Device Intelligence: A New Era in Fraud Detection (1)

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Device intelligence is presented as a fraud detection approach that analyzes hardware, software, browser, network, and behavioral characteristics to generate persistent, anonymized device fingerprints that are more difficult to spoof than cookies or traditional transaction-based checks. It can help identify account takeovers, synthetic identity fraud, bot traffic, multi-accounting, and return or friendly fraud by assigning devices risk scores based on their attributes and historical associations rather than automatically blocking them. Techniques including JavaScript, canvas, hardware, and WebRTC fingerprinting, often combined with behavioral biometrics, support more detailed device profiles, while continuous adaptation is needed to address evolving evasion methods. Didit says its platform combines device intelligence with identity verification, biometric authentication, AML screening, machine learning, customizable rules, real-time risk scoring, and reporting, and claims that integrating device intelligence can reduce fraud-detection false positives by up to 40%.

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