AI-Powered Claims Assessment Reference Architecture for Insurance

Data flows

The following are descriptions of the data flows shown in the AI-Powered Claims Assessment Reference Architecture diagram:

1. Capture structured and unstructured claims data from Guidewire, as well as telemetry and third-party sources such as documents, images and adjuster notes.
2. Use Databricks Auto Loader to ingest data efficiently into Delta Lake, applying the medallion architecture to separate raw (Bronze), refined (Silver) and business-ready (Gold) data layers.
3. Utilize Spark Declarative Pipelines to cleanse, enrich and unify data, performing claims classification and business rule enforcement to support accurate claims profiling.
4. Leverage Databricks SQL to run analytical queries over curated claims datasets, enabling dashboards and operational insights for claims lifecycle, risk scoring and fraud patterns.
5. Implement GenAI workflows including retrieval augmented generation (RAG), similarity search over embeddings and LLM-based Model Serving for intelligent claims triage, document Q&A and automated summarization.
6. Build interactive dashboards and enable natural language querying and present with Databricks Apps, allowing claims teams to explore data and insights intuitively and securely.

Benefits

Benefits of using the Databricks Platform for the AI-powered claims architecture include the following:

• Establish best-practices architecture for claims assessment use cases
• Learn about AI solutions on claims and image data, and how they differentiate Databricks as the industry leader