13
2012
ONLINE
FRAUD
REPORT
Online Payment Fraud Trends,
Merchant Practices and Benchmarks
TH
ANNUAL
CyberSource Online Fraud Report
Report & Survey
Methodology
Summary of Participants’ Profiles
The survey was conducted via online questionnaire by
Mindwave Research. Participating organizations completed
the survey between September 13th and October 12th 2011.
All participants were either responsible for or influenced
decisions regarding risk management in their companies.
12% of the survey participants use CyberSource fraud
management solutions.
This report is based on a survey of U.S. and Canadian
online merchants. Decision makers who participated in this
survey represent a blend of small, medium and large-sized
organizations based in North America. Merchant experience
levels range from companies in their first year of online
transactions to some of the largest retailers and digital
distribution entities in the world. Merchants participating in
the survey reported a total estimate of more than $83 billion
for their 2011 online sales.
Online Fraud Survey Wave
Total number of merchants participating
2007 2008
318 400
2009 2010
352 334
2011
325
Annual Online Revenue
Less than $5M
$5M to Less than $25M
$25M or More
56%
15%
29%
55%
14%
31%
56%
15%
29%
Duration of Online Selling
Less than One Year
1-2 Years
3-4 Years
5 or More Years
5% 11% 5% 6%
5%
13%12% 16%11% 12%
16%13% 14%19% 15%
66% 64% 65% 64% 68%
Risk Management Responsibility
Ultimately Responsible
Influence Decision
55%58% 54%55% 50%
45%42% 46%45% 50%
53%
18%
29%
54%
14%
32%
Get Tailored Views of Risk Management Pipeline Metrics
™
For sales assistance or to get a view crafted for your company’s size and industry, please contact CyberSource at
+1 888 330 2300 (international: +1 650 432 7350) or online at www.cybersource.com/contact_us.
For additional information, whitepapers and webinars:
Fraud Management Solutions: visit www.cybersource.com/products_and_services/fraud_management/
Global Payment Solutions: visit www.cybersource.com/products_and_services/global_payment_services/
Payment Security Solutions: visit www.cybersource.com/products_and_services/payment_security/
© 2012 CyberSource Corporation. All rights reserved.
iii
CyberSource Online Fraud Report
Table of Contents
EXECUTIVE SUMMARY. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
STAGE 1: AUTOMATED SCREENING. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Fraud Detection Tools Used During Automated Screening.. . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Planned Automated Screening Tool Usage 2012. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Automated Decision/Rules Systems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
STAGE 2: MANUAL REVIEW. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
Manual Order Review Rates. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
Tools Used/Planned During Manual Review. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Review Operations Efficiency. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
STAGE 3: ORDER DISPOSITIONING (ACCEPT/REJECT)��������������������� 9
Post-Review Order Acceptance Rates.
Overall Order Rejection Rates. . . . . .
International Orders Riskier. . . . . . .
Estimating Valid Order Rejection. . . .
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. 10
STAGE 4: FRAUD CLAIM MANAGEMENT������������������������������� 11
Types of Fraudulent Transactions. .
Friendly Fraud. . . . . . . . . . . . .
Fighting Chargebacks. . . . . . . . .
Fraud Rate Metrics. . . . . . . . . .
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11
11
11
12
TUNING & MANAGEMENT. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
Clean Fraud Still a Problem. . . . . . .
Tracking Mobile Fraud. . . . . . . . . .
Size of Fraud Management Budgets. .
Budget Allocation. . . . . . . . . . . . .
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14
14
14
15
Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
RESOURCES & SOLUTIONS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
ABOUT CYBERSOURCE.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
© 2012 CyberSource Corporation. All rights reserved.
v
CyberSource Online Fraud Report
Executive Summary
Managing online fraud continues to be a significant and
growing cost for merchants of all sizes. To better understand
the impact of payment fraud for online merchants, CyberSource
sponsors annual surveys addressing the detection, prevention
and management of online fraud. This report summarizes
findings from our 13th annual survey. Note: this report provides
benchmarks on total fraud rates (chargebacks and credits
issued directly to consumers by merchants). As such, these
metrics tend to be higher than those reported by banks
and card schemes, which generally base reported rates on
chargeback activity only.
Estimated $3.4 Billion Lost to Online Fraud
In 2011, merchants reported losing an average of 1.0% of total
online revenue to fraud. Although 2011 showed an uptick in
revenue loss rate versus the prior year, merchants reported
a 33% decrease in the percent of orders lost to fraud, 0.6%.
Using 2011 industry market projections1 on eCommerce
in North America, we estimate that the total revenue loss
translates to approximately $3.4 billion, a $700 million increase
over 2010 results (see Chart #1).
Chargebacks Account for less than 50%
of Fraud
This year’s survey probed the percent of fraud losses accounted
for by chargebacks. Overall, merchants continue to report that
chargebacks accounted for less than half of fraud losses —
41%, on average. The majority of fraud loss is due to merchants
issuing a credit to reverse a charge in response to a consumer’s
claim of fraudulent account use, or because of subsequent
information from additional orders that indicate likelihood of
fraud on the recently placed order. For digital goods with instant
fulfillment, credits could be issued afterwards, once fraud has
been detected.
International Order Acceptance is Riskier
Accepting international orders is riskier than domestic orders.
Merchants reported an international fraud rate by order rate of
2.0%, more than three times higher than domestic. In response
to the higher perceived risk, merchants rejected international
orders at a rate nearly three times higher than domestic orders
(7.3% vs. 2.8%, respectively).
In 2011, the order rejection rate continued to increase as it
has done since 2009. Merchants reported that they reject an
average of 2.8% of orders due to suspicion of payment fraud.
% Revenue Lost to Online Fraud
Online Revenue Loss Due to Fraud
Estimated $3.4B in 2011
1
4.0%
4.5
3.5%
$4.0
2.9
3.0%
$3.7
$3.1
2.5%
2.0%
1.7
1.8
1.6
1.4
1.5%
1.4
1.4
1.2
0.9
1.0%
1.0
3.0
$2.6
$3.4
$3.3
3.5
$ Loss in Billions
% Online Revenue Lost
4.0
3.2
$2.8
$2.7
2.5
$2.1
2.0
$1.7
$1.9
1.5
1.0
0.5%
0.5
0.0%
0.0
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
1 Based on eMarketer projections, with an 8% uplift to account for the merchant segments
covered by the survey but not by eMarketer’s market sizing.
