Infrastructure for Reinsurance Optimization & Placement

Reinsurance optimization requires explicit, current documentation of risk appetite statements, retention targets, capital constraints, and treaty evaluation criteria. The AI must know that the organization's catastrophe PML retention threshold is 10% of surplus, that quota share is preferred over excess-of-loss for certain lines—these strategic parameters must be findable and current. Without L3 formality, the AI models treaty structures that contradict undocumented capital strategy.

Reinsurance optimization relies on systematic capture of catastrophe model outputs (PML curves, OEP/AEP distributions), current treaty terms and pricing, and portfolio exposure data for all in-force policies. Template-driven capture ensures these fields are consistently recorded after each modeling run and renewal negotiation. Sporadic or free-text capture of cat model outputs makes scenario comparison impossible.

Reinsurance scenario modeling requires formal ontology connecting Portfolio.Exposure to Peril.PMLCurve to ReinsuranceTreaty.Terms with explicit relationships: Treaty.ExcessOfLoss.AttachmentPoint linked to Portfolio.Concentration.Geography and Capital.RetentionLimit. Without machine-readable schema defining these entities and constraints, the AI cannot compute ROE impact of competing treaty structures or map specific treaty conditions to capital efficiency metrics.

The reinsurance optimization engine must query portfolio exposure data across all in-force policies, access catastrophe model outputs, retrieve current treaty terms, and pull capital and surplus levels. API access to the underwriting system, actuarial modeling platforms, and financial systems enables the AI to assemble the inputs needed for scenario modeling. Batch-only export/import creates data staleness that undermines renewal negotiation timing.

Reinsurance optimization depends on current data: cat model outputs update after major loss events or model version releases, treaty terms change at renewal, and capital levels shift quarterly. Event-triggered maintenance ensures that when a major hurricane updates the cat model or surplus changes materially, the optimization inputs refresh automatically rather than waiting for a scheduled quarterly review.

Effective reinsurance optimization requires connecting the underwriting system (exposure data), actuarial platform (cat model outputs), financial systems (capital/surplus), and reinsurance management system (treaty terms). API-based connections between these systems allow the AI to assemble the full input dataset for scenario modeling without manual data assembly by the actuarial team at each renewal cycle.