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.

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.