SEON

Most organizations are thinking about AI in fraud prevention the wrong way: they treat it as a magic black box that will somehow absorb messy data, spit out a risk score and make their problems disappear. In reality, this mindset creates new risks, because opaque models without clear and cohesive explanations are almost impossible to defend to regulators, customers or interna...

For the past two years, organizations have raced to integrate generative models and machine learning capabilities into their environments, driven by the promise of unprecedented operational efficiency. Many B2B tech leaders now find themselves staring at sophisticated algorithms that process information without actively moving the needle on revenue or decision speed. The expe...

International expansion tends to surface a problem that most brands don’t anticipate: the fraud tools that protected their domestic business start misreading customers the moment cross‑border order volume picks up. Chargebacks climb, manual review queues grow and approval rates drop in the very regions expected to drive growth.

The increase in fraud is only part of ...

Most digital commerce brands watch their chargeback rates closely, and they must — card networks set thresholds, processors enforce consequences and the dispute management process consumes real operational time. For example, under Visa’s updated Acq...

Most brands measure fraud by their chargeback rate. It’s a reasonable proxy, but it’s an incomplete accounting if you’re running high-volume campaigns on Meta, TikTok or Search. The chargeback rate captures the value of the orders you lost. However, it doesn’t capture what you paid to acquire the fraudster who placed it. When you factor in customer acquisition cost, the true ...