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.

The pandemic has forced criminal gangs to come up with new ways to move money around. In turn, this has upped the stakes for anti-money laundering (AML) teams tasked with detecting suspicious financial transactions and following them back to their source. Key to their strategies are new AI tools. While some larger, older financial institutions have been slower to adapt their rule-based legacy systems, smaller, newer firms are using machine learning to look out for anomalous activity, whatever it might be. It is hard to assess the exact scale of the problem.

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.

Doing business in Sudan isn't easy, but back in 2011, it should have been possible. "We were working with major telecoms company Zain based out of southern Sudan at the time – before the country split in two," explains Charlie Tryon, chief executive of Maris, an investment holding company that operates across east and southern Africa. "Sudan was on the US's Office of Foreign Assets Control (OFAC) sanctions list, but the south was exempt, so it should have been OK for us to work here. We took out all the precautions we needed – did all the necessary paperwork, spoke to all the right people, told the banks, made sure we had permission from OFAC and notified all other regulatory bodies about our cross-border transaction – everything. "But the money we transferred from our business bank account to Zain was still stopped," he says. Tryon's transaction failed to pass compliance tests. In a bind, he was forced to physically move thousands of dollars from a bank account in Uganda to Sudan to pay his suppliers – at great personal risk. "We had suitcases full of cash that we took via plane and car into Sudan," says Tryon. "This isn't the ideal way to run a business, but at the time we had very little choice." A lot has changed since 2011. Know-your-customer and anti-money laundering (AML) screening is increasingly automated, helping to remove some of the delays caused by strict compliance measures. On top of this, banks, retailers, payment service providers (PSPs) and other businesses involved in the money transfer process are using artificial intelligence (AI) to make much more accurate decisions about payments. "At the time, we spent a huge amount of money and time trying to set this right," says Tryon. "It included international travel, lobbying, meetings with banks and regulators.

As Singapore currently has one of the highest rates of card fraud in the world, in order to combat this, in 2017 Singapore's financial industry came together to commit to data analytics as a means of fighting financial crime. As such, Featurespace's technological platform uses machine learning to detect anomalies in individual behaviour for fraud and risk management, and it was developed by computer scientists in the laboratories of Cambridge University. The real-time ARIC platform uses Adaptive Behavioural Analytics to self-learn and continuously respond to new customer data. By understanding the behaviour of each individual banking and credit card customer, ARIC identifies new and known attacks, while blocking fraud at the moment it occurs. ARIC reduces false positives, by 70%, increasing revenue, and reducing customer friction.

Featurespace is a world-leading machine learning software company, fighting fraud on the frontline for clients in financial services, gaming and insurance. If you're a bright person, with a flexible, positive attitude, we want to hear from you.

When it comes to digital payments, honest players around the globe – genuine consumers, merchants, processors and financial institutions – want two things: speed and security. But since the introduction of EMV, fraudsters have shifted their focus toward card-not-present (CNP) transactions (a February 2018 study by Javelin revealed fraudulent transactions are 81 percent more likely to occur online than at the POS). In a new article in The Green Sheet, Dave Excell, founder and CTO at Featurespace, explains why the application of machine learning models is essential to preserving the integrity of transactions around the globe by delivering a risk- and friction-free experience. Click here to read Dave's article: "Machine learning can safeguard global payments".

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.

Everyday we hear about Artificial Intelligence (AI) and Machine learning in different contexts. Entrepreneurs like Elon Musk fear that it will doom the human race. Notably, the Russian President Mr Vladimir Putin was recently quoted telling students that whoever masters AI will rule the world. When ordinary people hear about A.I their thoughts immediately venture towards machine based programs encountered in movies like Star Trek and Star Wars. This is not the only form of AI that exists. We have previously covered Indian start-ups working in AI.