Fraugster

Fraugster, a startup that uses AI to detect payment fraud, raises $5M

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Fraugster, a German and Israeli startup that has developed Artificial Intelligence (AI) technology to help eliminate payment fraud, has raised $5 million in funding.

Earlybird led the round, alongside existing investors Speedinvest, Seedcamp and an unnamed large Swiss family office. The new capital will be used to add to Fraugster’s headcount as it expands internationally.

Founded in 2014 by Max Laemmle, who previously co-founded payment gateway company Better Payment, and Chen Zamir, who I’m told has spent more than a decade in different analytics and risk management roles including five years at PayPal, Fraugster says it’s already handling almost $15 billion in transaction volume for “several thousand” international merchants and payment service providers, including (and most notably) Visa.

Its AI-powered fraud detection technology learns from each transaction in real-time and claims to be able to anticipate fraudulent attacks even before they happen. The result is that Fraugster can reduce fraud by 70 per cent while increasing conversion rates by as much as 35 per cent. The point of any fraud detection technology, AI-driven or otherwise, is to stop fraudulent transactions whilst eliminating false positives.

“We founded Fraugster because the entire payment risk market is based on outdated technology,” the startup’s CEO and co-founder Max Laemmle tells me. “Existing rule-based systems as well as classical machine learning solutions are expensive and too slow to adapt to new fraud patterns in real-time. We have invented a self-learning algorithm that mimics the thought process of a human analyst, but with the scalability of a machine, and gives decisions in as little as 15 milliseconds”.

Once integrated, Fraugster starts collecting transaction data points such as name, email address, and billing and shipping address. This is then enriched with around 2,000 extra data points, such as an IP latency check to measure the real distance from the user, IP connection type, distance between key strokes, and email name match. Then the enriched dataset is sent to the AI engine for analysis.

“At the heart of our AI engine is a very powerful algorithm which can mimic the thought process of a human analyst reviewing a transaction. As a result, we can analyze the story behind every transaction and say with precision which transactions are fraud and which aren’t,” explains Laemmle.

“You get a score or decision. Results are completely transparent (and not a black box), so you can understand exactly why a transaction was blocked or accepted. On top of this, our speeds are as low as 15ms. The reason why we’re so fast is because we’ve invented our own in-memory database technology”.

Fraugster cites competitors as incumbent enterprise level companies like FICO or SAS, which it claims are based on outdated technology.

Adds Laemmle: “At Fraugster, we do not use any rules, models or pre-defined segments. We don’t use a single fixed algorithm to analyze transactions either. Our engine reinvents itself with every new transaction. This lets us understand transactions individually and therefore decide which one is fraudulent and which one isn’t. As a result, we can offer unprecedented accuracy and the ability to foresee fraudulent transactions before they happen”.