Tech Explained: Here’s a simplified explanation of the latest technology update around Tech Explained: Accelerate insurance customer and data migration with AI in Simple Termsand what it means for users..
Generative AI introduces efficiency and automation into traditionally manual activities such as source to target mapping. For migrations involving 500+ data fields, GenAI proposes 60–70% of mappings with high confidence, reducing mapping phase duration by 40–50%. Where GenAI is not yet in use, similar efficiencies can be achieved over time by building reusable mapping libraries and pattern catalogues across successive migrations.
In practice, much of this efficiency comes from starting with preconfigured data models and mapping templates for leading policy administration platforms, such as Life400, LifeAsia and other industry solutions, and then tailoring them to each client’s specific structures and controls.
Additional GenAI capabilities include:
- automated test-case generation based on existing data and process patterns
- data lineage documentation that shows how each key data element moves and transforms across the migration pipeline
- real-time technical documentation and change logs that reduce the typical documentation drift seen in lengthy or multi-wave programs.
When embedded in a governed migration platform and combined with LC/NC tools, these capabilities enable insurers to industrialise migrations, apply consistent controls across batches of funds or investors, and reuse patterns for subsequent transformations.
In global financial services migrations of comparable complexity, this model has enabled organisations to:
- reduce migration timelines by 30–40%
- cut rework and remediation effort by 25–35%
- accelerate mapping phases by up to 50%
- decommission legacy platforms with confidence, not contingency plans.
Once a reusable migration platform and pattern library is in place, each additional insurance book migration benefits from existing mappings, rules and test assets. Anchoring this platform on reference data models (e.g. relationship between product–policy–customer–advisers) for widely used industry solutions further accelerates delivery, as many core entities and relationships are already understood and codified.