Fraugster partners with Elvah to tackle fraud in the e-commerce sector


Last week, payment information provider Fraugster announced it had partnered with e-mobility company Elvah to create a new managed payment protection service. Going forward, Elvah will provide users with chargeback protection, risk management, and credit scoring through a single AI-powered platform.

The service allows Elvah to better detect identity fraud thanks to an AI-based fraud prevention engine, which provides real-time risk scores for e-commerce transactions. The engine uses over 2500 variables in each transaction to decide whether the payment is approved or blocked.

The engine does not rely on a fixed algorithm to identify fraud, but rather uses three main machine learning models. One is a machine learning model designed to detect complex, well-defined fraud patterns. Another example is a logistic regression model to measure the strength of cause-and-effect relationships in structured data sets. There is also an AI-powered cluster model that can identify fraudulent patterns that are not based on historical data or other ML models.

The challenge of reducing fraud

The announcement comes as identity fraud has continued to be a serious threat to e-commerce providers, enterprises and consumers alike, costing: ecommerce fraud increase from $17.5 billion in 2020 to $20 billion last year.

An important reason for this increase is that the costs of resolving fraud has increased after the COVID-19 pandemic, with every $1 lost to fraud costing retailers $3.60 in costs to mitigate, compared to $3.13 pre-pandemic.

As the cost of fraud continues to rise, it’s clear that e-commerce providers and businesses need to evolve if they want to detect and prevent fraud. This is challenging because many organizations continue to rely on disjointed data pipelines that make it difficult to gain coherent insight into the status of fraud.

“The e-commerce ecosystem continues to operate in data silos that limit the potential for data pooling, network intelligence, and the application of AI and machine learning,” said Fraugster CEO, Christian Mangold.

At the same time, many of the fraud prevention solutions leverage organizations that fail to provide accurate insights at scale. “Most of the fraud prevention technologies in use use outdated and inaccurate methods that don’t leverage data and AI in the service of automation and smarter business decisions,” said Mangold.

Fraugster seeks to help organizations detect fraud at scale by creating a single AI fraud prevention platform that organizations can use to proactively manage fraud risk and protect against chargebacks, while increasing visibility so they can continue to comply with the growing legal requirements.

A Brief Look at the Fraud Detection and Prevention Market

The provider is part of the global fraud detection and prevention market, which researchers expect to grow from $24.8 billion in 2021 to $65.8 billion in 2026 as organizations seek to reduce revenue lost to fraud.

Fraugster isn’t the only company using AI to reduce ecommerce fraud, competing directly with fortan e-commerce fraud detection company that analyzes transactions and makes real-time decisions about whether or not to approve transactions, and recently raised $300 million as part of a financing round last year alongside a $3 billion valuation.

Another competitor is sifta payment fraud prevention provider, which uses real-time machine learning to automatically respond to fraudulent activity, while raising $50 million last year and a total rating of $1 billion.

However, Fraugster’s team believes its AI’s higher accuracy in detecting fraud sets it apart from competing solutions like Sift, which claim to reduce fraud by 50%.

“We continue to offer our customers an average fraud reduction of 60% and the approval rate is increasing, ranging from 5-15%. This means we have enabled our customers to generate additional sales in the tens of millions and significantly reduce fraud losses,” Mangold said.

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