Fraud is evolving at an unprecedented pace. As fraudsters deploy increasingly sophisticated tactics, financial institutions face a constant battle to protect their customers while minimizing losses. Traditional fraud detection methods, which rely on institution-specific data, often fall short in identifying new fraud patterns before significant damage is done.

To stay ahead, financial institutions need better insights – ones that aren’t limited to their own data. This is where consortium data comes in. By pooling transactional and fraud intelligence from thousands of financial institutions, consortium data offers unparalleled visibility into emerging threats, allowing for faster, more accurate fraud prevention.

At Rippleshot, we harness the power of consortium data to proactively detect and help stop fraud before it happens. With a proprietary network of over 5,000 contributing financial institutions and analysis of more than 50 million daily transactions, Rippleshot delivers the intelligence banks and credit unions need to proactively safeguard their portfolios.

What is Consortium Data?

Consortium data refers to a shared pool of anonymized payment and fraud data contributed by multiple financial institutions. Unlike isolated, institution-specific data, which only captures a fraction of fraudulent activity, consortium data provides a broader, more comprehensive view of fraud trends across the entire industry.

The analysis of transactional behaviors across thousands of financial institutions through consortium data enables early detection of fraud schemes, such as card testing, refund phishing, and merchant breaches, long before they become widespread.

How Rippleshot Leverages Consortium Data

1. Scale: The Power of 5,000+ Institutions

Rippleshot’s proprietary consortium includes transactional data from over 5,000 banks, credit unions, and payment processors, making it one of the most extensive fraud detection networks in the industry. By analyzing these million daily transactions, Rippleshot provides financial institutions with a continuous stream of insights into compromised merchants, high-risk merchants and high-risk transactions.

Specifically, here’s how consortium data can help financial institutions scale their fraud prevention efforts:

• A large dataset of millions of transactions across various payment ecosystems ensures fraud detection models are robust and accurate.
• AI-driven fraud analytics allow credit unions and mid-sized banks to scale their fraud detection operations to match those of larger banks.
• Proactive insights ensure timing is never an issue, helping fraud teams anticipate the next attack rather than react after the damage is done.

It’s not just the scale of the consortium data that matters – it’s the quality too. Thanks to Rippleshot’s impressive partners, the quality of the data is of the highest caliber. 

2. AI and Machine Learning for Predictive Fraud Detection

Rippleshot uses this data, applying advanced AI and machine learning to identify fraudulent patterns. Our models continuously learn from new fraud cases, ensuring that financial institutions receive proactive fraud intelligence before fraudulent transactions impact their customers.

3. Actionable Intelligence: Stopping Fraud Before It Happens

Consortium data is only valuable if it leads to actionable outcomes. Rippleshot transforms raw data into predictive insights that help financial institutions:

• Identify and block fraudulent merchants in real-time before they process high volumes of unauthorized transactions.
• Pinpoint compromised cards that are most likely to be used in fraud, allowing for proactive mitigation strategies.
• Optimize fraud rules to minimize false positives while maximizing fraud prevention.

4. Card Risk Scoring for Smarter Decisions

Rippleshot assigns risk scores to individual cards and alerts high-risk merchants based on consortium data analysis. This helps financial institutions make informed decisions on:

• Whether a merchant is high-risk and should be blocked.
• Whether a card is likely to experience fraud and needs reissuance.
• How to adjust fraud rules to prevent similar incidents in the future.

Consortium Data as a Differentiator

Rippleshot’s fraud prevention solutions stand out because of their unmatched access to industry-wide data. Here’s how consortium data gives Rippleshot an edge over traditional fraud detection methods:

1. Higher Accuracy with Lower False Positives

One of the biggest challenges in fraud prevention is balancing security with customer experience. Too many false positives can frustrate customers, leading to unnecessary declines and operational inefficiencies. Rippleshot’s fraud intel used in real-time rules, built on consortium data, reduce false positive rates (FPR) by 46% compared to the industry average.

2. More Comprehensive Insights

Rippleshot detects fraud up to 180 days before traditional network alerts from Visa and Mastercard, helping financial institutions stay way ahead of fraudsters.

3. Cost Savings and ROI

Early fraud detection and proactive rules lead to significant financial benefits. Financial institutions can save hundreds of thousands of dollars, thousands of hours of manual work, and achieve significant ROI almost immediately. 

What’s more, without consortium data, fraud analysts spend significant time manually reviewing transactions, trying to detect suspicious patterns – many of which may lead to dead ends. Rippleshot's AI-driven analysis eliminates this inefficiency, allowing fraud teams to focus on strategic decision-making rather than time-consuming investigations that may yield no answers.

4. Industry Collaboration for Stronger Fraud Prevention

Unlike fraud detection models based solely on a single institution’s data, Rippleshot’s consortium-based approach ensures that all participating institutions benefit from shared intelligence. This means that when one institution detects a new fraud pattern, others in the network can immediately adapt their fraud rules to prevent similar attacks.

Case Studies: Real-World Impact of Rippleshot’s Consortium Data

Case Study 1: Stopping Risky Merchants for a $2bn+ Financial Institution

A southeastern financial institution was experiencing a rise in fraudulent purchases. Their fraud team partnered with Rippleshot, gaining intelligence based on Rippleshot's consortium data. The result: new fraud rules that identified broader fraud trends.

Results:

• Proactive identification and blocking of high-risk merchants
• False positive rates reduced by 46%
• 7x ROI achieved in the first year

Case Study 2: Reducing Card Reissues for a $600m Financial Institution

Before implementing Rippleshot’s Sonar solution, a financial institution’s average fraud rate was 4.77 bps, high for this particular financial institution, leading to high operational costs and customer dissatisfaction. With Sonar’s early fraud detection capabilities, the institution was able to significantly reduce fraud and unnecessary reissuances while improving fraud prevention.

Results:

• The financial institution's fraud rate decreased by 58% to 1.99 bps
• There was a 45% decrease in fraud dollars lost
• The financial institution experienced a 25% reduction in fraudulent cards
• The ratio of fraudulent cards to active cards improved by 25%

Conclusion

The fight against fraud requires scale, speed, and intelligence – and consortium data is the key to unlocking all three. Rippleshot’s industry-leading fraud detection solutions, powered by proactive intelligence from over 5,000 financial institutions, enable banks and credit unions to stay ahead of fraudsters, minimize losses, and provide a seamless experience for their customers.

With AI-driven insights, proactive fraud rules, and an extensive fraud consortium, Rippleshot is transforming how financial institutions prevent fraud.

Is your institution ready to take fraud prevention to the next level? Learn more about how Rippleshot’s consortium data solutions can help you predict, pinpoint, and protect against fraud.