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Policy churn
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Insurance · Enterprise Solution

Policy churn

Predict lapse risk and trigger retention offers.

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LIVE AI ENGINE
Real-Time Execution Pipeline

Policy churn · Live Event Stream

STREAMING
STAGE 01INGEST
Enterprise Data
Lakehouse & APIs
STAGE 02ACTIVE
Breww AI Kernel
Multi-Agent Engine
STAGE 03ACTION
Automated Action
Low Latency SLA
EVENT MONITOR · INSURANCE
ONLINE

[INGEST] Multi-source telemetry batch synced (2,480 items)

p99: 12.4ms
Throughput: 420 ev/s
ZERO-DRIFT SLA
The problem

What we hear

Retention teams learn about lapse only after non-payment; win-back is expensive.

Our approach

How we solve it

Lapse-probability models keyed to behavioral, service, and macro signals, wired into your CRM cadence.

Outcomes

Measured impact

  • Earlier retention outreach at lower per-save cost
  • Personalized offer selection based on policyholder value
  • Fewer non-pay lapses through proactive billing intervention

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Portfolio exposure under climate and event scenarios.

Agent productivity

Next-best action for every producer and territory.

Talk to us

Bring policy churn into production.

Share a couple of lines about your setup — a senior partner will get back within a business day.

Discuss this use case

Tell us about your organization and what problem you are solving.

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Policy churn — Insurance · DataCaffe.ai