Fingerprint, a leading company in device intelligence for fraud prevention, has introduced a new AI feature to improve its Suspect Score system. This update allows businesses to train their fraud detection models using their own data, making the system more accurate and tailored to their specific needs. The goal is to help fraud teams detect threats more effectively while keeping full transparency and control over the process.

Why Traditional Fraud Detection Falls Short

Static fraud detection models have been the standard for a while, but they struggle to keep up with modern tactics. Hackers and bots are getting smarter, often bypassing fixed rules and signals. Meanwhile, legitimate users are increasingly using privacy tools like VPNs, which can make traditional signals less reliable. This creates a challenge for fraud teams trying to differentiate between genuine users and malicious actors.

Because of these challenges, organizations need more flexible and adaptive solutions. Static models can quickly become outdated, leading to false positives or missed threats. Manual tuning of these models is time-consuming and often ineffective, especially when fraud patterns change rapidly. This is where AI-powered recommendations come into play, offering a smarter way to stay ahead of evolving threats.

How AI-Enhanced Suspect Score Works

Fingerprint’s new AI feature allows companies to upload their own labeled fraud data. This data trains the system to recognize the unique patterns and behaviors specific to each business. As a result, the Suspect Score becomes more precise, reducing false alarms and catching more genuine threats.

The system combines customer data with Fingerprint’s existing Smart Signals — real-time device intelligence insights — to generate optimized signal weights. These weights help prioritize the most relevant signals for each organization’s fraud patterns. Businesses can preview these recommendations before applying any changes, giving them full visibility and control over how their fraud scores are calculated.

One of the key benefits is that organizations can retrain the model whenever they gather new data. This keeps the fraud detection system aligned with current threats, even as fraud tactics evolve. Overall, the AI-powered Suspect Score offers a more adaptive, accurate, and customizable approach to fraud prevention.

By shifting from static to adaptive scoring, Fingerprint sets a new standard in fraud detection. The system continuously learns from each organization’s data, providing ongoing optimization without sacrificing transparency or control. This makes it easier for fraud teams to respond quickly to new threats and reduce false positives, ultimately making online environments safer for everyone.

Inspired by

• https://ai-techpark.com/fingerprint-unveils-ai-enhanced-suspect-score/