As Latin American markets draw increasing global interest, several microfinance leaders with proven success across Asia and Europe have begun expanding into the region.
In recent conversations I had with some of these companies, it became clear that – despite arriving with strong portfolios and tested models for customer acquisition and risk assessment – they quickly found themselves navigating a very different landscape.
A Core Challenge for Fintechs Expanding into LATAM
Early results revealed a major challenge: the risk levels in their LATAM portfolios were significantly higher than anticipated. The same strategies that delivered results elsewhere struggled to offer the depth and responsiveness required in this new context. In a region defined by high exposure to digital fraud, a large informal economy, and limited credit data coverage, traditional models quickly lose both precision and agility.
This is also something I’ve seen firsthand in my work with fintechs operating in Latin America. It underscores a key principle: fintechs scaling in LATAM must go beyond adjusting. They need to fundamentally rethink how they assess risk, detect fraud, and segment users in an environment where structured data is often limited or unreliable.
In this article, I’ll share key risk fronts that fintechs face when entering the LATAM market – and how digital signals, especially those derived from device behavior and contextual analysis, can help build stronger, more adaptive decision systems.
1. Network Manipulation: The First Line of Attack
One of the most common fraud vectors in LATAM is manipulation of the network environment. Fraudsters frequently use VPNs, proxies, emulators, or foreign IP addresses to conceal their real location and bypass geo-based or IP reputation filters.
This not only distorts geographic analysis but also allows attackers to test multiple identity combinations from a single source – often without being detected.
Empirical evidence from our clients in Mexico supports this trend. JuicyScore’s latest analysis shows that a significant portion of credit traffic arrives with signs of network tampering – including VPN usage, proxy routing, or mismatched IPs.
Moreover, JuicyScore’s data indicated that this type of traffic exceeded typical market levels by 50–300%. See more in our study: How JuicyScore Drives Fintech Growth in Mexico
The problem is that relying solely on IP addresses or declared geolocation is no longer enough. IPs are easily compromised – and attackers know how to rotate them or emulate “legitimate” connections. That’s why it’s essential to use advanced fingerprinting tools that evaluate multiple layers of the digital environment instead of depending on a single signal.
2. Device Identity: The New Battleground
Fraud tactics have moved beyond network spoofing. Today, bad actors also forge device identities – emulating browsers, cloning configurations, rotating virtual environments, and automating processes to generate dozens or even hundreds of fake profiles.
This means a single fraudster can appear as an entirely new user on each attempt, slipping past duplication rules and deceiving risk models that rely on conventional attributes.
In Colombia, the Chief Risk Officer of a large-scale fintech told us they had clear evidence of organized fraud rings operating within their traffic. Devices were linked across multiple profiles, systematically creating synthetic identities. Their legacy risk prevention system was unable to identify these patterns – posing a serious threat to portfolio performance.
A deep device fingerprint enables detection of these sophisticated manipulations in real time – before the application reaches the decision engine.
3. Credit Invisibles: The Dilemma of Unscored Traffic
Fintechs exist to fill the gaps left by traditional banking – to serve the millions who remain unbanked. This mission, however, brings a structural challenge: a large portion of the traffic reaching microcredit and BNPL platforms comes from users without any credit history – making it difficult to translate the available behavioral data into well-grounded risk decisions.
Even in relatively advanced markets like Mexico, half the population is still excluded from the financial system, according to the World Bank’s 2024 data. This means that in many cases, there’s no historical data to support a credit decision.
As a result, many potentially creditworthy users are rejected due to lack of background.
This is where an additional layer of digital signals makes a tangible difference.
JuicyScore helps transform that “invisible” traffic – users without credit history – into well-grounded risk decisions. By analyzing the technical quality of the device, the stability of the connection, the traceability of the digital environment, and alternative signals of repayment capacity, it’s possible to identify segments with low risk and high financial inclusion potential.
4. Credit Bureaus See the Past – Devices Reveal Intent
One of the most common mistakes in risk evaluation is assuming that a strong credit history guarantees future good behavior. But even when credit bureau data is available – and corresponds to a real identity – it says nothing about the applicant’s current intent.
Past solvency is not the same as present willingness to repay.
In LATAM markets, where planned over-indebtedness (loan stacking) and opportunistic fraud are prevalent, this distinction becomes crucial. A user with a high credit score may simultaneously apply to five fintechs from the same device – with no intention of repaying any of them.
That’s where digital environment signals – like those captured by JuicyScore – can play a decisive role:
- We analyze the device, not the individual.
- We detect if the same IP or device environment has been used to submit applications across multiple institutions — a sign of coordinated fraud or high-risk behavior.
- We flag technical inconsistencies and suspicious patterns that point to manipulation or automation.
This enables a shift from a model focused on “Can they pay?” to one that asks a more telling question: “Do they intend to pay?”
Conclusion
Sustainable fintech growth in LATAM hinges on the ability to outpace fraud with smarter, data-driven tools. From what I’ve seen on the ground, fintech expansion in the region is far from slowing down — but to ensure that growth is sustainable, risk strategies must evolve just as quickly as fraud tactics do.
In this context, investing in early detection technologies based on device behavior is not only essential for minimizing losses – it’s also the key to expanding credit access with greater precision and confidence.