Tag Archives: Operational Resilience

Integrating AI and LLMs into Regulated Financial Services Data Platforms

How AI fits into Bronze/Silver/Gold without breaking lineage, PIT, or SMCR: This article sets out a regulator-defensible approach to integrating AI and LLMs into UK Financial Services data platforms (structurally accurate for 2025/2026). It argues that AI must operate as a governed consumer and orchestrator of a temporal medallion architecture, not a parallel system. By defining four permitted integration patterns, PIT-aware RAG, controlled Bronze embeddings, anonymised fine-tuning, and agentic orchestration, it shows how to preserve lineage, point-in-time truth, and SMCR accountability while enabling practical AI use under PRA/FCA scrutiny.

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East/West vs North/South Promotion Lifecycles: How Modern Financial Services Data Platforms Support Operational Stability and Analytical Freedom Simultaneously

This article argues that modern Financial Services (FS) data platforms must deliberately support two distinct but complementary promotion lifecycles. The well known and understood North/South lifecycle provides operational stability, governance, and regulatory safety for customer-facing and auditor-visible systems. In parallel, the East/West lifecycle enables analytical exploration, experimentation, and rapid innovation for data science and analytics teams. By mapping these lifecycles onto layered data architectures (Bronze to Platinum) and introducing clear promotion gates, FS organisations can protect operational integrity while sustaining analytical freedom and innovation.

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Using SCD2 in the Bronze Layer with a Non-SCD2 Silver Layer: A Modern Data Architecture Pattern for UK Financial Services

UK Financial Services firms increasingly implement SCD2 history in the Bronze layer while providing simplified, non-SCD2 current-state views in the Silver layer. This pattern preserves full historical auditability for FCA/PRA compliance and regulatory forensics, while delivering cleaner, faster, easier-to-use datasets for analytics, BI, and data science. It separates “truth” from “insight,” improves governance, supports Data Mesh models, reduces duplicated logic, and enables deterministic rebuilds across the lakehouse. In regulated UK Financial Services today, it is the only pattern I have seen that satisfies the full, real-world constraint set with no material trade-offs.

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