Sift

Fraud Solution Profile

Sift is a fraud prevention platform that helps businesses tell the difference between legitimate customers and fraudsters, in real time, at the scale of global commerce. It was built on the idea that fraud rarely happens in isolation: the same fraud rings, networks of compromised accounts, stolen cards, and fake identities move across merchants, industries, and borders, testing whichever surface has the least friction that day. Sift’s core asset is its Global Data Network, a pooled, anonymized set of events, drawn from transactions, account activity, device fingerprints, and behavioral patterns across its customer base and processing ~1 trillion annual events, which lets it spot patterns no single business could see on its own: the same stolen card tested across several merchants, the same device behind a dozen fake accounts, the same fraud ring cycling through loyalty programs at different companies.

Every login, transaction, or account change is assessed for risk in real time by AI/ML-driven models and returned as a decision: allow, block, or send to manual review. Businesses can act on that decision directly or route it through their own internal workflows and decision logic, keeping control over where automation ends and human judgment begins. These models retrain continuously on new patterns pulled from across the network, so detection keeps pace with shifting fraud tactics rather than lagging behind them. This also feeds Sift’s entry point control approach: catching risk early in the customer journey, at signup or first transaction, rather than only after damage is done.

The practical effect is twofold. First, it catches coordinated fraud that a single company would never spot on its own. Second, it protects legitimate customers from unnecessary friction, since an overly aggressive fraud system creates false declines, and false declines can cost a business as much real revenue as the fraud itself would have.

On top of that foundation sits a decisioning layer covering payment fraud, account takeover, and fake account creation. Newer capabilities include Global Profile intelligence, which brings network-level identity context into the decisioning workflow so analysts can recognize trusted users faster and investigate linked risk with more clarity, and attack and anomaly detection, which flags when key metrics move outside expected baselines so teams can catch emerging attack patterns before losses spread.

Sift’s positioning centers on the idea that context, not just instinct or static decision logic, is what makes fraud prevention effective. Where legacy systems rely on rigid rule sets or isolated point solutions, Sift positions itself as the AI/ML-driven layer that sits above both, orchestrating decisions using the fuller picture. It serves industries where trust and transaction speed both matter commercially: digital commerce, software-as-a-service, fintech, marketplaces, and iGaming among them.