"You don't want to be lagging behind, because that puts you in the spotlight," said Alma Angotti, a former senior enforcer with the U.S. Treasury Department's Financial Crimes Enforcement Network who now works as a partner at the consultancy Guidehouse Inc. "It will at some point be a regulatory expectation." The more stringent compliance expectations of President Biden's administration have prompted companies that are lagging to try to catch up, Ms. Angotti said. New AI-focused offerings are designed to reform the laborious approach to compliance that has long prevailed at many financial institutions and other entities. Compliance departments traditionally have used painstaking manual approaches to try to find the proverbial needle of crime amid the mountainous haystack of legitimate transactions and well-behaved clients. AI can do the job better, require less staff and enable continuous check-ups on customers and transactions for money-laundering issues and sanctions violations, proponents say.

The window financial institutions (FIs) have to determine "good" customers from "bad" lasts milliseconds. As fraudsters steal their unwitting victims' online identities, intercept SMS messages, mask device locations to commit payments fraud, banks and other firms need to be able to spot "signs" hidden in the eCommerce deluge that can separate genuine transactions from fraudulent ones. It's a $40 billion problem, that, as Dave Excell, founder of Featurespace, told Karen Webster, needs deep learning networks and a range of automated advanced technologies and models to construct the best lines of defense against the fraudsters. Two new patents, leveraging those advanced technologies, can help FIs pinpoint behavioral changes and identify high-risk behavior -- stopping fraud and financial crime before it happens. Featurespace said Monday (July 12) it had filed those two global patents, aimed at transforming network architecture and risk scoring to protect customers and accounts.

Today, Featurespace introduces Automated Deep Behavioral Networks for the card and payments industry, providing a deeper layer of defense to protect consumers from scams, account takeover, card and payments fraud, which cost an estimated $42 billion in 2020. "The significance of this development goes beyond the scope of addressing enterprise financial crime. "The significance of this development goes beyond the scope of addressing enterprise financial crime. It's truly the next generation of machine learning," said Dave Excell, founder of Featurespace. A breakthrough in deep learning technology, this invention required an entirely new way to architect and engineer machine learning platforms. Automated Deep Behavioral Networks is a new architecture based on Recurrent Neural Networks that is only available through the latest version of the ARIC Risk Hub. Deep learning technology has various applications, such as in natural language processing for the prediction of the next word in a sentence, however its use in preventing fraud in card and payments fraud detection has not been optimized to protect companies and consumers from card and payments fraud. With this invention, that challenge is solved. Transactions are intermittent, making contextual understanding of time critical to predicting behavior. Previously, building effective machine learning models for fraud prevention required data scientists to have deep domain expertise to identify and select appropriate data features – a laborious, yet vital step. Featurespace Research developed Automated Deep Behavioral Networks to automate feature discovery and introduce memory cells with native understanding of the significance of time in transaction flows, improving upon the market-leading performance of the company's Adaptive Behavioral Analytics. Detecting fraud before the victim's money leaves the account is the best line of defense against scams, account takeover, card and payment fraud attacks. Excell continued, "As real-time payments, digital transformation and consumer demand require the instantaneous movement of money, our role is to ensure the industry has the best tools for protecting their organizations and consumers from financial crime.

Machine-learning models trained on normal behavior are showing cracks --forcing humans to step in to set them straight. People weren't just searching, they were buying too--and in bulk. The majority of people looking for masks ended up buying the new Amazon #1 Best Seller, "Face Mask, Pack of 50". When covid-19 hit, we started buying things we'd never bought before. The shift was sudden: the mainstays of Amazon's top ten--phone cases, phone chargers, Lego--were knocked off the charts in just a few days.

Deep within the encrypted bowels of the dark Web, beyond the reach of regular search engines, hackers and cybercriminals are brazenly trading a new breed of digital fakes. Yet unlike AI-generated deepfake audio and video--which embarrass the likes of politicians and celebrities by making them appear to say or do things they never would--this new breed of imitators is aimed squarely at relieving us of our hard-earned cash. Comprising highly detailed fake user profiles known as digital doppelgängers, these entities convincingly mimic numerous facets of our digital device IDs, alongside many of our tell-tale online behaviors when conducting transactions and e-shopping. The result: credit card fraudsters can use these doppelgängers to attempt to evade the machine-learning-based anomaly-detecting antifraud measures upon which banks and payments service providers have come to rely. It is proving to be big criminal business: many tens of thousands of doppelgängers are now being sold on the dark Web.

The digitally driven retail environment has caused lots of adapt-or-die challenges for payment processors and merchant acquirers, including spotting fraud as it moves from one device and channel to another. "One of the attacks that I've seen in particular is to perform a low-value fraudulent transaction in one channel to build trust from within the system, then do a larger-value transaction elsewhere," said David Excell, founder of Featurespace, a British company that feeds artificial intelligence into behavior analysis in an attempt to track, predict and stamp out fraud that moves among channels. "There are still friction points that should be solved," Excell said. "Just last week my credit card was blocked because of a recurring monthly insurance payment that had been on the card for four months. That's where you can apply AI. To improve spotting that and make the experience better. But there's still a long way to go." Find out how ARIC Fraud Hub fights fraud with adaptive behavioural analytics & book a demo.

For people who play the video game Counter Strike online, it's hard enough watching your back at the best of times. In the fast-paced first-person shooter, there are always players with quicker reflexes or a sharper eye. But at the height of its popularity a few years ago, people started to come up against other players with skills that were too good to be true. Games like Counter Strike and Half Life – another shooter that was very popular online – had a problem with players who used software cheats that steadied their aim or let them see through walls. So in 2006, when the stakes were raised by an online competition with cash prizes, an unusual pair of referees were called in.

For people who play the video game Counter Strike online, it's hard enough watching your back at the best of times. In the fast-paced first-person shooter, there are always players with quicker reflexes or a sharper eye. But at the height of its popularity a few years ago, people started to come up against other players with skills that were too good to be true. Games like Counter Strike and Half Life – another shooter that was very popular online – had a problem with players who used software cheats that steadied their aim or let them see through walls. So in 2006, when the stakes were raised by an online competition with cash prizes, an unusual pair of referees were called in.