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Bears, mice, and moles aren't enough: a better approach for preventing fraud
Traditional fraud defenses struggle to keep up with modern threats like residential botnets and AI-powered automation. This article introduces Stytch’s practical framework for evaluating and improving fraud prevention, with a focus on better signal gathering, smarter decisioning, and resilient enforcement.
In the last year, we’ve observed increasing fraud activity, from low-effort automated spam (made easier by LLMs, unfortunately) to sophisticated account takeover attacks from residential botnets. It may seem obvious to break down the fraud prevention process in this way - it’s very similar to John Boyd’s OODA loop concept - but it gives us a common language and separation of concerns. For example, credential stuffing attacks almost always have a high rate of attempts to login to accounts that don’t exist at all - that’s data known to the authentication system, but it doesn’t show up at all in the attributes that Device Fingerprinting measures.
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