New AI-powered recommendations enable enterprises to train fraud scoring on their own data for more accurate detection<\/p>\n
Fingerprint, a leader in device intelligence for fraud prevention, today announced the addition of AI-powered recommendations to its Suspect Score solution.The enhancement gives Fingerprint customers an adaptive, intelligent fraud score trained on their own labeled data, improving detection accuracy while maintaining full transparency and control.<\/p>\n
Static scoring models fail to keep pace with increasingly dynamic, traffic-specific fraud patterns. Fraud teams lack the time and resources to continuously analyze signal interactions and tune model weights for their unique needs. With Fingerprint\u2019s AI-powered recommendations, fraud teams can eliminate manual tuning, save valuable time and resources, and make their fraud detection adaptive to evolving threats.<\/p>\n
\u201cFraud patterns vary by business and evolve constantly, rendering manual tuning obsolete,\u201d said Valentin Vasilyev, CTO and co-founder at Fingerprint. \u201cOur AI-powered recommendations remove that bottleneck by training on each customer\u2019s labeled data, making Suspect Score customizable, accurate, and easy for customers to use.\u201d<\/p>\n
Adaptive Fraud Detection for Evolving Threats<\/p>\n
Sophisticated AI agents and bots can bypass static detection models, leaving organizations vulnerable to modern fraud tactics. Compounding the challenge, legitimate users are\u00a0increasingly adopting\u00a0privacy tools like VPNs, complicating traditional signal weighting.<\/p>\n
Fingerprint\u2019s enhanced Suspect Score addresses this issue with a production-ready machine learning (ML) system. Built on Fingerprint\u2019s suite of\u00a0Smart Signals\u00a0\u2014 actionable real-time device intelligence insights \u2014 Suspect Score already delivers powerful fraud indicators. Now, enterprise fraud and security teams can upload labeled fraud data to train the ML system on their unique traffic patterns as threats evolve.<\/p>\n
Using this data, Fingerprint\u2019s updated Suspect Score:<\/p>\n
Intelligently analyzes customer data alongside Smart Signals to generate optimized signal weights tailored to a customer\u2019s specific fraud patterns<\/p>\n
Adjusts signal weights based on patterns observed in a customer\u2019s fraud data to reduce false positives while maintaining accuracy<\/p>\n
Provides a preview of all recommendations before customers apply changes with a single click, giving users full visibility and control over their scoring<\/p>\n
As threats evolve, organizations can retrain their scoring with up-to-date data to keep detection aligned with real-world fraud behavior.<\/p>\n
A New Standard for Fraud Detection<\/p>\n
Fingerprint\u2019s AI-powered Suspect Score recommendations shift fraud detection from static to adaptive. By tailoring detection to each organization\u2019s unique traffic patterns, Fingerprint sets a new data-driven standard, providing continuous optimization without sacrificing transparency or control.<\/p>\n
AI-powered Suspect Score recommendations are now available to all Fingerprint customers with access to Smart Signals. Existing customers can begin training customized scoring models through the Fingerprint dashboard.The post Fingerprint Unveils AI-Enhanced Suspect Score first appeared on AI-Tech Park.<\/p>\n","protected":false},"excerpt":{"rendered":"
New AI-powered recommendations enable enterprises to train fraud scoring on their own data for more accurate detection Fingerprint, a leader in device intelligence for fraud prevention, today announced the addition of AI-powered recommendations to its Suspect Score solution.The enhancement gives Fingerprint customers an adaptive, intelligent fraud score trained on their…<\/p>\n
The post Fingerprint Unveils AI-Enhanced Suspect Score<\/a> first appeared on AI-Tech Park<\/a>.<\/p>\n<\/div>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"site-container-style":"default","site-container-layout":"default","site-sidebar-layout":"default","disable-article-header":"default","disable-site-header":"default","disable-site-footer":"default","disable-content-area-spacing":"default","footnotes":""},"categories":[1,38],"tags":[3],"class_list":["post-2422","post","type-post","status-publish","format-standard","hentry","category-ai-and-ml","category-machine-learning","tag-ai"],"yoast_head":"\n