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
© 2012 CyberSource Corporation. All rights reserved.
1
CyberSource Online Fraud Report
Manual Review Rates are Slightly Up
Mobile Fraud Risk is Mostly Unknown
After remaining at 72% in the last two years, the percent of
merchants conducting manual review increased to 75% in
2011, with 27% of orders routed to manual review (vs. 24%
in 2010). Merchants continue to rely heavily on manual
review teams as a defense against fraud, with the review
teams accessing an average of 4.2 systems to research and
disposition the order (vs. 4.0 in 2010).
This year, we asked about merchant perception of fraud in the
mobile channel, which we defined as either commerce on a
mobile-optimized website or through a mobile app. Overall, 92%
of merchants do not know their mobile fraud rates, 7% perceive
that mobile fraud rates are the same or lower than online fraud,
while 1% perceive mobile fraud to be slightly higher.
Total Risk Management Pipeline View
Opportunities to Streamline Fraud
Management
As eCommerce sales continue to grow, scalability will become
more of a pressing issue. Merchants continue to face the
challenge of screening more online orders while keeping
manual review staffing and fraud rates as low as possible. Yet
82% of merchants reported that their fraud budgets will stay the
same or decrease, and only 18% reported that they will not be
increasing their order review staff.
With more volume and limited resources, emphasizing and
improving automated fraud detection capabilities is a top
priority for 55% of the merchants surveyed. The need for better
automation is understandable when 75% of manually reviewed
orders are ultimately accepted. To be successful, fraud
managers will need to adopt tools and practices to reduce the
number of orders being routed to review, as well as enable their
review teams to operate more efficiently.
Businesses that concentrate solely on minimizing chargebacks
may not see the complete financial picture. Online payment
fraud impacts profits from online sales in multiple ways.
Besides direct revenue losses, the cost of stolen goods/
services and associated delivery/fulfillment costs, there are
the additional customer experience “costs” of rejecting valid
orders, staffing manual review, administration of fraud claims,
as well as challenges with scaling fraud management operations
as business grows. Merchants can realize certain efficiencies
by taking a total risk management pipeline view of operations
and costs. While the fraud rate is one metric to monitor (and
maintain within industry and card scheme limits), an end-toend view is required to achieve the optimal financial outcome.
In 2011, these “profit leaks” in the Risk Management Pipeline™
impact as much as 30% of orders for mid-sized merchants
and as much as 13% of orders for larger merchants — driven
primarily by too many orders being manually reviewed, which
restricts profits, operating efficiency and scalability. This report
details key metrics and practices at each point in the pipeline
to provide you with benchmarks and insight. Custom views
of these benchmarks and practices are available through
CyberSource — see end of report for contact information.
Risk Management Pipeline
Order
Automated
Screening
PROFIT LEAKS
Manual
Review
Staffing &
Scalability
Lost
Sales
75% of merchants review
orders; these merchants review
27% of orders, on average
2.8% Avg. Reject Rate
for U.S./ Canadian
orders (all merchants)
52% of fraud management
budget is spent on review staff
costs
75% of these merchants have no
plans to change manual order
review staffing during 2012
2
Accept /
Reject
Fraud Claim
Management
Fraud Loss &
Administration
1.0% Average Fraud Loss
41% from chargebacks
59% from credit issued
Retained
Revenue
CyberSource Online Fraud Report
Stage 1: Automated Screening
Order
Automated
Screening
Manual
Review
Accept /
Reject
Fraud Claim
Management
Retained
Revenue
Tuning and Management
Fraud Detection Tools Used During
Automated Screening
We define fraud detection tools as those used to assess the
probability of risk associated with a transaction. They are
categorized into four groups: validation services, proprietary
data, purchase device tracing, and multi-merchant data.
Results from these tools drive the decision to accept, reject or
review the transaction, either through an automated rules-based
system or manually. In 2011, 56% of merchants surveyed
utilize an automated screening system.
Of these, 68% of merchants report using at least 3 tools in
their automated screening solution and an average of 4.9 tools
overall. Larger merchants processing higher order volumes use
an average of 8 tools. Sheer order volume could necessitate
automated screening or more sophistication to address fraud,
as well drive fraudsters to target these merchants more often. In
addition, larger merchants consistently show higher utilization
of more tools, as shown in Chart #2, which highlights the most
popular fraud detection tools used.
Ninety-seven percent of merchants use one or more validation
tools. Some are provided by the card schemes, such as Card
Verification Number (CVN), Address Verification Service (AVS),
and payer authentication services (Verified by Visa, MasterCard
SecureCode), and others are provided by third-party verification
services.
Not surprisingly, CVN and AVS show the most widespread
adoption, as they are provided at no additional cost. But in
terms of effectiveness, with the larger merchants, few cited CVN
and AVS in one of their top three tools in terms of effectiveness
(see Chart #3). This could be explained by the relative ease of
obtaining CVNs on the black market, and the limited availability
of AVS data outside of North America.
In terms of leveraging customer history, 67% of merchants
currently use or are planning to use their own proprietary data.
In particular, the use of company-specific fraud-scoring models
and customer website behavior analysis grew in 2011.
2
Fraud Detection Tool Usage 2011
All Merchants
Validation Services (Net)
Card Verification Number (CVN)
Address Verification Service (AVS)
Postal address validation services
Verified by Visa/MasterCard SecureCode
Telephone # verification/reverse lookup
Social networking sites
Paid-for public records services
Credit history check
Out-of-wallet or in-wallet challenge/response systems
39
25
22
10 7
9 6
55
5 2
Proprietary Data/Customer History (Net)
41
Purchase Device Tracing (Net)
48
40
25
16
Multi-Merchant Data/Purchase History (Net)
Shared negative lists – shared hotlists
Multi-merchant purchase velocity
Other
17
14
67
43
14
38
13
35
8
30
11
22
20
20
11
Customer order history
Negative lists (in-house lists)
Order velocity monitoring
Fraud scoring model – company specific
Customer website behavior analysis
Positive lists
IP geolocation information
Device “fingerprinting”
97
79
77
13
20
18
30
17
15
15
15
4 8
Merchants $25M+ Online Revenue
Validation Services (Net)
Card Verification Number (CVN)
Address Verification Service (AVS)
Postal address validation services
Verified by Visa/MasterCard SecureCode
Telephone # verification/reverse lookup
Social networking sites
Paid-for public records services
Credit history check
Out-of-wallet or in-wallet challenge/response systems
100
86
86
47
24
20
22
8 6
9 4
12
14
37
4
12
2
Proprietary Data/Customer History (Net)
89
Customer order history
Negative lists (in-house lists)
Order velocity monitoring
Fraud scoring model – company specific
Customer website behavior analysis
Positive lists
62
8
75
29
39
6
14
20
18
84
68
IP geolocation information
Device “fingerprinting”
20
43
Multi-Merchant Data/Purchase History (Net)
40
51
Shared negative lists – shared hotlists
Multi-merchant purchase velocity
% Using OR Planning to Use
10
63
62
Purchase Device Tracing (Net)
Other
12
4
29
27
22
18
34
% Currently Using
% Planning to Implement
© 2012 CyberSource Corporation. All rights reserved.
