The Problem with Reactive Conservation
Static outreach sequences were designed for a world where data was expensive to move. Today, Majesco Policy and Salesforce Financial Services Cloud hold everything needed to score lapse risk dynamically — premium payment behavior, cash value trends, policy loan history, prior conservation interactions — but the conservation team still runs on a calendar, not a risk model. High-value lapse risk surfaces only after the grace period clock starts. At that point, the policyholder has already made a decision, at least informally, and the team is playing catch-up instead of prevention.
How an AI Agent Shifts the Conservation Model
An AI Labor Company agent mines conservation outreach history from Majesco Policy and Salesforce, learns which signal combinations — payment skips, loan utilization, product type, face amount — have historically predicted lapse, and runs weekly probability scoring across the full in-force UL portfolio. When a policy crosses a risk threshold, the agent generates a personalized outreach sequence drawing on the policyholder's specific policy details and routes high-value cases to a conservation specialist — before the grace period opens. Twilio handles outbound contact; DocuSign supports any conservation option that requires policyholder acknowledgment. The conservation team works exceptions and high-stakes decisions; the agent handles routine monitoring and first-touch outreach.
What This Is Actually Worth
Conservation is a revenue protection play with a direct P&L line: lapsed policies represent lost future premium, lost investment spread, and replaced mortality/longevity exposure that can't be recovered. A 15-25% improvement in conservation rate on identified at-risk policies — the kind of result that runs on better timing and personalization — translates to real in-force premium retained. The labor side is also significant: teams in this position typically carry $250K–$600K/year in conservation ops labor, and an agent that handles routine scoring and first-touch outreach can reduce manual handling by 60–80%, typically going live and producing results in about five weeks.
How does the agent handle NAIC grace period rules and state-specific requirements?
The agent is configured with state-specific grace period windows from your Majesco Policy data. It surfaces high-risk policies well before the applicable grace period opens so the conservation team has a meaningful intervention window. It does not make policy decisions — it routes to specialists who retain authority over all conservation actions.
Does this require replacing our existing conservation workflow?
No. The agent layers on top of Majesco Policy and Salesforce Financial Services Cloud, running weekly scoring and generating outreach without replacing existing systems. Conservation specialists receive prioritized queues and retain full decision authority. The existing letter sequence can continue running for lower-risk segments while the agent handles proactive high-risk intervention.
What happens if the lapse probability score is wrong on a high-value policy?
The agent surfaces risk scores with supporting signals, not autonomous decisions. A specialist reviews every high-value escalation before outreach occurs. This means false positives generate unnecessary but low-cost contacts; false negatives are the same rate as the current reactive model. Over time, the model improves as analyst feedback is incorporated.