While emerging markets are the primary drivers of global e-commerce growth, they also harbor a heightened level of cyber risk that activates defensive reflexes across the entire financial chain. The impact of this risk is quantifiable and can be severe. According to the 2025 Global eCommerce Payments & Fraud Report by MRC and Visa, Latin America leads global revenue losses to fraud at 4.1%, a figure that stands significantly higher than North America (3.6%), Europe (2.8%), and the Asia-Pacific region (2.6%).

This environment of high volatility—marked by identity theft, phishing, and social engineering scams —forces issuers, merchants, and card schemes into a defensive posture. The result is a cycle of hyper-vigilance where merchants and issuers implement increasingly aggressive security layers to protect their margins.

The weight of this risk also dictates consumer behavior. According to the Experian Global Fraud Snapshot 2025, 51% of consumers in Brazil reported having been victims of fraud, up from 42% in the previous year. This high exposure affects consumer's priorities: 86% of Brazilian consumers prefer interacting with brands they perceive as secure, and 62% are willing to pay extra for a demonstrably safer online experience. Security has ceased to be a backend feature and has become a driver of brand preference.

However, this necessary vigilance has reached a breaking point. Over-correction has led to a "fear of fraud" that actively damages the very economy it intends to protect. In Colombia, a study by LexisNexis Risk Solutions found that 82% of businesses believe overly rigid prevention measures are currently sabotaging their sales conversion. 

Finding the ideal balance between performance and security is a constant challenge. Companies need anti-fraud strategies that simultaneously protect against losses and maintain a high approval rate for legitimate transactions, without adding excessive friction to the customer experience. 

This delicate balance requires advanced technology and constant adaptation.

While actual fraud is a pervasive threat, the unintended wave of false positives can impact commercial growth. When a prevention model fails to account for the unique realities of a region, it misinterprets legitimate consumption as a form of attack. 

Arthur Queiroz, Merchant Operations Senior Manager at EBANX, notes that when transactions are processed without a local context, legitimate behavioral patterns can be misinterpreted as risk signals. 

A first-time online purchase by a consumer using a newly issued credit card — who often lacks a formal credit history due to the prevalence of informal economies — is a classic trigger for these false declines. Standardized filters can treat a "clean slate" as a high-risk signal when, in reality, it simply represents a newly included digital consumer. In these contexts, the consumer is effectively penalized for their recent entry into the formal financial system, a transition common across emerging markets.

The consequences of this misidentification extend beyond a single lost transaction: it can trigger an erosion of customer trust and brand equity. ClearSale’s study on consumer attitudes reveals that 44% of Mexican consumers claim they would never shop with an online business again after experiencing a legitimate payment being unfairly declined. Furthermore, 45% of these consumers would use social media to register complaints, creating a reputational spillover that can derail a merchant’s growth strategy. 

For businesses, false declines can act as silent killers of customer lifetime value.

To solve the paradox of high risk and high declines, anti-fraud systems are transitioning toward an intelligence-first approach. What began as simple velocity checks and static rules has evolved into a realm dominated by artificial intelligence and behavioral analytics. 

This requires models that do not merely act as gatekeepers but as behavioral analysts capable of decoding intent. The evolution of AI in payments is helping businesses move away from rigid, pre-trained models toward systems that prioritize granular, real-time data ingestion.

Advanced models are now capable of analyzing billions of data points in real-time to distinguish legitimate customers from complex fraud attempts with granular precision. For any business operating in today's high-risk digital economy, adopting these state-of-the-art anti-fraud solutions has become an essential "must-have" to balance security needs with high performance and conversion rates.

Eduardo de Abreu, Chief Product Officer at EBANX, emphasizes that material gains in performance are possible through the integration of "tacit knowledge" into AI training. 

"Assertiveness in these markets depends on the quantity and quality of local data signals," Abreu notes. "It isn’t enough to look at transactional data points. We must transcribe human expertise—the understanding of specific 'windows of opportunity' that fraudsters exploit and the 'explorer' behaviors of first-time buyers—into machine-readable signals. This consistency is the true quantum leap for AI results."

