AI-Powered Payment Fraud Prevention That Stops Attacks Before They Hit

**OVERVIEW**
Fraud teams are constantly trying to stay ahead of increasingly complex, fast-moving threats. Traditional fraud tools often leave gaps—either reacting too late, missing cross-institution patterns, or requiring heavy manual analysis. Rippleshot gives fraud teams what they’ve been asking for: data-driven, AI-generated intelligence that proactively blocks fraud before it happens.

With Rippleshot, financial institutions deploy sophisticated, low false positive fraud rules and execute proactive measures on high-risk cards. Our proprietary consortium aggregates data from 5,000+ institutions, processing over 50 million daily transactions, generating predictive fraud signals and unmatched intelligence.

Rippleshot solutions strengthen fraud strategies by leveraging threat patterns from the consortium—delivering faster detection, intel for decision rules, and preemptive card protection to stop fraud before it happens.

Trusted by more than 1,800 financial institutions, Rippleshot leverages AI, machine learning, and data analyst expertise to transform traditional reactive fraud detection into predictive risk mitigation. Our platform delivers actionable intelligence and automated response capabilities that surpass the analytical scope achievable through isolated fraud detection systems.

In an era of rapidly evolving fraud attacks, Rippleshot provides the technological edge financial institutions need to stay ahead of sophisticated threat actors.

This listing contains high-risk merchants and demographic data that Rippleshot has identified as false and/or fraudulent. No personally identifiable information (PII) is contained in the dataset. The dataset is ideal for financial institutions looking to shift from reactive to proactive fraud prevention, and to leverage AI and consortium data to increase operational efficiencies and gain visibility into fraud other financial institutions are experiencing.

**USE CASES - HOW FRAUD TEAMS USE RIPPLESHOT CONSORTIUM DATA**
- **Write rules:** Implement a rule to block merchants identified as fraudulent or fake.
- **Identify risk:** Compare high-risk merchants to your existing rules to uncover potential vulnerabilities
- **Analyze data:** Reference the high-risk merchants when reviewing fraud and chargeback data
- **Leverage industry threats:** Monitor merchants affecting other financial institutions to stay ahead of emerging risks.

**BENEFITS**
- **Increased Visibility:** Identify fraud patterns based on analysis derived from consortium data of 50 million daily transactions, surpassing typical card processor data capabilities
- **Operational Efficiency:** AI and ML generated fraud intelligence delivers timely data, enabling fraud teams to spend less time analyzing and more efficiently block high-risk merchants.
- **Improved Fraud Detection:** Provide fraud teams with knowledge to proactively block risky merchants and stop fraud before it happens
- **Data Driven Results:** Fraud detection through AI-driven models, ML and expert data analysis
- **Ease of Use:** Rapid deployment within your existing fraud processes and workflows
- **Cardholder Trust:** Proactive fraud control minimizes card holder friction; legitimate transactions proceed smoothly, fraudulent transactions are blocked, and you maintain cardholder confidence
- **Minimize Losses:** Earlier card fraud prevention based on consortium intelligence allows fraud teams to act earlier, minimizing card holder impact and optimizing operational costs.
- **Measurable ROI:** No-cost trial allows financial institutions to analyze fraud prevention results prior to making a purchasing decision

**CASE STUDY**
2B+ Financial Institution results:
- 7X ROI in first year
- $150,000 fraud savings in first year
- $50k fraud savings in a single event

**PRODUCT DETAILS**
The dataset is generated using AI/machine learning and consortium data from 5,000+ financial institutions. It includes Merchant IDs which should be referenced in a blocking rule in your rules engine or tool.

**FIELD NAMES**
- Merchant ID
- Merchant Name
- Merchant Category Code (MCC)
- Merchant City
- Merchant State
- Merchant Postal Code
- Merchant Country

**There is no personally identifiable information (PII) contained in the dataset.**

**GUIDANCE**
The dataset is used in a blocking rule. The user copies ONLY the Merchant IDs into a tableset. The other data provided is for reference only.

In a fraud rules engine, a fraud analyst can create a blocking rule to automatically decline transactions that meet certain criteria. To block all transactions from a list of high-risk merchants, the analyst would write a rule that checks if the merchant ID on the transaction matches any merchant ID in a predefined list. This list, often called a tableset, would be uploaded to the rules engine and linked to the rule. When the rule runs, any transaction involving a merchant ID on the list would be automatically blocked, stopping fraud before it hits your financial institution.

**ADDITIONAL DETAILS**
This is one dataset among many available from Rippleshot. Rippleshot would welcome the opportunity to learn more about your challenges in detecting and stopping fraud to determine additional datasets that would provide value. For more details on our offerings or to discuss a custom dataset, please visit our [website](www.rippleshot.com) or contact us at [e-mail].
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