Sponsored Content - Transparency & Control Month
Approaching fraud prevention with a Digital Trust & Safety mindset takes more than understanding that online fraud presents a unique battlefront for risk teams. Beyond encouraging the adoption of a new platform or process, implementing it successfully means remodeling business strategies and reconsidering metrics to better meet the dynamic challenges and opportunities of the digital world.
While adapting existing tech and tactics, merchants must consider three core elements of Digital Trust & Safety: automation, context, and data.
At minimum, time-consuming manual tasks like verification cause bottlenecks and delays. But at their most damaging, those slow-downs snowball into decision fatigue, higher false-positive rates, mounting chargebacks, friction-heavy customer experiences, surging fraud, and lost revenue.
Merchant risk teams that rely solely on manual review are consistently resource-constrained, with less time to focus on critical tasks (like chargeback disputes) that truly require human analysis. But before implementing Digital Trust & Safety, risk teams should research which fraud prevention solutions are best suited to support automating risk mitigation tasks at scale. They’ll also want to consider how the solution handles growth—something that happens rapidly for many online businesses, causing fraud to balloon just as quickly.
Choosing an AI-based or machine learning-based fraud platform enables businesses to stop different types of abuse without slowing expansion. Trust and safety teams should ask the following questions as they investigate:
- Can the solution grow alongside the business?
- Does the solution easily ingest and take action on key user data, e.g., email address, shipping address, login attempts, etc.?
- Can the solution integrate with existing parts of the tech stack?
- What is the risk tolerance of the business, based on specific factors like profit margins, growth stage, and customer acquisition costs?
- Will the solution be able to automatically adapt to changing behaviors, and more accurately assess risk over time?
Answering these questions enables fraud teams to make informed decisions about the next phase of deploying Digital Trust & Safety: determining how the specific circumstances of the business either exacerbate challenges or present opportunities.
Context gives trust and safety teams the ability to make accurate, data-driven decisions. But many companies use multiple disparate tools to identify and fight fraud, surface industry trends, assess consumer behaviors, and build trust with customers.
But it’s rarely simple—or even possible—to integrate these different systems and platforms into a cohesive stack that can be managed from a single command center. Developer resources (which are always hard to come by) are usually needed, and fraud data often ends up incomplete or siloed in various tools. An ideal platform orchestrates the entire fraud prevention process without negatively impacting growth or protection. Trust and safety teams should look for platforms that:
- Can scale alongside the business
- Integrate easily with the company’s current core tech stack using little-to-no developer or engineering resources
- Allow bulk actions, such as blocking all orders from a specific email address
- Provide up-to-the-minute insights into how fraud is impacting the business
With a solution powered by real-time machine learning, all of these things are possible—but to be effective and accurate, it needs a variety of proprietary, actionable data.
Decide and Deploy with Data
Data shapes strategy. It can influence a business and its customers permanently; internally, even small adjustments to a handful of KPIs can significantly impact bottom line performance and top line growth.
For real-time fraud prevention to work consistently, while continually adapting and improving in real time, it’s critical to have quick access to both internal and external performance metrics and indicators. When launching a Digital Trust & Safety approach, risk teams should be prepared to deliver properly pulled data that’s clean and timely, and includes the following types of information (and then some):
- Chargeback data
- Network data
- True false-positive rates
- Historical backtesting
- Block rates
Fraud doesn’t wait for merchants to level up their risk mitigation strategies, and Digital Trust
& Safety is the only approach that allows businesses to defend against multiple types of abuse in real time and without giving up growth. Download Sift’s complete guide to implementing Digital Trust & Safety here.