New CA Technologies Payment Security Solution Reduces Online Fraud Loss
by 25 Percent
by 25 Percent
CA Applies Real-Time Behavioral Analytics and Machine
Learning to Largest Pool of Online Transaction Data to Stop Fraud Instantly
Learning to Largest Pool of Online Transaction Data to Stop Fraud Instantly
Singapore – May 5, 2017 – CA Technologies (NASDAQ:CA) today announced CA Risk Analytics Network,
the payment industry’s only card-issuer network that stops card-not-present
fraud instantly for network members using real-time behavior analytics, machine
learning and global transaction data to reduce online fraud losses by an
average of 25 percent* – a potential of US$2.2 billion in savings.**
the payment industry’s only card-issuer network that stops card-not-present
fraud instantly for network members using real-time behavior analytics, machine
learning and global transaction data to reduce online fraud losses by an
average of 25 percent* – a potential of US$2.2 billion in savings.**
As a cloud-based service, CA Risk Analytics Network incorporates a new
advanced neural network model, backed by real-time machine learning, to protect 3-D Secure card-not-present (CNP) transactions. It learns from, and
adapts to, suspected fraudulent transactions in an average of five
milliseconds, instantly closing the gap for potential fraud using the same card
or device across all members of the network.
advanced neural network model, backed by real-time machine learning, to protect 3-D Secure card-not-present (CNP) transactions. It learns from, and
adapts to, suspected fraudulent transactions in an average of five
milliseconds, instantly closing the gap for potential fraud using the same card
or device across all members of the network.
According to Javelin’s 2017 Identity Fraud Report, explosive growth in
card-not-present fraud, driven by the increasing e-commerce and m-commerce
volume, as well as theEMV liability shift,
contributed to the rise of existing-card fraud. “Just as e-commerce is
displacing point-of-sale transactions, the same is true for the channels in
which fraudsters choose to conduct their business. Among consumers, there was a
42 percent increase in those who had their cards misused in a CNP transaction
in 2016, compared to 2015 levels,” the report showed.
card-not-present fraud, driven by the increasing e-commerce and m-commerce
volume, as well as theEMV liability shift,
contributed to the rise of existing-card fraud. “Just as e-commerce is
displacing point-of-sale transactions, the same is true for the channels in
which fraudsters choose to conduct their business. Among consumers, there was a
42 percent increase in those who had their cards misused in a CNP transaction
in 2016, compared to 2015 levels,” the report showed.
“Detecting anomalies quickly and ensuring frictionless authentication
are the first steps in preventing card-not-present fraud without impacting
legitimate cardholder transactions,” said Terrence Clark, general manager for
CA Technologies Payment Security solutions. “Our data scientists have applied
advanced analytics and new, real-time, machine learning algorithms to the
global pool of 3-D Secure, e-commerce transaction data and device insights
maintained by the CA Payment Security Suite. This provides faster and more
accurate online fraud detection and prevention, reducing fraud losses for
network members while streamlining online shopping experiences for consumers.”
are the first steps in preventing card-not-present fraud without impacting
legitimate cardholder transactions,” said Terrence Clark, general manager for
CA Technologies Payment Security solutions. “Our data scientists have applied
advanced analytics and new, real-time, machine learning algorithms to the
global pool of 3-D Secure, e-commerce transaction data and device insights
maintained by the CA Payment Security Suite. This provides faster and more
accurate online fraud detection and prevention, reducing fraud losses for
network members while streamlining online shopping experiences for consumers.”
CA’s payment security solutions protect billions of online transactions
supporting hundreds of millions of cards and thousands of card portfolios
worldwide. CA Risk Analytics Network is open to card issuers with portfolios of
any size: from global banks with millions of cardholders, to smaller, or
regional financial institutions.
supporting hundreds of millions of cards and thousands of card portfolios
worldwide. CA Risk Analytics Network is open to card issuers with portfolios of
any size: from global banks with millions of cardholders, to smaller, or
regional financial institutions.
Support for
3-D Secure protocols today and in the future
3-D Secure protocols today and in the future
CA Risk Analytics Network and the CA Payment Security Suite support the 3-D
Secure specification today, and will support the new EMV 3-D Secure 2.0 specification, which addresses
authentication and security for card-not-present, e-commerce transactions using
smart phones, mobile apps, digital wallets and other forms of digital payment.
The 2.0 protocol will make extensive use of device data, giving CA Risk Analytics
Network subscribers a growing new source of information to reduce fraud and
optimize the customer experience across all consumer shopping devices and all
versions of the 3-D Secure protocol. Support for both the 1.0 and 2.0
specifications is important as adoption rates of the updated specification
among card issuers and merchants will vary.
Secure specification today, and will support the new EMV 3-D Secure 2.0 specification, which addresses
authentication and security for card-not-present, e-commerce transactions using
smart phones, mobile apps, digital wallets and other forms of digital payment.
The 2.0 protocol will make extensive use of device data, giving CA Risk Analytics
Network subscribers a growing new source of information to reduce fraud and
optimize the customer experience across all consumer shopping devices and all
versions of the 3-D Secure protocol. Support for both the 1.0 and 2.0
specifications is important as adoption rates of the updated specification
among card issuers and merchants will vary.
Resources
*Data based on applying the new CA Risk Analytics Network fraud model to
historical customer data over a 90-day period.
historical customer data over a 90-day period.
** Potential savings based on existing-card account fraud of US$8.8
billion in 2016, reported in “2017 Identity Fraud: Securing the Connected
Life,” a Javelin Strategy & Research survey conducted among 5,028 U.S.
adults over age 18.
billion in 2016, reported in “2017 Identity Fraud: Securing the Connected
Life,” a Javelin Strategy & Research survey conducted among 5,028 U.S.
adults over age 18.
For the LATEST tech updates,
FOLLOW us on our Twitter
LIKE us on our FaceBook
SUBSCRIBE to us on our YouTube Channel!