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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 fraudulent transactions. That became Seon, which they launched in 2017.
“For the past couple of years we’ve tripled our revenue year-on-year, and have been onboarding new clients and expanding like crazy into new markets,” says Jendruszak.
In the next 12 months, Seon plans to increase its headcount by 150 people in total, doubling its teams in London and Jakarta, adding another 80 to 90 people in its founding location Budapest, and doubling its team in the US.
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 compete because it’s already conquered the more varied regulatory landscape of Europe — which Kadar says US rivals are struggling to tackle.
Jendruszak won’t confirm specific acquisition targets, but is keen to explain that they will be instrumental to the company’s plans to move beyond know-your-customer (KYC).
The company is plotting a move into anti-money laundering products on the consumer side, sanction checking, politically exposed person checking, and is also evaluating how it can begin offering risk analysis and even digital footprinting products to evaluate businesses as well as consumers.
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.
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.”
building @tellimer. #engineering, #machinelearning and #crypto
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