The beginning of 2026 marks the beginning of a stage in which the banking sector demands real, responsible and measurable impacts. And only by converting investments in artificial intelligence into tangible results will it be possible to strengthen trust with clients and regulators and demonstrate that innovation can coexist with responsibility.
In a scenario of economic uncertainty and technological changes, the banking sector is moving to remain competitive in an environment marked by the dominance of AI. However, the past year has shown entities that experimenting with AI is no longer enough. That is why, facing this new year, SAS predicts that organizations that integrate advanced solutions and combine them with human talent will obtain a decisive competitive advantage. Furthermore, given the need for a solid governance framework, a priority that is already part of the sector’s daily life will be intensified: strengthening the authentication not only of people, but also of the digital agents that act on their behalf.
Apply quantitative models
Along these lines, Mónica Gutiérrez, SAS private sector sales director for Spain and Portugal, states: “The banking sector will leave behind the experimental phase to focus on results. AI will only generate advantage if it is verifiable, has a clear governance framework and protects critical data from contamination. It will be equally important to use AI agents to convert unstructured data into rapid decisions and apply quantitative models to gain efficiency and better control risk, maintaining the trust of clients and regulators.”
The leading data and AI company anticipates that operational efficiency, customer experience and risk control in the banking sector will be decided by the ability of entities to deploy responsible AI, protect critical data and authenticate human and digital identities.
Trust will be measured with verified intelligence
AI has already optimized critical processes, but it has also fueled an overreliance on automated decisions. In 2026, the new reference in the banking sector will be verifiable transparency: auditing of models, explainability of decisions and traceability between the analytical result and business metrics. Trust will no longer be a perception but will be based on evidence such as the impact on customer satisfaction, regulatory compliance and risk-adjusted profitability.
Data vaults to protect against contamination
The expansion of generative AI and synthetic data introduces an increasing risk of contamination of critical repositories. Therefore, the banking sector will move towards governed digital “vaults” for key data, with restricted access, separation of environments and automated controls capable of detecting quality degradations before they affect risk, fraud or compliance models.
GenAI to turn the unstructured into decisions
According to a study by SAS and FT Longitude, more than 80% of business information remains in unstructured formats and continues to grow rapidly. In 2026, the banking industry will use knowledge agents based on large language models and retrieval augmented generation (RAG) to accelerate access to that content and transform it into actionable responses. The objective will be to streamline decision-making, achieve more proactive risk management and offer a more personalized and consistent customer relationship across all channels.
AI and quantitative credit to gain efficiency in fixed income
Entities will increasingly use AI to better decide where to put money. These systems will combine indicators that change at high speed, such as economic data, news or market developments, to adjust budgets almost in real time and seek greater profitability with the lowest possible risk. If the dual objective of anticipating unforeseen events and detecting new opportunities is to be achieved, the banking sector will need quality data, strict controls over its use and models capable of being reviewed and updated quickly.
The quantum leap: from testing to production
Quantum computing will begin to move out of experimental territory and into production in use cases where traditional methods fall short. Optimization, anomaly detection, fraud and risk management will be some of the areas where this emerging technology can be differential. Entities that adopt these tools early, accompanied by specialized teams and rigorous validation processes, will be able to achieve measurable competitive advantages as the ecosystem matures.
Banks will increasingly use AI to better decide where to put money
While 2026 is shaping up to be a turning point, innovation will only count if it is verifiable, operational and has a well-structured governance framework. Organizations that measure AI by results, protect their critical data, combine human teams and intelligent agents, and strengthen their control of risk and digital identity will be the ones that turn predictions into real value for the banking industry.
