Overview

In early April, Rippleshot’s consortium data detected a concerning trend: a rise in fraudulent activity tied to a merchant known only as “Happiness.” At first glance, the merchant seemed ordinary. But Rippleshot’s advanced analytics and pattern recognition capabilities told a different story.

The Problem

Many financial institutions lacked the visibility to see the early signs. Transaction scores didn’t always indicate risk. Yet, fraud was slipping through the cracks. Without a broad, shared dataset, individual institutions couldn’t detect the bigger pattern.

The Consortium Advantage

Rippleshot’s proprietary data consortium – powered by over 50 million daily card transactions across 5,000+ financial institutions – picked up on the anomalies. Happiness was identified as a high-risk merchant. Based on the intelligence, Rippleshot’s Rules Assist platform recommended a proactive rule to block all transactions from this merchant.

Discovery During a Demo

Shortly after issuing the alert, Rippleshot held a demo with a prospective client. During the presentation, the financial institution’s fraud manager noted they had already incurred significant fraud losses tied to Happiness. It was an “aha” moment that validated the importance of early detection and shared intelligence.

The Results

Since April, Rippleshot has observed over $250,000 in approved fraud tied to Happiness across financial institutions – fraud that would have been blocked entirely if Rippleshot’s rule had been implemented. This case is a powerful reminder: consortium data enables early action, not just reaction.

Key Takeaways

• Fraud was detected weeks before many financial institutions were aware of the threat
• A real customer confirmed losses during a live Rules Assist demo
• Over a quarter million dollars in fraud could have been blocked proactively

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