CNP fraud is showing no signs of deceleration and is, in fact, expanding rapidly. New trends and new threats call for a revolutionary approach to effectively stop fraud. Learn how to deploy solutions that can quickly distinguish between genuine and fraudulent transactions and take appropriate action instantly. Explore the CA Risk Analytics Network.
For the past decade, financial institutions have created sophisticated digital platforms for consumers to access, save, share and interact with their financial accounts. As sophisticated as these digital platforms have become, cyber criminals continue to pose an ever-present risk for everyone – from individual consumers to large corporations.
In his recent article, 2018 Outlook: Customer Experience and Security Strike a Balance,
Andrew Davies, vice president of global market strategy for Fiserv’s Financial Crime Risk Management division, explains how and why security will become a key differentiator for financial institutions as they respond to a changing landscape, which includes:
• Global payment initiatives
• Open Banking standards
• Artificial intelligence and machine learning
• Consumer demand for real-time fraud prevention and detection
Cyber-crime is forecast to cost the global economy $6 trillion by 2021, up from $3 trillion in 2016. Described by some as the “greatest threat to every company in the world”, public concern for the safety of data is growing – not just in how criminals might use stolen data to commit fraud, but also in how personal data is used by the organizations we engage with.
The recent economic downturn has created some formidable challenges for the retail banking industry. Fraud and identify theft are on the rise, costing banks big money and raising customer concerns about security.
The response to possible bank card fraud is one of the most important factors affecting the relationship that customers have with their bank. For customer-centric financial institutions who issue millions of bank cards, any instance of possible fraud is both a business risk to be managed and an opportunity to strengthen customer relationships.
Cyren examined 11.7 million inbound emails at companies using various email security solutions to measure any possible “security gaps” in their protection,and identify any potential risks for the companies. Solutions tested ranged from hosted email services with included security filtering to on-premises email security gateways.
The study was conducted during September and October 2017, and revealed that, on average, 10.5% of email delivered to users after being scanned by their current email security solution was spam, phishing, or malware email. The report also summarizes results for three separate cases, which illustrate how penetration rates can vary across different companies.
Imagine getting into your car and saying, “Take me to work,” and then enjoying an automated
drive as you read the morning news. We are getting very close to that kind of
scenario, and companies like Ford expect to have production vehicles in the latter part
Driverless cars are just one popular example of machine learning. It’s also used in
countless applications such as predicting fraud, identifying terrorists, recommending
the right products to customers at the right time, and correctly identifying medical
symptoms to prescribe appropriate treatments.
The concept of machine learning has been around for decades. What’s new is that
it can now be applied to huge quantities of data. Cheaper data storage, distributed
processing, more powerful computers and new analytical opportunities have dramatically
increased interest in machine learning systems. Other reasons for the increased
momentum include: maturing capabilities with methods and algorithms refactored to
run in memory; the
Industry leaders from the banking and vendor landscape are working to streamline the
customer experience while closing the opportunities for fraud and exposure. Balancing
security and convenience will require an approach that combines consumer-facing
authentication (such as passwords, PINs and biometrics) with background security
measures (such as transaction and session-behavior analytics).
The 2016 ACFE Report to the Nations on Occupational Fraud and Abuse analyzed 2,410 occupational fraud cases that caused a total loss of more than $6.3 billion.8 Victim organizations that lacked anti-fraud controls suffered double the amount of median losses.
SAS’ unique, hybrid approach to insider threat deterrence – which combines traditional detection methods and investigative methodologies with behavioral analysis – enables complete, continuous monitoring. As a result, government agencies and companies can take pre-emptive action before damaging incidents occur. Equally important, SAS solutions are powerful yet simple to use, reducing the need to hire a cadre of high-end data modelers and analytics specialists. Automation of data integration and analytics processing makes it easy to deploy into daily operations.
“If we had done anything differently in Washington state, we would have done it faster,” said Hammersburg. “The key message is that fraud prevention – dealing with risk and program integrity – is not a cost issue, it’s a saving. When you can truly quantify the
positive impact to the bottom line of a company or government agency, you shift the recognition that this is not an expense but that it’s a saving.”
Some government organizations may be concerned that a rigorous program to shine a light on the underground economy will shine a brighter light on how much they didn’t know until now. Don’t let that stop you, said Hammersburg. “You have the opportunity
to really get ahead of it now. Turn a risk into an opportunity going forward.”
Traditional procurement fraud detection methods – manual searches; spreadsheets; and simple slice, dice, filtering and reporting from silo systems – are not keeping pace. Whatever tools and processes are in place now, there’s always the opportunity to evolve to a higher level for more accurate detection – more high-value alerts and fewer
Tax fraud is already prevalent, and fraudsters are more sophisticated and automated than ever. To get ahead of the game in detecting fraud
and protecting revenue, tax agencies need to leverage more advanced and predictive analytics. Legacy processes, systems, and attitudes
need not stand in the way. To explore the challenges, opportunities, and value of tax fraud analytics, IIA spoke with Deborah Pianko, a
Government Fraud Solutions Architect within the SAS Security Intelligence practice.
