Fingerprint has introduced a significant upgrade to its Suspect Score solution by adding AI-powered recommendations, marking a major step forward in device intelligence for fraud prevention. With this enhancement, customers can now leverage an adaptive and intelligent fraud scoring system trained on their own labeled data. As a result, organizations can improve detection accuracy while still maintaining complete transparency and control over their fraud prevention strategies.
Traditionally, static scoring models have struggled to keep up with rapidly evolving and highly dynamic fraud patterns. Moreover, fraud teams often lack the time and resources required to continuously analyze signal interactions and fine-tune model parameters. Consequently, many organizations face inefficiencies and gaps in fraud detection. However, with Fingerprint’s new AI-driven recommendations, businesses can eliminate manual tuning processes, save valuable time, and adapt more effectively to emerging threats.
“Fraud patterns vary by business and evolve constantly, rendering manual tuning obsolete,” said Valentin Vasilyev, CTO and co-founder at Fingerprint. “Our AI-powered recommendations remove that bottleneck by training on each customer’s labeled data, making Suspect Score customizable, accurate, and easy for customers to use.”
Furthermore, the need for adaptive fraud detection has become increasingly critical. Sophisticated AI-driven bots and automated agents are now capable of bypassing traditional static detection systems, leaving organizations exposed to modern fraud tactics. At the same time, the growing use of privacy tools such as VPNs by legitimate users adds another layer of complexity, making it harder to accurately weigh and interpret signals.
To address these challenges, Fingerprint has enhanced its Suspect Score with a production-ready machine learning system. Built on its Smart Signals framework which provides real-time, actionable device intelligence the solution already delivers strong fraud indicators. Now, organizations can upload their own labeled fraud data, enabling the system to learn and adapt to unique traffic patterns as threats evolve.
In addition, the upgraded Suspect Score introduces several advanced capabilities. It intelligently analyzes customer data alongside Smart Signals to generate optimized signal weights tailored to specific fraud patterns. It also dynamically adjusts these weights based on observed trends, helping reduce false positives while maintaining high detection accuracy. Importantly, users can preview all AI-generated recommendations before applying them with a single click, ensuring full visibility and control over any changes.
As fraud tactics continue to evolve, organizations can retrain their scoring models using updated data. Therefore, businesses can ensure that their fraud detection systems remain aligned with real-world behavior and emerging risks.
Ultimately, Fingerprint’s AI-powered Suspect Score recommendations represent a shift from static to adaptive fraud detection. By enabling continuous optimization based on real-time data and organization-specific patterns, the company is setting a new standard for data-driven fraud prevention. Additionally, this approach allows enterprises to enhance security without compromising transparency or operational control.
The AI-powered recommendations are now available to all Fingerprint customers with access to Smart Signals, and existing users can begin training their customized models directly through the Fingerprint dashboard.
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