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Understanding the Card Fraud Lifecycle :  A Guide For Private Label Issuers  The Problems, Tools, Techniques and Technologies   Christopher Uriarte   Chief Technology Officer &  Head of International Development Retail Decisions PLRT Conference May 1, 2009 Philadelphia, PA
About Retail Decisions:  A Market Leader ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Retail Decisions (ReD) is a London-based specialty provider of transaction and card issuing service to banks, retailers, oil companies and telcos worldwide
Sample of ReD’s Clients and Focus Sectors Travel Telephony Retail Oil Banking Europe America Asia Pacific Other
Where We Sit & Where the Data Comes From Fraud Prevention & Gateway Services (CP&CNP) ReDShield TM ReD1Gateway TM CardExpress TM Fraud Prevention for Acquirers & Processors PRISM TM Fraud Prevention for Issuers PRISM TM Fraud Prevention for Merchants Fraud Prevention for Banking Institutions
Fraud Control Life Cycle Solutions implemented to reduce fraud Time lag for solutions to take affect New solution is implemented to reduce fraud Familiarity with weaknesses in cards and technology increases fraud Fraud begins to rise as new technologies are cracked and new weaknesses are found 2002 2009 ??? ??? Are We Here Now??? Implies Innovation Time Value of fraud
Counterfeit fraud driven by growth of plastic ,[object Object],170,000 cards seized in Taipei, Taiwan
Regular Occurrences of Organized & Social Engineering Efforts Arrests in card scam Wednesday, February 28, 2007 By Paul Grimaldi Journal Staff Writer   Arraigned yesterday in the thefts of credit-card and debit-card information — and more than $100,000  The men allegedly stole the information by switching out checkout lane keypads with one of their own machines and then retrieving the units a few days later so they could copy the account data.  To achieve this, they took shelf stocking positions at the supermarket, which gave them legitimate access to the facility during late hours in the evening.  They recorded the stolen information on blank bank cards that they used to get money from ATMs in the area, the police said.
Implanted chips Criminals implant a chip directly into Point of Sale equipment The chip holds up to 1,000 account numbers Major occurrences in Taiwan, Malaysia and Brazil
Purpose Built Skimmer – Old Technology, But Still Popular ,[object Object],[object Object],[object Object],[object Object]
Personalizing the Card Low tech: Embossing only “ Higher tech”: Transplanting  Skimmed Card Data 3712 345XX8 95004
The Trend:  No Longer a Low-Tech Crime ,[object Object],[object Object],[object Object]
What This Means In Regards to Fraud ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Typical Merchant Fraud Assessment Process Merchant  Order  System, Storefront, Website, etc. ACCEPT  ORDER DENY ORDER CHALLENGE ORDER (Manually Review) Fraud Prevention System and Tools (Proprietary or Outsourced) 90%+ Of All Orders ~2% Of All Orders 2%-8% Of All Orders (Where Applicable) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Examples:
Finding the Balance Between Key Metrics ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Highlighted in red :  The most typical and critical results in each respective category When This Happens: This Could Happen: Manual Review Rates Increase   ,[object Object],[object Object],[object Object],[object Object],[object Object],Manual Review Rates Decrease   ,[object Object],[object Object],[object Object],[object Object],Hard Deny Rates Increase   ,[object Object],[object Object],[object Object],[object Object]
Fraud Prevention Systems:  Real-Time or Batch ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Merchants must first decide whether they require a Real-Time or Batch (non-real-time) fraud prevention system:  Sophisticated Systems Can Combine BOTH Techniques
