Talking online fraud prevention: Jeff Sakasegawa, Sift Science

• Describe the Sift Science product
“Sift Science is the fastest-growing trust platform in the travel industry preventing fraud and increasing revenue for top travel companies around the world. We are the only company to use Live Machine Learning, automatically utilising learnings from our global network of customers, as well as our customer’s own data, to provide a clear view of good and bad users. This lets our customers in the travel space reduce checkout friction and the need for costly authentication systems. Sift Science is ideal for growing companies and enterprises, offering a holistic solution that can solve multiple fraud problems through a single integration.”

• How big is the e-commerce fraud marketplace, and do you know what proportion is in travel and hospitality ?
“This past year, we saw an increase of more than 30 per cent in e-commerce fraud attacks compared with 2016. There was $57.8 billion of eCommerce fraud losses from Q2 2016 to Q2 2017. For both website and mobile channels, the airline industry indicates that they lose about 1.2 per cent of their revenue from payment-related fraud, up from one per cent in a 2014 study.”

• Sift Science recently raised $53 million in series D funding – who are your investors and how are you looking to expand?
“Our Series D round was led by Stripes Group, with previous investors Union Square Ventures, Spark Capital, and Insight Venture Partners participating. This brings our total funding to $107 million.”

• Who are your clients and do you have some in the hospitality and travel markets?
“Sift Science serves internet-based companies across a wide range of industries including travel and hospitality. Our clients in the hospitality sector include AirBnB, Hotel Tonight, Traveloka, Fareportal and Destinia.”

• What are the challenges the boutique and lifestyle hotelier faces with online fraud?
“Not only do travel companies deal with payment fraud in the form of stolen credit cards, but they also face fake accounts, content abuse (if they incorporate reviews and other user-generated content), and account takeover (loyalty fraud). The teams responsible for managing these different types of abuse may be located in different departments, using disparate tools, which makes it challenging to take a unified approach to managing risk.”

“Out of all these types of fraud, account takeover through loyalty programs is the latest target for fraudsters. As account takeover (ATO) soars, loyalty points accounts in particular are being preyed upon in greater numbers; of all non-card present fraud that occurred in 2016, four per cent of attacks were on loyalty and rewards points accounts, but that number jumped to 11 per cent in 2017. In 2016, 48 per cent of online businesses experienced an increase in ATO over the previous year, and ATO losses reached $2.3 billion.”

“Fraudsters aren’t just interested in hacking accounts for points – they’ve also found ways to cheat loyalty programs by racking up points illegitimately. Using an airline loyalty program as an example, the criminal often acquires stolen credit card information in bulk and then uses it to purchase multiple airline tickets. These transactions accrue a massive amount of loyalty points, which the criminal then redeems before the fraud is discovered. Once the cardholder of the stolen information discovers the fraud, they file a chargeback.”

• Do you have some simple tips for the boutique and lifestyle hotelier to follow?
“Payment fraud and loyalty program fraud have been rampant in the travel industry, yet many companies are still relying on an outdated and reactive fraud prevention approach. With higher-value purchases at stake, some companies have been hesitant to move on from legacy rules systems to layer on a machine learning approach. The result? Higher rejection rates, an unnecessary and expensive reliance on two-factor authentication, and more customer insults. Fraud prevention directly impacts their top line.”

“Some also are afraid to move on from legacy rules systems by a fear of the unknown with respect to loss rates. Legacy rules often have known loss figures, even if they’re poor, that fraud managers want to hold onto. Oftentimes companies won’t even A/B test legacy vs. machine learning to see if there would be a benefit to one over the other. While rules may still be used to prevent fraud today, machine learning is gaining prominence as a more nimble approach advanced by forward thinking travel companies. Machine learning improves accuracy in catching fraudsters, has ability to positively alter the user experience, can make real-time decisions, is adaptable, draws on network effect, and more.”

• Briefly outline the benefits of your product to an independent hotelier?
“It can offer parity with fraud solutions for lifestyle hoteliers backed by chains – lifestyle hoteliers that have the financial backing of chains often have large, multinational revenue streams. Independent hoteliers may not yet be at this scale, or even have a desire to. That being said, partnering with Sift Science will give them access to the same (if not more so) tooling, data scientist, and engineering resources. Leveraging Sift’s online machine learning models, independent hoteliers can identify new fraud trends as they happen and safeguard themselves against fraud, cyber-attacks (e.g. data breaches). Sift Science has the world’s largest database of fraud and abuse-related data collected from our global network in real time, and maps it against meaning, relationship structure, and relevancy – often in company- and market-specific ways.”

“Sift Science also has the ability to scale without dedicated personal resources. Headcount for an independent hotelier is a treasured resource. It stands to reason that you could not staff an entire risk team even if you needed it. Partnering with Sift, you can not only block abuse systematically but also improve the efficacy of the fraud resources you may already have with more targeted reviews.”

• What are the company goals over the next two years?
“To continue iterating on our SaaS platform which has evolved from one single product to stop payment abuse (chargebacks) to a full suite of services to prevent fraud like Account Takeovers and malicious and spammy content. And to safeguard all digital interactions for our growing global network of customers so they can ultimately provide better and differentiated user experiences.”

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