3
CyberSource Online Fraud Report
3
Most Effective Fraud
Management Tools
Multi-merchant data and purchase history is less utilized in
comparison to the other three categories, but can be quite
useful to merchants. Statistically, the average top ten Internet
merchant sees only 1/100th of the actual annual online
transaction activity, at best. Multi-merchant data gives a wider
view of activity to detect subtle fraud patterns, by providing
a broader pool of transactions for merchants to analyze for
linkages and potential fraud.
Validation Services (Net)
33
Paid-for public records services
26
Address Verification Service (AVS)
25
Card Verification Number (CVN)
Contact customer to verify order
24
Verified by Visa OR
MasterCard SecureCode
18
17
Credit history check
10
Telephone # verification/reverse lookup
7
Contact card issuer/Amex CVP
5
Postal address validation services
Validation using social networking sites
Tool selected as one of
“TopThree” most effective
fraud tools by 25%+ of
those using it
4
Google Maps lookup
0
Out-of-wallet or in-wallet
challenge/response systems
0
Proprietary Data/Customer
History (Net)
Fraud scoring model – company specific
39
Negative lists (in-house lists)
38
29
Order velocity monitoring
27
Customer order history
Customer website behavior analysis
Positive lists
Multi-merchant data can be difficult to obtain without the use
of a third-party provider, due to legal and privacy regulations.
Similarly, shared negative lists are subject to legal regulations
and are only as good as the information provided by others —
and in some cases the data can be outdated or inaccurate.
Shared data is only effective if there is a sufficiently large
volume of current, accurate data to correlate and analyze. The
use of shared negative lists fell by 3% among large merchants,
yet use of multi-merchant purchase velocity increased by 16%
(vs. 2010). For merchants of all sizes, shared negative lists grew
by 2%, and multi-merchant purchase velocity grew by 6%.
Planned Automated Screening Tool
Usage 2012
4
3
Device Fingerprinting Highest on “Plan to Buy” Lists
Purchase Device Tracing (Net)
Fifty-seven percent of surveyed merchants plan to add one
or more new fraud detection tools in the next twelve months.
Device fingerprinting and customer website behavior analytics
are the two tools that most merchants expect to evaluate for
adoption in the next year.
46
Device fingerprint results
45
Device “fingerprinting”
37
IP geolocation information
Multi-Merchant Data/Purchase
History (Net)
44
Multi-merchant purchase velocity
34
Shared negative lists — shared hotlists
0%
10%
20%
30%
40%
50%
Base: Merchants with annual online sales ≥$25M who use tool:
automated or manual (excludes None)
Customer website behavior analysis is one of the newer
fraud tools available. It attempts to assess whether or not the
customer’s visit and website activity is consistent with that of
a typical user. For instance, the time spent on checkout or the
number of pages viewed varies greatly between a typical user
and a fraudster using a bot. Although adoption is currently
relatively low, it may increase as fraudsters learn how to
circumvent traditional mitigation tools.
Purchase device tracing are tools that attempt to validate the
device and location of the network from which the order is
being placed. They continue to show broader adoption. For the
4
fraudster, bypassing device fingerprinting and IP geolocation
requires more sophistication than just obtaining stolen customer
identity and payment data. As a result, device fingerprinting and
IP geolocation were cited most often by large merchants as one
of their top three effective tools.
As in past years, card scheme payer authentication services
figure prominently in merchants’ future plans. The 2011 survey
results show that 25% of merchants currently use one or more
of the available payer authentication services, and 20% say they
are interested in deploying within the next twelve months.
Despite significant interest in implementing payer authentication
systems over the past few years, we have seen relatively slow
adoption of payer authentication since we started tracking this
tool in 2003. But with recent bank and regulatory mandates
on using payer authentication, particularly abroad, merchant
adoption is expected to increase, at least for those merchants
that have localized websites.
Automated Decision/Rules Systems
Automated Order Screening
Fifty-six percent of merchants utilize an automated decisioning
system. These systems apply a merchant’s business rules to
CyberSource Online Fraud Report
evaluate risk on incoming orders in real-time. As companies
grow in size and the number of fraud detection tools increases,
leveraging these assets as part of an automated order screening
solution will become increasingly imperative. Decision systems
can help organizations quickly analyze data from incoming
transactions and assess their risk, thereby enabling merchants
to scale their businesses as order volumes increase.
BEST PRACTICE
advice
To improve fraud detection and combat fraud, focus on
gathering as much data as possible on every transaction,
no matter how trivial it may seem. The larger the data
pool, the more likely you are to detect fraud.
Because fraud patterns are dynamic, automated screening
systems allow merchants to implement changes quickly;
54% of merchants say that changes are implemented
instantly. Furthermore, over 80% of the merchants surveyed
have a confidence level of 75% or higher that the changes
implemented will have the intended results.
Using this broad dataset as the foundation, correlate
each individual transaction element with historical
instances of fraud to determine its predictive value.
Moreover, correlate the various combinations of these
elements with fraud, to uncover the subtle interplay
between data elements that are indicative of fraud.
Results of Automated Screening
By applying a rigorous statistical approach to your data,
you can derive analytic models that present a strong,
multi-layered defense against fraud attacks.
The automated order screening process generates three
outcomes: 1) order acceptance without further review, 2) orders
flagged for further review and 3) automatic order rejection.
Some merchants allow this initial automated screen to cancel
orders without further human intervention. Forty-eight percent
of all merchants cancelled some orders as a result of their
automated screening process and 60% of large merchants
indicated they cancelled some orders at this stage (see Chart #4).
