This is where AI truly transforms an agent’s role, turning them into a strategic advisor rather than just a salesperson. By analyzing data on an ongoing basis, AI gives agents a “heads-up” on lead needs they might not have even considered yet. The system is constantly working in the background to find the perfect moment to connect.
Practical Examples: #
- Predictive Policy Needs: An AI can analyze a lead‘s current policy details, age, and family status. Based on this data, it could predict a client’s future needs. For example, a young client who purchased a small policy years ago might be predicted to need more coverage as their income increases or as they have children. The AI alerts the broker to a cross-selling or up-selling opportunity at just the right time.
- Lapse and Retention Risk Prediction: AI systems can monitor client payment history and engagement with the broker’s emails and communications. If a client misses a payment or stops opening newsletters, the AI can flag them as a “retention risk.” This proactive alert allows the broker to personally reach out and offer assistance before the policy lapses, preserving the relationship and the client’s coverage.
- Cross-Policy Recommendations: For a client who has multiple policies (e.g., life and disability insurance), the AI can analyze their full portfolio to identify gaps in coverage. For instance, if a client has life insurance but no critical illness or long-term care policy, the AI can generate a recommendation, giving the broker a tailored reason to schedule a policy review with the client.