Fighting Fraud 2019

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R A C O N T E U R . N E T 07 Artificial intelligence (AI) can help retailers rapidly identify and prevent ecommerce fraud, but human oversight is still essential a good or bad decision is inconsist- ent, "then the machine will star t to learn things which a human would quite clearly understand are not correct". This could, for example, result in AI that becomes more conservative as time goes on. "For instance, each time a fraudulent order is shipped and comes back as a chargeback, the machine learns not to ship sim - ilar orders," says Mr Whitehead. "Eventually, the machine ratchets down the number of orders a mer- chant is shipping and invariably some of the declined orders were actually legitimate." Criminals will always look to cir- cumvent the ecommerce fraud pre- vention systems that merchants put in place and some are already using AI for just this purpose. It's there- fore essential that online retailers employ multiple methods of ecom- merce fraud prevention and layers of control, says Jackie Barwell, direc- tor of fraud product management at ACI Worldwide. Positive profiling, for instance, builds a comprehensive picture of customers at the individual level through behavioural data, exter- nally confirmed fraud intelligence and a wide range of customer iden- tifiers. "Rather than the traditional route of screening each transaction, this focuses fraud screening on the person behind that transaction," Ms Barwell explains. She adds that the technique is especially useful for new ecom- merce methods such as click and collect, "where there is not as much time available to conduct post-trans- action, real-time analysis". Other new ecommerce services will no doubt arrive in the future and fraudsters will inevitably seek to exploit them. But as long as online retailers have AI in their armoury, they should manage to stay ahead of cybercriminals looking to profit from one of modern life's greatest gifts, the option to shop from the comfort of your home. those where the card and cardholder are physically present. In fact, a study by LexisNexis Risk Solutions found that fraud via remote chan- nels, such as online and mobile, is up to seven times harder to prevent than fraud in person. So if an online retailer's ecom- merce fraud prevention system isn't up to scratch, it can cost them dearly. Indeed, Juniper Research predicts that CNP fraud could cost online retailers more than £58 bil- lion over the next few years. The tools and techniques criminals use to carry out chargeback fraud, where the consumer makes an online purchase with their own credit card and requests a chargeback from hanks to the internet, we no longer need to go to the shops; instead, the shops come to us. In a few clicks you can order everything from the latest dig- ital gadgets to dog food, from the comfort of your sofa. And same- day delivery options mean you can receive items faster than ever. But the speedy online transactions and one-click purchasing systems that underpin the ecommerce sector don't just make life easier for con- sumers; they make things easier for fraudsters too. Successful ecommerce retailers receive thousands of orders a day, and these card-not-present (CNP) purchases are harder to verify than Andrew Neel/Unsplash T Duncan Jeffries What's more, they can find cases of fraud that no human is likely to spot. "By deploying constantly learn- ing machines that use the data from many thousands of mer- chants around the world, retail- ers have the sort of broad vision necessary to spot fraud and orders that are far out of the norm," says Ed Whitehead, managing director, Europe, Middle East and Africa, at Signifyd, a fraud protection com- pany that detects fraud and reim- burses merchants for fraudulent chargebacks on approved orders. When AI recognises an out- lier order, it can either automati- cally block it or refer it to a human expert for review. "The best way to use AI is to use it to solve the simple cases," says Paul Weathersby, senior director of product management at LexisNexis Risk Solutions UK. "A person is better at making decisions, so you could use the machine for cases which are fairly easy to process and improve the customer experience, and then pull out the exceptions that some - one needs to look at." Mr Whitehead agrees that a degree of human oversight is a key part of effective AI-based ecom- merce fraud prevention. "There are certain tasks that machines are good at, those requiring speed and scale, and there are tasks that humans are good at, those requir- ing intuition and experience," he says. "Combining the two creates a powerful shield to fraud while also recognising legitimate orders that might include some red flags." Data feeding into an unsuper- vised machine-learning model also needs to be properly mon- itored. Otherwise, says Mr Weathersby: "The vast amounts of data an unsupervised model works through can produce rules that don't make sense based on data which is quite hard to locate." He adds that if the method for supplying a machine-learning tool with feedback on what constitutes Identifying and tackling online fraud with AI their bank after receiving the item, or take over online accounts are constantly changing and increas- ingly sophisticated. "Traditional approaches to fight- ing fraud, such as rules engines and scoring, are too fixed to adapt to this shape-shifting nature of fraud," says Eido Gal, co-founder and chief exec- utive of Riskified, which provides an ecommerce fraud prevention solution and chargeback protection service for high-volume and enter- prise merchants. Mr Gal claims AI solutions that learn from each transaction and improve their accuracy are much more effective than these legacy methods of ecommerce fraud prevention. "Fraudsters take many different approaches to appear as a legitimate cardholder," he says. "They may use a proxy, spoof a device or take over a cardholder's retail account. A well-designed AI solution examines the links across these datapoints, compares them with historic orders and instantly determines when something is wrong." AI and machine-learning tools look at hundreds of datapoints across bil - lions of transactions to identify pat- terns that might constitute fraud. [With AI] retailers have the sort of broad vision necessary to spot fraud and orders that are far out of the norm INTERNE T/ECOMMERCE FR AUD LOS SES Losses on UK-issued cards (£m) UK Finance 2019 E C O M M E R C E 2010 2009 2012 2011 2014 2013 2016 2015 2018 2017 153 135 140 140 190 219 262 310 310 393

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