Who are we? We are humble but brilliant with a healthy dose of swagger. We start with the customer and finish as one team. We are Sift Science.
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In a nutshell, Sift Science provides a platform that provides a one stop shop for organizations to trust their users and better understand their data. We got our start with Y Combinator in the Summer 2011 class, presenting a visionary mission to democratize machine learning and make it easy for anyone not just the Googles and Amazons of the world - to harness its power. Payment fraud detection stood out as an ideal problem to solve as a first step in our mission. The initial pitch generated a lot of buzz, and landed a $1.6M invest ment led by Max Levchin, co-founder of PayPal.
“We’re going to catch anyone who tries to f*** with our customers.” Jason at Y Combinator demo day
June 2011
SIFT SCIENCE FOUNDED
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“We knew we wanted to do something with machine learning — that was the core premise of the company. We believed ML would transform a lot of industries and there would be lots of opportunities to change how problems would be solved. We went around interviewing a bunch of friends in the Bay Area and asked them what challenges their businesses were facing. Fraud emerged as a recurring theme. We didn’t know about fraud at this time. As we dug more into this industry and this problem, it felt like the right moment to get involved. The legacy players had stagnated and had been focused on making rules systems 10% better rather than 10x better. We wanted to democratize machine learning and build a platform that other businesses and developers could plug into and bring machine learning to the masses. Fraud prevention was the starting point.” Jason Tan
September 2011
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$1.6 M Seed Round Raised
In keeping with the spirit of our first value, Sift’s founding team started with the customer, in this case, Airbnb, and enabled them to apply world class machine learning to prevent fraud. We tailored the first version of our API specifically to their needs. The Sift team also built other aspects of the product, including the Score API and the Console, in response to customer feedback. We gained customers at a slow clip in the early days, and conversations with customers like Lista sowed the seeds for future abuse types.
2012
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“We found an online set of forum posts, and on this forum, Brian Armstrong (Airbnb at that time) had posted complaints about the Google Prediction API because different things weren’t working. We figured let’s just build our own prediction API that didn’t have the problems Brian complained about. I emailed him about it, and Brian decided to give it a shot. That was a huge tipping point. The brand of Airbnb helped us close other customers in the next year. Classic case of hustling and the right time, right place.” Jason Tan
2013
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“Brian needed an API to get a Score back for the user. And oh crap! We need a score API. Everything we built was very organic. We got our second customer. One of their requests was to view the data and so the Console was born. At the time, tooling for teams was bad. Our vision was to make those people happy and make their jobs great and help sell the product within,and that happened. Everyone who used the Console had a whizbang experience and was blown away by what we could do.� Fred Sadaghiani
March 2013
Series A $4 M Round Raised
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In 2013, the whole team (we were smaller back then) attended the Merchant Risk Council (MRC) Event in Las Vegas, touting the slogan “Fight Fraud with machine learning.” This marked the company’s first foray into the fraud prevention spotlight. No one was talking about tackling fraud this way, and we left with many leads we converted to more customers.
March 2013 First time at MRC 7
“We decided that if we’re going to be in the world of fraud, we need to go to these conferences and establish a name and brand for ourselves. We went to MRC and had a big, bold banner behind the booth. The banner said, “Fight fraud with Machine Learning.” And no one was using language like this. No one was talking about ML. We also decided to wear lab coats. We’re in the booth area. We’re the youngest people there wearing jeans and t-shirts with lab coats where everyone else was business casual attire. So we stood out like a sore thumb AND we were positioned right by a food service. And that was the best thing ever. 1) We looked out of place 2) We had a bold sign 3) We were by the food We started to build this huge amount of buzz. When we left MRC, we had hundreds of contacts. And that’s when we learned this is something powerful. It turned out to be a tremendous event for us. We’ve gone back and won awards.” Fred Sadaghiani
2014
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May 2014
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$18 M
Leveraging our real-time machine learning technology to fight fraud, customers saw stellar results lower fraud, less time spent on manual review, and increased conversion, to name a few. Sift Scores were so accurate they could identify both bad AND good users, opening up opportunities to grow their businesses. We slowly built out Sales and Marketing to complement our engineering team and grow our base beyond e-commerce into travel, ticketing, and more.
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2014
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First company retreat at Sea Ranch
2015
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“Sift Science helps us to identify more good customers and reduce the number of transactions that have to be authenticated, thus reducing payment friction and increasing overall conversion.� Wayan Tresna Perdana, Sr. Product Manager, Traveloka
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“When we started using Sift Science in the be ginning, Harry’s chargeback rate decreased by about 85% which is great for us because it helps us continue to be a company that people can trust shopping with.” Karen Chien, Trust and Safety at Harry’s
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Soon enough, Indeed and Zillow approached us to ask if we could help detect fake job and rental listings, respectively. We didn’t know if it would work, but we were open to trying, and the risk that fraud comes in many shapes and sizes and that our technology was versa-
We soon found that we enabled our customers to do more than just fight fraud. Companies like Entropay, Shutterstock, and OpenTable came to us with stories about increasing conversion and decreasing friction for users. The Console proved to be a powerful tool in our customers’ arsenals. It made their lives easier by helping them hunt down fraudsters with unparalleled speed and accuracy. In response to this feedback, we made the Console more information-dense and added automation features to further improve their processes.
