Fingerprint Device Intelligence for E-Commerce is a visitor identification and fraud prevention platform designed for online retail environments. It assigns a stable visitorID to each device using 100+ browser and device signals combined with machine learning, enabling merchants to identify returning visitors accurately across sessions — including those using incognito mode, VPNs, or other evasion techniques. The platform provides a set of "Smart Signals" that surface device and network risk indicators in real time. These signals include VPN detection, proxy detection, Tor detection, bot detection, incognito mode detection, browser tampering detection, virtual machine detection, cloned app detection, IP blocklist checks, geolocation spoofing detection, jailbreak detection, Frida instrumentation detection, and raw device attribute collection. For e-commerce specifically, the product targets four primary fraud scenarios: - Chargeback fraud: identifying devices linked to disputed transactions - Account takeover: flagging login attempts using stolen credentials - Promo abuse: detecting repeated exploitation of discounts or promotional offers - Automated threats: identifying malicious bots at the point of interaction On the user experience side, the visitorID can be used to silently verify trusted devices during checkout, reducing the need for additional MFA steps and lowering cart abandonment rates. The identification process runs in the background via a lightweight JavaScript API, integrating into existing rules-based fraud workflows without replacing them. The API response includes browser details, IP geolocation, confidence scores, and device attribute data. SDKs are available for JavaScript, React, and SPA frameworks.