nicolas vedrenne, payment fraud

Payment Fraud

EDAYS INTERNATIONAL E-COMMERCE CONFERENCE MOSCOW, JUNE 4-5, 2015

About the MRC

EDAYS INTERNATIONAL E-COMMERCE CONFERENCE – MOSCOW, JUNE 4-5, 2015 2

About the MRC

• For ePayment and eFraud• Non profit association• Created in 2000 in the US and 2011 in Europe• 400 Merchant companies • 50 Solution Providers

Networking / Education / Bechmarking / Advocacy


First, let’s do Mathand

get your attention


Payment Fraud: Business Case 1


Plane ticket to NY: 1.000€eTravel agent margin : 10€ (1%)If a fraud, then loss is: 1.000€

Travel sector Fraud: 0.29%

Fraud is 29% of profit for Travel

Payment Fraud: Business Case 2


Fraude for Physical Goods: 0.38%Internal fraud Costs multiplicator: x 2.93Total cost of Fraud:

1.113%

If your marging is: 10%

Fraud is 11.3% of profit for Physical Goods

How much is Fraud


Payment Fraud: 0.53% in average


USA BEL FRA DEU CAN GBR NLD AUS ITA ESP0.00%

0.10%

0.20%

0.30%

0.40%

0.50%

0.60%

0.70%

0.80%0.72% 0.71%

0.65%0.58%

0.52%0.46%

0.41% 0.39% 0.38%

0.27%

% of Ecommerce Sales Lost to Fraud in Top 10 Countries

Payment Fraud varies by sector


Subscription Services

Other Services, excl travel

Digital Goods Physical Goods

Travel / Tickets

0.00%

0.20%

0.40%

0.60%

0.80%

1.00%

1.20%

1.40%

1.60%

1.46%1.04%

0.80%0.42% 0.38% 0.29%

Global Fraud Rate by Merchant Type

Type of Payment Fraud


Triangulation schemes

Coupon / Discount / Refund abuse

Identity theft

Account takeover

Money laundering

Phishing / pharming / whaling

Clean fraud

0% 10% 20% 30% 40% 50% 60% 70%

4%

5%

9%

11%

13%

18%

28%

7%

6%

14%

13%

10%

12%

17%

8%

2%

15%

19%

8%

15%

7%

Fraud attacks in terms of frequency experienced by merchants

Rank #1Rank #2Rank #3

Organized Fraud vs. Friendly Fraud


0%

10%

20%

30%

40%

50%

60%

57%

30% 26%15% 6%

27%

How Merchants Determine Friendly Fraud

Friendly Fraud25%

Other75%

% Loss Merchants Believe was “Friendly Fraud”

11

What to do ?


Multi-layered approach


Biometric indicators (e.g. voice recognition)

Two-factor phone authentication

Credit history check

Paid-for public records services

Payer Authentication (3D Secure)

Postal address validation services

Email address validation

Google Maps lookup

Social networking site

Telephone number verification/reverse lookup

AVS

CVN

0% 20% 40% 60% 80% 100%

7%

15%

29%

37%

47%

50%

55%

57%

57%

66%

87%

3%

3%

3%

18%

5%

12%

1%

2%

3%

5%

5%

Currently in use

Plan to add in 12 months

Multi-layered approach


Other

Device 'fingerprinting'

IP geolocation information

Multi-merchant purchase velocity/identity morphing models

Shared negative lists - shared hotlists

Customer website visitor behavior analysis

Positive lists

Fraud scoring model - company specific

Order velocity monitoring

Customer order history

Negative lists (in-house lists)

0% 20% 40% 60% 80% 100%

9%

51%

77%

21%

40%

24%

57%

72%

73%

78%

82%

18%

8%

7%

6%

16%

7%

8%

5%

5%

6%

2 Warnings


Warning 1: False Positive


0.00%

0.50%

1.00%

1.50%

2.00%

2.50%

3.00%

2.64%

% Sales Rejected or Denied due to Suspicions of Fraud in Top Country

8%

57%

11%

5%

6%

13%

% of Transactions Rejected for Fraud thought to be False Positives

0%0.1-5%5.1-10%10.1-15%15.1-20%>20%

Warning 1: Abandon Rate


1 Reminder


Remember: Investment is key


EDAYS INTERNATIONAL E-COMMERCE CONFERENCE MOSCOW, JUNE 4-5, 2015

Exclusivity fore-DAYS attendees:

Let’s keep in touch!

• [email protected]

• +34 91 524 7512

• www.merchantriskcouncil.org


nicolas vedrenne, payment fraud

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