Fingerprint launches Fraud Score trained on customer fraud outcomes
Original title: Fingerprint trains specialized models to detect distinct fraud types
Separate models cover payment fraud, account takeover, fake accounts and promotion abuse, and retrain as customers add outcomes.
Fingerprint launched Fraud Score, a predictive signal that trains specialized models on each customer's fraud outcomes and Fingerprint device-intelligence data. The company says teams can use the scores directly or through its API instead of maintaining their own models. In a head-to-head evaluation Fingerprint says Fraud Score caught nearly 50% more fraud than one customer's in-house model without blocking more legitimate users. Covered types include payment fraud, account takeover, multi-accounting, fake accounts and promotion abuse.
Unconfirmed: The nearly 50% gain is Fingerprint's evaluation against one customer's model, not an independent study.
- Fingerprint says models retrain continuously as customers submit new fraud outcomes.
- A Flink engineer said blocking a visitor ID alone did not stop more capable fraudsters.
- Javelin analyst Suzanne Sando said static one-size-fits-all risk scores are no longer enough.
- Vendors
- Fingerprint
- Products
- Fraud Score
- Organizations
- Javelin Strategy & ResearchFlink
From the original article
2 min read at helpnetsecurity.comFingerprint has launched Fraud Score, a predictive fraud signal trained directly on customers’ own fraud outcomes and powered by Fingerprint’s device intelligence and machine learning models. Fraud Score learns the fraud types at each organization to help them identify fraud more accurately without having to build and maintain their own models.
By Industry News · helpnetsecurity.com
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