Last week, payment intelligence provider Fraugster announced that it had formed a partnership with e-mobility company Elvah to create a new managed payment protection service. In the future, Elvah will offer users chargeback protection, risk management and credit scoring through a single AI-driven platform.
The service will enable Elvah to better detect identity fraud thanks to an AI-based fraud prevention engine, which offers real-time risk scoring for ecommerce transactions. The engine uses over 2,500 variables in each transaction to decide whether to approve or block the payment.
The engine doesn’t rely on a fixed algorithm to identify fraud but rather uses three main machine learning models. One is a self-learning model designed to catch complex, well-defined fraud patterns. Another is a logistic regression model to measure the strength of cause-and-effect relationships in structured data sets. There’s also an AI-powered clustering model that can identify fraudulent patterns that aren’t based on historical data or other ML models.
The challenge of mitigating fraud
The announcement comes as identity fraud has remained a serious threat to ecommerce providers, enterprises and consumers alike, with the cost of ecommerce fraud rising from $17.5 billion in 2020 to $20 billion last year.
One key reason for this increase has been that the cost of remediating fraud has increased following the COVID-19 pandemic, with each $1 lost to fraud costing retailers $3.60 in expenses to mitigate, compared to $3.13 pre-pandemic.
As the cost of fraud continues to increase, it’s clear that ecommerce providers and enterprises need to evolve if they want to spot and prevent frauds. This is a challenge because many organizations remain reliant on disjointed data pipelines that make it difficult to gain cohesive insights into the status of fraud.
“The ecommerce ecosystem continues to operate in data siloed that limits 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 use organizations that fail to offer accurate insights at scale. “Most fraud prevention technologies in operation use outdated and inaccurate methods that fail to leverage data and AI in the service of automation and smarter business decisions,” Mangold said.
Fraugster is trying to help organizations detect fraud at scale by creating a single AI fraud prevention platform that organizations can use to proactively manage the risk of fraud and protect against chargebacks, while increasing visibility so they can remain compliant with growing regulatory 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 attempt to mitigate revenue lost to fraud.
Fraugster isn’t the only company using AI to mitigate ecommerce fraud, and is directly competing with Forter, an ecommerce fraud detection company, which analyzes transactions and makes real-time decisions on whether to approve transactions or not, and most recently raised $300 million as part of a funding round last year alongside a $3 billion valuation.
Another competitor is Sift, a payment fraud prevention provider, which uses real-time machine learning to automatically respond to fraudulent activity, while raising $50 million last year and achieving a total valuation of $1 billion.
However, Fraugster’s team believes that the higher accuracy of its AI in detecting fraud is what differentiates itself from competing solutions like Sift, which claim to decrease fraud by 50%.
“We continue to deliver an average fraud reduction of 60% for our customers, and approval rate increases, 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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