4
Are Inbound Orders Rejected Based Solely On Automated Screening?
All Merchants
Merchants $25M+ Online Revenue
35%
Yes
48%
43%
No
52%
No
40%
Yes
60%
13%
17%
Yes, but generally ONLY if customer is on our negative list
Yes, if automated tests indicate too much risk OR customer is on our negative list
No, generally all suspicious orders are outsorted for manual review
Base: Merchants using automated services/technologies
© 2012 CyberSource Corporation. All rights reserved.
5
CyberSource Online Fraud Report
Stage 2: Manual Review
Order
Automated
Screening
Manual
Review
Accept /
Reject
Fraud Claim
Management
Retained
Revenue
Tuning and Management
Orders which do not pass the automated order screening stage
typically enter a manual review queue. During manual review,
additional information is often collected to determine if orders
should be accepted or rejected due to excessive fraud risk.
Manual review is often the largest portion of an organization’s
fraud management operations, representing just over half
of the budget. Staff overhead is costly, limits scalability and
could potentially impact customer satisfaction as order volume
increases, due to the inherent latency associated with manual
review processes.
Seventy-five percent of merchants surveyed do not anticipate
a change in staffing levels in 2012, yet retail eCommerce is
forecasted to grow by 11.3% in 20122. This presents significant
challenges to profit growth, especially if the total number of
orders to be manually reviewed increases in lockstep with the
total increase of online sales.
Merchants could divert more staff time to order review, or
increase staffing levels, but both options would increase
overhead costs. Alternatively, merchants can allow more time
to process orders, but they would need to consider impacts to
customer service in the event of shipping delays. Another option
would be to fine-tune their automated decisioning system to
accurately disposition more orders up front, thereby sending
fewer orders to manual review.
5
Manual Review Trends
80%
70%
72
69
75
72
60%
50%
40%
32
28
30%
22
20
20%
27
24
17
17
10%
0%
2009
2008
2010
2011
% Merchants Performing Manual Order Review
% Orders Reviewed by Merchants Practicing Review
% of Orders Reviewed Overall (Net Review)
Average % of Orders Manually Reviewed 6
(for merchants engaged in review)
50%
Manual Order Review Rates
Seventy-five percent of merchants surveyed conduct manual
review, a percentage that has remained relatively stable over
the last few years. For those merchants that do conduct manual
reviews, the percentage of all orders that are manually reviewed
increased slightly, to 27% (see Chart #5). Review rates for
merchants with up to $25 million in online sales are more than
double those of larger merchants (see Chart #6). Lower order
volume and the lack of an automated order screening system
may account for the higher review rates — only 43% of these
smaller merchants have an automated order screening system.
2009
45
40%
30%
37
28
2010
2011
35
33
33
27
24
22
22 21
20%
12
10%
2 eMarketer, “U.S. Retail Sales, 2009 – 2015”
9
0%
Overall
<$5M
$5M –
<$25M
$25M –
<$100M
Annual Online Revenue
6
15
12
$100M+
CyberSource Online Fraud Report
Tools Used/Planned During Manual Review
Although many of the same fraud detection tools are used for
both automated and manual reviews, the review team typically
leverages additional tools and processes when attempting to
validate an order. “Customer order history” and “Contacting the
customer” continue to be employed most often. The tools that
have seen the highest growth in adoption are Google Maps for
investigating delivery addresses, along with IP geolocation (see
Chart #7).
In addition to the aforementioned tools, “Using device
fingerprinting results” (where results of device fingerprinting
information are exposed in the manual review screens) and
“Postal address validation services” have seen more adoption
with large merchants in 2011. “Using device fingerprinting
results” was the most cited by merchants as a technology they
would most likely be implementing in 2012.
Review Operations Efficiency
Reviewer Efficiency
7
Fraud Detection Tool Usage 2011
All Merchants
Validation Services
91
Contact customer to verify order
65
55 10
54 7
Telephone # verification/reverse lookup
Google Maps lookup
48 10
19
Postal address validation services
40
Contact card issuer/Amex CVP
33
24 6
Social networking sites
Paid for public records services
Credit history check
17
5 8
Customer order history
41
21
Positive lists
14
75
27
Multi-Merchant Data/Purchase History
17
Shared negative lists – shared hotlists
50
23
75 4
80 7
73
Google Maps lookup
55 7
62 7
Contact card issuer/Amex CVP
55 4
50 7
Social networking sites
Paid for public records services
14
92
Proprietary Data/Customer History
83
Customer order history
63 7
Negative lists (in-house lists)
Customer website behavior
38
Positive lists
37
18
14
IP geolocation information
33
28
Shared negative lists – shared hotlists
Other
% Using OR Planning to Implement
50
37
Multi-Merchant Data/Purchase History
18
2
% Currently Using
6
5
Overall
<$5M
$5M –
<$25M
$25M –
<$100M
$100M+
Annual Online Revenue
2009
2010
2011
Merchants reported that reviewers accessed or input data into
an average of 4.2 systems, with 10% of merchants reporting
usage of 10 or more systems. The inability to seamlessly
integrate or automatically interface with these multiple systems
negatively impacts reviewer productivity, which is exacerbated
as the number of systems in use increases.
Final Order Disposition
72
62 11
Purchase Device Tracing
Device fingerprint results
25
5
97
Postal address validation services
45
0
Validation Services
Telephone # verification/reverse lookup
100100
60
50
50
25
Contact customer to verify order
10
60
30
4/2
Merchants $25M+ Online Revenue
Credit history check
100
100
53
46 11
36
IP geolocation information
Other
125
8
Purchase Device Tracing
150
150
8
19
35
Customer website behavior
Device fingerprint results
150
81
69 10
Negative lists (in-house lists)
8
Orders Manually Reviewed per Day,
Per Reviewer
8
Proprietary Data/Customer History
The median number of orders a reviewer processes in a day
ranged from 6 for small merchants to 100 for large merchants
(see Chart #8). Large merchants who have case management
systems typically achieve twice the throughput per reviewer
in the manual review stage, in part due to more sophisticated
review systems and experienced investigators.
% Planning to Implement
(in the next 12 months)
Automated screening and manual order review ultimately
result in order acceptance or rejection. A relatively high
percentage of orders manually reviewed are ultimately
accepted (see next section). This highlights the need for
merchants to continue improving automated screening
accuracy and reduce their reliance on expensive manual
review processes. A look at order reject and acceptance
rates follows in Stage 3 of the pipeline review.
