How Entrepay strengthened its fraud detection strategy

Fraud prevention doesn’t just protect a business — it strengthens it. That mindset guided Entrepay’s partnership with Mastercard and its adoption of the Acquirer Security Program (ASP), with the Ghost Model playing a pivotal role in transforming its fraud detection efforts. Entrepay, a payment technology company headquartered in Brazil, processes between seven and eight million transactions each month and provides acquiring infrastructure and white‐label solutions for sub‐acquirers and fintechs. With innovation, technology and security at its core, the company continues to grow as a key player in Brazil’s fast‐moving payments ecosystem.

Entrepay faced a growing risk from ghost merchants: fraudulent entities that mimic legitimate businesses, commit fraud and vanish before being detected. Traditional monitoring approaches left blind spots, making it difficult to uncover these hidden threats in time. The team relied on slow, manual processes and piloted external tools, but none delivered the speed or precision needed to keep pace. Fraud continued to evolve, with high‑risk sectors such as gambling and betting proving notoriously difficult to monitor. Entrepay needed a solution to rapidly detect, score and prioritize suspicious behavior, giving teams the ability to act quickly on high‐risk cases.

Entrepay turned to Mastercard’s Acquirer Security Program, a fraud and risk management solution that helps acquirers detect hidden fraud and monitor high‑risk merchants at scale.

Within it, the Ghost Model, an artificial intelligence (AI)‑powered scoring tool driven by predictive analytics, became central to the company’s approach. The Ghost Merchant Risk Score analyzes transactional and risk‑related data to assess the likelihood that a merchant is a short‑lived operation set up to commit fraud. This allowed Entrepay to identify high‑risk suspicious merchants and take faster, data‑driven action.

As part of Mastercard’s suite of adaptive AI models, the Ghost Model is regularly refined to keep pace with evolving fraud patterns — supporting proactive, high‑confidence fraud prevention.

The results were transformative. In just three months of testing, the Ghost Model flagged a targeted set of high‑risk merchants — and the findings were clear. Of those flagged, 26% were confirmed as actual fraud or suspicious activity, including merchants engaged in unauthorized lending and high‑volume jewelry sales.

Using the Ghost Model, Entrepay blocked an average of 50 high‑risk merchants per month and prevented an estimated $300,000 in fraud losses each month. With Mastercard’s broader Acquirer Security Program, investigations that previously relied on manual review were prioritized and addressed more quickly, contributing to a 42% improvement in fraud prevention efficiency. The team could focus on the most urgent threats and reduce the company’s overall exposure.

These outcomes validated the Ghost Model as a proactive, precision‑driven fraud prevention tool. By integrating it into their daily workflow, Entrepay shifted from reactive, manual reviews to a score‑driven approach — embedding faster, smarter decision‑making and stronger financial protection across their fraud operations.

The Ghost Model, part of Mastercard’s Acquirer Security Program, has become central to Entrepay’s fraud strategy, using predictive scoring to help the team identify high‐risk merchants faster and act before losses occur. Companies looking to detect merchant fraud earlier, reduce investigation time and fortify their fraud strategy should consider partnering with Mastercard.