AI Agents

Context

Built for insurance in the Renewals & upsell stack, this agent is used to predict policy lapse risk early—well before renewal or premium due dates. Typical scenarios include monitoring in-force books for emerging lapse risk, prioritizing retention lists for agents or outbound teams, and feeding renewal journeys where timing matters.

What it does

The agent analyzes recent policyholder behavior and produces an explainable lapse risk score with the key drivers. It looks at payment patterns (late/partial/missed), engagement signals (portal/app logins, correspondence opens), cover changes and endorsements, claim history, and service interactions that often precede lapse. It then outputs who is at risk, why, and the window in which to act—so retention teams can intervene with the right message or option before the decision is made.

Core AI functions

Per-policy baselining and cohorting to define “normal” engagement; change-point and trend detection on payment and usage; feature construction across recency/frequency/amounts for payments, claims, and interactions; association of service issues with subsequent lapse; and reason-code generation that explains movements (e.g., “two consecutive late payments” or “reduced engagement post-endorsement”). Thresholds and look-back windows are tuned to your persistency targets.

Problem solved

Lapse indicators are scattered across billing, servicing, and digital systems—and often noticed after the fact. Generic lists waste retention effort. This agent consolidates the earliest reliable indicators into a single, ranked view with reasons, so outreach is timely and focused.

Business impact

Persistency improves because at-risk policies are contacted earlier; retention actions are better targeted and more effective; customer experience strengthens as interventions feel relevant; and acquisition costs are protected by saving policies already on the books.

Integration and adjacent use cases

Integration is light–moderate: read billing/payment status, digital engagement, service/complaint markers, claims, and basic policy attributes from your warehouse or stream; write risk flags, scores, and reasons to CRM or renewal orchestration—no core changes required.

Common combinations in this stack:

• Premium change impact explainer (to clarify pricing shifts),

• Usage-based data interpreter (telematics) (to translate driving/usage into renewal context),

• Product suitability re-matcher (to propose a better-fit product), and

• Cross-sell / upsell trigger agent (to surface relevant add-ons when retention is secured).