© 2012 CyberSource Corporation. All rights reserved.
7
CyberSource Online Fraud Report
BEST PRACTICE
advice
To optimize the manual review process, focus on how
your reviewers can access the information they need in
the most intuitive and direct way possible. Use a case
management system that can consolidate all of the
information relating to the order in one place.
Provide a structured framework and checklist for
investigating orders to your review team, which help
to streamline the process and ensure consistency in
dispositioning orders.
Finally, measure the performance of your review team by
looking at key metrics (such as orders reviewed per day,
length of time in queue, chargebacks from the manual
review process and by reviewer), to identify areas for
improvement at both the reviewer and team level.
8
CyberSource Online Fraud Report
Stage 3: Order Dispositioning (Accept / Reject)
Automated
Screening
Order
Manual
Review
Accept /
Reject
Fraud Claim
Management
Retained
Revenue
Tuning and Management
Post-Review Order Acceptance Rates
Of the orders that are manually reviewed, on average, 75%
of them are ultimately accepted. Over half of the merchants
surveyed state that they accept over 90% of the orders placed
into review (see Chart #9). With three out of every four orders
accepted, these merchants are incurring significant expense to
review legitimate orders (see Chart #10). Either excessively high
acceptance or rejection rates can indicate more orders than
necessary are being diverted to manual review, a situation that
can be corrected by tuning automated screening rules/detectors
to isolate and systematically disposition more of these orders.
90%
80%
% Acceptance Rate
1-9%
3
10-19%
3
20-29%
2
30-39%
2
40-49%
70-79%
60%
50%
40%
30%
31
25
28
19
17
20%
10%
Overall
<$5M
$5M –
<$25M
$25M –
<$100M
$100M+
Annual Online Revenue
Accepted
Rejected
Overall Order Rejection Rates
7
(Overall and by Merchant Size)
4
3.9
4%
3.3
5
13
80-89%
7
2.7 2.8
2.6
2.8
3.0
2.7
2.1
49
90-99%
11
Average % Orders Rejected Due to
Suspicion of Fraud
1
3%
100%
69
Order reject rates can either reflect true fraud risk or signal a
potential “profit leak” in terms of customer insults. In 2011,
merchants reported rejecting an average of 2.8% of orders
domestically (see Chart #11).
50-59%
60-69%
81
72
9
4
0%
83
75
70%
0%
% of Merchants Reporting This Level
of Post-Review Acceptance
10
Post-Review Acceptance Trends
2.3
2%
1%
0%
Overall
<$5M
$5M –
<$25M
$25M –
<$100M
$100M+
Annual Online Revenue
2010
2011
© 2012 CyberSource Corporation. All rights reserved.
9
CyberSource Online Fraud Report
Merchants with more than $100 million in online sales
continued to make progress in reducing rejection rates, while
smaller merchants with less than $5 million in online sales
showed slightly higher rejection rates. As expected, merchants
selling higher ticket, physical goods tend to reject more orders
(i.e., consumer electronics, apparel/jewelry) in comparison to
digital goods (see Chart #12).
13
Order Rejection Trends
14%
12%
11.1
10.9
10%
7.7
8%
Average % Orders Rejected Due to
Suspicion of Fraud
4.2
4%
2.9
2.4
2.7
2.8
2%
5.9
2010
5%
0%
2011
4.7
4%
3%
7.3
6%
(Overall and by Industry)
6%
7.6
12
3.6
3.8
2.9
2.4
1.8
1%
0%
Apparel/
Jewelry
2010
2011
3.1 3.0
2.5
Consumer
Electronics
2009
% of International Orders Rejected
3.2
2%
Overall
2008
% U.S./Canada Orders Rejected due to Suspicion of Fraud
3.9
2.7 2.8
2007
Household/
General
Merchandise
Physical
Goods
Digital
Goods &
Services
All Services
International Orders Riskier
The order rejection rate for international orders is nearly
three times higher than domestic orders — 7.3% vs. 2.8%,
respectively (see Chart #13). Fraud screening is more
challenging for international orders, as standard validation tools
may not be readily available or may be more costly to implement
Address and telephone records, credit checks, and public
records vary by country. The actual fraud rate experienced
on international orders supports a cautious approach to order
acceptance, as merchants report the fraud risk on international
orders is significantly higher than that of domestic orders.
Estimating Valid Order Rejection
Orders are rejected due to suspicion of fraud, but within that
pool, inevitably, are valid orders from customers that were
erroneously rejected. In addition to the lost sales from these
good customers, merchants try to avoid this type of “customer
insult,” as lost customers and negative word of mouth can
adversely impact future revenues.
Fifty-two percent of large merchants surveyed attempt to
estimate valid order rejection rates, but it is difficult to measure
accurately or with a degree of confidence. Of the merchants
that provided input on how they track valid order rejections,
most cited tracking customer complaints or customer feedback
on the orders. However, any of these methods understates the
true “false positive” rate.
BEST PRACTICE
advice
To focus the review team’s efforts on truly questionable
orders, maximize the number of automated decisions.
To this end, analyze the profiles of orders that are
accepted during manual review and determine if there
are common characteristics from which you could build
effective “auto-pass” screening rules.
As a general rule of thumb, half of the orders that
are manually reviewed should be accepted. Order
acceptance and rejection rates that skew above 50%
signal opportunities to shift more of the manual order
evaluations to automated screening.
10
CyberSource Online Fraud Report
Stage 4: Fraud Claim Management
Automated
Screening
Order
Manual
Review
Accept /
Reject
Fraud Claim
Management
Retained
Revenue
Tuning and Management
Types of Fraudulent Transactions
Friendly Fraud
We define fraudulent orders as either chargebacks or a credits
issued to consumers claiming fraudulent use of their accounts.
As a result, actual fraud rates reported tend to be higher than
those cited by banks or card schemes. Fraudulent orders
have a significant impact on bottom line profits. Although
chargebacks are the most often cited metric, merchants report
that chargebacks actually account for only 41% of all fraud
claims (see Chart #14).
“Friendly fraud” occurs when a merchant receives a claim
because the cardholder denies making the purchase or
receiving the order, yet the goods or services were actually
received. In some instances, the order may have been placed
by a family member or friend that has access to the buyer’s
cardholder information. Chart #15 shows that 60% of merchants
perceive friendly fraud has increased over the past two years.
14
% of Fraud Claims: Chargebacks vs.
