’
Trading off added prevention for increased
production




                                         Un-rocked Boat
Fraud Prevention




                   New Business ‘Production’
       Fraud Data Matching                         Credit Accounts       Extended Credit
                                                   Name                  Applications
                       Exact / Fuzzy               Address               Name
New Application                                    Date of birth         Address
Name                                               Product               Date of birth
Address                                            Performance           Biographical data
Date of birth
Telephone              Miss - Match
Previous address
Bank Sort / Acct No.
Etc
                                                   Credit Applications   Public data
                       Validity Match              Name
                                                   Address
                                                                         ER
                                                                         CCJ’s
                                                   Date of birth         Bankruptcies IVA’s
                                                   Product               Notice of
                                                   Performance           Correction
                       Velocity Match
Existing Accounts                                             Other data
Name                                                          CIFAS
Address
Date of birth
                       Exclude Match                          Suspects
                                                              GAIN
Telephone                                                     Deceased
Bank Sort /Acct No                                            Redirections
Etc                    Results Evaluated
                                                              Sanctions
                       Decisioning - Scorecard
                                                              Etc
                       Client side / Bureau side
                       Referral policy
Fraud matching
                         Online        Batch

Detecting Existing   CIFAS           CIFAS / National
Frauds                               Hunter / Hunter

Detecting Existing   Exception /     Local / Corporate
Suspects             Caution Files   Hunter

                     Detect          National Hunter /
Preventing new                       Local Hunter
Frauds               Data sources
                     Fraud
                     Scorecards      CIFAS trawl
Detecting frauds
in clean accounts                    reports
                                     National Hunter
Latest trends in serious and
organised identity fraud
• The methodology used by the fraudsters with
  reference to case examples
• The impact of the fraud in terms of value of loss
  and spread of victims
• Good practice in the management of mass-multiple
  fraud cases of this sort
• Gaps in the fraud detection process that could be
  improved upon
• Opportunities for fraud prevention
• Taking care of the victim
Why?
• Maintain consumer confidence
• Operational costs of administering case are greater
  in reaction to a substantive fraud, than if prevented
      Human resource costs
      Data clean up
      Liaison with victim
      Case handling
      Court case - preparation of statement
• Reputational damage
      Poor PR
      Impact of not being trusted
Victims of fraud
• Dedicated Experian team established Summer 03
• 300-400 hours to clean up the mess
• £5k-8k costs
• 400 cases recorded since September 03
• Previous address fraud – 20 months to discover
• Average fraud £1400 per case
• Experian leading NCOA initiative
Prevention - self help - consumers
• Obtain a regular copy of your
  credit file
• Statutory nominal fee £2
• Monthly repeat copy - reduced
  price
• New online channel -
  WiseConsumer and
  CreditExpert
• Dedicated Victims of Fraud
  service - to assist victims
  affected by fraudsters using
  their name
Victim - relevance of data
• Transparent - every individual has a right to see
  data about themselves
• Legal requirement for accuracy, relevance etc.
• Accessible - easy to retrieve and report upon
• Immediate - usually very quick to deliver
• Delivers knowledge - allows the individual to
  assess their status
• Therefore data can help an individual protect
  themselves from the impact of ID fraud
      Know if someone is looking at their data
      Take immediate steps to reduce damage
     Victim - relevance of online channel
     - example
• Traditional - WiseConsumer
• New - CreditExpert
• Allows individuals to access
  their credit report online
• Ongoing file monitoring and
  alerts
• Mix of systems and physical
  KYC procedures prevent
  fraud
• Immediate and without delay
CreditExpert launched 13 September
CreditExpert - Member centre
CreditExpert - File alerts
Latest trends in serious and
  organised identity fraud

           Thank you!

        Any questions?
              Gareth Jones
 Fraud Products Director - Experian Ltd
    gareth.jones@uk.experian.com
             0115 992 2101