A UK retail bank

BankingBank

Automating evidence extraction for regulatory narratives

Reducing manual effort in regulatory narratives while improving traceability and consistency.

AI & DecisioningPlatform & Architecture

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Structured, explainable evidence model for key regulatory topics

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Automated extraction and aggregation of events and decisions into narrative templates

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Human-in-the-loop workflow for validation and enrichment

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Designed to evolve as regulation and internal controls change

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Engineering Facets

GovernanceComplianceAuditabilityDomain Alignment

Expertise

Domain-Driven Design Practice AI-Native Systems

Accelerators

Domain Reasoning Engine Human-in-the-Loop Responsible & Explainable AI Runtime Integrity Engineering

Capabilities

“The bottleneck in regulatory narratives isn't writing. It's assembling and reconciling evidence from fragmented systems with provenance intact.”

Context

Risk, compliance and legal teams were under pressure to produce detailed narratives explaining failures, near-misses and remediation actions. Much of this work was manual: assembling evidence from multiple systems, stitching together timelines, reconciling conflicting data. The bank wanted to improve quality and reduce manual drag without losing human judgment.

The Challenge

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Extract evidence across multiple platforms, logs and decision services
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Maintain an audit trail that regulators can test
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Ensure narratives remain consistent with source data even as systems and processes evolve
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Allow subject matter experts to shape the narrative while leveraging automation for heavy lifting

Our Approach

Evidence model and ontology

Defined canonical entities: decisions, events, approvals, controls, mitigations, incidents. Mapped system data to these entities via well-defined adapters.

Evidence extraction services

Built services to pull and normalise evidence from relevant systems (risk engines, fraud platforms, workflow tools, logs). Ensured each piece of evidence has provenance and contextual metadata.

Narrative templating and assembly

Developed narrative templates for common regulatory themes (e.g. breaches, remediation programmes, thematic reviews). Automated initial draft creation, stitched from structured evidence.

Human-in-the-loop review

Experts review and adjust drafts, with changes tracked and reconciled against the underlying evidence.

Engagement Timeline

Evidence Model Design

2 months

Defined canonical entities (decisions, events, approvals, controls) and mapped system data via well-defined adapters.

Extraction & Aggregation

3 months

Built extraction services to pull and normalise evidence from risk engines, fraud platforms, workflow tools and logs.

Narrative Assembly

2 months

Developed narrative templates for common regulatory themes with automated initial draft creation from structured evidence.

Review Workflow

1 month

Implemented human-in-the-loop review with change tracking and evidence reconciliation.

Outcomes

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Reduced cycle time for complex regulatory narratives.
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Improved internal confidence in the consistency and traceability of evidence.
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Created a reusable evidence and narrative capability that can support multiple regulations and jurisdictions.

Technologies & Patterns

Evidence extraction servicesOntology-driven data modelNarrative templating engineHuman-in-the-loop workflowsExplainable AI

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