Credit Issued by Merchant
2011 vs. 2010
15
“Friendly Fraud” over the Last
Two Years
(Overall and by Merchant Size)
Remained
the Same
100%
90%
41
80%
70%
59
62
64
80
60%
73
59
63
52
49
35%
Increased
60%
50%
40%
Decreased
59
30%
20%
41
38
36
20
10%
27
41
37
48
0%
2010
2011
Overall
2010
2011
<$5M
2010
2011
$5M – <$25M
2010
2011
$25M – <$100M
2010
2011
$100M+
Annual Online Revenue
Credits Issued
5%
51
Chargebacks
Fighting Chargebacks
Merchant practices vary with respect to contesting chargebacks
(“re-presentment”). On average, merchants re-present 56% of
their fraud-coded chargebacks, with nearly 30% re-presenting
all of their chargebacks (see Chart #16).
Considering the financial impact of both fraud claim routes
(chargebacks and credit issuance/reversal), some merchants
encourage direct consumer contact to address fraud claims and
thus avoid chargebacks from the acquiring bank/processor.
© 2012 CyberSource Corporation. All rights reserved.
11
CyberSource Online Fraud Report
Fraud Rate Metrics
16
Average % Total Fraud-Coded
Chargebacks Re-presented
% of Merchants Reporting This
Re-presentment Rate
(Overall and by Merchant Size)
14
0%
80%
73
3
10-19%
70%
60%
5
1-9%
59
56
14
20-29%
53
1
30-39%
50%
5
40-49%
40%
9
50-59%
30
30%
1
60-69%
20%
7
70-79%
10%
80-89%
0%
90-99%
Overall
<$5M
$5M – $25M – $100M+
<$25M <$100M
When monitoring the level and trend of online fraud loss,
we focus on three key metrics: 1) fraud rate by revenue
(revenue lost to fraud as a percent of total online sales); 2)
fraud rate by order (percent of accepted orders which turn
out to be fraudulent); and 3) the average value of a fraudulent
order relative to a valid order. Fraud rates vary widely by
merchant and depend on a variety of factors, such as onlinte
sales volume, type of products or services sold online, how
such products/services are delivered and paid for, and the
merchant’s fraud prevention capabilities. It is important that
merchants track key fraud metrics over time and evaluate
their performance relative to their peer group (both size and
industry).
Fraud Rate by Revenue
5
7
28
100%
Annual Online Revenue
Merchants report that they win, on average, 40% of the
chargebacks they dispute, resulting in a net recovery rate
of 27%. Using the average percent of chargebacks that are
disputed (56%), multiplied by the average win rate of 40%,
results in a net recovery rate of 22% (meaning 22% of all fraudcoded chargebacks are recovered). However, given the wide
disparity in the chargeback re-presentment rate, when these are
calculated on a merchant-by-merchant basis and then averaged,
the re-presentment win rate rises to 27% (see Chart #17).
Revenue loss measurement includes not only the value of
orders on which fraudulent chargebacks are received, but also
the cost of any credits issued to avoid such chargebacks. In
2011, the average fraud rate by revenue was 1.0%. The largest
merchants reported smaller revenue loss rates (0.4%) while
conversely, smaller merchants reported up to three times higher
(see Chart #18).
(Overall and by Merchant Size)
2010
1.4%
Fraud Chargeback Re-presentment:
Win Rate / Net Recovery Rate 2011
1.1 1.1
1.1
1.1
1.0
1.0
0.9
0.8%
(Overall and by Merchant Size)
0.6%
% Challenged
80%
1.0%
2011
1.2
1.2%
17
18
Average Fraud Rate by Revenue
2011 vs. 2010
% Net Recovery*
0.4
0.4%
0.4
70%
0.2%
60%
0%
50%
56
Avg
Win
Rate
20%
10%
Avg
Win
Rate
40%
40%
30%
Overall
44%
27
45%
73
Avg
Win
Rate
59
Avg
Win
Rate
34%
33
22
25%
30
Avg
Win
Rate
53
29
15
0%
Overall
<$5M
$5M –
<$25M
$25M –
<$100M
$100M+
Annual Online Revenue
*Net Recovery is expressed as a % of all fraud-coded chargebacks challenged
12
<$5M
$5M –
<$25M
$25M –
<$100M
Annual Online Revenue
$100M+
CyberSource Online Fraud Report
Fraud Rate by Order
Another key metric is the number of accepted orders that
later turn out to be fraudulent, expressed as a percent of total
accepted orders. In 2011, the fraud rate by order was 0.6%,
and ranged from 0.4% to 1.2% (see Chart #19).
20
Fraud Rate by Order
Domestic vs. International
5.0%
19
Average Rate by Order 2011 vs. 2010
(Overall and by Merchant Size)
4.0
4.0%
3.6
3.0%
2010
1.5%
2.7
2011
1.1
1.0%
0.9
1.1
1.2
0.7
2006
0.9
0.6
2007
2008
2009
2010
2011
International Orders
0.4
0%
<$5M
0.9
Domestic Orders
0.5
Overall
1.1
2.0
0.0%
0.8
0.6
0.5%
1.3
1.0%
0.9
0.9
2.1
2.0
2.0%
$5M –
<$25M
$25M –
<$100M
$100M+
Annual Online Revenue
Fraudulent Order Value
Historically, fraudulent orders tend to have higher values than
valid orders. In 2011, merchants reported a median fraudulent
order value of $250, vs. $150 for the valid order value.
International Orders Carry Higher Risk
Fifty-eight percent of merchants surveyed accepted orders
from outside the U.S. & Canada in 2011, with international
sales accounting for 17% of total orders, on average. Because
of the higher risk associated with cross-border transactions,
one in four merchants surveyed stopped accepting orders from
at least one country outside of North America. Fraud rates for
international orders are more than three times higher than
domestic orders (2.0% vs. 0.6%, respectively). However, the
fraud rate for international orders has remained relatively stable
since 2009 (see Chart #20).
BEST PRACTICE
advice
To minimize overall chargebacks, take steps to reduce
your exposure to friendly fraud. Clearly articulated terms
and conditions that the customer must acknowledge at
the outset often provide an effective front-line deterrent.
Follow this with other safeguards, such as confirmation
emails, activation links, or other online validations that
require customer input or action.
