INDUSTRY · BANKING

Decision Intelligence Software for Banking and Financial Services

Most AI programmes in banking stall before they deliver. Not because the models are wrong, but because the data underneath them is fragmented, inconsistent and ungoverned. Stratio gives banks the data foundation that makes AI reliable, scalable and defensible.

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CONTEXT

A Tier-1 bank. Multiple markets. Measurable results.

A regulated financial institution operating at scale across Latin America. Every figure below comes from a live engagement across credit risk, regulatory reporting and portfolio integration.

80%

less effort per regulatory submission

faster post-merger portfolio integration

Weeks

to first working AI use case in production

THE CHALLENGE

The AI adoption gap most banks are not talking about

Every bank has AI on the roadmap. Credit models, fraud detection, regulatory automation, customer analytics. Most of those programmes hit the same wall: source systems carry conflicting definitions, governance is a quarterly exercise rather than a live control, and when the regulator asks why a model produced a given output, the answer is not readily available.

The gap is not the AI. It is the data foundation the AI sits on. Without consistent business meaning and continuous governance, AI outputs cannot be explained, reproduced or trusted at scale. Stratio closes that gap, connecting to your existing infrastructure, enforcing governance from source to output, and delivering AI that carries a full audit trail your regulator can follow. Built and operated in Europe.

BENEFITS

What Stratio makes possible for banks

Two validated cases in production today. Each is a real engagement: what the customer needed, what we built, what changed.

From proof of concept to production

AI that works in the pilot works the same way at scale. Stratio gives every model and automated process a single governed view of your data, removing the inconsistencies that cause production failures before they reach the model.

Compliance as a continuous property, not a project

The banks that adapt fastest to regulatory change are the ones whose compliance processes run continuously. Stratio turns DORA, EU AI Act and Basel obligations into governed, automated workflows, so the next requirement is an update, not a programme.

AI your regulator can stand behind, not just your data team

Most banking AI deployments stall when the regulator asks for explainability and the answer is a black box. Stratio builds auditability into the architecture: every model output is explainable, every decision is logged, and the audit trail runs from source data to final output.

HOW IT WORKS

How Stratio works in a regulated bank

Stratio connects to your existing systems without moving data or disrupting operations. Nothing is replaced. Most banks have their first governed AI process running within weeks.

200+ native connectors to core banking systems, payment rails, risk platforms and cloud data warehouses. Zero data movement. Data residency stays within your perimeter.

Stratio establishes consistent definitions of what a customer, an exposure and a transaction mean across every system. One shared understanding across every department, model and regulatory report.

Access controls, data quality rules and end-to-end lineage run at runtime, not at quarter-end. Compliance posture is always current.

Automated processes handle credit, fraud and compliance checks within the limits your risk teams set. Every output is logged and explainable.

Stratio runs on-premises, as managed SaaS or within your own cloud environment. Data never leaves your infrastructure: a deployment model, not a contractual commitment.

USES CASES

Where Stratio makes the difference in banking

Validated cases in production today; four cases on the validated pipeline. Each is a real engagement — what the customer needed, what we built, what changed.

M&A integration in a third of the time

When the customer needed to integrate acquired customer portfolios across LATAM into a moving-target core platform, Stratio's semantic layer isolated the migration from the evolving data model and from origin differences — making the transformation logic reusable across markets and portfolios.