Infrastructure for Proactive Policy Review & Cross-Sell Recommendations

Cross-sell recommendation requires documented definitions of coverage gap criteria, product eligibility rules, and life event triggers that justify a specific recommendation. 'Homeowners without umbrella coverage who have high liability exposure' must be formalized: what liability threshold, what asset level, which products qualify, which states restrict unsolicited recommendations. These rules must be current and findable so the AI generates compliant, strategy-aligned recommendations—not generic upsell attempts based on undocumented assumptions.

Proactive policy review requires systematic capture of life event signals—property value changes, new vehicle additions, business formation indicators—alongside cross-sell outcomes (accepted, declined, agent-contacted). Template-driven capture via defined CRM workflows ensures that recommendation rationale and customer response are recorded with sufficient metadata to train propensity models. Without this, the system cannot learn which recommendation types convert by customer segment.

Coverage gap identification requires formal ontology mapping Customer.AssetProfile to CoverageType to EligibilityConditions. The relationship 'Customer owns home valued >$750K AND holds auto policy AND has no umbrella' → Recommend.Umbrella.WithLiabilityThreshold must be explicitly defined in machine-readable schema. Without entity relationships connecting property value, existing coverage lines, and product eligibility, the AI cannot compute gap exposure or generate contextually appropriate recommendations.

Cross-sell recommendations require API access to policy administration (all lines held by the customer), property data (dwelling values), claims history, and agent portals for notification delivery. The AI must assemble a multi-line customer view to identify coverage gaps. Customer portal display of recommendations requires read-write API access to the portal layer. Existing API coverage in modern policy admin systems supports this use case without requiring a unified access layer.

Product eligibility rules, coverage thresholds, and cross-sell criteria must update when products change, regulatory constraints shift, or market conditions alter the commercial viability of specific recommendations. Event-triggered maintenance ensures that when a new BOP product is filed and approved in a state, the recommendation engine immediately includes it in eligible suggestions for qualifying commercial auto customers—rather than waiting for the next quarterly review.

Proactive policy review requires integration across policy administration (all lines), property data sources, claims systems, product catalog, and agent/customer portal notification channels. API-based connections enable the AI to assemble a complete customer exposure profile and route recommendations to the right channel. Claims remain partially batch-integrated, which limits loss-ratio-aware recommendation filtering but does not prevent the core coverage-gap identification from functioning.