OneSpan launches AI-based risk
analytics to stop financial fraud
neSpan, a global leader in
software for trusted identities,
e-signatures and secure
transactions, has announced the
launch of its open API, cloud-based
Risk Analytics solution to help financial
institutions stop fraud, including account
takeover and new account fraud.
O
Account takeover and new account fraud
are the top two types of fraud challenging
financial institutions and in the US alone,
ID fraud accounted for US$16.8 billion
dollars in fraud losses in 2017.
Risk Analytics protects against these
and other fraudulent activities across
online and mobile channels using
Machine Learning-based risk analysis,
a form of Artificial Intelligence. This
analysis identifies fraud in real time,
predicts risk levels and takes immediate
action when fraud is detected.
threats keep evolving, it’s critical for
financial institutions to take proactive
measures and a layered approach to
security to monitor, detect and block
fraudulent transactions from happening
before they occur, ensuring the best
possible experience for the user.”
The use of Machine Learning enables
risk scoring to streamline processes,
reduce operational costs tied to manual
review and ultimately improve the user
experience through fewer false positives.
Risk Analytics is then able to take
immediate action to either allow, review
or block the transaction, based on
intelligent workflows incorporating bank-
defined security policies and rules.
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Issue 12
OneSpan CEO, Scott Clements
“Wherever money and data flows, fraud
will certainly follow,” said OneSpan CEO,
Scott Clements. “Real-time fraud detection using
advanced, Machine Learning-based risk
analytics enables financial institutions to
strengthen their security, lower fraud and
achieve regulatory compliance.”
“While stopping fraud has become
increasingly challenging because today’s OneSpan’s Risk Analytics is
available now. u
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During a financial transaction, Risk
Analytics collects and analyses data
from a variety of sources, including
devices, user behaviour, transactions,
digital channels and business
applications. The solution then scores
the user, device and transaction data,
and determines the risk associated with
that transaction.
Julie Conroy, Retail Banking and Payments
Research Director, Aite Group, added:
“Even as criminals’ attacks on digital
channels continue to escalate, financial
institutions are under intense competitive
pressure to reduce fraud and meet strict
regulatory compliance requirements while
growing their business.