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By Joe Vignolo /
24 May 2021
As fraud evolves and fraudsters adapt to rapidly changing market conditions and countermeasures that businesses deploy, a trust and safety strategy has never been more crucial to succeeding in today’s digital landscape. But there may still be some confusion as to the difference between traditional fraud prevention strategies and a trust and safety approach.
At its core, a trust and safety mindset is not only about protecting a business and its customers—it’s about providing tailored customer experiences that unlock revenue growth while reducing risk. Traditionally, fraud and risk teams have focused more on loss prevention, e.g., blocking orders, reducing chargebacks, and adding friction. But for a business to live up to the demands of today’s consumers, the customer experience can’t be an afterthought.
Leading online businesses have already made the move to trust and safety, with that shift in mindset is only expected to accelerate in the coming months and years. In fact, Geoff Huang, VP of Product at Sift, organized a webinar featuring Gartner analyst, Jonathan Care, research analyst at Gartner, to discuss the importance of trust and safety, and how a business can make the shift from a traditional fraud detection and prevention approach to a more customer-centric, growth-minded strategy. During the webinar, Care predicted that “by 2025, over 75% of e-business will adopt a trust and safety mindset, up from less than 45% today.”
In this article, we will summarize some of the insights and takeaways from the webinar that will aid in adopting trust and safety at your organization.
Care laid out the three components of successfully rolling out a trust and safety strategy:
To understand how these three pillars contribute to successfully implementing a trust and safety strategy, we will address each one separately.
Care emphasized that making the move to trust and safety needs to be sincere. It can’t be a change in name alone. Companies that successfully adopt a trust and safety approach get buy-in from all stakeholders and make it part of the company’s DNA. “They actually say, ‘this is part of our mission statement. This is part of how we deliver service to customers, and to be sincere, it’s how our risk teams will also work. They will prioritize creating an environment where our customers feel it’s okay to do business with us,’” according to Care.
Creating tailored customer experiences is really what differentiates traditional fraud prevention strategies from trust and safety. Dynamically adjusting the level of friction based on risk allows trusted or “known” customers to receive seamless experiences, while risky or “unknown” users encounter additional verification steps. This minimizes false positives and lost customer LTV (lifetime value)—all while protecting the business from fraudsters. Care sums up why customizable customer flows are so important:
“So if we have a customer that we know well, that we trust very well, and we see no signs of concern, no risk indicators of significance, we can ease and expedite the customer journey, whereas if we see someone who has a few risk signals—they’re coming from an unusual place, or their behavior isn’t quite right, or there’s something else that the systems are putting into our radar—we say, ‘okay, we need to perhaps make this a little bit more frictional.’ And the feedback from the world is that customers actually recognize and value this. They recognize when friction is useful, friction is protecting their accounts. Friction is stopping their online selling account from being misused. Friction is protecting stored value.”
Dynamically adjusting customer experiences is next to impossible if you don’t use technology that can adapt in real time while analyzing data from a variety of sources, as well as scale as the business grows. “All of this is accomplished and expedited by the use of highly automated fraud solutions, utilizing advanced machine learning techniques,” Care says.
Online merchants need to use scalable fraud prevention solutions that don’t undermine growth for the sake of protection. With a solution that effectively detects all types of abuse across a variety of channels, online businesses can enable their fraud teams to contribute directly to expansion—and give them the control they need to refine and scale fraud operations as the business matures. Machine learning (ML) is a core component of effective fraud prevention automation. ML instantly calibrates risk assessments based on new data and evolving fraud trends. It’s the only way for merchants to tailor the customer experience based on risk and achieve the accuracy required to drive online growth while stopping fraud before it happens —all while reducing false positives and lowering operational costs.
Simply adopting a trust and safety mindset, establishing a handful of effective and efficient processes, and implementing an ML-powered fraud prevention solution doesn’t mean you can call it a day. Care emphasizes the need for constant improvement: “We need to recognize that the threat landscape is not static. The things that are going on out there change and evolve, while we are changing, evolving internally. So we need to make sure that as the landscape changes, our approach must evolve, too.”
It’s important to understand that different industries have different challenges and opportunities, and therefore must adjust their trust and safety strategies accordingly. Sift uses velocity, volume, and value to understand the needs of specific industries. Care provides a summary of how those three aspects can negatively and positively affect the success of a trust and safety approach:
“As your delivery speed increases, you have more to lose, because if your delivery speed is fast, if things go wrong, if a fraudster gets into the journey, they can start racking up the losses really, really quickly.
“Again, as you receive more orders, you attract more fraudsters. And this is the tall poppy syndrome. The taller the poppy is, the more risk it is of being targeted. And of course, as value increases, so does fraud, especially if we’re talking about highly reusable, re-sellable goods and services,” says Care.
Fortunately, the right type of automated fraud prevention solution can adjust in real time and lead to seamless customer experiences and lower operational costs, resulting in higher revenue and growth. Automation through machine learning ensures a business’ fraud prevention efforts scale with order velocity, volume, or value. It also efficiently separates trusted users from potentially risky actions so that legitimate users don’t experience unnecessary friction. This creates a smoother, faster user experience.
If you’re interested in how Digital Trust & Safety can help address your specific challenges, prevent fraud, and help your business grow, take our new Sift Digital Trust & Safety Assessment. You’ll receive custom recommendations from our team of experts with decades of fraud-fighting experience at companies like Facebook, Square, and Google.
*Sift Digital Trust and Safety Online Event featuring Gartner analyst, 20th April, 2020. Based on Gartner, Create Trust and Safety on the Internet, Jonathan Care, Akif Khan, 29 June, 2020.
GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.
Joe Vignolo is the Director of Content Marketing at Sift, specializing in authentic storytelling that connects and converts. Before joining Sift, he ran content at Outreach and Datanyze and was an award-winning broadcast journalist in the San Francisco Bay Area. He also believes Point Break (the original) is a shining example of American cinema.
Stop fraud, break down data silos, and lower friction with Sift.