For years, social media data has been marketed as a shortcut to trust. If an email appears tied to a public profile or happens to be used across various online platforms, it must be legitimate—right?
Fraudsters are counting on businesses to think so.
Fake Reputations, Real Risk
On the surface, comparing transaction data to social media-linked information might help answer simple questions, like “Has this email been seen before?” or “Does this identity appear to have history?”
But that’s exactly the trap. Because in fraud prevention, history without context can result in more harm than value if the user assumes or projects context. For example, assuming that the social media account an email is associated with is legitimate. Social media is easy to manipulate: fraudsters can spin up new email addresses, pair them with leaked personal info, and gradually manufacture a convincing identity by signing up for online accounts, subscribing to marketing emails, and engaging with low-friction services. Over time, those actions create digital footprints that data aggregators mistake for legitimacy.
In 2023 it was reported that Meta detected and deleted 27 billion fake Facebook accounts. It’s reasonable to assume a fraud fighter could mistake a fake account with a legitimate account, providing a false sense of confidence about the legitimacy of a transaction.
The problem is that these reputation scores don’t reveal how or why a signal appeared—they just confirm that it exists. And many risk tools relying on this data don’t distinguish between high-integrity and low-integrity sources. Worse, some vendors pull data through informal channels, creating massive gaps in quality, consistency, and compliance.
Why Sift Doesn’t Rely on Shaky Signals
Sift takes a fundamentally different approach to identity intelligence that isn’t built on third-party guesswork or publicly scraped data. Instead, our platform delivers context-based decisions with Identity Trust XD, fueled by a proprietary global data consortium processing over one trillion events each year. This scale gives Sift the power to observe how identities behave across the entire digital economy—not just whether they exist, but how they act, evolve, and signal intent over time.
This means we can answer these types of questions with confidence:
- Has this identity been seen behaving consistently across real businesses?
- Does this combination of PII, device, and behavior reflect trustworthy patterns—or signs of synthetic abuse?
- Is this a returning customer, or a returning fraudster in disguise?
By rooting analysis in first-party behavioral data, not scraped or stitched-together reputation scores, we’re able to provide clarity and accuracy where other systems offer overwhelming noise or too little insight.
Trust Built on Behavior, Not Appearances
It’s tempting to think of a user’s identity in the binary—yes it’s them, no it’s not—but fraud doesn’t operate that way. Risk hides in gray areas, exploits shortcuts, and evolves alongside the systems meant to stop it.
The future of digital risk management won’t be built on reputational patchwork. Instead, it will be defined by an approach that looks beyond basic associations—like an email or phone number linked to a customer—and focuses on whether those details are tied to authentic, trustworthy behavior. That’s why more businesses are turning to solutions that let them see past surface-level trust signals and reveal the full scope and complexity of the risk they’re facing.
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