Fraud prevention teams are increasingly relying on digital fingerprinting as an effective tool to combat evolving threats and maintain user satisfaction. These digital fingerprints analyze subtle details of a user's device, browser, and network environment to generate a unique identifier that is difficult to spoof or erase, unlike cookies or login credentials. Various types of fingerprinting, including device, browser, TLS, canvas, WebGL, audio, behavioral, network, and media device fingerprinting, play unique roles in identifying and stopping fraud while allowing legitimate users to proceed seamlessly. Combining multiple fingerprinting methods enhances the ability to detect sophisticated fraud tactics, such as the use of virtual machines, automation frameworks, and anti-detection tools. Platforms like Fingerprint utilize over 100 signals to create a stable visitor ID that persists even when users clear cookies or change IP addresses, providing actionable insights such as bot, VPN, and browser tampering detection. Real-world applications include preventing account takeovers, payment fraud, bot attacks, and multi-accounting abuse, all while adhering to privacy regulations like GDPR and CCPA. By integrating device intelligence with internal data, fraud teams can act swiftly, reducing manual reviews and maintaining a smooth user experience.