June 2015 Formulas and Actions 15
In 2016 and 2017, we continued to lead the industry as a one stop shop for fraud fighting by rolling out products preventing Account Abuse, Content Abuse, Promo Abuse, and Account Takeover. Four years after the MRC debut in 2013, machine learning has grown in awareness but is still nascent in terms of adoption.
2016
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July 2016
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$30 M
July 2016 Multiple Abuse types launched; Workflows launched
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Our conversations with customers focused on their desire to go beyond mitigating fraud and toward creating excellent customer experiences. Merchants wanted to reduce the roadblocks to check-out for their customers, but although 90% of their traffic was likely legitimate, the 10% of suspicious traffic weighed on their product design and trust & safety teams. This got the team thinking: what defines the most successful businesses? They don't build with fraud in mind; they trust their customers. Think 1-click checkout and easy digital wallets.
2017
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Trust Platform Launch
“Creators trust the number they see on their account. Previously fraud was such an issue that creators experienced as well and did not really trust the number they saw in their account because a lot of it would turn out to be fraud. Now they can trust the amount that they see is really theirs and it will be deposited at the end of the month… Sift Science has made my life easier and made our brand more trustworthy.” Maritza at Patreon
“I’ve been lucky to talk to customers and the recurring theme is that our customers want to elevate the customer experience for their customers. They want to reduce friction and trust that their users are good. The life of a fraud analyst is fraught with problems and if they can offset that pressure with Sift.” Emily Chin
Sift Science opens Seattle office
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FIVE YEAR SIFT ANNIVERSARY RAP-Jason Tan
Five years, we’ll kick into higher gear Our journey of hopes, dreams, fears Our collective blood, sweat, tears Our vision and mission crystal clear A better internet operating on trust Better customer experiences without the fuss No more bad actors! I’m looking at you, Nicholas Cage Fraudsters beware, we’re turning the page Many hands make light work Our team one of many perks We’re a quirky, kind, passionate bunch Our culture eats strategy for breakfast and lunch Our sales team make the money Attracting prospects like bees to honey You’ll get results, no magic tricks No big egos, no one’s a dick
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People Ops, tirelessly keeps us alive So much food, snacks, drink and space to thrive But, we can all play our part No more dishes in the sink to start! Marketing tells our story, designs our schwag All that swagger when we wave our flag Billboards, content, events, we’ll broaden our reach So much machine learning for us to teach Biz Ops brings that Excel-lent thunder With a million ways to crunch the numbers Plans and graphs and charts for days We most certainly won’t lose our way ...
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Research & Development solve our customer needs Everything is possible! That is their creed Gnarly problems? Everyone’s up to the task We’ll do more, we’ll do it better, and of course we’ll do it fast From design to debugging, let’s go head-to-head Competitors may try, but they’re left for dead They hate us cuz they ain’t us, they want to see us fall But they’re got knives and we’ve got wrecking balls Five more years, the possibilities are endless We’ve got the foundation, momentum is tremendous We’re making it real, our ambitious dream One company, one unit, one drumbeat, one team So we say Trump, you won’t make America great! We’ll be the ones to eliminate deceit, fear, and hate
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Trust is the next chapter of the fraud and risk story. A ers fight fraud, the internet can be an unsafe place. T making customers happier. Now, with a single platfo multiple vectors of fraud and can automatically finetheir trustworthiness.
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As we’ve learned from years of helping customTrusting users allows business to grow by orm, online businesses protect themselves from -tune their users’ online experience based on
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“Trust is a natural extension of risk. Trust is a superset. Trustworthiness is a spectrum (fraud to trustworthy behavior). The big challenge we’ve seen is that they often think of things as trust in different buckets, and that’s wrong. As a business, you can only move at the speed of trust and the ability to trust customers quickly and at scale becomes a competitive advantage.” Jason Tan
“Trust requires us to extract the maximum utility of the data we have. The algorithms and the types of tools we’re using are much less important than the pooling of data together. This pool of data represents the union of all knowledge, across geos, verticals, businesses and what enables us to make online Trust happen.” Fred Sadaghiani
November 2017 Trustology customer appreciation event
Today
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In the last six years, we’ve grown the team from two people to nologies that build trust, but it’s our team and culture that serv ensures that we build products that our customers care about them even better champions. Setting the stage lets us come to on ideas but excellent to each other helps us get better with e Here’s to the next six years and beyond!
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o over 100. Our company DNA is rooted in products and techve as the real foundations for trust. Starting with the customer t, and our personal approach to solving their problems makes o a full understanding of problems we face, while being tough every iteration.
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inued t n o c e b To
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Move at the speed of Trust