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Experts have previously told Sifted that there are still multiple ways of undermining the processes that APIs like Seon’s provide. When run online, every single layer of the value chain can be exploited. This means Seon will be stuck in its own game of cat and mouse with fraudsters, too.
Kadar acknowledges that this is the inherent, constant battle. “In the case of reported false negatives or positives, the client can inform us via an API or manually,” he says. “We then retrain the algorithm based on this feedback, to cover new patterns and improve on the algorithm.”
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Seon operates on a usage-based pricing model, foregoing integration fees and instead charging customers each month based on how many transactions they make.
It uses machine learning to track data points that clients can customise according to their trouble areas.
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While the US might be their main target for now, gaining market share in US fraud prevention is no easy task. More established competitors in the market include $4.5bn Socure as well as Ravelin, Sift and Riskified.
Seon believes it can com...
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University friends Kadar and Jendruszak began developing what would become Seon while working on a crypto exchange venture in 2015 that was targeted by a slew of fake accounts.
To stop these attacks, they built their own solution to detect false accounts and quash fraud...
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