For example, if your business is subscription-driven,
send an email to the address provided, with a link to
activate the account. This serves two purposes: first, if
the email address is phony, then the fraudster will be
unable to activate the account. Second, if the email
account is valid, once the activation URL is clicked,
you’ll have a trail that you can use in the event that
the cardholder disputes charges.
Any or all of these safeguards will put you in a better
position to deter fraud or re-present any subsequent
chargeback, as you will have documentation of actions
taken by the customer.
Balance the safeguards required against the customer
experience — excessive customer friction would
negatively impact your legitimate customers.
By configuring your order flow with the appropriate
checks properly in place, you can maximize legitimate
revenue while deterring friendly fraud.
© 2012 CyberSource Corporation. All rights reserved.
13
CyberSource Online Fraud Report
Tuning & Management
Order
Automated
Screening
Manual
Review
Accept /
Reject
Fraud Claim
Management
Retained
Revenue
Tuning and Management
Clean Fraud Still a Problem
We define “clean” fraud as fraudulent orders that look and
behave like valid orders because they pass the typical fraud
checks put in place by a merchant. Forty-six percent of
merchants say that the fraudulent orders they’ve seen are
“cleaner” than what they experienced a year ago (see Chart
#21). This is down slightly from 2010 levels, but continues to be
the biggest change that merchants have noticed in 2011.
Fraudulent Orders Continue to Look
More Like Valid Orders
No
54%
21
lower than online fraud, while 1% believe that it’s higher. With
such a large percentage that “don’t know,” it will be interesting
to see how these results change as mobile becomes more of
an established channel.
Size of Fraud Management Budgets
How much are online merchants spending to mitigate fraud
risk? In 2011, survey results show that 25% of merchants
spend 0.5% or more of their online revenues to manage online
payment fraud, while 75% spend less than 0.5%. In 2011,
across all merchants the median ratio of fraud management
expense to sales was 0.1%. These spending estimates focus on
the cost of managing fraud risk (internal and external systems
and services, management development, and review staffing).
Direct fraud loss (chargebacks, lost goods and associated
shipping costs), as well as the opportunity cost associated with
valid order rejection, are not included (see Chart #22).
Yes
46%
= % of merchants that
claim current fraudulent
orders are cleaner than
those from 12 months ago
(Percent of Merchants Operating at Defined
Expense Level)
58
60%
Tracking Mobile Fraud
As the mobile channel continues to grow, merchants will need
to understand how fraud impacts mobile commerce. One
school of thought believes mobile is riskier (new channel,
harder to identify the device or IP address), while another
school of thought believes the opposite (closed mobile network,
passcodes on phones, omnipresent device).
Twenty-seven percent of merchants reported that they have
a mobile commerce website, while 20% reported having a
mobile app (Note: there is a high overlap of merchants that
offer both). However, 92% do not know their fraud rates in the
mobile channel. 7% believe that mobile fraud is the same or
14
22
How Much Merchants Spend on Fraud
Management
51
50%
2009 % of revenue spent*: 0.3%
47
2010 % of revenue spent*: 0.2%
2011 % of revenue spent*: 0.1%
40%
*Median
30%
20%
15
17 17
16
8
10%
10
8
10 11
13 13
6
0%
0% - <0.2%
.2% – <.5%
.5% – <1%
1% – <4%
% of Annual Online Revenue Spent to Manage Fraud
(Staff, Systems, Tools, etc., excluding Fraud Loss)
4%+
CyberSource Online Fraud Report
Budget Allocation
For many years, merchants have consistently spent just over
half their fraud management budgets on review staff, and 2011
was no different — on average, 52% was allocated to order
review staff (see Chart #23).
23
Average % Spending Allocation for
Fraud Management 2011
3rd Party Tools
Outsourcing
4%
MR tasks/
workflow
21%
Oder Review
Staff
25
Other
2%
Improving automated detection
and sorting capability
Process
Analytics
18%
Internal Tools
& Systems
21%
Top Priority Strategy /Area of Focus
2012
Automated
Detection
55%
Streamlining the tasks/workflow
occurring during the manual
review process
Improving process analytics
Outsourcing portions of review/
screening operations
Other
Planned Staffing Levels
for 2012
52%
27%
Increase
Same
Decrease
18%
75%
7%
Only 18% of merchants reported that they would be increasing
their staffing levels, as review staff costs remain the dominant
cost driver of fraud operations. In terms of budget, over 80%
reported that their budgets would remain flat or decrease in
comparison to last year (see Chart #24).
With such a heavy reliance on manual review coupled with
No Change
continued
online sales growth, order review teams will come
under more
pressure to review more orders in the same amount
78%
of time. Without the right tools and processes in place, orders in
queue will become a growing concern. As department budgets
remain tight, merchants will need to re-double their efforts to
automate more of the fraud management process while keeping
valid order conversion high and fraud loss low.
BEST PRACTICE
24
Expected Budget Change for
Fraud Management 2012
Increase
No Change
78%
19%
Decrease
Average % Fraud Mgmt
Budget Expected to
INCREASE
%
15
Average % Fraud Mgmt
Budget Expected to
DECREASE
%
15
4%
Automated detection and outsorting continues to be the most
cited area for process improvement attention in 2012, followed
by streamlining manual review (see Chart #25). Reducing
the need for manual review and increasing the efficiency and
effectiveness of reviewers is key to growing online business
profits and managing the total cost of online payment fraud.
advice
To optimize your fraud management operations,
maximize automated order screening capabilities while
streamlining workflow for your review team. Use a
decisioning system where business users can create
screening criteria based on order attributes, as well
as results and information provided from a host of
verification and validation services.
Look for portals where case management systems
are seamlessly integrated with relevant third-party
data sources. It’s imperative that reviewers access
one consolidated tool rather than having to leverage
multiple systems.
Finally, define and measure your key performance
indicators throughout the fraud management lifecycle.
Understand your performance baseline and objectives,
so you can identify where you can fine-tune your fraud
management operations. That which gets measured,
gets improved.
© 2012 CyberSource Corporation. All rights reserved.
15
CyberSource Online Fraud Report
Conclusion
(note: Chart 27 does not reflect all of the industries cited by
merchants). Each merchant is unique in terms of their business
objectives, fraud tolerance and risk, which is reflected in the
results shown in the charts.
To provide an overall assessment and basis of comparison, we
took a snapshot of average merchant performance across four
key performance indicators (KPIs): manual review rate, order
rejection rate, percent of orders lost to fraud (Fraud Rate —
by Order), and percent of revenue lost to fraud (Fraud Rate
— by Revenue), as shown in Charts #26 and #27.