One promising frontier in this evolution would be the use of multi-rail signals. In markets where consumers adopt alternative payment methods earlier or more intensively than cards, a strong transaction history on instant payments or digital wallets could serve as a proxy for trust when card usage is new or limited. A customer making a first credit card purchase, for example, might appear risky to traditional filters. However, their consistent, verified behavior across other payment rails could tell a very different story.

"A customer might be buying with a credit card for the first time, which standardized filters would flag as high risk because of the clean slate," says Alessandra Arduini, Data & AI Senior Manager at EBANX. "However, if identified that this same customer has a solid history of successful, document-verified transactions via instant payments like Pix, for example, that could become a signal of trust."

At EBANX, the proprietary anti-fraud solution uses a machine-learning approach to build a high-definition view of consumer identity. Arduini explains that the engine cross-references over 100 behavioral variables to validate a single purchase, creating tailor-made fraud-prevention strategies for merchants.

These criteria include variables such as account age, name-to-email correlation, and document validity. "Our model performs so well because it looks at the entire EBANX ecosystem," Arduini notes. "We analyze payments from different merchants and locations, allowing us to build features specific to each vertical and calibrate the model to be either conservative or permissive based on the merchant's specific strategy."

That's why the right anti-fraud model can improve approval rates, rather than blocking more transactions. "It’s about educating the model to think within our context," Arduini adds. "We bring our context to the model, explaining what performance looks like in these markets, rather than just waiting for a global model to adapt to us."

A fundamental flaw in legacy fraud prevention is the reliance on a "one-size-fits-all" model. Systems must be sector-aware and region-specific to be effective. 

André Peixoto, Director of Operations at EBANX, points out that a model built for developed countries can fail in emerging contexts if it doesn't recognize local consumption rules. Furthermore, the behavioral signature of a legitimate user varies wildly between industry verticals.

A sudden spike in purchase frequency that looks like a bot attack in a subscription-based SaaS merchant might be expected behavior in a Gaming platform during a major seasonal release. Well-calibrated, tailor-made models adjust their acceptance thresholds based on the specific industry’s ticket and purchase cadence.

In the Travel vertical, where AOVs are high and purchase frequency is low, a well-calibrated AI model can focus on recognizing and tracking a customer's identity across different transactions and in document validity. Conversely, in Gaming or digital microtransactions, the focus can shift to processing high velocity and identifying the specific windows of time when legitimate players are most active.

By recognizing these contextual shifts, models prevent legitimate "out-of-pattern" spending from being penalized. As Alessandra Arduini highlights, calibrating anti-fraud for clusters of merchants allows for a delicate balance of being permissive enough to drive performance while remaining strict enough to keep chargebacks under control.

Technological precision is only one part of the solution. The other is relationship-based. 

Arthur Queiroz, Merchant Operations Senior Manager at EBANX, identifies a "trust gap" in the payment chain, particularly in international acquiring. When a transaction is sent from a distant foreign acquirer to a local issuer, the absence of a pre-existing relationship or recognized data strategy often leads the domestic bank to block the transaction by default, to avoid assuming liability.

One clever way to bridge this gap is through a feature called Trusted MID Channels. This is a collaborative agreement where the local payment processor aligns its fraud strategy directly with the issuing banks. The processor acts as a guarantor, providing the issuer with high-level data transparency regarding the merchant’s low fraud indices. This synergy between human strategic alignment and technology precision can result in a performance increase of at least 5 percentage points.

"The payments market is fundamentally built on trust," Queiroz observes. "By establishing these local connections, we can align the risk appetite of the merchant with that of the issuer." When the issuer trusts the intermediary and its technology, it can safely lower its fraud thresholds. 

For many global merchants, the fraud statistics of emerging markets can seem insurmountable. As Queiroz highlights, while the reality of higher fraud is undeniable, it does not render the landscape unnavigable. 

“Comparing the fraud rate of emerging markets versus established ones often discourages merchants from exploring these countries, because they don't want to deal with it,” he says. “They think it's going to be a boogeyman, that the market is impractical to enter. And yes, [high fraud rates] it is the reality of the market, but that's not why you can't navigate it."

The solution lies not in avoiding the risk, but in managing it effectively. By pairing advanced, locally-attuned AI with the strategic trust built through local partnerships, merchants can effectively dismantle the paradox, turning a region of perceived risk into one of manageable, and highly rewarding, opportunity.