Fraud Prevention Systems:  Tools and Technologies ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Tool Example:  IP Geolocation Instantly compare an online customer's registered address with his real-world location to flag potential fraud Pre-emptively block web site access to certain locales or IP origination points known to be frequent sources of fraud Real-world location
Technique Example:  Combining IP Geolocation with Additional Transaction Analysis  Unusual combinations of location details I.P. address in California  Billing and/or Delivery address in London Card issued in Poland
Technique Example: Tumbling & Swapping
Technique Example: Email Tumbling & Swapping and Pattern Detection  John1 John2 John3 John4 John5 John6 John7 John8 John9 John10 @hotmail.com [email_address] [email_address] [email_address] [email_address] [email_address] [email_address] [email_address] [email_address] [email_address] [email_address] Up to10% fraud rate on free email domains
Technology Example:  Neural Networks ,[object Object],[object Object],[object Object],[object Object],[object Object],Neural Model Transaction Details Where does this fall?? GOOD??  BAD??  Score: 743 Scale:  0 to 999 0 = Good, 999=Fraud Transaction Details  (Required fields varies depending on features  & purpose of neural model) Score: 0 Score: 999 Good  (Non-Fraud) Historical Transactions Bad  (Confirmed Fraud) Historical Transactions Model Building And Training
The "More Tools Create Greater Complexity" Challenge No Matches Negative Data Device ID Check Address Validation Proxy Detection Neural Score Transaction Data Everything’ s  OK;  First time buyer Business Rules No History Address is Good;  No match of Name to Address Could be behind a University proxy Score: 362 NOW WHAT? Should you accept it?  Should you outright deny it?  Should you manually review it? Challenge:  Not just managing the individual components, but the sum of the parts!
The Real World Challenge ,[object Object],[object Object],[object Object],“ Islands of Fraud in a Sea of Good Transactions” Suspicious AVS Order Amount
The Real World Challenge:  Example ,[object Object],[object Object],Suspicious AVS Order Amount
The Real World Challenge:  Example ,[object Object],[object Object],Suspicious AVS Order Amount
The Real World Challenge:  Example ,[object Object],[object Object],[object Object],[object Object],Problem: Strong  Detection at the Cost of High False Positives! Rule 1 Rule 2 IP Geolocation Coverage Suspicious AVS Order Amount
The Real World Challenge:  What a Fraud Prevention System Should Do ,[object Object],[object Object],[object Object],[object Object],Suspicious AVS Order Amount
New Tools and Techniques Can Bring New Challenges ,[object Object],[object Object],[object Object],[object Object],Some may require additional customer data, such as SSN/last 4 or ask personal validation questions Cost per transaction increases when more techniques and technologies are added to the suite of fraud tools Fraud Evolves.  Will these be valid in 2 years?  1 year?  6 Months? Could lead to increased manual review costs, false positives and customer dissatisfaction
Merchant vs. Issuer Fraud Prevention ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Private Label transactions offer a unique “Issuer/Merchant” perspective, but consolidated Merchant / Issuing fraud prevention systems do not exist today!
Private Label Card Fraud Examples ,[object Object],[object Object],[object Object],[object Object],Private Label Cards Other Cards Types Fraud Rate: % of Transactions % of Overall $ Value % of Transactions % of Overall $ Value Large Retailer “A”  (Apparel, Home Goods) 0.08% 0.23% 0.16% 0.34% Large Retailer “B” (Mixed Retail) 0.44% 1.56% 0.41% 1.30% Large Retailer “C” (Mixed Retail) 0.50% 0.98% 1.5% 3.2%
Private Label Fraud:  So What Does This Mean? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Latest Trends:  Private Label Gift Card and Stored Value Card Fraud on The Rise ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Key: June – December  2008 [January-February 2009] Virtual Gift Cards Plastic Gift Cards Other Cards Types (Non-PL) Fraud Rate: % of Transactions % of Overall $ Value % of Transactions % of Overall $ Value % of Transactions % of Overall $ Value Large Retailer “A”  (Apparel, Home Goods) 0.80% [1.50%] 1.00% [1.70%] 0.03% [0.60%] 0.03% [0.90%] 0.16% 0.34% Large Retailer “B” (Mixed Retail) 4.10% 10.6% 2.10% 3.05% 0.41% 1.30% Large Retailer “C” (Mixed Retail) 1.70% [6.70%] 2.60% [5.5%] 0.70% [2.7%] 2.80% [2.6%] 1.5% 3.2%