Managing fraud is an ongoing balancing act where merchants
constantly weigh tradeoffs among fraud loss, customer
experience, and cost. If fraud is reduced, what happens to the
number of rejected orders? Will good customers be impacted?
If more orders are accepted, what happens to fraud, and how
will manual review support this? Having a structured framework
to address these tradeoffs can help merchants identify gaps
and improve overall fraud management processes.
KPIs vary by merchant size and by industry. In general, the
largest merchants tend to fare better. This may be driven by
potentially more resources, tools and experience in combating
online fraud, whereas smaller merchants may not have the
resources at their disposal.
KPIs are more varied across industry, which is to be expected
26
Order Reject Rate and Fraud Rates by Merchant Size
1.2
Manual Review Rate (avg = 27%)
Order Reject Rate (avg = 2.8%)
45
Fraud Rate – by Order (avg = 0.6%)
Fraud Rate – by Revenue (avg = 1.0%)
3.9
Percent
33
1.2
1.1
2.8
0.7
1.1
2.7
0.5
Average
2.3
12
<$5M
$5M – <$25M
0.4
15
0.4
$25M – <$100M
$100M+
27
Order Reject and Fraud Rates by Industry
Manual Review Rate (avg = 27%)
Order Reject Rate (avg = 2.8%)
4.7 1.0
Fraud Rate – by Order (avg = 0.6%)
Fraud Rate – by Revenue (avg = 1.0%)
1.5
3.9
37
3.8
Percent
26
1.3
0.7
0.7
1.0
28
1.0
29
0.7
3.2
3.0
0.6 1.0
28
Average
0.5 0.8
1.8
13
Consumer Electronics
16
Apparel/Jewelry
Household / General
Merchandise
Physical Goods
Digital Goods &
Services
All Services
CyberSource Online Fraud Report
Resources & Solutions
To find information on CyberSource’s industry-leading fraud
management solutions, self-paced webinars, and other
whitepapers on electronic payment management, visit our
online Resource Centers:
Americas:
Visit www.cybersource.com. For sales assistance, call
+1 888 330 2300 (international: +1 650 432 7350) or email
[email protected] (Latin America: [email protected])
Europe | Middle East | Africa:
Visit www.cybersource.co.uk. For sales assistance, call
+44 (0) 118 929 4840 or email [email protected]
stands ready to back your team, or even manage complete
portions of your operation. All of our services are backed by
business performance guarantees.
• Performance Monitoring provides supports for configuring
rules and detectors, and monitoring process performance.
• Screening Management includes Performance Monitoring,
plus our experienced review staff to manage manual order
review per your policies. Available 24/7.
CyberSource Payment Management
Solutions
Japan:
In addition to our fraud management solutions, CyberSource
offers a comprehensive portfolio of modular services and tools
to help your business manage your entire payment pipeline to
optimize sales results. All are available via one connection to
our web-based services.
Visit www.cybersource.co.jp. For sales assistance, call
+81 (0) 3 3548 9873 or email [email protected]
Global Payment Acceptance
Asia Pacific:
For sales assistance, call +65 6671 5020 or email
[email protected]
CyberSource Fraud Management Solutions
CyberSource’s industry-leading fraud management solutions
enable businesses to detect fraud sooner and accurately, as
well as streamline fraud management operations. With a hosted
fraud management system and managed risk services that
can supplement or manage complete portions of your review
process, CyberSource provides flexible and powerful options
that best meet your business needs.
CyberSource Decision Manager: Rule
Console and Fraud Detectors
Having more data enables you to gain more insightful
correlations to detect sophisticated fraud. Decision Manager is a
hosted system providing access to a full range of data generated
from global fraud detectors, multi-merchant and cross-industry
correlations, truth data and more. Decision Manager comes
with a business rule console that controls automated screening
and case routing, an advanced case management system, and
reporting and analytics.
Automatically screen more bookings up front, while providing your
review team with access to fraud detectors and customized rules
to help them review more orders, faster and more accurately.
Managed Risk Services
CyberSource Managed Risk Services enables you to scale your
expertise and capacity without adding fixed headcount. Our
staff of fraud analysts, review teams and chargeback experts
Accept payments worldwide using a merchant account from
your preferred provider: worldwide credit and debit cards,
regional cards, direct debit, bank transfers, electronic checks
and other payment types such as PayPal and Bill Me Later.
CyberSource also provides professional services to help you
integrate payment with front-end and back-office systems.
Payment Security
Remove payment data from your network, which is a great way
to streamline PCI compliance and mitigate security risk.
• Payment Tokenization and Hosted Payment Acceptance
Services: enables you to process payments without storing
or even transmitting payment data.
• Payment System Centralization: Our team of experts
will help you consolidate multiple payment systems into
a single, easy to manage system. Link legacy systems
to web-based services for rapid service expansion.
Optionally, CyberSource will also host, support and
manage these centralized payment systems in our secure
datacenters.
Professional Services
CyberSource maintains a team of experienced payment
consultants with proven systems integration expertise. Our
client services team is additionally available to help you monitor,
tune, or fully outsource portions of your payment operations.
© 2012 CyberSource Corporation. All rights reserved.
17
About CyberSource
CyberSource, a wholly-owned subsidiary of Visa Inc., is a
payment management company. Over 390,000 businesses
worldwide use CyberSource and Authorize.Net brand solutions
to process online payments, streamline fraud management,
and simplify payment security. The company is headquartered
in San Francisco and maintains offices throughout the world,
with regional headquarters in Singapore (Asia Pacific); Tokyo
(Japan), Miami/Sao Paulo (Latin America and the Caribbean),
and Reading, U.K. (Europe/Middle East/Africa). CyberSource
operates in Europe under agreement with Visa Europe. For
more information, please visit www.cybersource.com.
AmericaS
CyberSource HQ
Phone: +1 888 330 3200
+1 650 432 7350
Email: [email protected]
Latin America email: [email protected]
Europe
CyberSource Ltd
Phone: +44 (0) 118 929 4840
Email: [email protected]
Asia Pacific
CYBS Singapore Pte Ltd
Phone: +65 6671 5020
Email: [email protected]
Japan
CyberSource KK
Phone: +81 (0) 3 3548 9873
Email: [email protected]
© 2012 CyberSource Corporation. All rights reserved.