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Stop fraud, break down data silos, and lower friction with Sift.
- Achieve up to 285% ROI
- Increase user acceptance rates up to 99%
- Drop time spent on manual review up to 80%
By Angela Marrujo /
Fraud isn’t standing still. It’s an ever-evolving problem that can’t be solved with legacy fraud solutions that struggle to keep up and tend to be reactionary and rules-based. We sat down with our CEO, Jason Tan, to discuss why the time for Digital Trust & Safety is now, and why it’s the future of fraud prevention.
Jason: The legacy mindset is very much like airport security, guilty until proven innocent. The internet has a lot of friction, verification steps, and security checks that are outright unnecessary: you just put in your password 20 minutes ago and have to do it again, you have to pull out your phone to input two-factor authentication, you have to click on a bunch of pictures to prove that you’re not a robot, you have to answer a customer support phone call. All of that is part of a legacy mindset — it’s really a “security first and at all costs” mindset. It becomes a competitive disadvantage if you’re stuck thinking that way.
Underlying all of that is the limitation of legacy technology, which is primarily built on rules-based systems and device fingerprinting, which aren’t keeping up with the pace of today’s fraudsters and the pace of growth that businesses are experiencing. Rules-based systems are not dynamic. They are reactive and difficult to update, maintain, and scale, which results in a lot of false positives and negatives. And that’s a lose-lose for everyone. Rules-based systems are terrible.
Jason: Part of the problem is that we didn’t have anything to shift to. Legacy solutions were the best thing possible 10 years ago, but in the last 10 years technology has continued to compound and evolve at an alarmingly fast rate. Now we’re in the age of big data, machine learning, and data science and we have the ability to use machine learning to drive incredible accuracy and automation, which drastically reduces the operational burden and overhead of these systems.
This technology didn’t exist a decade ago but has become more mainstream now, which is going to require a mindset shift to get businesses to adopt. People are comfortable with the old way of doing things, and there’s a certain control you get with rules-based systems that feels very safe and natural compared to machine learning, which feels like a black box to some. Comfort needs to be built into the transformation so machine learning doesn’t seem as scary. Education is needed to explain why machine learning is usually a better approach.
Jason: We’re lucky that many of those businesses are our customers. These businesses are viewing the tradeoff between friction and security as a false one. They believe the best customer experience will be frictionless but also minimizes risk for the business. It isn’t either-or, it’s both. And what we see as their nomenclature is trust and safety, not trust or safety.
These businesses are developing teams that are de-siloed compared to legacy ways. Under legacy systems, Fraud teams are typically siloed on their own, not talking to other people, and the Product and Marketing teams are trying to get as many people through the door as possible. Digital Trust & Safety is a holistic, connected, de-siloed approach that thinks about balancing the scales on both ends and creating less disconnect.
Jason: It’s about tackling the problem from all its angles. It’s minimizing risk and preventing bad actors and fraud without inconveniencing good customers. TSA Pre-Check is a good example of what the default experience should be but the internet right now is anything but that. We all have a shared goal but Engineering and Product need to pair up with Risk and Security to achieve a balanced approach. Product and Engineering should look at this as a competitive advantage. Amazon’s 1-Click ordering is a competitive advantage — it intelligently removes friction while minimizing risk. Product and Engineering should think of it not as a nuisance but, if done right, as a mechanism to build brand loyalty and adoption.
Jason: In some ways, you’re eliminating the need to achieve balance; with the right technology you’ll be so accurate you’ll make the right decision in the moment, and can make decisions quicker than ever before. Traditionally you’d have to hire teams of humans to manually determine whether something is fraudulent, which introduces delays, operational burden, and potential mistakes. If you can automate that decision-making, which is what machine learning aims to do, you don’t have to choose and can have the best of all worlds. You can do things like one-click checkout but also introduce security hoops for someone who is actually suspicious. This is a bigger shift around static models of identity and risk to probabilistic models of identity and risk. Things aren’t so black and white anymore, and are more nuanced than, “You have the right SSN, you must be who you say you are.” We have technology that can work through those nuances and make better decisions.
Jason: If they don’t make the change now, they’ll lose market share. Digital Trust & Safety is a competitive advantage that separates winners from losers in the digital economy. You see this with who’s winning, with who’s getting funded: Airbnb, LinkedIn, etc. Many of our customers are able to provide frictionless experiences at every step and introduce security checks and verification steps only when truly necessary, which is a big part of why they’re winning.
Google, Facebook, Amazon, they don’t think this needs to be a compromise, they think they can have the best of both worlds and succeed at it. If your business isn’t thinking about this transformation, you’ll fall behind your competitors that are. Consumer expectations continue to grow: they want things fast and now, and if you’re unable to deliver they will leave you for someone else that can.
Jason: Less friction is the primary one. Additionally, over time, the business will know who you are so well and trust you so much that the experience is truly tailored for you. It will no longer be just about removing friction — because you can only remove so much — but businesses will now be giving personalized experiences.
There’s a lot of friction to remove first and foremost. Mobile is the other big part of this; as everything moves to a tiny screen, there isn’t much consumer patience to click around a bunch of things. Time is of the essence, so you have to earn their trust and business quickly.
Jason: They’re not wrong, but are they doing the best they can? Those legacy solutions are going to likely come at a cost and the cost comes in multiple forms. You have to hire a lot of people for manual review, which isn’t a good use of time or resources. You’re likely inconveniencing customers and operating at some customer insult rate. Think of the lost revenue and lost growth. Don’t think of growth as the flip side of risk management — it’s all connected.
Jason: We do it through three pieces: technology, community, and partnerships. The first piece is technology: we have world-class machine learning systems which ingest incredible amounts of data across our community and they are getting smarter every day. Stronger together, strength in numbers. On the partnerships side, we’re leveraging our Trust and Safety expertise to deliver a tailored service and outcomes for your business. These custom models are working in tandem with machine learning, operating in a scalable way, and we partner with businesses on every step of their journey to be a trusted, holistic, all-in-one Trust and Safety partner. That’s what sets us apart.
Interested in learning more about Sift and how Digital Trust & Safety can protect your business while driving revenue? Request a demo!
Angela Marrujo, Content Marketing Manager at Sift, is a lifelong gamer with a deep love for Nintendo, in particular. Illustration and music are her other passions. Angela is a San Francisco State University alumna and, prior to Sift, worked in PR and Marketing in the video game industry.
Stop fraud, break down data silos, and lower friction with Sift.