Artificial intelligence promises efficiency, personalization, and automation for the financial system. But its adoption in Argentine banks faces technical, cultural and regulatory barriers that can turn innovation into risk. This note reveals the main challenges and suggests ways to overcome them.
The Argentine context: opportunities and pressures
The Argentine banking sector is under high digital pressure. Competition with fintechs and digital customer expectations push towards AI adoption. According to local reports, several banks are already exploring virtual assistants, predictive models and automation of internal processes.
However, that momentum comes with risks that are often underestimated. Legacy architecture, the sensitivity of financial data, and the need for regulatory transparency compound local challenges.
Main “pains” when implementing AI in Argentine banks
Data Quality, Governance, and Silos
Powerful AI is only as good as the data that feeds its models. In many banks, data is scattered in silos (legacy, disconnected systems) or with errors, lack of standardization or incomplete records.
Without rigid data governance, AI can be trained on biased or inconsistent data, affecting critical decisions (credit, scoring, fraud detection).
The Inter-American Development Bank warns that, from the design of the AI project, data architecture, flow, storage and quality must be defined.
Transparency, explainability, and algorithmic biases
When an AI decides to reject a loan or modify risks, the customer tends to demand explanations. “Black box” models make it difficult to provide accountability.
In addition, if biases are not controlled, AI could exacerbate inequalities: for example, favoring urban profiles over users in rural areas. Academic studies warn that training models without explicit constraints can generate undesired results.
AI opens up attack vectors of its own: adversarial manipulation (altering inputs to fool the model), data poisoning (altering training), model access attacks.
There is also a growing risk that AI tools will be used against the bank itself (for example, to automate sophisticated attacks or fraud).
Legacy support and scalability
Many banks still run on old “core banking,” systems designed decades ago. Integrating AI models – cloud services, real-time data pipelines – with that infrastructure can lead to high integration costs, latencies, or technical conflicts.
The KPMG Argentina report points out that one of the strategic challenges is to integrate AI into the credit, account opening and compliance (KYC) processes without rewriting the entire back-end.
Specialized talent, culture and internal resilience
It’s not just about having data engineers and scientists: it’s about the entire organization—risk unit, legal, operations, customer service—understanding the role of AI.
Resistance to change, fear of automatic errors, or lack of trust in systems can delay actual adoption.
In addition, specialized talent (ML engineers, data scientists) is scarce and competitive, forcing banks to invest in retention, training, and internal innovation schemes.
Hidden costs and uncertain return
The AI budget goes beyond licenses or cloud services: it includes ongoing maintenance, audits, adversarial testing, monitoring, model updating, support, and more.
Some AI initiatives can exceed the originally estimated budget by 500% to 1,000%.
And the benefits (efficiency improvements, less fraud, higher retention) can take time to materialize, which stresses the internal evaluation of return on investment.
Regulation, Privacy & Compliance
Argentina has data protection frameworks (Personal Data Law) and bodies such as the Agency for Access to Public Information (AAIP) that oversee transparency and data use.
But AI-specific regulation in finance is still under construction. Banks must anticipate algorithm auditing requirements, explainability mechanisms, and regulatory oversight.
Globally, the Bank for International Settlements warns that the use of AI/ML in the financial sector poses prudential stability challenges if not managed well.
Best practices to mitigate risks
Proofs of Concept and gradual deployment: start with limited use cases to learn without exposing the entire system.
Data governance from the start: create a “data trust“, define roles (who modifies data, who accesses) and apply permanent quality control.
Algorithmic auditing and integrated explainability: choose “interpretable” models, generate bias reports, record decisions.
Adversarial security and continuous monitoring: incorporate stress tests, white hat tests and automatic alerts.
Transversal training and executive sponsorship: form teams at all levels, have the support of senior management.
Modular architecture and APIs: making it easier for AI components to evolve without remaking the entire stack.
Maintenance plan and continuous update: models age; Provide for retraining, withdrawals or adaptations.
Regulatory collaboration and compliance: dialogue with regulators, prepare transparency reports, and align with emerging standards.
In short, artificial intelligence can reconfigure Argentine banking: improve efficiency, anticipate risk, personalize services. But it’s not a plug & play solution.
Banks that move forward without addressing the “hidden pains”—data, security, culture, regulation—risk turning innovation into vulnerability.
The challenge is not only technological: it is institutional. Transformation must accompany a rigorous, responsible and gradual approach.
