Future-Proofing Device Recognition: Building Similarity into Fraud Detection to Protect User Privacy and Build Trust

As the stalwart of fraud detection for the last decade, device recognition – of laptops, smartphones, tablets etc - has formed the basis for many rule-based policies and machine-learning models to detect fraud.

Many organizations talk about a layered defense strategy, that starts with device recognition and layers multiple additional capabilities such as behavioral analytics, digital identity data and additional authentication checks. But the painful truth is that hard-to-detect fraud is still getting through.

So, what’s not working? Is device recognition getting less effective as a tool for detecting unusual behavior? Is it harder to spot genuinely high-risk behavior in amongst the grey noise of more locked down, generic operating systems? To best answer this question, it’s worth going on an evolutionary journey of device recognition technology. Let’s look at the evidence.

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