It’s no secret: 2021 was an ever-evolving year for financial crime fighters. Both threats and world events emerged quickly to forever change the risk landscape, and credit unions have needed to stay five steps ahead to combat these new fraud and anti-money laundering (FRAML) vulnerabilities.
One of the most important parts of doing business with a new customer is verifying that that customer is who he or she claims to be and that he or she represents the company it claims to represent. While there are myriad ways to do this, there’s a clear winner for online identity authentication.
As businesses work to keep out fraud and curb false declines in the year ahead, they must bear in mind that context is key. In the Digital Fraud Tracker, TSYS’ Dondi Black explains how companies can tap AI and machine learning to verify data points and use context — such as location — to go long on security while creating a frictionless customer experience.
João Moura, the CEO of Fraudio, discusses how AI models can outsmart merchant initiated fraud and help PSPs and acquirers onboard more merchants in order to grow faster, smarter, and safer
The acceleration of the digital transformation resulted in a surge of online transactions, greater adoption of digital payments, and increased fraud.
When it comes to preventing card fraud, issuers need all the help they can get. Fortunately, credit unions have access to a variety of tools, resources, and expertise in their fight against fraudsters’ ever-changing tactics.
Card not present (CNP) fraud is the dominant type of payment fraud that Strategic Link partner CO-OP Financial Services is seeing among its credit unions’ portfolios, comprising over 80% of fraud incidents across both debit and credit.
One key contributor to this rise has been the increasingly bold use of BIN attacks, one of the most common types of CNP fraud.
n.exchange, a cryptocurrency exchange specialising in fiat on- and off-ramp to make crypto investment user-friendly, unveils its crypto purchase credit card fraud attempt figures for 2019-2021. Its campaign to combat credit card fraud in cryptocurrency highlights a significant rise in fraudulent purchase attempts by cybercriminals using stolen card details, most of which was perpetrated from countries in the Western hemisphere.
The past two years has seen a rapid shift of work to remote and hybrid offices. The statistics show that hackers welcomed that shift and took advantage of the vulnerabilities and gaps in security by businesses.
Nearly half (40%) of merchants are reporting a rise in friendly fraud over the past 12 months, but the majority are struggling to challenge Google Pay and Apple Pay chargebacks successfully, research by Ravelin finds.
When you think of a credit union (CU), it’s easy to picture a place that values member relations and face-to-face banking.
While this business model has typically worked to the advantage of CUs, it can also leave some of these institutions ill-prepared for the COVID-fueled shift to digital-first banking — and the risk of online fraud that come with it.
Banking and financial institutions lose billions of dollars because of fraud. Machine learning can help detect and prevent fraud.
Machine learning algorithms can reveal fraud patterns much faster and more accurately than humans or traditional rule-based systems. Read this article to understand how exactly banks can benefit from ML-powered solutions in fraud detection.
Each year, banking and financial institutions from all over the world lose many billions of dollars because of fraud. Machine learning seems to be the most efficient technology for detecting and preventing fraud in this rapidly evolving sphere. From this article, you’ll understand how exactly banking and financial institutions can benefit from integrating ML algorithms. Plus, you’ll learn about the shortcomings of traditional fraud detection techniques.