Insurance Customer 360

You’re an insurance executive (COO, head of retail life, or chief customer officer) about to review the portfolio. You open Insurance Customer 360 on Cloudera AI—the app loads from a pre-seeded warehouse (synthetic UK/EU-style book by default) with churn scores already trained.

The header shows data freshness (warehouse, caches, churn scoring) so you trust the numbers aren’t stale. You might switch language (En / Fr / Es / He / Ar) to show localization; the UI and, when configured, AI responses follow that choice.

You land in The Business—the executive home for book, cohorts, and portfolio KPIs.   Watch the demo

• Protect revenue with a book-wide view of retention and value at risk
  Executives see total book value, 12‑month lapse outlook, and value at churn risk in one place, with cohort filters and trends—so leadership can spot exposure and concentration before renewals slip, not after.

• Focus scarce teams on the customers that matter most Churn scoring and a prioritized retention queue rank policyholders by material at-risk value; drill-down from portfolio KPIs to named customers replaces hunting across policy, investment, and service silos.

• Turn insight into action with AI on governed customer context Per-customer AI summaries, assistant Q&A over the book, and optional outreach drafts sit on the same warehouse as analytics—shortening the path from “what’s happening?” to “who do we contact and why?” without a separate tools chain.

Insurance carriers hold customer, policy, investment, and service data in separate systems, so leaders and frontline teams often rebuild the same picture in spreadsheets and multiple applications before they can answer a simple question about book health or a single relationship. The use case is to consolidate that information into one governed customer view that supports both portfolio oversight and individual servicing.

Insurance Customer 360 demonstrates that pattern on Cloudera AI: a unified warehouse feeds executive dashboards for book value, retention outlook, and value at churn risk, with filters and trends by cohort. Machine learning scores churn from behavior and interaction signals; the application ranks at-risk customers by financial impact so retention effort goes to the highest-value relationships first.

When an LLM is configured, the same consolidated record powers AI-generated customer summaries, an executive assistant over the book, and a retention work queue with short next-best actions and optional outreach drafts—aligned with the user’s selected language. The demo uses synthetic, territory-aware data so teams can evaluate the end-to-end story without real customer data.

The blueprint is meant to be adapted: swap the demo database for production tables in Cloudera Data Warehouse (for example Iceberg), connect real policy, CRM, and engagement feeds, and keep the dashboard and AI layer as the experience tier. The business outcome is faster, more consistent decisions on retention, growth, and customer experience from a single source of truth.

Insurance Customer 360 runs as a Cloudera AI prototype: automated setup installs dependencies, loads demo data, trains a churn model, and starts a web application.

Data
A governed warehouse holds customers, policies, investments, legal encumbrances, service interactions, and precomputed analytics caches. Demo data is synthetic and can be reseeded for different regions and languages. In production, the same pattern maps to a lakehouse or enterprise warehouse as the system of record.

Analytics and machine learning
A batch job scores churn from book behavior and engagement signals. Results drive risk tiers, portfolio KPIs, and prioritization of at-risk customers by value.

Application tier
A Python API serves portfolio and customer views, search and filtering, and admin functions. A React dashboard provides business, customer, engagement, and product workspaces plus an executive assistant sidebar. Optional generative AI streams summaries, retention actions, and outreach drafts using the consolidated customer record and the user’s selected language.

Deployment
The stack is packaged for Cloudera AI import or catalog launch. The UI ships as a prebuilt bundle; an optional build step refreshes it when translations or layout change. LLM connectivity (cloud or private endpoint) is optional and configured at deploy or runtime.

Modularity
Data sources, warehouse technology, churn model, and model provider can be swapped without changing the overall shape: one governed customer layer, analytics and ML on top, dashboard and assistant for users.