What can be accomplished? Customer Case Study – Top 10 Retailer ,[object Object],[object Object],[object Object],23 Retailer Chargeback Data
Customer  Case Study – Top 5 Digital Download / Gaming 24 ,[object Object],[object Object],[object Object],[object Object]
Customer Case Study  – Top 5 Global Travel Site (£ ‘000) 25 ,[object Object],Country Total Fraud Total Fraud stopped by ReD Percentage Stopped UK 1,356 1,186 88%  Germany 356 330 93%  France 504 476 95%  Italy 6 4 60%  TOTAL 2,221 1,996 90%
Day 1 Customer Case Study – Fraud Ring for Top 5 Jeweller  Individual IP address, phone numbers, email address, card numbers & shipping addresses all shared and used in one attack 26
Day 3 Customer Case Study – Fraud Ring  for Top 5 Jeweller  Individual IP address, phone numbers, email address, card numbers & shipping addresses all shared and used in one attack 27
Day 7 Tried to get  £30,000  worth of goods in just  7  days Customer Case Study – Fraud Ring  for Top 5 Jeweller  Individual IP address, phone numbers, email address, card numbers & shipping addresses all shared and used in one attack ,[object Object],28
Customer Case Study – ReD Shield Consistent Results  ,[object Object],[object Object],Total Chargeback Rate Total Chargeback Rate 29 Mobile Phone Operator Major High Street Retailer  (Pharmacy/Cosmetics, etc.)
Christopher Uriarte [email_address] US: +1 732.452.2440 UK: +44 (0) 1483 728700 Thank You! Please feel free to contact me with any questions!

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Understanding the Card Fraud Lifecycle : A Guide For Private Label Issuers

  • 1. Understanding the Card Fraud Lifecycle : A Guide For Private Label Issuers The Problems, Tools, Techniques and Technologies Christopher Uriarte Chief Technology Officer & Head of International Development Retail Decisions PLRT Conference May 1, 2009 Philadelphia, PA
  • 2.
  • 3. Sample of ReD’s Clients and Focus Sectors Travel Telephony Retail Oil Banking Europe America Asia Pacific Other
  • 4. Where We Sit & Where the Data Comes From Fraud Prevention & Gateway Services (CP&CNP) ReDShield TM ReD1Gateway TM CardExpress TM Fraud Prevention for Acquirers & Processors PRISM TM Fraud Prevention for Issuers PRISM TM Fraud Prevention for Merchants Fraud Prevention for Banking Institutions
  • 5. Fraud Control Life Cycle Solutions implemented to reduce fraud Time lag for solutions to take affect New solution is implemented to reduce fraud Familiarity with weaknesses in cards and technology increases fraud Fraud begins to rise as new technologies are cracked and new weaknesses are found 2002 2009 ??? ??? Are We Here Now??? Implies Innovation Time Value of fraud
  • 6.
  • 7. Regular Occurrences of Organized & Social Engineering Efforts Arrests in card scam Wednesday, February 28, 2007 By Paul Grimaldi Journal Staff Writer Arraigned yesterday in the thefts of credit-card and debit-card information — and more than $100,000 The men allegedly stole the information by switching out checkout lane keypads with one of their own machines and then retrieving the units a few days later so they could copy the account data. To achieve this, they took shelf stocking positions at the supermarket, which gave them legitimate access to the facility during late hours in the evening. They recorded the stolen information on blank bank cards that they used to get money from ATMs in the area, the police said.
  • 8. Implanted chips Criminals implant a chip directly into Point of Sale equipment The chip holds up to 1,000 account numbers Major occurrences in Taiwan, Malaysia and Brazil
  • 9.
  • 10. Personalizing the Card Low tech: Embossing only “ Higher tech”: Transplanting Skimmed Card Data 3712 345XX8 95004
  • 11.
  • 12.
  • 13.
  • 14.
