Get a practical, one-sitting blueprint for making advanced AI production-ready in finance. This micro course connects the dots between data strategy and regulatory compliance, showing how to move from traditional warehouses to federated and domain-oriented data systems. You’ll see where data lakes, synthetic data, and secure sandboxes fit, and how to design AI-ready architectures that align with GDPR, DORA, and Basel III. We’ll cover metadata management and data lineage for explainability, plus the role of APIs, Open Banking, and interoperability in building a trustworthy AI stack. Finish with a clear checklist you can apply to your organisation the same day.
You’ll learn how to:
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Map an AI data strategy from warehouses to federated/domain data platforms
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Use data lakes, synthetic data, and sandboxes to accelerate safe model development
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Translate GDPR, DORA, and Basel III requirements into concrete data and MLOps controls
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Implement metadata, lineage, and model traceability for audit-ready explainability
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Leverage APIs and Open Banking standards to enable secure, interoperable AI services
Who it’s for: Data leaders, architects, risk/compliance owners, and product teams in financial services who need a concise, actionable guide to AI data readiness.
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