Trust by design: Embedding Governance into AI at Banco do Brasil

For Banco do Brasil, one of Latin America’s largest and most respected financial institutions, scaling AI meant more than deploying models. It meant proving that every decision made by those models was fair, explainable and compliant.

With over 80 million customers and hundreds of AI use cases in production, the stakes were high. The bank needed to ensure that its AI systems aligned with ethical standards, regulatory frameworks and public expectations.

Across industries, AI adoption is accelerating—but so are the risks. In particular, financial institutions operate in high-stakes environments where the cost of failure is measured in lost trust, regulatory penalties and reputational damage.

According to IBM research:

• 73% of IT leaders are concerned about biased outcomes
• 79% worry about security vulnerabilities in AI systems
• Less than 30% of CROs and CFOs feel their organizations are adequately addressing compliance risks

The inherent complexity of managing AI systems amplifies these concerns. Organizations today operate within multifaceted stakeholder environments that include compliance officers, risk managers, IT leaders and data scientists. The absence of standardized documentation—combined with the manual nature of oversight processes—makes it difficult to scale AI initiatives efficiently.

At the same time, regulatory frameworks such as the EU AI Act, ISO 42001 and NIST AI RMF are evolving rapidly, requiring constant adaptation. Furthermore, the opaque behavior of AI models underscores the need for streamlined processes and technologies that promote explainability and auditability—helping address board-level concerns and avoid or mitigate costly non-compliance fines.

This context makes it clear: governance is a strategic necessity.

To address these challenges, Banco do Brasil collaborated with EY and IBM to co-develop a governance-first AI strategy designed to scale with confidence.

EY brought in its generative AI lifecycle and continuous AI monitoring frameworks, which are designed to define clear roles, responsibilities and evaluation checkpoints across the AI lifecycle. These frameworks introduced rigorous processes for model benchmarking, bias mitigation and vulnerability assessments.

IBM implemented this strategy through watsonx.governance®, a platform purpose-built to:

• Automate oversight across the AI lifecycle
• Monitor fairness, drift and performance in real time
• Streamline documentation for audits and regulatory reviews
• Align AI outputs with institutional values and responsible AI best practices

Together, these capabilities formed a unified system of control and trust, enabling Banco do Brasil to scale AI responsibly while maintaining institutional confidence.

With watsonx.governance, Banco do Brasil achieved more than just compliance—it redefined how innovation is governed and scaled across the enterprise. The platform served as a unifying layer that brought consistency to governance practices across both traditional and generative AI systems. Regardless of the model type or deployment environment, oversight became standardized and streamlined.

Through automation of governance workflows, Banco do Brasil reduced manual intervention and oversight efforts. This accelerated model approvals and shortened the time to deployment while maintaining compliance rigor.

Most importantly, watsonx.governance helped embed transparency and accountability into every stage of the AI lifecycle. This change wasn’t just a technical enhancement—it marked a cultural shift. Governance evolved from being a reactive safeguard to becoming a proactive enabler of innovation, empowering teams to move faster, with greater confidence and without compromising integrity.

Integrated dashboards and automated risk assessments now provide continuous visibility into potential exposures. When model metrics breach thresholds, alerts are triggered instantly. This approach enables teams to detect risks early, take corrective action and ensure compliance in real time.

Automated capturing of model metadata ensures full traceability across the AI ecosystem, enhancing accountability and simplifying audits.

Visibility into model behavior helped facilitate evaluation. Stakeholders—from data scientists to risk officers—gained the ability to trace AI outcomes back to their source, validate them against ethical and regulatory standards and uphold institutional values. This level of transparency was critical in building trust both internally and externally.

With ongoing surveillance of model behavior, teams can rapidly identify performance drift and implement timely corrections. This supports agility, resilience and sustained model trust.

The transformation at Banco do Brasil delivered real, measurable outcomes that reshaped how the institution approaches AI at scale.

By embedding governance into the core of its AI strategy, the bank accelerated the deployment of new use cases. This shift enabled faster innovation without compromising on oversight.

Collaboration across departments also improved significantly. Risk, audit and data teams—often siloed in traditional workflows—began working from a shared governance framework. This alignment fostered clearer communication, reduced redundancies and created a more agile response to regulatory demands.

Transparency became a defining feature of the bank’s AI operations. Internal stakeholders gained greater visibility into how models functioned and why they made certain decisions, while external regulators were met with clear, auditable documentation that demonstrated accountability and ethical rigor.

Most importantly, Banco do Brasil now has a system that is not only scalable but also adaptable. As new regulations emerge and AI capabilities evolve, the bank is now equipped with a resilient foundation that supports sustainable growth and positions governance as a long-term strategic advantage.

Governance is no longer a checkbox—it’s a competitive differentiator. Organizations that embed governance into their AI strategy are better positioned to:

• Navigate regulatory complexity by embedding adaptive frameworks that evolve alongside global standards like the EU AI Act and ISO 42001, reducing compliance lag and enabling faster market entry.
• Build trust with stakeholders, including regulators and partners, through clear, auditable AI systems that demonstrate ethical rigor and accountability—turning governance into a brand asset and a driver of stakeholder confidence.
• Accelerate innovation without compromising integrity by integrating governance into the AI development lifecycle, allowing teams to experiment and deploy at scale while maintaining alignment with responsible AI best practices and defined risk thresholds.

IBM watsonx.governance helps leaders move from compliance anxiety to confidence in AI outcomes, turning oversight into a source of strategic advantage.

Banco do Brasil’s journey shows what’s possible when governance is treated as a strategic asset, further fueling and accelerating ROI from AI initiatives. With watsonx.governance, the bank didn’t just manage risk—it built trust, drove efficiency and set a new standard for ethical AI in finance.

For tech leaders facing similar challenges, the message is clear: governance isn’t just about control—it’s the strategic lever for sustainable development, long-term growth and innovation.

Discover how watsonx.governance can help your organization scale AI responsibly