AI-Ready Data in Financial Services : The Delivery Playbook

In Part 1, we introduced why“AI-ready data” is a different bar in financial services: it must be fit for a specific use case and defensible under audit, model-risk, privacy, and regulatory scrutiny.

If you haven’t read Part 1 yet, start here: Optimizing Data Readiness for AI Modeling in FinancialServices

This Part 2 blog is the implementation guide. It translates the concept into an end-to-end delivery blueprint: what to build, what controls to put in place, what documentation to produce, and how to operationalize AI-ready data so it works reliably in production.

What makes part 2 different?

• Less theory, more execution: architecture, gates, and artifacts you can deliver.

• A practical approach for all data types: system data, external/third-party data, and unstructured data for RAG.

• A readiness scorecard and an AI-Ready Pack checklist to make progress measurable and repeatable.

A working definition we use in delivery

AI-ready data is governed, contextualized, and operationalized data that can be consumed repeatedly (by models and humans), at scale, with predictable quality, cost, and risk.

In financial services, that definition implies:

• Traceability: lineage and reproducibility down to what drove a decision.

• Controls: access, retention, masking, and purpose limitation enforced (not just documented).

• Operational reliability: SLAs, tests, monitors, and run books.

• Explainability readiness: features and inputs that can be reviewed and defended.

The scope: three data categories you must make AI-ready

Most AI programs stumble because AI-ready data gets reduced to a single pipeline. In practice, it is a portfolio of governed pipelines and assets spanning:

• System data: core operational platforms (lending, payments, trading, servicing, claims, CRM).

• External data: bureaus, KYC/AML utilities, market data, open banking feeds, alternative data.

• Unstructured data: policies, SOPs, underwriting notes, call transcripts, emails, documents, PDFs and scans.

How we organize it: medallion architecture plus AI layers

Medallion architecture makes readiness tangible: move from raw to standardized to certified assets, with explicit quality and governance gates at each stage.

Bronze: land data safely (raw but controlled)

• Immutable landing with source metadata (system of record, extract time, license constraints).

• Security from day 1: encryption, RBAC/ABAC, masking rules, retention tags.

• Schema versioning and ingestion logs (critical for third-party feeds).

Silver: standardize and validate (trusted operational data)

• Canonical schemas and identifiers (customer, account, entity, instrument).

• Quality checks and reconciliation against business rules.

• Reusable transformation patterns (not one-off scripts).

• For unstructured: normalize text, de-duplicate, redact, and enrich with metadata.

Gold: package for consumption (data products and features)

• Business-aligned data products with contracts, definitions, and SLAs.

• Curated aggregates and certified metrics.

• Reusable feature views / feature store for model consistency.

• For GenAI: retrieval-ready document collections and governed vector indexes tied to provenance.

AI layers that sit alongside Gold

• Feature layer: versioned, governed features with online/offline parity.

• Vector layer: embeddings index with metadata filters andchunk-to-document lineage.

• Semantic layer: glossary and ontology that standardize meaning.

• Observability layer: monitoring for pipeline health, drift, data breaks and cost.

Unstructured data delivery: make documents query-able with accountability

GenAI outcomes depend on unstructured data, but most teams jump to embeddings too early. We start by making documents query-able with accountability: every answer must be traceable back to authoritative sources under the right access controls.

The document readiness flow

1. Inventory and ownership: what exists, where it lives, who owns it, and what is authoritative.

2. Classification: sensitivity tags (PII/PCI/confidential), jurisdiction,and retention rules.

3. Canonical versioning: reduce duplicates, track effective dates and superseded content.

4. Parsing and redaction: OCR/extraction with traceable transforms;remove sensitive content where required.

5. Chunking by structure: chunk by headings/sections (not arbitrary tokens) and preserve citations to page/section.

6. Metadata-first indexing: business taxonomy, regulatory taxonomy, product/process context, access tags.

7. Hybrid retrieval: vector + keyword + structured filters (role, jurisdiction, effective date).

8. Evaluation and monitoring: retrieval quality harness, hallucination controls, drift as documents change.

This is the practical heart of our GenAI Data & Document Readiness Accelerator: it standardizes these steps so teams can move fast without creating a new compliance surface.

The AI-Ready Data Scorecard

To avoid vague conversations, we use a simple scorecard that measures readiness per domain and use case. It forces clarity on what must be true before AI can be trusted.