Infrastructure for Renewal Retention & Lapse Prediction

Lapse prediction requires documented definitions of what constitutes a retention-worthy account, which premium increase thresholds trigger at-risk classification, and what retention actions are authorized by segment (discount level, outreach method, coverage review offer). These business rules must be current and findable—not in senior agents' heads—so the model generates recommendations consistent with company strategy. State regulatory constraints on discount authorization must also be documented per jurisdiction.

Lapse prediction requires automated capture of behavioral signals at the moment they occur: premium change notifications sent, customer service calls logged, payment latency events, agent contact outcomes, and competitive quote request indicators. Manual capture misses the timing precision that makes early intervention valuable. The 60-90 day pre-renewal intervention window demands that behavioral signals are captured automatically from policy admin, CRM, and billing systems—not logged when someone remembers.

ML-based lapse prediction requires formal ontology mapping Policy to Customer to RenewalEvent to RetentionAction with defined propensity score calculations. Relationships must be explicit: Policy.PremiumIncreasePct + Customer.TenureYears + Customer.ClaimsHistory → LapseProbabilityScore. Without formal entity relationships and feature engineering rules encoded in schema, the model cannot compute consistent propensity scores across product lines and state variations.

The lapse prediction model must query policy history, claims records, billing behavior, and customer interaction logs to assemble the feature set for each at-risk account. API access to policy admin, claims, and billing systems provides the necessary data. Agent notification for high-value accounts requires write-back to CRM or agent portal. The existing API landscape in modern policy admin systems (Guidewire, Duck Creek) supports this without requiring a unified access layer.

Retention strategies and lapse risk thresholds must update when market conditions change—competitor rate cuts, catastrophe-driven premium spikes, or regulatory changes to cancellation rules. Event-triggered maintenance ensures the model's decision rules and recommended actions reflect current business context. A model trained on stable-market lapse patterns will generate misleading predictions after a 15% rate increase, if retention thresholds are not recalibrated.

Renewal retention prediction must integrate policy administration (renewal terms, premium changes), billing (payment history, lapse events), claims (loss ratio by account), CRM (customer interactions), and agent portals (notification delivery). API-based connections between these systems allow the model to assemble a complete customer view and route recommendations to the right channel. Claims and CRM are currently partially integrated via batch, which constrains feature freshness but does not prevent the capability from functioning.