Technology

Leveraging AI to prevent payment fraud in real time

Technology

Leveraging AI to prevent payment fraud in real time

Differentiating Technology Pillars

Real-Time Anomaly Detection

Our models can distinguish between large spikes of good transactions and spikes of bad ones. We stop scalable fraud early.

Tailored ML models

Through Dynamic Data Engineering we can collect and engineer unique fraud vectors for each customer that give our ML models an edge.

Advance behavioral analytics

Proprietary blend of algorithms that emphasize the analysis of contextual data that cannot be manipulated.

Ancillary Feedback Loops

Unique external data sources integration that gets us to crucial information fast.

What makes nSure.ai unique?

nSure.ai understands that digital goods fraud is unique, and fighting it requires a different, more accurate approach. These unique challenges led nSure.ai to develop an advanced AI risk engine that leverages deep learning techniques to accurately identify fraudulent transactions.
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nSure.ai has spent years modeling specific buyer patterns in the field of fraud-attractive goods, learning from millions of digital transactions while constantly adapting its machines to learn from evolving customer behaviors and fraudster attack vectors. Based on its advanced technology and specific expertise, nSure.ai is able to achieve a 98% approval rating on average, with no manual review or delayed delivery of product.
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With a rejection rate of just 2% compared to the industry average of 20%, that’s a lot of happy customers and a lot of of saved revenue.

Not your typical fraud protection

Fraud protection tools generally analyze each online transaction in an attempt to correctly decide which purchases to approve and which to decline. Yet on average, 15% of transactions are difficult to clearly define as either fraudulent or legitimate.
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This 15% of “grey“ transactions are manually reviewed to determine whether to accept or decline them. Digital goods don’t have time between purchase and delivery to conduct these manual checks.
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The result? Either a delayed delivery of e-goods, or a high rejection rate of legitimate customers, both leading to a substantial loss of revenue and a negative customer experience.