  • 15.
  • 16.
  • 17. Tool Example: IP Geolocation Instantly compare an online customer's registered address with his real-world location to flag potential fraud Pre-emptively block web site access to certain locales or IP origination points known to be frequent sources of fraud Real-world location
  • 18. Technique Example: Combining IP Geolocation with Additional Transaction Analysis Unusual combinations of location details I.P. address in California Billing and/or Delivery address in London Card issued in Poland
  • 20. Technique Example: Email Tumbling & Swapping and Pattern Detection John1 John2 John3 John4 John5 John6 John7 John8 John9 John10 @hotmail.com [email_address] [email_address] [email_address] [email_address] [email_address] [email_address] [email_address] [email_address] [email_address] [email_address] Up to10% fraud rate on free email domains
  • 21.
  • 22. The "More Tools Create Greater Complexity" Challenge No Matches Negative Data Device ID Check Address Validation Proxy Detection Neural Score Transaction Data Everything’ s OK; First time buyer Business Rules No History Address is Good; No match of Name to Address Could be behind a University proxy Score: 362 NOW WHAT? Should you accept it? Should you outright deny it? Should you manually review it? Challenge: Not just managing the individual components, but the sum of the parts!
  • 23.
  • 24.
  • 25.
  • 26.
  • 27.
  • 28.
  • 29.
  • 30.
  • 31.
  • 32.
  • 33.
  • 34.
  • 35.
  • 36. Day 1 Customer Case Study – Fraud Ring for Top 5 Jeweller Individual IP address, phone numbers, email address, card numbers & shipping addresses all shared and used in one attack 26
  • 37. Day 3 Customer Case Study – Fraud Ring for Top 5 Jeweller Individual IP address, phone numbers, email address, card numbers & shipping addresses all shared and used in one attack 27
  • 38.
  • 39.
  • 40. Christopher Uriarte [email_address] US: +1 732.452.2440 UK: +44 (0) 1483 728700 Thank You! Please feel free to contact me with any questions!

Editor's Notes

  1. Slides 1-4 (3 Minutes) Brief Introduction to Retail Decisions to provide the context of who we are, why we see so many transactions and where my data is coming from Slide 5 (3 Minutes) Explain the “Fraud Control Lifecycle” and the correlation between fraud prevention tools and overall industry fraud rates Slides 7-12 (4 Minutes) Examples of how criminals obtain card data and how the cards are monetized Demonstrate the current complexity of fraud rings against merchants Slide 13-14 (4 minutes) Overview of the typical merchant fraud assessment process Introduction to the key metrics that merchants user in fraud prevention processes Slides 15-20 (10 Minutes) Examples of current fraud prevention system types and fraud prevention tools Slides 21-27 (5 minutes) Real-world examples of the complexities faced when using fraud prevention tools Slide 28-30 (10 minutes) Fraud in the Private Label space Slides 31-37 (5 minutes) Case studies – What can be accomplished when merchant fraud prevention systems are put in place Estimated time: 45 Minutes
  2. MG
  3. MG Seven Day Attack using 55 delivery addresses, 30 computers, 64 email addresses, 55 credit cards and 50 telephone numbers. Attempted to steal £30,000 worth of fraud Day One, two, three, four, five, six seven. As more fraudsters share details see the picture growing Start with one phone number the use of 14 credit cards Day two
  4. MG Seven Day Attack using 55 delivery addresses, 30 computers, 64 email addresses, 55 credit cards and 50 telephone numbers. Attempted to steal £30,000 worth of fraud Day One, two, three, four, five, six seven. As more fraudsters share details see the picture growing Start with one phone number the use of 14 credit cards Day two
  5. Seven Day Attack using 55 delivery addresses, 30 computers, 64 email addresses, 55 credit cards and 50 telephone numbers. Attempted to steal £30,000 worth of fraud Day One, two, three, four, five, six seven. As more fraudsters share details see the picture growing Start with one phone number the use of 14 credit cards Day two