Preserve History.
Retire Risk.
Fully decommission legacy platforms without sacrificing 50+ years of policy truth.
The problem
Legacy systems persist
because inactive policies
cannot be ported.
Active policies can move to the modern platform. Inactive (closed) policies typically cannot — and that is why legacy systems remain online. Some are 40–50 years old, and underlying contracts may need to be retained for 50+ years.
This is not a modernisation debate. It is a risk and continuity reality.
Why legacy systems cannot be easily retired
- 01Expensive to maintain
- 02Running on obsolete technologies
- 03Partially documented — or not at all
- 04Dependent on shrinking SME knowledge
- 05Rules reachable only in contract text
Why inactive policies still matter
Closed policies remain
analytically essential.
Closed policy lifecycle data is not operational — but remains analytically essential for business intelligence and regulatory compliance. When this history is locked inside legacy platforms, analysts lose depth and decision quality degrades.
Inactive policies support critical analysis
- Long-tail claims analysis
- Actuarial modelling
- Regulatory reporting
- Audit traceability
- Historical risk comparison
Why traditional migration approaches stall
- Recreating decades of embedded business rules
- Mapping complex product structures for discontinued lines
- Rebuilding logic for products no longer in the current platform model
- Translating undocumented contract logic
That proved nearly impossible — so legacy systems stayed alive, not because they were ideal, but because they were irreplaceable.
Strategic benefits
Controlled risk reduction
through data preservation.
This is not modernisation for its own sake. Using a modern lakehouse approach, inactive policies can be extracted, harmonised, and made accessible without rebuilding decades of legacy logic in new transactional systems. Legacy systems can be retired while knowledge remains accessible.
Reduced technology risk
Faster actuarial reporting
Unified cross-system view
Lower long-term total cost of ownership
AI-ready historical foundation
The shift in approach
Preserve history. Harmonise data.
Retire risk.
The goal is no longer to rebuild legacy logic inside a new transactional system. The modern objective is to preserve history, harmonise data, retire risk, and maintain regulatory compliance — using a lakehouse architecture built for that purpose.
Full Snapshot Extraction
Complete extraction of inactive policy data from legacy platforms, preserving all historical states, contract terms, and lifecycle events without transformation loss.
Immutable Historical Layer
Inactive policy snapshots stored in an immutable lakehouse architecture with ACID compliance, ensuring no historical data can be accidentally modified or lost.
Time Travel & Versioning
Access any historical policy state at any point in time. Delta Lake enables version control and audit trails for complete regulatory compliance.
Unified Canonical Model
Data from multiple legacy platforms harmonised into a consistent, unified model that enables cross-system analysis without losing platform-specific details.
Governance & Lineage
Full data lineage tracking from source to destination. Automated governance workflows ensure compliance with regulatory requirements and audit standards.
AI-Ready Foundation
Modern lakehouse architecture enables advanced analytics, actuarial modelling, and AI-powered insights while legacy platforms can be safely retired.
Migration approach
From legacy platform to governed analytical asset.
A structured, validation-driven framework that preserves 50+ years of inactive policy history while enabling safe decommissioning of legacy platforms.
Legacy Discovery & Snapshot Extraction
Understand the legacy landscape in depth. Perform full snapshot extraction from ageing policy administration systems, capturing all inactive policy records, contract terms, and lifecycle events.
Business Rule & Contract Recovery
AI-assisted analysis detects patterns in historical outputs, recovering undocumented contract logic and embedded business rules — including rules reachable only in the original contract text.
Schema Harmonisation & Canonical Mapping
Data from multiple legacy platforms is harmonised into a unified canonical model. Cross-system mappings generated with full lineage tracking from source to destination.
Immutable Layer & Time Travel Validation
Inactive policy snapshots stored in an immutable historical layer with ACID compliance and time travel capabilities. Every record validated before decommissioning begins.
Governed Retirement & Compliance Handoff
Controlled legacy decommissioning with full governance, lineage, and regulatory compliance. Legacy systems retired while knowledge remains accessible and audit-ready.
Preservation architecture
Designed for long-term
historical preservation.
Because inactive policies must be retained for 50+ years, the preservation architecture prioritises immutability, governance, and accessibility at every level. By combining full snapshot extraction, lakehouse architecture, time travel capabilities, and unified data models, Qubiz ensures organisations can retire legacy platforms while preserving complete historical certainty.
Immutable Historical Layer
Inactive policy snapshots stored in an immutable architecture with ACID compliance. No historical data can be accidentally modified or lost after decommissioning.
Full Data Lineage
The complete journey of every policy record tracked from legacy source to modern lakehouse. Every transformation logged, versioned, and auditable for regulatory compliance.
Performance & Scalability
Designed for very large policy datasets: millions of closed policies, decades of historical contract data, and billions of lifecycle events. Distributed processing ensures reliable extraction.
Why Qubiz
Deep expertise in inactive
policy migration.
Qubiz combines consultancy-led modernisation strategy with hands-on engineering execution. We do not just migrate tables — we preserve history, harmonise data across platforms, and enable safe decommissioning.
We are comfortable working with undocumented policy contracts, obsolete technologies, multiple legacy platforms, and shrinking SME knowledge.
Outcomes for insurance providers
- Retire ageing legacy infrastructure safely
- Reduce technology and continuity risk
- Enable faster actuarial and regulatory reporting
- Create unified cross-platform analytical views
- Lower long-term total cost of ownership
- Build an AI-ready historical foundation
Strategic outcome
Operational liability
becomes governed
analytical asset.
Legacy platforms retired. Knowledge preserved. Fully accessible and audit-ready.
Frequently asked questions.
What is passive (inactive) policy data migration?
It is the structured process of extracting, preserving, and governing closed policy records from legacy platforms — enabling those platforms to be safely decommissioned while retaining full historical, analytical, and regulatory access to the data.
Why can't inactive policies simply move to the modern platform?
Active policies can migrate to modern systems because the underlying products are modelled there. Inactive policies typically cannot — the legacy products are not available in the new system, and rebuilding decades of embedded contract logic proved nearly impossible. The legacy platform stayed alive not because it was ideal, but because it was irreplaceable.
What is a lakehouse approach and why is it suited to this problem?
A lakehouse combines the scalability of a data lake with the governance and ACID compliance of a data warehouse. For inactive policies, it allows full snapshot extraction without forcing a rebuild of legacy business logic into a new transactional system — the history is preserved as-is, harmonised, and made queryable.
How long must inactive policy data be retained?
Some legacy platforms hold contracts from 40–50 years ago, and underlying policy contracts may need to be retained for 50 years or more under Swiss and European regulatory requirements. The preservation architecture is designed for that time horizon.
Do we need to document everything before starting?
No. The Qubiz approach is explicitly designed to work with undocumented contracts, partially documented systems, and shrinking SME knowledge. AI-assisted rule recovery extracts patterns from historical outputs rather than requiring upfront documentation.
What do we actually end up with after migration?
A governed, immutable, queryable historical layer containing the complete lifecycle of every inactive policy — with full data lineage, time travel capabilities, and regulatory traceability. The legacy platform can then be safely decommissioned.
What is legacy insurance platform decommissioning, and when is it possible?
Legacy insurance platform decommissioning is the controlled retirement of an ageing policy administration system once its data and rules have been fully extracted, preserved, and made accessible in a modern architecture. It becomes possible only when the historical data is no longer locked inside the old system — which is precisely what the passive policies migration achieves.