> For the complete documentation index, see [llms.txt](https://docs.rainbird.ai/rainbird/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.rainbird.ai/rainbird/getting-started/use-cases/real-time-fraud-claim-decisioning.md).

# Real-time fraud claim decisioning

**Decision:** Insurance - claims fraud.   **Pattern:** Automated pipeline

**Use case:** This solution replaced an overnight batch process with real-time decisioning, applying expert fraud reasoning to every flagged claim instead of only the ones a team had time to review manually. Find out more in our [case study](https://rainbird.ai/case-study/dacb-tackling-insurance-fraud-with-rainbird).

**End-user:** Claims fraud investigation team

**The decision:** Whether a claim flagged as potentially fraudulent should proceed, be investigated further, or be declined — based on the fraud indicators present and how they compare to known patterns.

**The knowledge graph:** Encodes the fraud investigation team's expert reasoning, capturing dozens of fraud indicators gathered from multiple source systems, so every flagged claim gets the same rigour previously only possible on a small manual sample.

**Integration:** Data — including fraud indicators pulled from multiple systems via RPA — feeds into Rainbird automatically. Rainbird reasons over it and returns a decision immediately, removing the overnight batch run and the manual backlog it used to create.

**Evidence:** Every decision returns a full explanation of the fraud indicators behind it, so the investigation team can act on flagged cases immediately without re-doing the reasoning themselves.


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