As fraud, waste, and abuse (FWA) cost government agencies and private companies billions each year, artificial intelligence (AI) has transformed how organizations prevent, monitor, and respond to FWA activity, powering advanced analytics, repeatable processes, and workflow automation and orchestration tools. Indeed, AI offers a wealth of tactics for combatting fraud, such as advanced authorization for credit card transactions, deep learning to combat false positives, and behavioral analytics. However, here we explore another facet of AI's fraud-fighting potential: helping all of the pieces work in concert towards a "fused" FWA solution. Fraud targeting and tactics have grown more sophisticated--and expansive--in recent years. The rapid proliferation of apps, payment platforms, and digital assets entering the financial services ecosystem has often outpaced regulatory oversight, and many of these channels have become a magnet for illicit activity.

Attackers often look to take advantage of spikes in trends to launch attacks and trick innocent consumers into downloading malware or parting with sensitive, often financial, information. We saw it at the end of last year, when hackers took advantage of the increase in communication around Strong Customer Authentication (SCA) to steal credentials, as well as during Black Friday and Cyber Monday. Sadly, hackers are now jumping on the back of the widespread attention around the Coronavirus to try and bait victims into opening malicious attachments that they believe to be instructions around how to stay safe. Researchers at IBM X-Force have identified several campaigns where opening the attachment results in an Emotet downloader being installed silently in the background. Similarly, Kaspersky revealed that they've found "malicious pdf, mp4 and docx files disguised as documents relating to the newly discovered Coronavirus. The file names imply that they include virus protection instructions, current threat developments, and even virus detection techniques."

When it comes to defeating fraud, is your AI reality falling short of AI hype? Are you concerned your organization isn't realizing the AI-powered results you were led to expect? There's a lot of hype around AI, but it takes more than just great algorithms to realize true value. The good news is, your organization can achieve the kind of results you deserve, with dCube, the comprehensive AI-powered fraud management solution from DataVisor. Please expect a confirmation email shortly.

I'm loath to use the term, but Stripe is a revolutionary product. It allows pretty much anyone to accept card payments just by adding a few lines of code to their site, without having to deal with the hassle of compliance and security. As you might expect, Stripe has gotten really good at identifying legitimate transactions from ones that aren't exactly kosher. Now, it's bundled its experience into a new product it's calling Radar 2.0, which allows large businesses to take their fraud protections into their own hands. A component of Radar 2.0, called Radar for Fraud Teams, is aimed at large enterprises.

National Westminster Bank in London is tracking every movement customers make on its website or mobile app, looking for behavior that doesn't match past actions or clues that users are not who they purport to be. Behavioral biometric software is catching on in the industry, typically as one of an array of measures to prevent digital banking fraud. The recent cybertheft at Tesco Bank in Edinburgh, Scotland, in which about $3 million was stolen from 9,000 customer accounts, has brought the need for such defenses to the forefront. "I've seen banks in the U.K. use behavioral biometrics and I've seen a lot of interest amongst major U.S. banks," said Avivah Litan, a vice president at Gartner. "Behavioral biometrics has proven to reduce false positives."

In recent months, machine learning has become somewhat of a buzzword, with major players such as Google highlighting the positive impact it can have for businesses. Defined as a subset of artificial intelligence, machine learning focuses on the development of computer programmes that can teach themselves, and grow and change in response to new data. With 90% of the world's data having been created in the last two years alone, the ability to develop automated processes to efficiently adapt to new information is invaluable. For mobile operators, machine learning has the potential to drive huge benefits, in particular when it comes to tackling fraud. In 2016 alone, telcos are expected to face global losses of $294 billion, making it vital that they look to utilise all tools at their disposal to combat such a pressing issue, says Raul Gomes Azevedo, director Product Development, WeDo Technologies. For starters, fraud management involves identifying specific profiles and behaviour, and checking whether everything's running as expected, or if there are any anomalies.