Latin America Faces GenAI and Fintech Risks in 2026

For 2026, the stability of Latin America’s financial sector will hinge on managing risks arising from generative AI (GenAI), disparities in regional regulatory maturity, and the growing interconnectivity of Fintech ecosystems.

Systemic vulnerability is not confined to isolated attack vectors but emerges from sophisticated, cross-cutting threats. “The true competitive advantage will not be in avoiding all attacks, but in responding better than anyone else. Financial institutions must combine vision, data, and talent capable of anticipating the unexpected,” says Eric Mejía, CTO, Banco Sabadell.

This view highlights a shift from a reactive defensive posture to an operational resilience model focused on response capacity and adaptability to complex incidents.

The 2026 risk landscape is  shaped by technologies that have transformed the traditional security perimeter. The acceleration of GenAI has democratized tools that once required advanced expertise, fueling hyper-personalized phishing campaigns and high-fidelity deepfakes targeting biometric authentication and social engineering protocols.

Fragmented regulation in Latin America compounds these technological risks. The cybersecurity maturity gap among countries creates arbitrage opportunities that attackers exploit to infiltrate interconnected ecosystems. In an Open Finance environment, the vulnerability of a single institution or supply chain provider can compromise the integrity of the entire transactional network. This is particularly critical in instant payment systems, such as account-to-account transfers, where rapid settlement leaves little margin for fraud detection.

Infrastructure and Technological Preparedness

To address these challenges, financial institutions are redesigning their defense architectures by adopting quantitative risk methodologies and integrating managed security service providers with 24/7 monitoring. Investment is increasingly directed toward post-quantum cryptography (PQC) solutions. While commercial-scale quantum computing is still developing, safeguarding current data against future decryption capabilities is a long-term compliance and security priority.

Fraud prevention systems have evolved from static rules to adaptive machine learning (ML) models that analyze behavioral patterns in real time, enabling the suspension of suspicious transactions without affecting legitimate users. Multifactor authentication and advanced biometrics are becoming baseline regulatory requirements, alongside enhanced transparency and incident reporting obligations.

Supervisory bodies are expected to adopt a more facilitative role. Regulatory sandboxes will allow institutions to test technologies such as blockchain and AI in controlled environments, balancing risk mitigation with the flexibility needed to foster financial innovation.