As explored in this paper, the SAS Fraud Framework supports a complete, modular,
enterprise-level program integrity solution that helps payers prevent, detect and
manage fraud, waste and abuse across all silos and lines of business. Its fully integrated
components offer both top-down and bottom-up functionality for exposing hidden and
risky networks. This approach gives payers enhanced detection capabilities, greater
insight into case management and improved operational efficiency while decreasing
overall cost of ownership. The result is highly effective, early, and even preventative
detection of fraud, waste, abuse and corruption that improves operational efficiency
and reduces health care costs.
First, today’s digitally oriented customers expect banks to provide an ever-higher quality experience defined by speed and the flexibility to conduct business across many channels. They’ve grown accustomed to going online and transferring money between accounts, for example, and using their mobile device to make payments and check their account balance. These kinds of experiences have raised the bar in terms of customer expectations – and banks need to keep up, or risk losing customers. This is particularly true of millennial customers, as they have little regard for loyalty, which banks have traditionally relied on to build their business. Once frustrated by inconvenience, they don’t hesitate to switch banks – and thanks to the internet, this is now a fast, painless process.
Insurers have long been plagued by fraud, error, waste, and abuse in health care payments. The costs are huge – amounting to as much as 25 percent of payments made. Today’s data management and
analytics platforms promise breakthroughs by incorporating comparative and behavioral data to predict as well as detect loss in all its forms. To explore the opportunities and how insurers can capitalize on them, IIA spoke with Ben Wright, Sr. Solutions Architect in SAS’s Security Intelligence Global Practice.
Financial institutions (FIs) must support the channels and services that consumers demand in order to remain competitive with each other and with disruptive competitors. To that end, supporting account opening, delivering new transactional features, and facilitating payments through digital channels have become table stakes. Unfortunately, the speed and convenience that these capabilities afford is a benefit to consumers and fraudsters alike. To successfully prevent fraud while retaining the benefits of offering digital financial services, FIs must understand how fraudsters are exploiting these capabilities and fight fraud with customer experience in mind.
Published By: Oracle CX
Published Date: Oct 19, 2017
In today’s IT infrastructure, data security can no longer be treated as an afterthought, because billions
of dollars are lost each year to computer intrusions and data exposures. This issue is compounded by
the aggressive build-out for cloud computing. Big data and machine learning applications that perform
tasks such as fraud and intrusion detection, trend detection, and click-stream and social media
analysis all require forward-thinking solutions and enough compute power to deliver the performance
required in a rapidly evolving digital marketplace. Companies increasingly need to drive the speed of
business up, and organizations need to support their customers with real-time data. The task of
managing sensitive information while capturing, analyzing, and acting upon massive volumes of data
every hour of every day has become critical.
These challenges have dramatically changed the way that IT systems are architected, provisioned,
and run compared to the past few decades. Most companies
Websites provide online businesses with an unprecedented level of contact with customers and end
users. However, they also place business information where it can be easily accessed by third parties –
often using automated tools known as “bots”. For many organizations, bots represent up to 50%
or more of their overall website traffic, from good bots engaged in essential business tasks to bad
bots conducting fraudulent activities. Regardless of business impact, bot traffic can reduce website
performance for legitimate users and increase IT costs. Organizations need a flexible framework to
better manage their interaction with different categories of bots and the impact that bots have on
their business and IT infrastructure.
A fundamental people-process-technology transformation enables businesses to remain
competitive in today’s innovation economy. Initiatives such as advanced security, fraud detection
services, connected consumer Internet of Things (IoT) devices, augmented or virtual reality
experience, machine and deep learning, and cognitively enabled applications drive superior
business outcomes such as predictive marketing and maintenance.
Superior business outcomes require businesses to consider IT a core competency. For IT, an
agile, elastic, and scalable IT infrastructure forms the crucial underpinning for a superior service
delivery model. The more up to date the infrastructure, the more capable it is of supporting the
scale and complexity of a changing application landscape. Current-generation applications must
be supplemented and eventually supplanted with next-generation (also known as cloud-native)
applications — each with very different infrastructure requirements. Keeping infrastructure up
A compromised account is 17 times more valuable than a stolen credit card number. That’s why fraud bots, loaded with stolen credentials, use their lists of username/password pairs on thousands of websites. Credential stuffing bots can lead to data theft, customer identity fraud, and account takeover on your site.
Learn about the risk to your business from credential stuffing bots in the Akamai infographic, Credential Stuffing 101: The Risk of Bots to Your Business.
While mobile threats are still largely mischiefware and have not yet broken the device’s security model but instead more focused on for-pay texting scams or stealing personal information, the dangers are still rife. This white paper from BlueCoat examines the mobile threat landscape and the behavioral patterns of mobile users that make them most vulnerable to data loss, malicious applications, fraud and other mobile threats.
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