Insurance Regulatory Data Exchange Harmonization
Standardizes insurance data semantics across systems using the NGDS Object Model to support interoperable regulatory reporting and reduce mismatches between business processes and underlying data elements.
The Problem
“Insurance Regulatory Data Exchange Harmonization with NGDS Semantic Mapping”
Organizations face these key challenges:
Different insurance systems use inconsistent names and meanings for the same business concept
Regulatory reporting teams rely on manual reconciliation between business definitions and technical fields
Point-to-point mappings are brittle and expensive to maintain across many systems
Code sets and enumerations differ by source platform and reporting jurisdiction
Impact When Solved
The Shift
Human Does
- •Collect source data dictionaries, interface specs, and regulatory templates from each system
- •Reconcile field meanings, code sets, and reporting interpretations through manual reviews
- •Document point-to-point mappings and transformation rules in spreadsheets and ETL specifications
- •Validate submission outputs, investigate mismatches, and coordinate corrections across reporting cycles
Automation
Human Does
- •Approve NGDS-aligned mappings and resolve low-confidence semantic conflicts
- •Decide policy for code-set normalization, reporting interpretation, and version changes
- •Review exceptions, lineage evidence, and validation failures before publication or submission
AI Handles
- •Analyze schemas, dictionaries, and regulatory templates to propose NGDS entity and attribute mappings
- •Generate explainable crosswalks, lineage links, and normalized code-set recommendations across source systems
- •Monitor source and reporting changes, assess mapping impact, and flag conflicts or gaps for review
- •Execute approved harmonization, validation, and exception triage to produce consistent reporting-ready outputs
Operating Intelligence
How Insurance Regulatory Data Exchange Harmonization runs once it is live
AI runs the first three steps autonomously.
Humans own every decision.
The system gets smarter each cycle.
Who is in control at each step
Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.
Step 1
Assemble Context
Step 2
Analyze
Step 3
Recommend
Step 4
Human Decision
Step 5
Execute
Step 6
Feedback
AI lead
Autonomous execution
Human lead
Approval, override, feedback
AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.
The Loop
6 steps
Assemble Context
Combine the relevant records, signals, and constraints.
Analyze
Evaluate options, risk, and likely outcomes.
Recommend
Present a ranked recommendation with supporting rationale.
Human Decision
A human accepts, edits, or rejects the recommendation.
Authority gates · 1
The system must not publish or submit regulatory reporting outputs without approval from a regulatory reporting lead or designated governance approver [S1].
Why this step is human
The decision carries real-world consequences that require professional judgment and accountability.
Execute
Carry out the approved action in the operating workflow.
Feedback
Outcome data improves future recommendations.
1 operating angles mapped
Operational Depth
Technologies
Technologies commonly used in Insurance Regulatory Data Exchange Harmonization implementations:
Key Players
Companies actively working on Insurance Regulatory Data Exchange Harmonization solutions: